Author response:
We thank the reviewers for their thoughtful and constructive comments, which greatly helped us to clarify, quantify, and strengthen both our findings and interpretations. Below, we provide a point-by-point response to each comment and describe the corresponding changes made.
Public Reviews:
Reviewer #1 (Public review):
Summary:
The manuscript by Rayan et al. aims to elucidate the role of RNA as a context-dependent modulator of liquid-liquid phase separation (LLPS), aggregation, and bioactivity of the amyloidogenic peptides PSMα3 and LL-37, motivated by their structural and functional similarities.
Strengths:
The authors combine extensive biophysical characterization with cell-based assays to investigate how RNA differentially regulates peptide aggregation states and associated cytotoxic and antimicrobial functions.
Weaknesses:
While the study addresses an interesting and timely question with potentially broad implications for host-pathogen interactions and amyloid biology, several aspects of the experimental design and data analysis require further clarification and strengthening.
Major Comments:
(1) In Figure 1A, the author showed "stronger binding affinity" based on shifts at lower peptide concentrations, but no quantitative binding parameters (e.g., apparent Kd, fraction bound, or densitometric analysis) are presented. This claim would be better supported by including: (i) A binding curve with quantification of free vs bound RNA band intensities ,(ii) Replicates and error estimates (mean {plus minus} SD).
We thank the reviewer for this suggestion. To quantitatively support the binding differences observed in Figure 1A, we have now performed densitometric analysis of the EMSA data and included the results in Figure S1. The analysis showed that the Kd for PSMα3 binding to polyAU and polyA RNA is in the same order of magnitude but lower for the polyAU, indicating a stronger binding. A description was added to the results in lines 137-145 of the revised version.
(2) The authors report droplet formation at low RNA (50 ng/µL) but protein aggregation at high RNA (400 ng/µL) through fluorescence microscopy. However, no intermediate RNA concentrations (e.g., 100-300 ng/µL) are tested or discussed, leaving a critical gap in understanding the full phase diagram and transition mechanisms.
Our initial choice of 50 ng/µL (low RNA) and 400 ng/µL (high RNA) was guided by a broader RNA titration performed by turbidity measurements across 0, 10, 20, 50, 100, 200, and 400 ng/µL (Figure S2 in the revised version). In this screen, turbidity increased up to 50 ng/µL and then decreased dose-dependently from 100–400 ng/µL. We interpret this non-monotonic behavior as consistent with a transition from a dropletrich regime (maximal light scattering at intermediate dense-phase volume) toward conditions where assemblies become larger and/or more compact and sediment out of the optical path. This is described in lines 158-161 of the revised version.
Of note, additional intermediate RNA conditions (100 and 200 ng/µL) are included in Figure S14 (of the revised version). While these experiments were performed under the heat-shock perturbation, they nevertheless support the central point that RNA tunes assembly state across intermediate concentrations rather than producing a binary low/high outcome.
Importantly, we agree with the reviewer that a full phase diagram would be the most rigorous way to define the transition mechanism. However, establishing csat and constructing a complete phase diagram would require systematic measurements of dilute-phase concentrations (e.g., centrifugation/quantification or fluorescence calibration), controlled ionic strength titrations, and time-resolved mapping, which is beyond the scope of the present study. We have therefore revised the text to avoid implying that we provide a complete phase diagram. Instead, we frame our results as a qualitative with multi-assay characterization showing that RNA concentration drives a shift from liquid-like condensates (at low RNA) toward solid-like assemblies (at high RNA), with an intermediate regime suggested by the turbidity transition and supported by additional imaging under stress. Finally, to address the “critical gap” concern directly, we add a sentence (lines 239-241) stating that: “Future work will be required to quantitatively define the phase boundaries and delineate the dominant mechanisms, such as sedimentation, dissolution, or coarsening/aging, across intermediate RNA concentrations.
(3) Additionally, the behaviour of PSMα3 in the absence of RNA under LLPS conditions is not shown. Without protein-only data, it is difficult to assess if droplets are RNA-induced or if protein has a weak baseline LLPS that RNA tunes. The saturation concentration (csat) for PSMα3 phase separation, either in the absence or presence of RNA, should be reported.
In response to the reviewer’s request, we have added Figure 2F, which shows PSMα3 alone in the absence of RNA under the same conditions. PSMα3 does not form droplets in this condition, indicating that condensate formation is RNA-dependent in the tested conditions. This is referred to in the text in lines 190-193 of the revised version. Please see our response about determining the csat in the response to the previous comment.
(4) For a convincing LLPS claim, it is important to show: Quantitative FRAP curves (mobile fraction and half-time of recovery) rather than only microscopy images and qualitative statements.
We have included quantitative FRAP analysis in Figure S4 of the revised version, showing normalized recovery curves along with extracted mobile fractions and half-times of recovery (t₁/₂). These quantitative measurements support the dynamic nature of the PSMα3–RNA. This is referred to in the text in lines 179-184 of the revised version.
(5) The manuscript highly relies on fluorescence microscopy to show colocalization. However, the colocalization is presented in a qualitative manner only. The manuscript would benefit from the inclusion of quantitative metrics (e.g., Pearson's correlation coefficient, Manders' overlap coefficients, or intensity correlation analysis).
In response, we have added quantitative colocalization analysis to the revised manuscript. Specifically, we now report Pearson’s correlation coefficients and Manders’ overlap coefficients for the dual-channel fluorescence microscopy datasets in Figure S5 of the revised version. These metrics provide an objective measure of codistribution and complement the qualitative imaging.
The analysis supports that at low RNA concentrations (droplet/condensate conditions), PSMα3 and RNA show strong colocalization, consistent with RNA being incorporated within, or closely associated with, the peptide-rich phase. In contrast, at high RNA concentrations, where the assemblies are more solid-like/amyloid-positive, the quantitative coefficients decrease, consistent with reduced overlap and an apparent spatial demixing in which RNA becomes partially excluded from the peptide-rich structures. This is referred to in the text in lines 194-203 of the revised version.
(6) In Figures 3 B and 3C, the contrast between "no AT630 at 30 min, strong at 2 h" (50 ng/μL) and "strong at 30 min" (400 ng/μL) is compelling, but a simple quantification (e.g., mean fluorescence intensity per area) would greatly increase rigor.
We have included quantitative analysis of AmyTracker630 fluorescence intensity in Figure S6 of the revised version, reporting the mean fluorescence intensity per area for the indicated conditions and time points. This quantification supports the qualitative differences observed in Figures 3B and 3C. This is now referred to in the text in lines 233-236 of the revised version.
(7) In Figure S3 ssCD data, if possible, indicate whether the α-helical signal increases with RNA concentration or shows a non-linear dependence, which might link to the LLPS vs solid aggregate regimes.
The ssCD spectra displayed in Figure S7 in the revised version (corresponding to Figure S3 in the original submission) show that the α-helical signature of PSMα3 is markedly enhanced in the presence of RNA compared to peptide alone, as evidenced by increased signal intensity, deeper minima, and more pronounced spectral features characteristic of α-helical structure. Importantly, this enhancement is more pronounced at 400 ng/µL Poly(AU) RNA than at 50 ng/µL, particularly after 2 hours of coincubation, indicating that RNA concentration influences the stabilization of α-helical assemblies. This is now more specifically detailed in the text in lines 258-263 of the revised version.
We note that solid-state CD does not allow direct quantitative deconvolution of secondary structure content (e.g., % helix) in the same manner as solution CD, due to sample anisotropy, scattering, and orientation effects inherent to dried or aggregated films. Consequently, our interpretation is qualitative rather than strictly quantitative. The ssCD data therefore suggest a non-linear dependence on RNA concentration, rather than a simple linear dose–response. This is also expected considering that phase transition, suggested by the other findings, is intrinsically non-linear.
(8) In Figure 5B, FRAP recovery in dying cells may reflect artifactual mobility rather than biological relevance. Additionally, the absence of quantification data limits interpretation; providing recovery curves would clarify relevance.
We added quantitative FRAP analysis of the effect on PSMα3 within HeLa cells, shown in Figure S8 of the revised version. Compared to PSMα3 assemblies in vitro, nucleolar PSMα3 exhibits slower fluorescence recovery and a reduced mobile fraction. The nucleolus represents a highly crowded, RNA-rich cellular environment, which is expected to impose additional constraints on molecular mobility and likely contributes to the slower recovery kinetics observed in cells. This is now more specifically detailed in the text in lines 324-333 and discussed in lines 597-607 of the revised version.
(9) The narrative conflates cytotoxicity endpoints (membrane damage, PI staining, aggregates) with localization data (nucleolar foci), creating ambiguity about whether nucleolar targeting drives toxicity or is a consequence of cell death. Separating toxicity assessment from localization analysis, or clearly demonstrating that nucleolar accumulation precedes cytotoxicity, would resolve this ambiguity.
We thank the reviewer for raising this important point. We agree that, in the current dataset, cytotoxicity readouts (membrane damage, PI staining, aggregate formation) and subcellular localization (nucleolar accumulation) are observed in close temporal proximity, which limits our ability to unambiguously assign causality. In the experiments presented here, PSMα3 was applied at concentrations known to induce rapid membrane disruption and cytotoxicity in HeLa cells. Under these conditions, PSMα3 accumulates on cellular membranes and penetrates into the cell and nucleus on very short timescales (seconds to minutes), likely preceding the temporal resolution accessible by standard live-cell fluorescence microscopy. As a result, nucleolar accumulation and cytotoxic endpoints are detected essentially concurrently, precluding a definitive determination of whether nucleolar association actively drives toxicity or occurs as a downstream consequence of membrane permeabilization and cell damage.
We therefore emphasize that, in this study, nucleolar localization is presented as a phenomenological observation consistent with RNA-rich compartment association, rather than as a demonstrated causal mechanism of cytotoxicity. We have revised the Discussion (lines 597-607) to clarify this distinction and to avoid implying that nucleolar targeting is the primary driver of cell death.
We agree that resolving this ambiguity would require systematic time-resolved and concentration-dependent experiments, including analysis at sub-toxic PSMα3 concentrations below the membrane-disruptive threshold, combined with orthogonal imaging approaches. Such experiments are planned for future work but are beyond the scope of the present study.
(10) In Figure 8, to strengthen the LLPS assignment for LL-37, additional evidence, such as FRAP analysis or observation of droplet fusion events, would be valuable. This is particularly relevant given that the heat shock conditions (65 °C for 15 minutes) could potentially induce partial denaturation or nonspecific coacervation.
In response to this comment, we have added FRAP analysis of LL-37 assemblies in the revised manuscript (Figure S12), including representative images and corresponding fluorescence recovery curves. The FRAP measurements show minimal fluorescence recovery over the acquisition window, indicating that the LL-37–RNA assemblies formed under these conditions are largely immobile and solid-like, rather than liquid-like droplets. This is now referred to in the text in lines 458-462 of the revised version.
Reviewer #2 (Public review):
In this paper, Rayan et al. report that RNA influences cytotoxic activity of the staphylococcal secreted peptide cytolysin PSMalpha3 versus human cells and E. coli by impacting its aggregation. The authors used sophisticated methods of structural analysis and described the associated liquid-liquid phase separation. They also compare the influence of RNA on the aggregation and activity of LL-37, which shows differences from that on PSMalpha3.
Strengths:
That RNA impacts PSM cytotoxicity when co-incubated in vitro becomes clear.
Weaknesses:
I have two major and fundamental problems with this study:
(1) The premise, as stated in the introduction and elsewhere, that PSMalpha3 amyloids are biologically functional, is highly debatable and has never been conclusively substantiated. The property that matters most for the present study, cytotoxicity, is generally attributed to PSM monomers, not amyloids. The likely erroneous notion that PSM amyloids are the predominant cytotoxic form is derived from an earlier study by the authors that has described a specific amyloid structure of aggregated PSMalpha3. Other authors have later produced evidence that, quite unsurprisingly, indicated that aggregation into amyloids decreases, rather than increases, PSM cytotoxicity. Unfortunately, yet other groups have, in the meantime, published in-vitro studies on "functional amyloids" by PSMs without critically challenging the concept of PSM amyloid "functionality". Of note, the authors' own data in the present study, which show strongly decreased cytotoxicity of PSMalpha3 after prolonged incubation, are in agreement with monomer-associated cytotoxicity as they can be easily explained by the removal of biologically active monomers from the solution.
We thank the reviewer for this important critique and agree that direct cytotoxicity is most plausibly mediated by soluble PSM species, while extensive fibrillation generally reduces toxicity by depleting these forms, a conclusion supported by our data and by other studies (e.g., Zheng et al 2018 and Yao et al 2019). We do not propose mature amyloid fibrils as the primary toxic entities. Rather, we use the term functional amyloid in a regulatory sense, consistent with other biological amyloids whose fibrillar states modulate activity (e.g., hormone storage amyloids or RNA-binding proteins).
In line with emerging findings, we interpret PSMα3 toxicity as arising from a dynamic assembly process rather than from a single static molecular species. We previously showed that PSMα3 forms cross-α fibrils that are thermodynamically and mechanically less stable than cross-β amyloids and readily disassemble upon heat stress, fully restoring cytotoxic activity (Rayan et al., 2023). This behavior contrasts with PSMα1, which forms highly stable cross-β fibrils that do not recover activity after heat shock, suggesting that the limited thermostability of PSMα3 is an evolved feature enabling reversible switching between inactive (stored) and active states.
Consistent with this view, both PSMα1 and PSMα3 are cytotoxic in their soluble states, yet mutants unable to fibrillate lose activity, indicating that fibrillation is required but not itself the toxic end state (Tayeb-Fligelman et al., 2017, 2020; Malishev et al., 2018). Our other studies further show that cytotoxicity toward human cells correlates with inherent or lipid-induced α-helical assemblies, rather than with inert β-sheet amyloids (RagonisBachar et al., 2022, 2026; Salinas 2020, Bücker 2022). Together, these findings support a model in which membrane-associated, dynamic α-helical assembly, which requires continuous exchange between soluble species and growing fibrils, drives membrane disruption, potentially through lipid recruitment or extraction, analogous to mechanisms proposed for human amyloids such as islet amyloid polypeptide (Sparr et al., 2004).
In the present study, we further show that RNA reshapes this dynamic landscape: while PSMα3 alone progressively loses activity upon incubation, co-incubation with RNA preserves cytotoxicity by stabilizing bioactive polymorphs and condensate-like states, whereas high RNA concentrations promote solid aggregation but nevertheless preserve activity. Thus, aggregation is neither inherently functional nor toxic, but context-dependent and environmentally regulated. Taken together, our data support a model in which PSMα3 amyloids act as a dynamic reservoir, enabling S. aureus to tune virulence by reversibly shifting between dormant and active states in response to environmental cues such as heat or RNA.
This is now discussed in lines 56-76 and 523-553 of the revised version.
(2) That RNA may interfere with PSM aggregation and influence activity is not very surprising, given that PSM attachment to nucleic acids - while not studied in as much detail as here - has been described. Importantly, it does not become clear whether this effect has biologically significant consequences beyond influencing, again not surprisingly, cytotoxicity in vitro. The authors do show in nice microscopic analyses that labeled PSMalpha3 attaches to nuclei when incubated with HeLa cells. However, given that the cells are killed rapidly by membrane perturbation by the applied PSM concentrations, it remains unclear and untested whether the attachment to nucleic acids in dying cells makes any contribution to PSM-induced cell death or has any other biological significance.
We thank the reviewer for this important point and agree that PSM–nucleic acid interactions are not unexpected and that our data do not support a direct intracellular role for RNA binding in mediating cytotoxicity. Accordingly, we do not propose nucleolar or nuclear association of PSMα3 as a causal mechanism of cell death. At the concentrations used, PSMα3 induces rapid membrane disruption, and nucleic acid association is observed along with membrane attachment, precluding conclusions about intracellular function. This limitation is now explicitly clarified in the revised manuscript. The biological significance of our findings lies instead in extracellular and environmental contexts, where PSMα3 encounters abundant nucleic acids, such as RNA or DNA released from damaged host cells or present in biofilms as now addressed in lines 622631. Our data show that RNA modulates PSMα3 aggregation trajectories, shifting the balance between liquid-like condensates and solid aggregates, and thereby regulates the persistence and timing of cytotoxic activity. In this framework, RNA acts as a context-dependent regulator of virulence, rather than as an intracellular cytotoxic cofactor, an aspect which would be studied in depth in future work. This is now addressed in the text in lines 597-607 of the revised version.
Reviewer #3 (Public review):
Summary:
The manuscript by Rayan et al. aims to investigate the role of RNA in modulating both virulent amyloid and host-defense peptides, with the objective of understanding their self-assembly mechanisms, morphological features, and aggregation pathways.
Strengths:
The overall content is well-structured with a logical flow of ideas that effectively conveys the research objectives.
Weaknesses:
(1) Figure 2 displays representative FRAP images demonstrating fluorescence recovery within seconds. To gain a more comprehensive understanding of how recovery after photobleaching varies under different conditions, it is recommended to supplement these images with corresponding quantitative fluorescence recovery curves for analysis.
In response to this comment, we have supplemented the representative FRAP images with quantitative fluorescence recovery curves, reporting normalized recovery kinetics for the indicated conditions. These data are now provided in Figure S4 of the revised manuscript, allowing direct comparison of recovery behavior across conditions (shown by microscopy in Figure 2). In addition, we have included quantitative FRAP analyses for the cellular imaging shown in Figure 5 (presented in Figure S8) and for LL-37 assemblies formed under heat-shock conditions (Figure S12). Together, these additions provide a quantitative framework for interpreting the FRAP results and strengthen the distinction between liquid-like and solid-like assembly states.
(2) Ostwald ripening typically leads to the shrinkage or even disappearance of smaller droplets, accompanied by the further growth of large droplets. However, the droplet size in Figure 2D decreases significantly after 2 h of incubation. This observation prompts the question, what is the driving force underlying RNA-regulated phase separation and phase transition?
We thank the reviewer for this observation. Across multiple samples, we consistently observe a coexistence of small droplets and larger aggregates, rather than systematic growth of larger droplets at the expense of smaller ones or a uniform decrease in droplet size. In addition, the timescales examined do not allow us to reliably assess whether diffusion-driven droplet coalescence is fast enough to draw firm conclusions about droplet size evolution. This is now addressed in the text in lines 181-184 of the revised version.
A decrease in droplet size over time is nevertheless observed in some instances and is more consistent with a time-dependent conversion of initially liquid-like condensates into more solid-like assemblies, which would reduce molecular mobility and suppress droplet coalescence. In parallel, progressive fibril formation may act as a sink for soluble peptide, leading to partial dissolution or shrinkage of less mature condensates. Together, these observations are consistent with a non-equilibrium aging process, in which RNAregulated assemblies evolve from dynamic condensates toward more solid structures rather than following equilibrium Ostwald ripening.
(3) The manuscript aims to study the role of RNA in modulating PSMα3 aggregation by using solution-state NMR to obtain residue-specific structural information. The current NMR data, as described in the method and figure captions, were recorded in the absence of RNA. Whether RNA binding induces conformational changes of PSMα3, and how these changes alter the NMR spectra? Also, the sequential NOE walk between neighboring residues can be annotated on the spectrum for clarity.
The solution-state NMR experiments were performed specifically to characterize the potential binding of EGCG to PSMα3. Due to the strong tendency of PSMα3 to undergo rapid aggregation and line broadening upon RNA addition, solutionstate NMR spectra in the presence of RNA could not be obtained at sufficient quality for residue-specific analysis. As suggested, we have updated and annotated the sequential NOE walk between neighboring residues on the relevant NOESY spectra to improve clarity.
(4) The authors claim that LL-37 shares functional, sequence, and structural similarities with PSMα3. However, no droplet formation was observed of LL-37 in the presence of RNA only. The authors then applied thermal stress to induce phase separation of LL-37. What are the main factors contributing to the different phase behaviors exhibited by LL37 and PSMα3? What are the differences in the conformation of amyloid aggregates and the kinetics of aggregation between the condensation-induced aggregation in the presence of RNA and the conventional nucleation-elongation process in the absence of RNA for these two proteins?”
We appreciate this important question and have clarified both the basis of the comparison and the origin of the divergent phase behaviors of LL-37 and PSMα3. While PSMα3 and LL-37 share key properties as short, cationic, amphipathic α-helical peptides that self-assemble and interact with nucleic acids, they differ fundamentally in their assembly architectures. PSMα3 is an amyloidogenic peptide that forms cross-α amyloid fibrils, in which α-helices stack perpendicular to the fibril axis. In contrast, LL-37 can form fibrillar or sheet-like assemblies (observed in cryo grids), but these lack canonical amyloid features without clear cross-α or cross-β amyloid order, as so far observed by crystal structures. This is now clarified in different parts of the text of the revised version. Thus, the comparison between the two peptides is functional and physicochemical rather than implying identical amyloid mechanisms. These structural differences likely underlie their distinct phase behaviors.
Because LL-37 does not follow a classical amyloid nucleation–elongation pathway, and high-resolution structural information (e.g., cryo-EM) is currently lacking, partly due to its sheet-like, non-twisted morphology (unpublished results), it is not possible to directly compare aggregation kinetics or nucleation mechanisms between LL-37 and PSMα3. It is possible that amyloidogenic systems such as PSMα3 exhibit greater flexibility in prefibrillar and fibrillar polymorphism, enabling RNA-regulated phase behavior, whereas nonamyloid assemblies such as LL-37 are more prone to stress-induced solid aggregation. We note that this interpretation is necessarily tentative and does not imply a general rule, but rather reflects differences evident in the present system.
