Main

Innate immune signalling transduces small signals to robust responses such as programmed cell death and/or inflammation. While the binding of a pathogen-associated molecular pattern (PAMP) to receptors initiates signaling, this initial interaction releases insufficient energy to directly change cell state. That tiny signal must be amplified in an energy consuming process that is fundamental to understanding the architecture and evolution of these critical signaling networks.^1–3 Innate immune signaling networks from bacteria to humans amplify signaling through interactions between nonenzymatic death fold domains (DFDs).^4–6 However, the energetic basis for DFD function remains enigmatic (Figure 1A).

Figure 1.

DFDs commonly form paracrystalline polymers that can template their own growth when free subunits exceed their saturation concentration (C_sat). Polymers are functionally initiated by PAMP-bound receptor oligomerization. To preclude signaling through spontaneous nucleation, DFDs are presumed to be effectively subsaturated prior to activation, through a combination of low basal expression, subcellular compartmentalization, post-translational modifications, and regulatory interactions.^4,7–9 We previously discovered, however, that the DFD-containing adaptor Bcl10 exhibits an intrinsic nucleation barrier that is large enough to support persistent deep supersaturation in vivo, allowing it to amplify signaling independently of orthogonal input.¹⁰

Here, we propose that DFDs broadly function as long-term energy reservoirs via metastable supersaturation, a state where a protein exceeds C_sat yet remains in solution due to structurally-determined nucleation barriers. This mechanism would allow cells to respond to PAMPs immediately, decisively, and independently of metabolism, which is frequently compromised during infection.¹¹ For DFDs to function in this manner, their endogenous concentration would need to greatly exceed their respective C_sat values, while they nevertheless remain soluble over timescales spanning the window of vulnerability to infection, i.e. the full lifetimes of cells. Cells would therefore be continuously susceptible to spontaneous death and inflammation through stochastic nucleation events, imposing a fundamental tradeoff between immunity and longevity.

We here use a combination of biophysical, bioinformatic, and cytological approaches to investigate the capacity and functional relevance of supersaturation by human DFDs. Our results collectively uncover the energetic basis for signal amplification by DFDs and in turn a thermodynamic drive to die.

A select subset of DFDs have intrinsic nucleation barriers enabling persistent supersaturation

To systematically survey the ability of DFDs to supersaturate, we compiled an exhaustive set of 109 structurally independent human DFDs (Table S1 and Figure S1A) and then characterized their tendency to self-assemble in near-physiological conditions while minimizing interference from other proteins. For this purpose we used distributed amphifluoric FRET (DAmFRET) in an orthogonal eukaryotic host that completely lacks endogenous DFDs and their associated regulatory machinery –– Saccharomyces cerevisiae. DAmFRET produces a snapshot of the population-level distribution of single-cell measurements of ratiometric FRET (“AmFRET”) between two fluorescent forms of the same protein species.¹² The data revealed a diversity of behaviours (Figure 1B-C, S1C, and Table S1), ranging from no self-association to self-association in all cells at all concentrations. A minority (21) of the DFDs transitioned from no to high AmFRET in a discontinuous manner, the signature of a large intrinsic nucleation barrier.^10,12,13

Using high-throughput confocal microscopy, we observed subcellular assemblies for most of the DFDs that populated AmFRET-positive states, but not those with entirely low AmFRET (Figure 1B). Nucleation barriers increase with the entropic cost of assembly.^12,14 Assemblies with large barriers therefore tend to be more ordered than those without. Ordered assembly often manifests as long filaments in cells.^10,15,16 As predicted, most of the assemblies of DFDs that had transitioned discontinuously had fibrillar morphologies, whereas those that had transitioned continuously (low-to-high DAmFRET) instead mostly formed spherical or amorphous puncta (Figure 1B, S1E-G).

For discontinuous DFDs, the soluble protomers in the low FRET cells are hypothetically poised to assemble; they just lack a structural template to get them started. We tested this hypothesis by co-expressing genetically encoded “seeds” (Figure 1D-E),¹⁰ and found that the seeds caused cells to switch to the high AmFRET population for most discontinuous DFDs but not continuous DFDs (Figure 1F, S1H, Table S1). We further verified that assembly was mediated by native DFD subunit interfaces rather than amyloid-like misfolding (Supplemental Information, Figure S2A-B). Altogether, these data confirm that a subset of DFDs have native ensembles that can supersaturate in a soluble form to power subsequent switch-like assembly.

The magnitude of supersaturation in vivo is determined by the ratio of a protein’s total concentration to its C_sat, which reflects the strength of interactions between subunits in its assembled structure.¹⁷ Total concentration and C_sat can evolve independently by gene regulation and DFD sequence, respectively. Consequently, if the function of discontinuous DFDs involves supersaturation, we would expect evolution to have lowered their C_sat while raising their expression relative to continuous DFDs. To test these predictions, we first analyzed the relationship of nucleation barriers to C_sat, as determined by each protein’s transition concentration in the presence of seed. As expected, discontinuous DFDs exhibited lower C_sat values than continuous DFDs (Figure 1G, Table S1), and in the absence of seed achieved soluble concentrations that exceeded them by at least four-fold on average (Figure 1H).

Nucleation barriers are a characteristic feature of inflammatory signalosome adaptors

We next evaluated the relationship of nucleation barriers to DFD concentrations in vivo. Using published proteomic datasets,^18–20 we found that proteins with discontinuous DFDs tend to be expressed more abundantly than those with continuous DFDs, both at the cell type and tissue levels (Figure 2A, S3A-B and Table S1). We then used a high-coverage transcriptomics dataset²¹ to ask how C_sat values relate to DFD gene expression in primary immune cells. Remarkably, transcript abundances significantly anticorrelated with C_sat in 17 out 18 canonical immune cell populations (Figure 2B and S3C). Such a relationship is highly unusual for soluble proteins, whose expression instead tends to strongly correlate with C_sat.²² These analyses collectively indicate that discontinuous DFDs are likely to be functionally supersaturated in their endogenous physiological contexts.

Figure 2.

Given the low frequency of nucleation barriers among DFDs, is there an enrichment for them somewhere within the signaling network? A protein’s importance to a biological response can be approximated by its centrality in the corresponding network. Central proteins tend to be essential,²³ more abundant,²⁴ slower evolving,²⁵ and more frequently targeted by pathogens than noncentral proteins.^24,26 Proteins that have many direct interactions, or high “degree centrality”, are hubs in protein-protein interaction networks. Proteins that bridge many other nodes, or have high “betweenness centrality”, act as bottlenecks that control information flow through the network. We observed that proteins with discontinuous and/or seedable DFDs have greater degree and betweenness centralities (Figure 2C and S3D), consistent with their controlling signaling.

For a DFD nucleation barrier to enable on-demand assembly, the DFD must remain supersaturated even as a full-length (FL) protein. The potential for rapid, on-demand assembly would be diminished if other parts of the protein prematurely trigger nucleation or destabilize the assembled state. We therefore evaluated the phase behaviours of 21 diverse DFD-containing FL multidomain proteins. Most of the proteins behaved the same way as their respective DFDs (Figure 2D, Figure S4A, and Table S2). Six proteins suppressed the ability of their DFDs to supersaturate, resulting in continuous low or moderate DAmFRET that we found reflects autoinhibited oligomerization (Figure S4B), consistent with prior findings for multiple DFD-containing receptors.^7,27–32 Only five of the FL proteins retained or enhanced their DFDs’ nucleation barriers: ASC, BCL10, FADD, MAVS, and TRADD (Figure 2D, Figure S4A, and Table S2). Strikingly, they span all four subfamilies and function specifically as adaptors in well-characterized signalosomes linking pathogen sensing to inflammation and/or inflammatory cell death (Figure 2E). In contrast, signalosomes with primarily non-immunity functions –– the apoptosome, the PIDDosome, and the ectodysplasin (EDA) receptor complex –– lacked supersaturation (Figure 2G). Consistent with their functioning through supersaturation, only the former set of adaptors are more abundantly expressed than their cognate receptors in the human body (Figure 2F). These data collectively identify the adaptors, specifically, of inflammatory signalosomes as energy reservoirs for signal amplification.

Nucleation barriers facilitate signal amplification in human cells

To explore the functionality of supersaturation, we focused on two modes of programmed cell death signaling that differed in this regard: intrinsic apoptosis (no nucleation barrier) and pyroptosis (with nucleation barrier). The former occurs downstream of persistent intracellular stresses.^33–35 The latter is instead triggered by minute levels of PAMPs and therefore involves greater signal amplification.

To test if the absence of nucleation barriers limits the sensitivity of the apoptosome, we adapted an optogenetic approach^10,36–38 to precisely control apoptosome initiation. HEK293T cells lack the inflammasome constituents NLRC4, ASC, and CASP1, allowing us to repurpose their DFDs in this cell line. Accordingly, we transduced the cells with mScarlet-I fusions of either the non-supersaturable WT apoptosome effector, CASP9, or a chimeric version that harbored the supersaturable DFD of CASP1 in place of its own (CASP9^CASP1CARD). We simultaneously transduced the cells with blue light-inducible seeds of the cognate upstream DFD –– opto-APAF1 or opto-NLRC4, to respectively create non-supersaturable and supersaturable versions of the apoptosome (Figure 3A). Before comparing amplification, we first assessed the abilities of the reconstituted apoptosomes to simply engage CASP9 when active, by measuring the activation of CASP3/7, the downstream target of CASP9, after one minute of blue light stimulation. Both cell lines activated a fluorescent CASP3/7 reporter to the same extent at this time point (Figure 3B), confirming that both apoptosomes can in principle signal effectively.

Figure 3.

To now assess signal amplification, we evaluated both the persistence of DFD assemblies and their ability to commit cells to apoptosis following an otherwise sublethal stimulus. We anticipated that cells expressing the non-supersaturable apoptosome will 1) form reversible clusters and consequently 2) survive a short blue light stimulus. In contrast, cells expressing the supersaturable apoptosome will 1) form irreversible clusters that prolong signaling, and consequently 2) die even after a short blue light stimulus.

To test prediction 1, we stimulated the cells with blue light for one minute and monitored subsequent protein localization. We found that both apoptosomes formed puncta in essentially all cells within one minute (Figure 3C-D; Movie S1). The non-supersaturable apoptosomes then dissolved over the course of the next 10 minutes. In contrast, the supersaturable apoptosomes instead continued to grow at a constant rate for at least the next 20 minutes (Figure 3C-D; Movie S2).

To test prediction 2, we measured cell death two hours after the initiation of blue light stimulation for either one minute or the entire two hours. Neither apoptosome induced cell death in the absence of blue light (Figure 3E). The non-supersaturable version induced death in approximately 18% and 55% of cells following the short and long stimulation, respectively (Figure 3E), confirming the expected dose-dependence of signaling. In contrast, the supersaturable apoptosome induced death in most cells even upon short stimulation, confirming that supersaturation-mediated DFD polymerization amplifies signaling.

Innate immune adaptors are endogenously supersaturated

We next asked if pyroptosis and extrinsic apoptosis are indeed powered by adaptor supersaturation in vivo. We first induced pyroptosis in human THP-1 monocytes by treating them with poly(dA:dT), a ligand for the inflammasome receptor, AIM2. By 18 hours, approximately 70% of the cells were dead or dying. We then deleted PYCARD to determine if cell death depended on ASC, the adaptor for the inflammasome. Death was delayed, but not eliminated (Figure 4A). We then also deleted the adaptor for extrinsic apoptosis, FADD, as it has been shown to co-assemble with AIM2 in pathogen-triggered PANoptosis.³⁹ Death was reduced further still (Figure 4A and S5B), confirming that AIM2 activation can induce cell death through both ASC and FADD.

Figure 4.

To eliminate the potentially confounding effects of orthogonal dsDNA sensors,⁴⁰ we next placed AIM2^PYD under blue light control and confirmed that the resulting fusion protein –– “opto-AIM2” –– grants direct control over seed formation (Figure 4B and 4C, top). Using this system, a transient blue light exposure induced AIM2 clustering within seconds and cell death within ten minutes (Figure 4C-D and Movie S3-S4). Next, we evaluated if cell death depended on ASC or FADD. We found that stimulating WT cells with a one second blue laser pulse every 15 minutes killed essentially all of them within one hour (Figure 4E). In contrast, only half of cells lacking ASC (PYCARD-KO) died, and they did so with delayed kinetics consistent with FADD-driven apoptosis.^41–43

Finally, we directly assessed inflammasome nucleation by AIM2^PYD seeds. To do so we reconstituted the PYCARD-KO with mScarlet-I-tagged ASC and titrated its expression to well below endogenous levels so as to circumvent any potential for the fusion tag to enhance the protein’s oligomerization (although this would be unexpected;⁴⁴ Figure S5C-D). We then tracked AIM2^PYD and ASC localization following a 10-second laser pulse. Both proteins began to cluster almost immediately. AIM2^PYD clusters ceased growing by 5 minutes, whereas ASC clusters continued to grow for the full duration of the time course (Figure 4F). The plasma membrane concomitantly permeabilized as a result of gasdermin D (GSDMD) activation,⁴⁵ and to an extent that increased with the level of ASC expression (Figure 4G). These data collectively confirm that endogenous ASC is highly supersaturated prior to stimulation, and that the extent of supersaturation determines the extent of signal amplification.

Given that inflammatory signalosomes frequently initiate programmed cell death, and that a kinetic barrier governs their activity, the susceptibility of cells to aberrant cell death through spontaneous nucleating fluctuations is expected to increase with the level of adaptor supersaturation in vivo. This would constrain cell lifespan if such nucleation occurs with appreciable frequency. To explore this possibility, we asked how adaptor supersaturation (as approximated by the ratio of mRNA levels to C_sat values) relates to the turnover rates of each cell type in the human body.⁴⁶ We found that short-lived cells such as monocytes indeed have greater DFD supersaturation than long-lived cells such as neurons (Figure 4H). Among individual DFD proteins, cell turnover correlated especially strongly with the expression level of ASC (Figure S5F-G), suggesting that lifespan may be limited by the thermodynamic drive for inflammatory signal amplification.

The nucleating interactome is highly specific

DFDs share the same fold and are co-expressed in many of the same cells. While spontaneous nucleation of supersaturable DFDs poses an inherent risk of inadvertently activating end fate cell signalling, this possibility is multiplied by the risk of cross-activation between DFDs in different pathways. To what extent do they nucleate each other? To determine specificity and systematically map the nucleating interactome of DFDs, we mated our library of seed-expressing yeast strains with a library of strains expressing each mEos3-fused DFD (Figure S6A) to create over 10,000 arrayed diploid yeast strains that we then screened by DAmFRET (Figure S6B and C).

In total, we identified 171 nucleating interactions, representing just ∼1.6% of the total library (Figure S6D-F and Table S4). The interactions were largely constrained not just to members of the same subfamily, but to members of the same signaling subnetwork even within subfamilies (Figure 5A-B). For example, CARDs of the CBM signalosome nucleated each other but not CARDs of the inflammasome, and vice versa. As an exception, PYDs of the inflammasome and DEDs of the Death-Inducing Signaling Complex (DISC) nucleated each other (Figure 5C). These interactions are consistent with the residual cell death we observed in the PYCARD FADD double knockout, as well as previously observed crosstalk between these pathways and the close phylogenetic relationship between PYDs and DEDs.^42,47–49

Figure 5.

On the whole, the observed network of nucleating interactions reveal that DFDs from different pathways generally function independently of each other, allowing adaptors to serve as orthogonal energy reservoirs for their respective signalosomes (as schematized in Figure 2E).

DFD nucleation barriers are deeply conserved

While all multicellular organisms have innate immunity, among eukaryotes DFDs occur only in animals. ASC and the PYD subfamily of DFDs first appeared in fish, while FADD and the other DFD subfamilies can be traced all the way back to sponges.^50,51 To investigate conservation of nucleation barriers, we characterized by DAmFRET the phase behaviours of DFD-containing constituents of a basal inflammasome –– NLRP3, ASC, and CASP1 from zebrafish (Danio rerio); and a basal DISC –– A0A1X7U321, FADD, and CASP8 from the model sponge, Amphimedon queenslandica. As for their human counterparts, the fish and sponge adaptors –– but not the receptors and effectors –– were supersaturable (Figure 6A and S7A-B).

Figure 6.

The ancestor of animals acquired DFDs horizontally from bacteria (Figure 6B).^5,52,53 Bacterial DFDs occur predominantly in multicellular lineages where they function with caspases and other effector domains in multi-protein antiphage defence systems.^54,55 While bacteria generally lack ASC– and FADD-like adaptor proteins, our hypothesis predicts that one protein in each signaling pathway will nevertheless feature an intrinsic nucleation barrier. To investigate, we tested via DAmFRET four pairs of DFDs from representative bacterial innate immunity operons (Table S2).⁵⁴ As expected, multiple DFDs had intrinsic nucleation barriers (Table S2). Moreover, consistent with the trend for animal signalosomes, this property was limited to just one DFD in each operon (Figure 6C). These results suggest that the function of DFDs as energy reservoirs preceded the evolution of animals.

Discussion

Our systematic investigation reveals that metastable supersaturation of a select subset of adaptor proteins provides an energetic basis for DFD-mediated signal amplification.

Seminal studies on the structures of DFDs have led to the common view that DFDs generally function to scaffold signaling through homotypic self-assembly.^7,56 We were therefore surprised to find that less than half of the 109 human DFDs can self-assemble, and many of the ones that can, lack sufficient nucleation barriers to appreciably supersaturate, precluding them from amplifying signals without additional energy expenditure by the cell. The only DFDs to supersaturate in full-length protein contexts are the adaptors of inflammatory signalosomes. The sparsity of this property revealed a hierarchy of DFD protein functions, wherein a core set of signal amplifying adaptors each interact with a sequence-specified set of receptors, effectors, and regulators that categorically lack nucleation barriers. Because nucleation involves only a tiny fraction of the supersaturated adaptors, they are poised to amplify minute signals from their cognate receptors and transduce them to their cognate effectors, precisely as required for innate immunity. Because this attribute is conserved across homologous animal adaptors and into the DFD progenitors of multicellular bacteria, we conclude that energy storage for on-demand signaling may be the principal ancestral function of DFDs.

Myd88, a prominent inflammatory adaptor that links Toll-like receptors (TLRs) to pro-inflammatory NF-κB,⁵⁷ defied this trend by assembling without a nucleation barrier in our experiments. This is consistent with its endogenous occurrence as hexameric oligomers prior to stimulation,⁵⁸ and their coalescence to numerous puncta following stimulation.⁵⁹ We speculate that its close collaboration with kinases and ubiquitin ligases, which perform conventional ATP-fueled signal amplification, achieves the switch-like response of TLR pathways. Other ancient innate immune signaling systems such as the viral dsDNA sensor, cGAS, likewise amplify signaling through nucleotide hydrolysis.^5,60

We liken these supersaturated DFD adaptors to ‘phase-change materials’ in industrial thermal batteries, accumulating potential energy for privatized deployment upon nucleation,^61,62 unlike signaling cascades that rely on shared chemical fuels like ATP. As the discharged state of the battery, DFD assemblies are effectively irreversible, committing the cell to terminal responses like death. This battery function rationalizes DFD prevalence over more common assembly modes like liquid-liquid phase separation (LLPS; Figure 6D). While LLPS offers sensitivity that can enhance PAMP detection,^63,64 its rapidly diminishing nucleation barrier with increasing supersaturation^65–67 makes it unsuitable for long-term, high-energy storage needed for rapid signal amplification.

Signalosome effectors activate through simple induced-proximity mechanisms that lack specific structural requirements of the DFDs themselves,^{10,37,68–70} which suggests that the structures of DFD polymers are determined by a function of the solution phase prior to activation. Our finding that the function is energy storage rationalizes the strikingly regular structure of DFD polymers. The evolution of this function would have been driven by selection against premature assembly, favoring paracrystalline structures whose infrequent nucleation allows energy storage over a cell’s lifetime. This differs from selection for a specific functional polymer structure, as seen for example in microtubules. This principle may also explain the use of other paracrystalline modules like TIR domains and functional amyloids in immune signaling, as well as the dearth of non-nucleated polymers like DIX and SAM domains that abound in other signaling pathways.⁷¹

The ability of a protein fold to crystallize has few structural constraints, allowing relatively small changes in sequence to produce orthogonal signaling modules. The resulting evolvability may be essential in the never-ending arms race against pathogens. High specificity also insulates pathways from each other and from cellular processes and metabolic fluctuations that could aberrantly activate them to lethal consequence.^72–74

Our findings imply that cells perpetually await death. The cumulative certainty of stochastic nucleation appears to be reflected in the observed relationship of DFD supersaturation to mortality rates across human cell types. We speculate that this underpins a fundamental tradeoff between innate immunity and lifespan, potentially contributing to age-related inflammation and stem cell exhaustion⁷⁵.

Methods

Reagents and antibodies

Hygromycin B (Invivogen, ant-hg-1), Penicillin-Streptomycin (ThermoFisher, 1514014gp), PMA (BioVision, 1544-5), Puromycin (Invivogen, ant-pr-1), Sytox Orange (ThermoFisher, S11368), CellTox (Promega, G8741), Annexin V Alexa568 (1:200, ThermoFisher, A13202), Annexin V Alexa488 (1:200, ThermoFisher, A13201), Incucyte® Caspase-3/7 Dye (Sartorius, 4440). Antibodies, anti-ASC (Santa Cruz Biotechnology, sc-514414), anti-FADD (Sigma, 05-486), anti-Actin (Santa Cruz Biotechnology, sc-8432) were obtained from the indicated vendors.

Structural analyses

Twelve human proteins contain two DFDs, typically one closely following the other. To determine if the DFDs in such pairs should be evaluated independently or together, we used the predicted aligned error (PAE) matrix generated by AlphaFold3.⁷⁹ PAE is the expected positional error at residue x if the predicted and actual structures are aligned on residue y. Seven of the DFD pairs exhibited very low interdomain PAE scores comparable to those of the component DFDs (Figure S1B, bottom), suggesting a conserved fixed geometric relationship between the domains. We therefore considered these tandem DFDs as single members of their respective subfamilies. Similarly, we excluded the annotated⁶ PYD-like domain of CENP-N because the PAE matrix and experimental structures show that it is in fact part of a larger non-DFD.

Plasmid construction

Yeast expression plasmids were made as previously described.¹² Briefly, we used a high copy episomal vector, V08, which contains inverted BsaI sites to support Golden Gate cloning, followed by a rigid helical linker 4x(EAAAR) and mEos3.1. This vector drives the expression of proteins from a GAL1 promoter and contains the auxotrophic marker URA3. The vector V12 is identical to V08 except that mEos3.1 and linker precedes rather than follows the BsaI sites, for expressing proteins with an N-terminal fusion. Inserts were ordered as yeast codon-optimized GeneArt Strings (Thermo Fisher) flanked by Type IIs restriction sites for ligation between BsaI sites in V08 and V12. Fusions were made opposite the native N– or C-terminus of each DFD to minimize non-native steric effects. All other inserts were cloned into respective vectors via Gibson assembly between the promoter and respective tag. All plasmids were verified by Sanger sequencing. All expression plasmids are listed in Table S1.

Lentivirus vectors were as previously described.¹⁰ Briefly, optogenetic constructs were cloned into pLV-EF1a-IRES-Hygro (Addgene #85134) which encodes a hygromycin B resistance cassette. To create lentiviral vectors expressing the the optogenetic constructs fused with miRFP670nano⁸⁰ and Cry2, the corresponding sequences of AIM2^PYD, APAF1^CARD, NLRC4^CARD were inserted via Gibson assembly into pLV-EF1a-IRES-Hygro. Finally, The doxycycline-controlled lentiviral vectors were cloned via Gibson assembly with the respective coding sequences from PYCARD, CASP9, CASP1, and mScarlet-I into pCW57.1 (Addgene #41393). All lentivirus vectors are listed in Table S3.

Yeast strain construction

Unseeded DAmFRET experiments were conducted using strain rhy1713.¹² To create strains expressing DFD seeds, we first transformed AseI digests of each DFD plasmid along with a plasmid expressing Cas9 and a guideRNA targeting the URA3 markers into rhy2153. This strain contains a genomic landing pad consisting of natMX followed by the tetO7 promoter and counterselectable URA3 ORFs derived from C. albicans and K. lactis, and stop-µNS-mCardinal as described.¹⁰ Successful integration of the insert replaces the URA3 marker with the gene of interest and fuses to the protein’s C-terminus μNS-mCardinal, under the control of a doxycycline-repressible promoter. Transformants were selected for resistance to 5-FOA and validated for successful seed integration by detection of mCardinal expression using flow cytometry. The arrayed library of resulting strains was then mated to each of the rhy1713 strains expressing separate DFD-mEos3.1 fusions, by pinning each pair of strains together onto agar omnitrays containing SD-URA+NAT+dox media. The resulting colonies were then pinned into liquid SD-URA+NAT+dox for continued diploid selection and creation of glycerol stocks. The entire nucleating interaction screening consisted of 384 96-well plates.

DAmFRET assay preparation and data collection

We performed DAmFRET as previously described.¹² Briefly, single transformant yeast colonies were inoculated in 200 μL of SD-URA in a 96-well microplate well and incubated in a Heidolph Titramax platform shaker at 30°C, 1350 RPM overnight. Cells were washed with sterile water, resuspended in galactose-containing media, and allowed to continue incubating for approximately 20 h. Microplates were then illuminated for 25 min with 320–500 nm violet light to photoconvert a fraction of mEos3 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.

For the nucleating interaction screen, glycerol stock plates were pinned into liquid SD-URA without dox and incubated for 16 h at 30°C with 1350 RPM shaking overnight. We then resuspended cells in fresh SD-URA media and continued incubation for an additional 20 h. After this, we resuspended cells in SGal-URA and continued incubation for 20 h to induce protein expression. Finally, we resuspended cells in fresh SGal-URA for 4 h prior to DAmFRET data collection. The library was then consolidated into 96 384-well plates.

DAmFRET data were collected on a ZE5 cell analyzer cytometer. 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 425/35 nm or 593/52 nm emission, respectively. Acceptor fluorescence was detected with 561 nm excitation and 589/15 nm emission. For each well, we collected a volume of 13 μL, resulting in approximately 500,000 events 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). For nucleating interactions, we included an additional channel for mCardinal intensity with 561 nm excitation and 670/30 nm emission.

DAmFRET data analysis

Data were processed on FCS Express Plus 6.04.0015 software (De Novo). Events were gated for single unbudded cells by FSC vs. SSC, followed by gating of live cells with low autofluorescence and positive donor and acceptor fluorescence. With the exception of TNFRSF10A^DD (TRAIL-R1) (rhx2933) which failed to express with either its C– or N-terminus tagged, all expression plasmids were processed. Plots represent the distribution of AmFRET (FRET intensity/acceptor intensity) vs. acceptor intensity (protein expression).

We then analysed the data as previously described.¹³ Briefly, FCS files were gated using an automated R-script running in flowCore. Before gating, the forward scatter (FS00.A, FS00.W, FS00.H), side scatter (SS02.A), donor fluorescence (FL03.A), and autofluorescence (FL17.A) channels were transformed using a logicle transform in R. Single cells were gated using FS00.A vs SS02.A and FS00.H vs FS00.W. These were gated for expressing cells using FL03.A vs FL17.A. Cells falling within these gates were then exported as FCS3.0 files for further analysis.

DAmFRET histograms were divided into 64 logarithmically spaced bins across a predetermined range large enough to accommodate all data sets. The upper gate values were determined for each bin as the 99th percentile of the DAmFRET distribution in that bin. We used the expression of mEos3.1 alone to delineate the region of a DAmFRET plot that corresponds to no assembly. For all samples, cells falling above this region are considered to contain protein assemblies (FRET-positive). The fraction of cells in the assembled population was plotted as a ratio to the total cells in the bin for all 64 bins. The gross fraction of such cells expressing a given protein is reported as fgate.

Determination of continuity

We initially attempted to classify each DAmFRET dataset as one-state or two-state, and discontinuous or continuous for the latter, using an algorithm previously developed for this purpose.⁸¹ However, the algorithm invariably misclassified discontinuous datasets as “continuous” when the AmFRET level of the high FRET state changed with concentration, i.e. exhibited positive or negative slope as for FAS^DD and CASP2^CARD, respectively. Due to this limitation, we adopted a different method.

To determine the continuity of an adequately expressed plasmid, we analysed the distribution of AmFRET values about the transition region. To do so, we fit a spline to the median AmFRET values across binned concentrations that met a density cutoff defined by a minimum of 20 cells, a minimum cell density of 500, and no more than 25% of cells being defined as an outlier. The bin density is measured by the number of cells divided by the interquartile range (IQR) of AmFRET values within that bin. The number of bins was determined using Scott’s rule for number of bins in a histogram. The median AmFRET values were calculated for each bin, and then denoised using the Python SciPy.signal.sosfiltfilt, resulting in the spline fit. This was bootstrapped 100 times with the average of all bootstraps reported as the final spline fit. The resulting spline was used to determine the transition region. The transition point is defined as the concentration with the greatest change in AmFRET. Therefore, this value was calculated as the maximum of the first derivative of the fitted spline, using the numpy.diff package. The transition range is defined as the region between the maximum positive and negative rate of change, indicating where the transition between FRET states is starting and ending, respectively. The transition starting point is calculated as the maximum of the second derivative that lies before the transition point, and the transition endpoint is calculated as the minimum of the second derivative that lies after the transition point. This is done on all 100 bootstrapped splines, with the median of each measure reported. Hartigan & Hartigan’s dip test for unimodality was used to determine the continuity of AmFRET values within the determined transition range. Plots are classified as “discontinuous” or “continuous” for p-values less than or greater than 0.05, respectively.

Plasmids that were determined to have a continuous transition were further classified as uniformly low (“low”), uniformly high (“high”), or transitioned from low to high with increasing concentration (“low to high”). This was done by extracting the minimum and ending AmFRET values from the spline fit. As a reference for the AmFRET value that indicates the start of a high FRET state, the AmFRET value of the transition start point of a control plasmid, rhx0927, was used. For each plasmid, if its minimum and ending AmFRET values fell below the reference, it was classified as low. If both fell above, it was classified as high. Those with a minimum value below and an ending value above were classified as “low to high”. The majority classification of replicates for each plasmid is reported.

Calculating centrality measures

To determine betweenness and degree of centrality, we extracted interactions involving DFD-containing proteins (listed in Table S1) from STRING version 12.0,⁸² considering only physical interactions with scores of 900 or higher. Using NetworkX v3.1, we analysed these interactions as an undirected graph to calculate betweenness and degree of centrality. For proteins with multiple DFDs, we classified a protein as discontinuous if any of its DFDs were identified as discontinuous.

Determination of positive nucleating interactions

We first excluded files with fewer than 2500 events positive for mEos3 or mean acceptor intensities less than 3.5 p.d.u. From this, any DFD or seed left with less than 25% of their original instances after filtering were removed from the analysis completely. Next, we identified nucleating interactions as DFD pairs that decreased the C50 and increased the fraction assembled (fgate). We standardized all variables for each experimental batch of DFDs to a mean of 0 and variance of 1. We then determined the outlier degree for C50 and fgate based on the number of interquartile ranges below or above the median for these values. This was done directionally on a per-DFD basis. We defined the “nucleating interactions” for a given mEos3-fused DFD as those whose mean of these two values (reported as “seedability”) is greater than or equal to 3 standard deviations above the mean of all seedability values. We confirmed that the seedability values for most DFD pairs partitioned with that of two negative controls included for each DFD.

To evaluate the reproducibility of our assay, we replicated it for a set of 36 DFDs. This replicate analysis mirrored the original, except it utilized the DFD distributions and cutoff values from the first set. The Pearson correlation (R) between the two sets was 0.91 (p < 0.0001). To minimize the impact of random variations in negatives and outliers, we excluded double negative instances, resulting in a slightly altered Pearson correlation (R) of 0.90 (p < 0.0001). Of the 3423 DFD + seed combinations reassessed, 16 showed inconsistent hit-calling, indicating an assay consistency rate of 99.53% with a 95% confidence interval ranging from 99.30% to 99.76%.

Approximating C_sat and supersaturability

To generate an average DAmFRET curve, we computed the mean of each histogram bin in the DAmFRET dataset, focusing on bins containing a minimum of 100 cells. The average DAmFRET curve for each DFD in the presence of its self-seed was fit to a Weibull function as follows. We first calculated the average AmFRET value in each concentration bin. The resulting curves resemble the fraction-assembled curves except that the asymptote is the maximum AmFRET value rather than 1. Therefore, we used the following equation for fitting:

Here Amp is the AmFRET asymptotic value of the curve, c is the concentration, C50_seeded is the concentration at which the curve, in the presence of its self-seed, has reached 50% of its asymptotic value, and a describes the steepness of the stretched exponential. Initial values of the parameters were chosen based on Gaussian smoothed versions of the curves and constrained in the fit to at minimum a 2-fold change from those initial guesses. The a parameter was constrained between 0.1 and 10 based on expected reasonable values of that parameter. At low and high concentration values, the average AmFRET values are clearly unstable and influenced by noise and minor compensation errors. Therefore, we chose the beginning and ending points of each curve by visual inspection, choosing starting points where the curve begins to increase and ending points where the curve levels off. Error values were determined from Monte Carlo simulations as in the fitting of fraction assembly.

To approximate the supersaturability of DFDs, we used the ratio of the average C50 in the presence of the respective DFD’s seed (C50_seeded) to the average C50 in the presence of a null-seed (C50_stochastic). Ratios were only calculated between C50s obtained from the same batch run.

Cell culture

HEK293T cells and THP-1 cells were purchased from ATCC. THP-1 PYCARD-KO (thp-koascz) cells were purchased from InvivoGen. 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 1640 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).

Generation of stable cell lines

Stable cell lines were created as described.¹⁰ Briefly, 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. Lentivirus was harvested and incubated with 293T with polybrene or infected at 1000x g for 1 h for THP-1 cells. For transduction of pCW57.1 derived vectors, HEK293T and THP-1 cells were selected with Puromycin (1 μg/mL) for 7 days. After this time, cells were sorted for positive expression of mScarlet-I and expanded in continuing selection with puromycin. For transduction of plasmids encoding fusions to miRFP670nano-Cryclust, THP-1 and HEK293T cells were selected with hygromycin B (350 μg/mL and 150 μg/mL, respectively) for 7 days. Cells were sorted for positive expression of miRFP670nano and expanded for further experiments with continued selection. To generate THP-1 PYCARD-KO + FADD-KO cells, sgRNA targeting FADD exon1 was cloned into the lentiCRISPR v2-Blast (Addgene #83480). This vector was packaged into lentivirus as described above. THP-1 PYCARD-KO cells were transduced using spinfection and supplemented with polybrene. 24 h after spinfection, media was replaced. 48 h 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 analysed by immunoblot to confirm the absence of FADD protein and sequence-verified.

High-content imaging analysis

High-content imaging was performed on the Opera Phenix high-content screening system (PerkinElmer) using a 63x water immersion objective. Briefly, yeast (rhy2977) transformed with individual plasmids were cultured and induced as for DAmFRET assays. Then, 10 μL were transferred into a well containing 90 μL of SGal-URA of a 96-well optically clear flat-bottom plate (PerkinElmer 6055302). Data analysis of the high content imaging was performed in Fiji.

Images of mEos3 were acquired using 488 nm excitation and a standard GFP filter set. Small z-stacks were acquired over 5 µm total range with 1 µm steps. The image containing the brightest mEos3 signal was used. The mEos3 signal was then background-subtracted with a rolling ball radius of 100 pixels then found and converted to Fiji ROIs using the Fiji Default method of traditional image thresholding. The mean, standard deviation, and aspect ratio (AR) were measured for each object. The coefficient of variation (CV) in pixel intensity was calculated for every object following the formula: CV = Std Dev/Mean*100. The wells were divided into 3 categories based on their AR and CV. Objects that had a CV > 55 and an AR > 1.159 were designated “fibrillar”. Objects that had a CV > 55 and an AR < 1.16 were designated “punctate”. Finally, objects that had a CV < 55 and an AR < 1.16 were designated “diffuse”. These cutoffs were determined manually from a visual inspection of the data. The results were then manually verified for all wells. Three plasmids (rhx2935, rhx2637, rhx0989) were inconsistently classified by these cutoffs due to heterogeneous morphologies of their assemblies. Three others (rhx1113, rhx1097, rhx2937) had anomalously high AR due to low expression. Plasmids rhx4763 – rhx4767 were acquired in a second data set that used different laser powers and integration times. Hence, for these plasmids specifically, “fibrillar” had a CV > 17 and an AR > 1.4, “punctate” had a CV > 17 and an AR < 1.41, and “diffuse” had a CV < 18 and an AR < 1.41. These cutoffs were determined manually from a visual inspection of the data. The results were then manually verified for several wells.

Fluorescence microscopy and optogenetic nucleation

The yeast and mammalian cells were imaged in an LSM 780 microscope with a 63x Plan-Apochromat (NA = 1.40) objective. T-Sapphire was excited with a 405 nm laser. mEos3 and mScarlet-I were excited with a 488 nm and 561 nm laser, respectively. For time-lapse imaging, samples were maintained at 37°C and 5% CO₂ with a stage top incubator. To stimulate Cry2clust we used the 488 nm laser at a power setting of 50% for a pulse of 10 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. Pyroptosis events were tracked by incorporating the Sytox Orange reagent into the cell. To quantify the CV, images were subjected to an in-house Fiji adapted implementation of Cellpose for cellular segmentation.⁸³ The Cellpose-generated regions of interest (ROIs) were used to measure specified imaging channels.

For quantification of cell death events using IncuCyte (Sartorius), THP-1 cells were plated on a 24 or 96 well plate at a density of 4×10⁸/well or 1×10⁸/well, respectively, with PMA (10 ng/mL) for 16 h. Media was replaced with fresh media supplemented with Annexin V-Alexa488 and Sytox Orange (1:1000). For AIM2-Cry2clust activation, an initial collection of unexposed measurements was taken for 30 min. Then, the plate was exposed to 488 nm laser every 5 min. For treatments with poly(dA:dT), cells were treated and immediately subjected to imaging every 30 min for 19 h. Positive cells for either fluorophore were identified using the integrated software in the IncuCyte instrument.

For optogenetic activation of APAF1^CARD and NCLR4^CARD, HEK293T cells expressing lentivirus constructs were seeded on a 35-mm dish (ibidi) at a density of 4×10⁴/mL with 2 mL of media. The next day, dox was added at a concentration of 1 µg/mL to induce the expression of mScarlet-I tagged proteins. 24 h after protein induction, media was replaced with fresh media supplemented with Incucyte® Caspase-3/7 Dye (1:1000) 2 h prior to the experiment or Annexin V-Alexa488. Cells were imaged 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). A region of interest (ROI) was selected to induce optogenetic activation for indicated times using a 488 nm laser at 50% laser power for the indicated time ranging from a fraction of a second to 30 s. ROIs were generated by Cellpose segmentation algorithm around each cell contour. These ROIs were then used to measure the area, mean, standard deviation, and integrated density of each cell on the 488 nm and 560 nm fluorescence channels.

Protein immunodetection

We performed capillary based protein immunodetection (Wes, ProteinSimple) as described.¹⁰ Briefly, protein lysates were prepared as per recommended manufacturer instructions to a final concentration of 1 μg/mL. An assay plate was filled with samples, blocking reagent, primary antibodies (1:50 dilution for anti-Actin, 1:200 dilution for anti PYCARD), HRP-conjugated secondary antibodies and chemiluminescent substrate. The plate was subjected to electrophoretic protein separation and immunodetection in the fully automated capillary system. The rulting data was 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 western blot, cells were centrifuged at 1000 x g for 5 min and resuspended in lysis buffer (50 mM Tris (pH 7.4), 137 mM NaCl, 1 mM EDTA, 1% Triton X-100, 10 mM DTT (dithiothreitol), cOmplete Protease Inhibitor (1 tablet / 10 mL) (Roche, 11697498001). Protein lysates were resolved on a NuPAGE™ 4 to 12%, Bis-Tris gel and transferred onto a PVDF membrane (IPVH00010, Millipore) using the Pierce Power Blotter (ThermoFisher). The membrane was blocked with 5% skim milk and incubated overnight with the antibodies: anti-Actin (1:1000, sc-8432), anti-FADD (1:500, 05-486) and anti-PYCARD (1:1000, sc-514414). Primary antibody was removed by several washes with TBS + 0.1% Tween-20 and then subjected to incubation with secondary antibody (anti-mouse-HRP, 7076S, Cell Signaling Technology). The detection of protein bands was then carried out using enhanced chemiluminescence (ECL) (SuperSignal West Pico Chemiluminescent Substrate, ThermoFisher, 34577). The chemiluminescent signal was acquired by placing the membrane in a film cassette and exposing it to X-ray film (Kodak) at varying durations in a darkroom. After exposure, films were developed using an automatic film processor.

Semidenaturating Detergent-Agarose Gel Electrophoresis (SDD-AGE)

SDD-AGE was performed as previously described.¹² Briefly, cells were lysed using a 2010 Geno/Grinder with bead-beating. Samples were prepared with 2% sarkosyl and separated in a 1.5% agarose gel with 0.1% SDS. The distribution of mEos3-fused proteins was analysed directly in the gel with a GE Typhoon Imaging System. Images were processed to remove background using a 250-pixel rolling ball, cropped, and contrast-adjusted.

Quantification and statistical analysis

Two-sided Student’s t-tests were used for significance testing unless stated otherwise for two sample comparisons. The graphs represent the means ± SEM of independent biological experiments unless stated otherwise. Statistical analysis was performed using GraphPad Prism 9, Python and R packages.

Supplementary figures

Figure S1.

Figure S2.

Figure S3.

Figure S4.

Figure S5.

Figure S6.

Figure S7.

Supplementary text

Experiments to investigate the nature of DFD assembly

DAmFRET data do not clarify if self-assembly involves native DFD interactions rather than amyloid-like misfolding.¹² To address this question, we introduced point mutations to disrupt assembly via conserved known interfaces between folded DFD subunits.^16,27 Across the multiple DFDs examined, all such mutations indeed reduced or eliminated the high-AmFRET population (Figure S2A). To directly evaluate the nature of DFD assembly in our experiments, we subjected the seeded and unseeded cells to semi-denaturing detergent-agarose gel electrophoresis (SDD-AGE), a technique that distinguishes amyloids from other protein states based on their detergent-resistance and size dispersity.⁸⁴ We found that unlike our amyloid control (RIPK1^RHIM), none of the DFD assemblies survived sarkosyl exposure (Figure S2B), consistent with their retaining the death fold rather than misfolding into amyloid. Ongoing work is elucidating the physical basis of these nucleation barriers.

Data availability

All DAmFRET data can be accessed interactively on a dedicated website https://simrcompbio.shinyapps.io/HalfmannLab-Nucleating_Interactome/. Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-2387

Acknowledgements

We thank Nick Grishin and Lisa Kinch for creating structure-guided alignments of DFDs early in this work, and Mark Miller for assistance with illustrations. 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.

Additional information

Author Contributions

Conceptualization, A.R.G., S.V., and R.H.; Methodology, A.R.G., T.M., S.V., J.J.L., J.R.U. and R.H.; Formal Analysis, A.R.G., T.M., J.J.L. and J.R.U.; Investigation, A.R.G., T.M., S.V., J.J.L., J.W., X.S., and D.B.; Writing – Original Draft, A.R.G. and R.H.; Writing – Review & Editing, A.R.G., S.V., T.M. and R.H.; Funding Acquisition, A.R.G. and R.H.; Supervision, R.H.

Funding

National Institutes of Health (R01GM130927)

National Institutes of Health (F99AG068511)

American Cancer Society

https://doi.org/10.53354/acs.rsg-19-217-01-ccg.pc.gr.90697

Stowers Institute for Medical Research

Additional files

Supplemental Table 1. Metadata, construct specifics, and data for the human DFDs analysed in this study, related to Figures 1 and 2. ND (not determined). NA (not applicable/available). FL (full-length). ^arhx1073b, rhx1102b, rhx1108, and rhx1135 did not have a majority automated classification and were therefore classified by manual inspection. ^bInspection of the DAmFRET plots of the six nonseedable discontinuous DFDs reveal that most are only slightly discontinuous, and include CARD9^CARD which we previously showed to have a negligible nucleation barrier,²⁷ indicating that at least some of these six are misclassified due to heterogeneity between cells around C_sat.

Supplemental Table 2. Metadata, construct specifics, and data for additional proteins analysed in this study, related to Figure 2. FL (full-length). ^aMutations were made to inactivate enzymatic activity for cell death effectors. ^brhx4655 did not have a majority automated classification due to differences in expression level between experiments, and was therefore classified by manual inspection.

Supplemental Table 3. Specifics of lentivirus vectors used in this study, related to Figures 4 and 5.

Supplemental Table 4. Nucleating interactome of DFDs, related to Figure 5. Note that plasmids rhx2938, rhx1071, rhx2934, rhx1113, rhx1064, rhx2933, and rhx1368; and seeds from rhx2938, rhx2933 and rhx2934; did not pass quality control as outlined in methods and were therefore omitted from the analysis.

Supplemental Table 5. Transcript and protein abundance statistics, related to Figure S3.

Supplemental Movie 1. Time-lapse movie of cells expressing opto-APAF1 (top) and CASP9-mScarlet-I (bottom) showing one minute of blue light induction and subsequent recovery, related to Figure 3. Non-supersaturable opto-APAF1 allows clusters to dissolve following stimulation. Scaler bar 10 µm.

Supplemental Movie 2. Time-lapse movie of cells expressing opto-NLRC4 (top) and CASP9(CASP1^CARD)-mScarlet-I (bottom), showing one minute of blue light induction and subsequent recovery, related to Figure 3. Supersaturable opto-NLRC4 continues to drive cluster growth following stimulation. Scaler bar 10 µm.

Supplemental Movie 3. Time-lapse movie of cells expressing opto-AIM2, following 10 seconds of blue light induction, related to Figure 4. Left panel: opto-AIM2. Middle panel: Sytox Orange fluorescence. Right panel: Bright field. Following 10 seconds of blue light induction, opto-AIM2 clusters form, leading to cell death (Sytox Orange uptake). Scale bar: 10 µm.

Supplemental Movie 4. Time-lapse movie of cells expressing opto-AIM2(F27G), following 10 seconds of blue light induction, related to Figure 4. Left panel: opto-AIM2(F27G). Middle panel: Sytox Orange fluorescence. Right panel: Bright field. Following 10 seconds of blue light induction, opto-AIM2(F27G) clusters form and rapidly dissociate, failing to induce cell death. Scale bar: 10 µm.