1. Biochemistry and Chemical Biology
  2. Microbiology and Infectious Disease

Cross-talk between individual phenol-soluble modulins in Staphylococcus aureus biofilm enables rapid and efficient amyloid formation

  1. Masihuz Zaman
  2. Maria Andreasen  Is a corresponding author
  1. Aarhus University, Department of Biomedicine, Denmark
https://doi.org/10.7554/eLife.59776
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  1. Masihuz Zaman
  2. Maria Andreasen
(2020)
Cross-talk between individual phenol-soluble modulins in Staphylococcus aureus biofilm enables rapid and efficient amyloid formation
eLife 9:e59776.
https://doi.org/10.7554/eLife.59776
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Abstract

The infective ability of the opportunistic pathogen Staphylococcus aureus, recognized as the most frequent cause of biofilm-associated infections, is associated with biofilm-mediated resistance to host immune response. Phenol-soluble modulins (PSM) comprise the structural scaffold of S. aureus biofilms through self-assembly into functional amyloids, but the role of individual PSMs during biofilm formation remains poorly understood and the molecular pathways of PSM self-assembly are yet to be identified. Here we demonstrate high degree of cooperation between individual PSMs during functional amyloid formation. PSMα3 initiates the aggregation, forming unstable aggregates capable of seeding other PSMs resulting in stable amyloid structures. Using chemical kinetics we dissect the molecular mechanism of aggregation of individual PSMs showing that PSMα1, PSMα3 and PSMβ1 display secondary nucleation whereas PSMβ2 aggregates through primary nucleation and elongation. Our findings suggest that various PSMs have evolved to ensure fast and efficient biofilm formation through cooperation between individual peptides.

Introduction

Aggregated proteins in the form of functional amyloids are widespread in nature (Pham et al., 2014). In humans, functional amyloids assist in immunity, reproduction, and hormone secretion (Maji et al., 2009). However, in various bacterial strains they provide structural stability as the major protein component of the self-produced polymeric matrix in biofilms (Dueholm et al., 2010; Evans and Chapman, 2014; Romero et al., 2010; Schwartz et al., 2012). Functional amyloids increase the bacteria’s ability toward a variety of environmental insults, increasing their persistence in the host as well as promoting resistance to antimicrobial drugs and the immune system (Gallo et al., 2015; Marmont et al., 2017; Van Gerven et al., 2018). The well-studied curli machinery in Escherichia coli (Evans and Chapman, 2014), Fap system in Pseudomonas fluorescens (Dueholm et al., 2010), TasA system in Bacillus subtilis (Romero et al., 2010), along with phenol-soluble modulins (PSMs) in Staphylococcus aureus (Schwartz et al., 2012) are some of the major bacterial functional amyloid systems that have been reported so far.

For S. aureus biofilm formation PSMs have been recognized as a crucial factor. In their soluble monomeric form they hinder host immune response by recruiting, activating, and lysing human neutrophils while also promoting biofilm dissociation (Schwartz et al., 2012). However, self-assembly of PSMs into amyloid fibrils fortify the biofilm matrix to resist disassembly by mechanical stress and matrix degrading enzymes (Bleem et al., 2017). The genes encoding the core family of PSMs peptides are highly conserved and located in psmα operon (PSMα1–PSMα4) and psmβ operon (PSMβ1 and PSMβ2), and the δ-toxin is encoded within the coding sequence of RNAIII (Peschel and Otto, 2013 Table 2). High expression of PSMαs,~20 residues in length, increases virulence potential of methicillin-resistant S. aureus (Wang et al., 2007). Moreover, PSMα3, the most cytotoxic and lytic PSM, enhances its toxicity to human cells upon fibrillation (Tayeb-Fligelman et al., 2017). Despite lower concentrations, the larger PSMβs,~44 residues in length, seem to have the most pronounced impact on biofilm structuring (Periasamy et al., 2012). Despite the formation of functional amyloids in S. aureus by PSMs, many questions remain about the intrinsic molecular mechanism by which they self-assemble and what molecular events trigger the formation of fibrillar structure from their monomeric precursor peptide. Here we apply a combination of chemical kinetic studies along with biophysical techniques to explore the relative importance of different microscopic steps involved in the mechanism of fibril formation of PSMs peptides.

Results

Chemical kinetics reveals different aggregation mechanisms for different PSMs

To investigate the dominating mechanism of aggregation for the individual PSMs we used chemical kinetics to analyze the aggregation of all the seven individual PSM peptides under quiescent conditions. Recently, kinetic models of protein aggregation (Knowles et al., 2009; Meisl et al., 2016) have been effectively applied to numerous model systems in biomolecular self-assembly (Cohen et al., 2013; Collins et al., 2004; Meisl et al., 2014). Through these models, the aggregation kinetics ascertain the rates and reaction orders of the underlying molecular events, allowing for the determination of the dominating molecular mechanism of formation of new aggregates. Aggregation kinetics of all seven PSMs peptides (PSMα1–4, PSMβ1 and 2, and δ-toxin) was monitored using Thioflavin T (ThT) fluorescence intensity (LeVine, 1993). For PSMα1, PSMα3, PSMβ1, and PSMβ2 reproducible aggregation curves were observed (Figure 1a–c and Figure 1—figure supplement 1), while for the rest of the PSM peptides (PSMα2, PSMα4, and δ-toxin) no reproducible aggregation was observed (Figure 1—figure supplement 2a–c). An increase in ThT fluorescence is observed for PSMα4 although this was not sigmoidal in shape and also not reproducible (Figure 1—figure supplement 2b). The timescale for the completion of aggregation differs significantly between the PSM ranging from ~1 hr for PSMα3 and up to ~70 hr for PSMα1. Furthermore, the aggregation of PSMβ1 was carried out at concentrations of microgram per milliliter compared to concentrations at milligram per milliliter for the other PSM peptides since at higher concentrations of PSMβ1 the lag-time during aggregation becomes monomer independent suggesting a saturation effect (Figure 1—figure supplement 2d).

Figure 1 with 4 supplements see all
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Experimental kinetic data for the aggregation of phenol-soluble modulin (PSMs) peptides from monomeric peptides.

(a) Aggregation of PSMα1 (0.05–1.0 mg/mL) fitted to a secondary nucleation model. (b) Aggregation of PSMα3 (0.2–1.0 mg/mL) fitted to a secondary nucleation model. (c) Aggregation of PSMβ1 (20–50 µg/mL) fitted to a secondary nucleation model. (d) Aggregation of PSMα1 in the presence and absence of low concentrations of preformed seeds (monomers: 0.5 mg/mL, seeds: 0–250 nM). Significant effects on the rate of aggregation were observed. (e) Aggregation of PSMα3 in the presence and absence of low concentrations of preformed seeds (monomers: 0.4 mg/mL, seeds: 0–250 nM). Significant effects on the rate of aggregation were observed. (f) Aggregation of PSMβ1 in the presence and absence of low concentration of preformed seeds (monomers: 0.025 mg/mL, seeds: 0–100 nM). Significant effects on the rate of aggregation were observed. (g) Aggregation of PSMβ2 (0.05–1.0 mg/mL) fitted to a nucleation-elongation model. (h) Aggregation of PSMβ2 in the presence and absence of low concentration of preformed seeds (monomers: 0.05 mg/mL, seeds: 0–100 nM). No significant effects on the rate of aggregation are evident. (i) Schematic illustration of the microscopic steps in PSM aggregation. Monomers of PSMβ2 nucleate through primary nucleation (rate constant: kn) and the aggregates grow by elongation (rate constant: k+). Additionally monomers of PSMα1, PSMα3, and PSMβ1 nucleate through secondary nucleation on the surface of already existing aggregate (rate constant: k2). All kinetic experiments were carried out in triplicates. Parameters from the data fitting are summarized in Table 1.

Figure 1—source data 1

Kinetic data for phenol soluble peptide aggregation.

https://cdn.elifesciences.org/articles/59776/elife-59776-fig1-data1-v2.xlsx
Table 1
Kinetic parameters obtained from fitting of data in Figure 1 using the web server AmyloFit.

nc and n2 are the reaction order of the primary and secondary nucleation process respectively, kn and k2 are rate constants for the primary and secondary nucleation process, and k+ is the rate constant for the elongation of existing fibrils.

ParametersPSMα1PSMα3PSMβ1PSMβ2
Dominating mechanismSecondary nucleationSecondary nucleationSecondary nucleationNucleation- elongation
Mean squared residual error (MRE)3.11 × 10−31.38 × 10−33.33 × 10−34.75 × 10−3
k+kn (M−nch−2)6.98 × 10−52751.86 × 101848.8
nc (−)7.84 × 10−60.63.920.572
k+k2 (M−nch−2)1295.17 × 1064.23 × 103–
n2 (−)1.66 × 10−30.1230.2–
Table 2
PSM peptide sequences.

The peptide sequence of the seven different PSMs from S. aureus.

PSMα1MGIIAGIIKVIKSLIEQFTGK
PSMα2MGIIAGIIKFIKGLIEKFTGK
PSMα3MEFVAKLFKFFKDLLGKFLGNN
PSMα4MAIVGTIIKIIKAIIDIFAK
PSMβ1MEGLFNAIKDTVTAAINNDGAKLGTSIVSIVENGVGLLGKLFGF
PSMβ2MTGLAEAIANTVQAAQQHDSVKLGTSIVDIVANGVGLLGKLFGF
δ-toxinMAQDIISTIG DLVKWIIDTVNKFTKK

To elucidate the dominating aggregation mechanism of PSMα1, PSMα3, PSMβ1, and PSMβ2, kinetic data were globally fitted at all monomeric concentrations concurrently by kinetic equations using the Amylofit interface (http://www.amylofit.ch.cam.ac.uk/fit) (Meisl et al., 2016). High quality global fits were achieved for all four peptides assuming a secondary nucleation mechanism for PSMα1, PSMα3, and PSMβ1, and a primary nucleation and elongation mechanism for PSMβ2. The presence of a single dominating aggregation mechanism for all four peptides is seen in the linear correlation between the half-time and the initial monomer concentration (Meisl et al., 2016; Figure 1—figure supplement 3a–d). In this simple nucleation-elongation (or linear self-assembly) model, the protein monomers form an initial nucleus with rate constant (kn) and reaction order (nc) which grow by elongation through the addition of monomers to the fibrils ends with rate constant (k+). The secondary nucleation model additionally involves nucleus formation catalyzed by existing aggregates. In this model system, k act as a combined parameter that controls the proliferation through secondary pathways with secondary process rate constant (k2) and secondary pathway reaction order (n2) with respect to monomer (Meisl et al., 2016). Secondary nucleation dominated aggregation mechanisms have previously been reported for disease-related amyloid fibrils, for example Aβ peptides (Cohen et al., 2013; Meisl et al., 2014), insulin (Foderà et al., 2008), α-synuclein (Buell et al., 2014; Gaspar et al., 2017), and islet amyloid poly peptide (Ruschak and Miranker, 2007) whereas the nucleation-elongation model has previously been linked to functional amyloids from E. coli and Pseudomonas (Andreasen et al., 2019).

In order to confirm the dominating mechanism of aggregation of the four peptides based on chemical kinetics, seeded aggregation in a regime of very low seed concentrations (nano-molar range) to monomeric concentration was conducted. This type of experiments delivers a direct means of probing the ability of fibrils to self-replicate (Arosio et al., 2014; Buell et al., 2014). The presence of preformed fibril seeds can accelerate the aggregation process by two different mechanisms, namely elongation and surface-catalyzed secondary nucleation. The presence of low amounts of seeds eliminates the rate limiting step of primary nucleation when secondary nucleation is present but no changes in the kinetics will be observed when only primary nucleation and elongation is present as the low amounts of seed do not eliminate the need for more nuclei to be formed before the elongation process dominates. Indeed a decrease in the lag phase was observed with increasing seed concentration for PSMα1, PSMα3, and PSMβ1 (Figure 1d–f and h), supporting the observation that secondary nucleation is the dominating molecular mechanism for the formation of new aggregates of PSMα1, PSMα3, and PSMβ1 peptide (Cohen et al., 2012). The seeding effect is more clearly visible in PSMα1 in comparison to PSMα3 and PSMβ1. However, if we do the comparison through raw data we have found that almost 50% reduction in lag phase was observed in the presence of low regime of the preformed seeds in PSMβ1. Despite the very fast kinetics of PSMα3, the reduction in lag phase was significant and can be clearly observed (Figure 1e). The aggregation kinetics of PSMβ2 was not affected by the presence of low amounts of seeds confirming the lack of self-replication processes in the form of surface catalyzed secondary nucleation (Figure 1h). The general mechanism underlying formation of new aggregates from monomers of the PSM peptides from both primary and secondary pathways is shown in Figure 1i.

Elongation rates differ by a factor of 1000 between fastest and slowest PSM

The relative contributions of elongation rate constant (k+) were investigated in the presence of high concentration of preformed fibril seeds. The global fitting of the kinetic data yields a product of the elongation rate constant and the primary nucleation rate constant (knk+); however, in the presence of high amounts of preformed seeds, the intrinsic nucleation process becomes negligible, and hence the aggregation under this type of experiments is only dependent on elongation of the aggregates (Cohen et al., 2012). The initial increase in aggregate mass was measured through linear fits to the early points of the aggregation process (Rasmussen et al., 2019; Weiffert et al., 2019; Figure 1—figure supplement 4a, c, e and g). The estimated elongation rate constants for PSMα1 and PSMβ2 were found to be 0.2 mM/h2 and 0.5 mM/h2 respectively, differing by a factor of ~2, which is insignificant. Contrary to PSMα1 and PSMβ2 the estimated elongation rate constant for PSMβ1, which aggregates at very low monomeric concentrations, was found to be 0.2 µM/h2 and hence a factor 1000 smaller than for PSMα1 and PSMβ2. In contrast, the elongation rate constant of PSMα3 was found to be 16.6 mM/h2 exceeding the values of PSMα1 and PSMβ2 ~80- and ~35-fold, respectively. The stronger effect of elongation of PSMα3 in comparison with the other three peptides suggests that interactions with the fibrils of PSMα3 could be important during the assembly reactions in biofilm formation compared with the assembly of free monomers of other peptides into fibrils.

Secondary structure analysis confirms α-helical structure of PSMα3 and β-sheet structure for other PSMs

The changes in secondary structure of the peptides following aggregation was monitored using benchtop circular dichroism (Jasco), synchrotron radiation circular dichroism (SRCD) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. The CD spectra (Jasco) of monomers of all PSMs peptides prior to aggregation all show double minima at 208 and 222 nm indicative of α-helical structure consistent with previous observations (Da et al., 2017; Towle et al., 2016; Figure 2—figure supplement 1a). Additionally, based on peak intensity at 190 and 210 nm, higher degree of α-helical secondary structure is observed for PSMα1 and PSMα3 in comparison to PSMα4 and PSMβ peptides also consistent with previous observations (Laabei et al., 2014). This observed increase in α-helicity in PSMα1 and PSMα3 peptides compared to PSMα4 could be due to the presence of helix stabilizing alanine residue at fifth position in PSMα1 and PSMα3 in comparison to the presence of the helix destabilizing glycine in PSMα4 (Laabei et al., 2014). Upon aggregation the SRCD spectrum of the peptides changes displaying a single minimum at approximately 218 nm (typical β-sheet signal) for PSMα1 and PSMα4, and at 220 nm for PSMβ1 and PSMβ2 indicative of β-sheet rich structure (Figure 2a). The peak positions for PSMα1 and PSMα4 are in good agreement with previous findings (Marinelli et al., 2016); however, we also find amyloid-like structures in the β-group of PSMs. Despite the lack of sigmoidal aggregation curves, for PSMα4 changes in the SRCD spectrum upon incubation was observed. This indicates a transition from α-helical structure to a structure with increased β-sheet content upon aggregation and is consistent with the data previously published (Dueholm et al., 2010; Romero et al., 2010). The SRCD spectrum of aggregated PSMα3 is still displaying a double minimum with minima shifted to 208 nm and 228 nm indicative of α-helical structure being present in the aggregates although this helical structure is different from that observed in the monomeric peptide. This observation is consistent with the reported cross-α-helical structure of PSMα3 aggregates (Tayeb-Fligelman et al., 2017). To further probe the contribution of the individual structural components to the SRCD spectra each spectrum was deconvoluted using the analysis programs Selecon3, Contin, and CDSSTR in the DichroWeb server (Whitmore and Wallace, 2004; Whitmore and Wallace, 2008; Figure 2b and Figure 1—figure supplement 1). Indeed the major structural contribution to the SRCD spectrum for PSMα3 aggregates is α-helical (~70%). For PSMα1 and PSMα4 the major structural components are β-sheet (33% and 35% respectively) and unordered structure (39% and 34% respectively). Despite the single minima indicative of predominantly β-sheet structure observed for PSMβ1 and PSMβ2 the major structural components are α-helix (35% and 40% respectively) and unordered structure (33% and 35% respectively) with less contribution from β-sheet structure (24% and16 % respectively). Monomeric δ-toxin exhibited high degree of α-helicity structure based on CD peak intensity at 190 and 208 nm consistent with previous reports (Laabei et al., 2014). However, no significant structural changes were observed for PSMα2 and δ-toxin, which upon incubation at tested conditions still displayed spectra characteristic of α-helix (Figure 2—figure supplement 1), which is also consistent with the previous observations (Marinelli et al., 2016). However, we observe only one minimum at around 208 nm of δ-toxin in comparison to monomeric δ-toxin, which possess two minima at around 208 and 224 nm (Figure 2—figure supplement 1a and b). This is consistent with the lack of aggregation seen for these peptides by ThT fluorescence.

Figure 2 with 2 supplements see all
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Structural comparison of fibrils formed by different phenol-soluble modulins (PSMs) variants.

(a) Synchrotron radiation (SR) far UV-CD spectra of PSMα1, PSMα3, PSMα4, PSMβ1, and PSMβ2 fibrils recorded after 7 days of incubated samples except for PSMα3 which is recorded after 1 hr of incubated samples. (b) Deconvolution of the SRCD spectra of fibrils of PSM variants into the individual structural components. (c) Fourier transform infrared (FTIR) spectroscopy of the amide I’ region (1600–1700 cm−1) of fibrils of PSMs variants. PSMα1, PSMα4, PSMβ1, and PSMβ2 show a peak at 1625 cm−1 corresponding to rigid amyloid fibrils. In contrast, PSMα3 shows a peak at and 1654 cm−1indicating α-helical structure in the fibrils. (d) Deconvolution of the FTIR spectra of fibrils of the PSM variants into the individual structural components. (e) CD (Jasco) thermal scans from 20°C to 95°C of PSMα1, PSMα3 (1 hr), PSMα3 (7 days), PSMα4, PSMβ1, and PSMβ2 fibrils.

Figure 2—source data 1

Source data for secondary structural analysis of phenol solube modulin aggregates.

https://cdn.elifesciences.org/articles/59776/elife-59776-fig2-data1-v2.xlsx

Consistent with the CD data, the FTIR spectra of PSMα1, PSMα4, PSMβ1, and PSMβ2 were found to be very similar to each other with a well-defined intense peak at ~1625 cm−1 indicative of amyloid β-sheet and a minor shoulder at ~1667 cm−1 indicative of β-turns (Dueholm et al., 2011; Gasset et al., 1992; Zandomeneghi et al., 2004; Figure 2c). The secondary structure composition of the fibrils was estimated using deconvolution of the spectra followed by conventional fitting program and summarized in Figure 2d and Figure 2—figure supplements 2 and Tables 3 and 4. The percent secondary structure contribution and peaks of PSMα1 and PSMα4 from our findings are quite similar to the previous findings (Marinelli et al., 2016) irrespective of different experimental conditions. Additionally, a band around 1654 cm−1 usually assigned to helical/random conformations is also noticed for all peptides, which may reflect a certain equilibrium between residual helical soluble states and a predominant aggregated assembly (Marinelli et al., 2016). Moreover, absence of high intensity signal ~1690 cm−1 (characteristics of anti-parallel beta-sheet) in PSMα1 and PSMα4 suggests that β-sheets are packed in the fibrils in parallel arrangement. In good agreement with the CD data and previous reports, the FTIR spectra of PSMα3 aggregates shows significantly higher content of α-helical structure relative to other PSMs peptide fibrils as shown by a more intense band in the spectrum of at 1654 cm−1, indicative of α-helical structure (Kong and Yu, 2007; Tayeb-Fligelman et al., 2020).

Table 3
Structural contribution to FTIR spectra.

Percentage contribution of various structural components for the fibrils of PSM variants based on deconvolution of FTIR spectra along with peak position.

PeptidePeak position% β-sheet% α-helix% β turns
PSMα11626, 1653,166768.5826.329.10
PSMα31635, 165443.9456.06-
PSMα41625, 165569.1230.88-
PSMβ11625, 165666.8033.2-
PSMβ21624,1644, 166466.2220.0513.73
Table 4
Structural contribution from deconvolution of CD spectra.

Percentage contribution of various structural components for the fibrils of PSMphenol-soluble modulin variants based on deconvolution of SRCD spectra using the DichroWeb server using the reference data setSP175 for the Selecon3, Contin, and CDSSTR analysis programs.

Peptide% α-helix% β-sheet% Turns% Unordered
PSMα116.133.110.538.5
PSMα369.311.36.113.2
PSMα423.431.010.135.4
PSMβ134.523.810.133.0
PSMβ239.516.410.634.7

Freshly formed PSMα3 aggregates are unstable but become stable through lateral association during maturation

The stability of the PSM aggregates was tested using CD spectroscopy (Jasco). Figure 2e shows the CD signal at 220 nm for fibrils (PSMα1, PSMα3, PSMα4, and PSMβ2) from 25°C to 95°C. All peptide fibrils spectra except PSMα3 indicate thermally stable β-sheet structure. Even at 95°C, there is no indication of the loss of β-sheet structure of PSMα1, PSMα4, and PSMβ2, as judged by the stable negative peak at 220 nm. However, freshly formed (1-hr old) PSMα3 fibrils are thermally unstable as a loss of structure is seen above 50°C which is consistent with the previous studies of fragments of PSMα3 (Salinas et al., 2018). Interestingly the lateral association of aggregates of PSMα3 upon further incubation (7 days; see Figure 3) renders the fibrils thermally stable and no changes in structure is seen upon heating to 95°C indicating that the lateral association of the aggregates stabilizes the structure. The stability of the aggregates toward chemical denaturants was tested using urea (Figure 2—figure supplement 1c and d). Again, aggregates of PSMα3 (1-hr old) are the only ones susceptible toward disassembly (5–8 M urea) while no apparent effect is observed for PSMα1, PSMα4, PSMβ1, and PSMβ2 fibrils.

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Morphology of aggregates of phenol-soluble modulin (PSMs) peptides.

Transmission electron microscopic image of the end state of reaction for samples initially composed of (a) PSMα1 fibrils, (b) PSMα3 fibrils after 1 hr of incubation, (c) PSMα3 fibrils after 7 days of incubation, (d) PSMα4 fibrils, (e) PSMβ1 fibrils, and (f) PSMβ2 fibrils. Please note that scale bar changes.

The morphological features of aggregates were examined using transmission electron microscopy (TEM). After 7 days of incubation, PSMα1 formed stretches of entangled fibrils (Figure 3a). In addition, bulky dense aggregates surrounded by a network of fibrils were observed. In contrast, PSMα3 incubated for 1 hr generated short and unbranched fibrils (Figure 3b), which, upon 2-day incubation, associates laterally to form stacks (Figure 3—figure supplement 1c) and further associates to form entangles networks of fibrils after 7 days of incubation (Figure 3c). Aggregates of PSMβ2 showed entangled fibrils marginally thicker and more dispersed than PSMα1 and PSMα3 (Figure 3f). Aggregates of PSMβ1 also formed entangled networks of fibrils (Figure 3e). No aggregated species could be observed for PSMα2 and δ-toxin (Figure 3—figure supplement 1a–b), consistent with the lack of aggregation as seen by the lack of increase in ThT fluorescence upon incubation. Interestingly, PSMα4 that lacked reproducible ThT kinetics but displayed β-sheet structure using CD and FTIR spectroscopy displays very thin fibrils visible at higher magnification with some distribution of spherical aggregates (Figure 3d). However, overall, these data are in good agreement with the recorded kinetics and structural data.

PSMα1 displays promiscuous cross-seeding while other PSMs display selective cross-seeding abilities

The interplay between individual PSM peptides during formation of functional amyloids was investigated using cross-seeding experiments where the ability of aggregates of one PSM peptide to seed the aggregation of the other PSM peptides was tested. Cross-seeding experiments using 20% preformed fibril seeds of PSMα3, PSMβ1, and PSMβ2 and monomers PSMα1 were performed. It can be seen that compared to the non-seeded aggregation seeds of all the other aggregating PSM peptides PSMα3 and PSMβ1–2 accelerated the aggregation process (Figure 4 and Figure 4—figure supplement 1). Similar to PSMα1, PSMβ1 aggregation is also accelerated by the presence of all the other types of preformed fibril seeds, namely PSMα1, PSMα3, and PSMβ2 seeds (Figure 4e). Although the resulting ThT fluorescence intensity is lower than the unseeded aggregates the lag-phase is no longer present when seeds of the other PSM peptides are present. Unlike PSMα1 and PSMβ1 the fast aggregating PSMα3 is cross-seeded by PSMα1 and PSMβ2 but not PSMβ1 (Figure 4c). This indicates that the cross-seeding capability of the PSM peptide aggregates is selective rather than universal among the PSM peptides. Similar to PSMα3 the aggregation of PSMβ2 is accelerated by only PSMα1 and PSMβ1 whereas PSMα3 seeded interestingly enhanced the lag phase of PSMβ2 dramatically (Figure 4f). Due to the presence of 20% preformed fibril seeds the initial ThT fluorescence signal is higher for the seeded experiments compared to the unseeded experiments for all the different PSM peptides. This effect is due to binding of ThT to the preformed fibril seeds at the beginning of experiment.

Figure 4 with 2 supplements see all
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Cross-seeding phenol-soluble modulins (PSMs) variant.

(a) Aggregation of PSMα1 (0.25 mg/mL) in the absence of seeds and in the presence of 20% (20 µM) preformed PSMα3 seeds, PSMβ1 seeds, and PSMβ2 seeds. (b) Aggregation of PSMα2 (0.25 mg/mL) in the absence of seeds and in the presence of 20% (20 µM) preformed PSMα1 seeds, PSMα3 seeds, PSMβ1 seeds, and PSMβ2 seeds. (c) Aggregation of PSMα3 (0.25 mg/mL) in the absence of seeds and in the presence of 20% (20 µM) preformed PSMα1 seeds, PSMβ1 seeds, and PSMβ2 seeds. (d) Aggregation of PSMα4 (0.25 mg/mL) in the absence of seeds and in the presence of 20% (20 µM) preformed PSMα1 seeds, PSMα3 seeds, PSMβ1 seeds, and PSMβ2 seeds. (e) Aggregation of PSMβ1 (0.025 mg/mL) in the absence of seeds and in the presence of 20% (1 µM) preformed PSMα1 seeds, PSMα3 seeds, and PSMβ2 seeds. (f) Aggregation of PSMβ2 (0.25 mg/mL) in the absence of seeds and in the presence of 20% (10 µM) preformed PSMα1 seeds, PSMα3 seeds, and PSMβ1 seeds. (g) Aggregation of δ-toxin (0.25 mg/mL) in the absence of seeds and in the presence of 20% (20 µM) preformed PSMα1 seeds, PSMα3 seeds, PSMβ1 seeds, and PSMβ2 seeds. (h) Schematic representation of the cross-seeding interactions between the PSM variants during biofilm formation.

Figure 4—source data 1

Source data for the cross-seedding of phenol solube modulins.

https://cdn.elifesciences.org/articles/59776/elife-59776-fig4-data1-v2.xlsx

The PSM peptides that were not found to aggregate on their own at the conditions tested here, namely PSMα2, PSMα4, and δ-toxin, could all be induced to aggregate in the presence of different cross-seeds. Both PSMα2 and PSMα4 aggregation could be induced in the presence of PSMα1 and PSMβ1 seeds but not by PSMα3 and PSMβ2 seeds (Figure 4b and d). For δ-toxin aggregation could be induced by the presence of PSMα1 and PSMβ2 seeds but not PSMα3 seeds (Figure 4g). A slight increase in ThT fluorescence is also observed in the presence of PSMβ1 seeds but this is both very slow and a very small increase as compared to the increase seen in the presence of PSMα1 and PSMβ2 seeds.

Based on the cross-seeding analysis it is clear that the PSM peptides display selectivity in the interaction with preformed aggregates, which cannot be explained simply by the sequence similarities with in the PSM α- or β-group indicating an intricate interplay between the various PSM peptides during biofilm formation. It is also clear that PSMα1 is the most promiscuous of the PSM peptides as aggregation of PSMα1 is accelerated by all the other three PSM peptides that aggregate while also being able to accelerate the kinetics of aggregation of all the other PSM peptides, even the ones that do not aggregate on their own. Furthermore, there is no correlation between the presence or absence of secondary nucleation in the dominating aggregation mechanism for the individual peptides and the cross-seeding capacity. PSMβ2 that aggregate through a nucleation-elongation dominated aggregation mechanism can be seeded with some (PSMα1 and PSMβ1) but not all of the peptides (PSMα3) which aggregate through a mechanism dominated by secondary nucleation. On a similar note not all the PSM peptides that aggregate through a mechanism dominated by secondary nucleation can cross-seed each other as PSMβ2 is not cross-seeded by PSMα3. We therefore suggest a model describing the delicate interplay between individual PSM peptides in the formation of biofilm where the fast aggregating PSMα3 initiates the aggregation by forming unstable fibrils. These fibrils can then accelerate the aggregation of PSMα1 that forms stable fibrils capable of accelerating the aggregation of the majority of the remaining PSM peptides (PSMα2, PSMα3, PSMβ1, PSMβ2, and δ-toxin; Figure 4e). In this way the fast kinetics of PSMα3 may act as a catalyst for the whole system of aggregation of PSMs peptides during biofilm formation.

Discussion

PSMs peptides are major determinants and play an important and diverse role in the biofilm matrix in S. aureus (Peschel and Otto, 2013). Previous studies have shown that PSMs from S. aureus form functional amyloids that contribute to biofilm integrity and provide resistance to disruption, which is critical to the virulence of medical device-associated infections (Marinelli et al., 2016; Schwartz et al., 2012). However, there have been no efforts to date to establish a general picture for the self-assembly of PSMs peptides that brings together all the species in the aggregation cascade. In the present study, we have conducted a combination of detailed kinetic analysis with structural and morphological analysis to gain insights into the molecular and mechanistic steps, to determine how functional amyloid of PSMs, the biofilm determinant of S. aureus, forms and grows. This study involves studying separately the different process involved in the aggregation reaction, i.e., initial nucleation steps, growth of fibrils, and their amplification.

Earlier computational analysis of PSMs sequences (smallest staphylococcus toxins) already suggested that the peptides of individual families might display differential self-assembly properties (Marinelli et al., 2016). We first determined the rates of the various microscopic steps (concentration dependent) associated with the aggregation of PSMs peptides. Our results have shown that under quiescent conditions, a dominant contribution to the formation of new aggregates is a fibril catalyzed secondary nucleation pathway that is shared by different variant of the α-PSMs family, which sustain the integrity of biofilms (Schwartz et al., 2012), along with the β-PSM family (PSMβ1). In contrast to this, nucleation and elongation are the only processes contributing to the aggregation of PSMβ2, since the influence of secondary process that gives rise to self-replication of aggregates is negligible for this peptide. It is remarkable that even though PSMα1 and PSMα3 possess seven identical and additional 10 similar amino acids in their sequence (Bleem et al., 2017), they show distinct aggregation behavior as PSMα3 aggregates approximately 50 times faster than PSMα1, which specifies that the existence of distinct residue in PSMα3 might play a significant role in lowering the energy barrier for the steps in the conversion process of monomers to fibrils as observed for Aβ peptides (Meisl et al., 2014). Furthermore the fibrils formed by PSMα3 was found to be initially unstable as also observed before (Tayeb-Fligelman et al., 2017) but upon further incubation the fibrils associate laterally to form more mature stable fibrils while fibrils of PSMα1 was stable without the need for lateral association.

In the current study at quiescent conditions no aggregation kinetics were observed for PSMα4 despite observing β-sheet structure using CD and FTIR and monitoring very thin fibrils with TEM. Previous reports on the aggregation of PSMα4 involved incubation of up to 28 days or incubation under shaking conditions (Marinelli et al., 2016; Salinas et al., 2018). As shaking conditions during aggregation is known to increase fragmentation due to shear forces (Cohen et al., 2013) shaking conditions were excluded in the present study. Compared to the other PSM peptides PSMα4 is the one with both the lowest solubility score and the lowest calculated aggregation propensity when computing these using the CamSol algorithm (Sormanni et al., 2017; Sormanni et al., 2015; Figure 4—figure supplements 2 and Table 5). This could possibly explain the need for long incubation time and shaking conditions during aggregation.

Table 5
Solubility score of phenol-soluble modulin peptides.

Solubility score of different peptides calculated using the Camsol web server (http://www-mvsoftware.ch.cam.ac.uk/index.php/login).

Peptide NameSolubility Score
PSMα10.826149
PSMα20.883944
PSMα31.282062
PSMα40.022660
PSMβ11.038055
PSMβ20.907007
δ-toxin1.242128

Functional amyloids from gram-negative bacteria are mainly composed of a single protein such as CsgA in E. coli curli and FapC in Pseudomonas (Chapman et al., 2002; Dueholm et al., 2010). Along with the proteins incorporated into the functional amyloids a whole range of auxiliary proteins is expressed simultaneously. In the gram-positive bacteria S. aureus the functional amyloids in biofilms is made up of the different PSM peptides (Schwartz et al., 2012). The model suggested here accounts for the role of individual PSM peptides during formation of functional amyloids to stabilize the biofilm, hence allowing the bacteria an efficient way to form functional amyloids in a very short amount of time but at the cost of stability. The stability is later gained by the aggregation of other PSM. The most proinflammatory and cytotoxic PSMα3 (Wang et al., 2007) boosts the reaction of PSMα1 kinetics followed by enhancement of aggregation kinetics of rest of the PSMs peptides, which likely play a key role in stabilizing the biofilm matrix (Schwartz et al., 2012) and influences the biofilm development and structuring activities (Periasamy et al., 2012). The highly stable amyloidal structures thus serve as the building blocks cementing the biofilm and creating the rigidity that can explain the resistance of amyloid-containing biofilms. Overall, we note that the rates of individual kinetic steps in the process can differ by several orders of magnitude between different variants, whereas in previous reports a vast structural diversity of amyloid-like structures have also been reported for PSM peptides (Salinas et al., 2018). Further, in vitro studies also confirmed that contrary to what previously thought (Schwartz et al., 2012), not all PSMs form amyloid structures even at higher concentrations under the conditions tested here. Moreover, cross-seeding results show that given the right conditions, all tested PSMs can aggregate into ThT-binding species.

We conclude that the outcomes presented in this article may have significant implications for understanding the aggregation process of PSMs peptides during biofilm formation. These findings indicate a molecular interplay between individual PSM peptides during accumulation of PSMs amyloid fibrils in biofilms. This also suggests an important approach for suppressing the biofilm growth of S. aureus as PSMs have critical role during infection and represent a promising target for anti-staphylococcal activity (Cheung et al., 2014). Recently potential inhibitors of Aβ aggregation in Alzheimer’s and α-synuclein in Parkinson’s disease have been found to inhibit self-replication by secondary nucleation being the most promising candidate (Cohen et al., 2015). In the case of S. aureus biofilm forming amyloids this could also be a potential strategy as several of the PSM peptides aggregated through a secondary nucleation dominated mechanism. This could be possible by using inhibitors of amyloid formation as numerous studies have demonstrated that inhibitors of aggregation also tend to inhibit biofilm formation (Arita-Morioka et al., 2018; Arita-Morioka et al., 2015). In the context of the development of biofilm formation, the key processes and mechanisms revealed in this study are likely to contribute to the difficulty in controlling and to understanding the role of amyloid growth as a potentially limiting factor of biofilm formation.

Materials and methods

Key resources table
Reagent type
(species) or
resource		DesignationSource or
reference		IdentifiersAdditional
information
Peptide, recombinant proteinPSMα1GenScript Biotech, TheNetherlandsFormylation
(N-terminal)
Peptide, recombinant proteinPSMα2GenScript Biotech, TheNetherlandsFormylation (N-terminal)
Peptide, recombinant proteinPSMα3GenScript Biotech, TheNetherlandsFormylation (N-terminal)
Peptide, recombinant proteinPSMα4GenScript Biotech, TheNetherlandsFormylation (N-terminal)
Peptide, recombinant proteinPSMβ1GenScript Biotech, TheNetherlandsFormylation (N-terminal)
Peptide, recombinant proteinPSMβ1GenScript Biotech, TheNetherlandsFormylation (N-terminal)
Peptide, recombinant proteinδ-toxinGenScript Biotech, TheNetherlandsFormylation (N-terminal)
Chemical compound, drug2,2,2-Trifluoro-acetic acidSigma AldrichSigma T6508
Chemical compound, drugThioflavin TSigma AldrichSigma T3516
Chemical compound, drug1,1,1,3,3,3-Hexafluoro-2-propanolSigma AldrichAldrich-105228
Chemical compound, drugDMSOMerckCAS# 67-68-5
Software, algorithmAmylofithttps://www.amylofit.ch.
cam.ac.uk/
Software, algorithmDichrowebhttp://dichroweb.cryst.bbk.ac.uk/html/home.shtmlRRID:SCR_018125
Software, algorithmCamSolhttp://www-vendruscolo.ch.cam.ac.uk/.uk/camsolmethod.
Software, algorithmOPUS 5.5Bruker
Other96-well plate, half area, polystyrene, non-binding surfaceCorningProduct number 3881

Peptides and reagents

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N-terminally formylated PSM peptides (>95% purity) were purchased from GenScript Biotech, The Netherlands. ThT, trifluoroacetic acid (TFA), and hexafluoroisopropanol (HFIP) were purchased from Sigma Aldrich. Dimethyl sulfoxide (DMSO) was purchased from Merck. Ultra-pure water was used for the entire study.

Peptide pretreatment

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Lyophilized PSM peptide stocks were dissolved to a concentration of 0.5 mg mL−1 in a 1:1 mixture of HFIP and TFA followed by a 5 × 20 s sonication with 30 s intervals using a probe sonicator, and incubation at room temperature for 1 hr. The HFIP/TFA mixture was evaporated by speedvac at 1000 rpm for 3 hr at room temperature. Dried peptide stocks were stored at −80°C prior to use.

Preparation of samples for kinetics experiments

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All kinetic experiments were performed in 96-well black Corning polystyrene half-area microtiter plates with a non-binding surface incubated at 37°C in a Fluostar Omega plate reader (BMG Labtech, Germany). Aliquots of purified PSMs were thawed and dissolved in DMSO to a concentration of 10 mg mL−1 prior to use. Freshly dissolved peptides were diluted into sterile MilliQ water containing 0.04 mM ThT. To each well 100 μL of samples was added and the plate was sealed to prevent evaporation. The ThT fluorescence was measured every 10 min with an excitation filter of 450 nm and an emission filter of 482 nm at quiescent conditions. However, for PSMα3 the measurement was done every 15 s with same excitation and emission wavelength. The ThT fluorescence was followed by three repeats of each monomer concentration.

Pre-seeded kinetic assay

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Fibrils of different peptides were collected and sonicated for 3 × 10 s using a probe sonicator, at room temperature in low bind Eppendorf tubes (Axygen). Seeds were added to fresh monomer of corresponding peptide immediately before ThT measurements. In cross-seeding experiment, seeds (PSMα1 and α3, PSMβ1 and β2) were added to monomer of all other PSMs variants. ThT fluorescence was observed in the plate reader every 10 min under quiescent conditions.

Calculation of the elongation rate constant

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To estimate the rates of fibril elongation seeded aggregation with high concentration of preformed fibril seeds (20–50% of monomeric equivalents concentration) and fixed monomeric concentrations (0.25 mg/mL of PSMα1, 0.5 mg/mL of PSMα3, 0.025 mg/mL for PSMβ1, and 0.25 mg/mL for PSMβ2) was performed. The initial gradients (first 120 min for PSMα1, PSMβ1, and PSMβ2 and the first 120 s for PSMα3) of the kinetic curves were determined and plotted against the monomer concentration. Data points at higher concentration were excluded due to saturation effect of elongation.

Far-UV circular dichroism (CD) spectroscopy

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CD was performed on a JASCO-810 Spectrophotometer at 25°C, wavelength 200–250 nm with a step size of 0.1 nm, 2 nm bandwidth, and a scan speed of 50 nm/min. Samples were loaded in a 1 mm Quartz cuvette. Triplicate samples containing various peptide concentrations of each freshly dissolved peptide were pelleted and supernatant was transferred to clean sterile tube. The remaining pellet was resuspended in the same volume of MilliQ water followed by bath sonication and examined individually in far UV-CD. For each sample, the average of five scans was recorded and corrected for baseline contribution and the milliQ signal was subtracted.

Synchotron radiation circular dichroism (SRCD) spectroscopy

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The SRCD spectra of the various PSM fibrils were collected at the AU-CD beamline of the ASTRID2 synchrotron, Aarhus University, Denmark. To remove DMSO from the solution, fibrillated samples were centrifuged (13,000 rpm for 30 min), supernatants discarded, and the pellet resuspended in the same volume of MilliQ water and pellets of each sample were assayed separately. Three to five successive scans over the wavelength range from 170 to 280 nm were recorded at 25°C, using a 0.1 mm path length cuvette, at 1 nm intervals with a dwell time of 2 s. All SRCD spectra were processed and subtracted from their respective averaged baseline (solution containing all components of the sample, except the protein), smoothing with a seven pt Savitzky–Golay filter, and expressing the final SRCD spectra in mean residual ellipticity. The SRCD spectra of the individual PSM fibrils samples were deconvoluted using DichroWeb (Whitmore and Wallace, 2004; Whitmore and Wallace, 2008) to obtain the contribution from individual structural components. Each spectrum was fitted using the analysis programs Selecon3, Contin, and CDSSTR with the SP175 reference data set (Lees et al., 2006) and an average of the structural component contributions from the three analysis programs was used.

Fourier transform infrared spectroscopy

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Tensor 27 FTIR spectrometer (Bruker) equipped with attenuated total reflection accessory with a continuous flow of N2 gas was used to collect spectra of different aliquots. Fibrillated samples were collected and centrifuged (13,000 rpm for 30 min), supernatants discarded, and the pellet resuspended in half the original volume of MilliQ water to make samples more concentrated. Of each sample 5 µL was spread on ATR crystal and let dry under nitrogen gas to purge water vapors. Absorption spectra were recorded on the dry samples. For each spectrum 64 interferograms were accumulated with a spectral resolution of 2 cm−1 in the range from 1000 to 3998 cm−1 and spectra in the region 1600–1700 cm−1 are represented. The data were processed by baseline correction and interfering signals from H2O and CO2 were removed using the atmospheric compensation filter. Further, peak positions were assigned where the second order derivative had local minima and the intensity was modeled by Gaussian curve fitting using the OPUS 5.5 software. All absorbance spectra were normalized for comparative study.

Transmission electron microscopy (TEM)

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Fibrillated samples (all peptides) were collected following the ThT fibrillation kinetics assay by combining the contents of two to three wells from the plate. Five-microliter samples of all peptides were directly placed on carbon coated formvar grid (EM resolutions), allowed to adhere for 2 min, and washed with MilliQ water followed by negative staining with 2% uranyl acetate for 2 min. Further, the grids were washed twice with MilliQ water and blotted dry on filter paper. The samples were examined using a Morgagni 268 from FEI Phillips Electron microscopy, equipped with a CCD digital camera, and operated at an accelerating voltage of 80 KV.

Fibril stability

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CD (Jasco) spectra of fibrils (PSMα1, α3, and α4 and PSMβ1 and β2) were recorded from 25°C to 95°C with a step size of 0.1°C at 220 nm. Stability toward denaturants was tested by dialyzing fibrils (PSMα1, α3, and α4 and PSMβ1 and β2) containing various concentrations of urea (0–8 M) for 24 hr. One-hour incubated samples of PSMα3 fibrils were used for chemical stability.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1, 2 and 4 in addition to Figure 1 - Figure supplement 3, 4 and 5, Figure 2 - Figure supplement 1 and 3, and Figure 4 - Figure supplement 1 and 2.

The following data sets were generated
    1. Andreasen M
    2. Zaman M
    (2020) Dryad Digital Repository
    Staphylococcus aureus phenol soluble modulin aggregation kinetics.
    https://doi.org/10.5061/dryad.w6m905qmx

References

    1. Evans ML
    2. Chapman MR
    (2014) Curli biogenesis: order out of disorder
    Biochimica Et Biophysica Acta (BBA) - Molecular Cell Research 1843:1551–1558.
    https://doi.org/10.1016/j.bbamcr.2013.09.010

Decision letter

  1. Dominique Soldati-Favre
    Senior Editor; University of Geneva, Switzerland
  2. Manajit Hayer-Hartl
    Reviewing Editor; Max Planck Institute of Biochemistry, Germany
  3. Meytal Landau
    Reviewer; Technion - Israel Institute of Technology, Israel
  4. Matthew Chapman
    Reviewer; University of Michigan, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Biofilm formation plays a major role in the virulence of the pathogen Staphylococcus aureus by mediating resistance to the host immune response. In this study, Zaman and Andreasen used biophysical techniques to analyze the basic aggregation mechanism of individual phenol-soluble modulins (PSMs), the structural scaffold of S. aureus biofilms. They dissected the molecular events that trigger the formation of fibrillar structure in the monomeric precursor peptides, resulting in a model for how the PSMs collectively contribute during the biofilm formation process. The insights from this study may help in the development of treatment strategies for biofilm-forming methicillin-resistant S. aureus.

Decision letter after peer review:

Thank you for submitting your article "Cross-talk between individual phenol soluble modulins in S. aureus biofilm enables rapid and efficient amyloid formation" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Dominique Soldati-Favre as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Meytal Landau (Reviewer #2); Matthew Chapman (Reviewer #3).

The reviewers have discussed the reviews with one another and editors have judged that your manuscript is of interest, but additional experiments are required before it can be published. The Reviewing Editor has drafted this decision to help you prepare a revised submission. As you can see from the reviewers' comments, a better representation of the results and clear conclusion statements of what is new (identify the major take home message) should be implemented in the revised manuscript. The present data informs on the PSM mutual effects in vitro, but whether this sheds light on the actual interplay is not definitive. In addition, the technical points raised by the reviewers should be addressed.

Reviewer #1:

In this study, Zaman et al., specifically address the basic aggregation mechanism of individual phenol-soluble modulins (PSMs), the structural scaffold of the pathogen S. aureus biofilms. The aggregation kinetics of individual PSMs (PSMα1-4, PSMβ1-2 and δ-toxin) was determined using the Thioflavin T (ThT) assay. By fitting the aggregation kinetics into pre-defined equations, the authors deduce the aggregation mechanism of the individual PSMs. They find that PSMα1, PSMα3 and PSMβ1 aggregate via secondary nucleation (mechanism for disease related amyloid fibrils – nucleus formation catalysed by existing aggregates), while PSMβ2 aggregates via primary nucleation-elongation (mechanism linked to functional amyloids). They further analysed and compared the elongation rates in the presence of high concentrations of preformed fibril seeds (20-50%), and found that PSMα3 fibrils had the strongest effect on elongation, suggesting that interaction of PSMα3 fibrils contributes to the assembly process during biofilm formation. Secondary structural analysis by SRCD and FTIR revealed that PSMα3 aggregates have a significantly higher content of α-helical structure and are thermally unstable compared to other PSM peptide fibrils. TEM analysis of the aggregates supported the kinetics and secondary structural analysis of the individual PSMs. Finally, the authors performed cross-seeding experiments using 20% preformed fibrils to investigate the interplay between the PSMs. They show that fibrillation of the different PSMs is coordinated, enabling the rapid formation of an initially unstable amyloid structure into a very stable biofilm structure. The interplay between PSMs in biofilm formation offers new insights into the higher-order peptide-peptide interactions and may be useful for drug design towards treatment strategies of infections with biofilm-forming pathogens.

Essential revisions:

1) The idea of cross-talk between individual PSMs, the main message of the story, in the context of biofilm formation is attractive. However, the conclusion that PSMα1 seeds accelerate the aggregation of all other PSM peptides, based on data shown in Figure 4A, is overstated. The amount of ThT binding is really low and the extent of aggregation with PSMα1 seeds appears not more prominent than the aggregation of the individual PSMs (Figure 1—figure supplement 5).

2) The statement "Pre-formed seeds of PSMβ2 were able to accelerate the aggregation of PSMα1 and PSMβ1 while also inducing aggregation of PSMα2 and δ-toxin, Figure 4D", is not convincing based on the present figure display. The authors need to better display their data to convince the reader of the cooperation between individual PSMs. Except for PSMα1 being the promiscuous aggregator, it is difficult to see the interplay between the individual PSM peptides.

3) To establish a biologically relevant cross-talk, is it possible to verify the initial interaction of PSMα3 with PSMα1 by generating PSM mutants?

4) The model in Figure 4E suggests that the fast aggregating PSMα3 initiates biofilm formation by forming unstable fibrils, which accelerates the aggregation of PSMα1. Is the resulting stable fibril consisting of both PSMα3 and PSMα1?

Reviewer #2:

In the paper, the authors assessed the biophysical properties and fibrillation of seven PSM family members, which plays a major role in S. aureus virulence.

The main findings include:

1) PSMs show varied aggregation kinetics:

a) PSMα1, PSMα3, PSMβ1 and PSMβ2 showed reproducible ThT aggregation kinetics, while PSMα2, PSMα4 and δ-toxin did not. This partially overlaps with previous reports on PSMs fibrillation.

b) PSMα3 shows the fastest aggregation kinetics.

c) PSMs reach saturation in aggregation kinetics in different concentrations, with PSMβ1 showing saturation in relatively low concentration compared to other PSMs.

2) PSMs seeds (of the same peptide) show varied effects on aggregation lag time:

PSMα1>PSMα3>PSMβ1, while PSMβ2 shows no seeding effects. Thus,

3) PSMs show varied aggregation mechanisms:

a) A secondary nucleation mechanism for PSMα1, PSMα3 and PSMβ1, and a primary nucleation and elongation mechanism for PSMβ2.

b) PSMα1 and PSMβ2 show a similar elongation rate constant. PSMβ1, which aggregates at very low monomeric concentrations, showed a smaller elongation rate constant by a factor of 1000. In contrast, PSMα3 shows a higher elongation rate constant (few dozen fold) compared to PSMα1 and PSMβ2.

4) PSMs show varied cross-seeding effects (See notes – this is not obvious from the figures, as controls and zoom-in view into the graphs are needed).

a) PSMα1 seeds promoted self-aggregation and cross-seeded (accelerated/induced aggregation of) all other PSMs (PSMα2, PSMα3, PSMα4, PSMβ1, PSMβ2 and δ-toxin)

b) PSMα3 seeds promoted self-aggregation and cross-seeded (accelerated aggregation of) PSMα1 and PSMβ2.

c) PSMβ1 accelerated the aggregation of PSMα1 (and PSMβ2?).

d) PSMβ2 accelerated the aggregation of PSMα1 and PSMβ1, and induced aggregation of PSMα2 and δ-toxin. No effect on PSMα3 and PSMα4.

In the Conclusions the authors suggested a model in which the different properties of the PSMs allow regulation of biofilm formation and other functions in the bacteria.

In terms of impact, the PSMs play a major role in S. aureus virulence and are promising anti-virulence drug targets. Therefore, understanding their Interplay and properties can advance therapeutic approaches. In addition, they provide an excellent model system for understanding properties and mechanisms of functional amyloids, by providing a simplified system of naturally produced short peptides. The complexity in their fibrillation and functional properties and interplay, as also shown here, is fascinating considering their short sequences.

Essential revisions:

1) Figure 1 – In my opinion, seeding did not produce a significant effect on the rate of aggregation of PSMβ1. The effect on PSMα3 is present, but also not very significant. The differences might be related to the fibrillation lag time of the different PSMs. For PSMα1, with a long lag time, the seeding will be more significant.

2) Table 1 and figures – "MRE" needs to be explained.

3) Figure 1—figure supplement 5 – I wonder if controls at the same PSM concentration with no seeds should be added to the extrapolation.

4) Subsection “Elongation rates differ by a factor of 1000 between fastest and slowest PSM” – " The stronger effect of elongation of PSMα3 in comparison with other three peptides suggests that interactions with the fibrils increases the importance of PSMα3 fibrils in the assembly reactions compared with the assembly of free monomers of other peptides in the solution." Sentence is not completely clear.

5) Figure 2 – Solution CD is not the best way to look at secondary structures of fibrils, which are mostly insoluble. The CD spectra after incubation show that PSMβs had a higher helical content than β, while FTIR shows differently. This is probably related to the measurement in solution, which still contains high level of soluble species. At any case, this discrepancy needs to be acknowledged. Also, individual CDs for each peptide before and after Incubation will better show the trend of change (also in Figure 2—figure supplement 1B). This point becomes less critical since CD spectra of some PSMs were already shown in other papers. In that respect, you seriously need to cite, and compare results with at least some of the papers that show CD/FTIR of PSMs:

http://dx.doi.org/10.1021/acs.biochem.6b00615

https://www.sciencedirect.com/science/article/pii/S0005273614003149

https://www.nature.com/articles/srep34552

https://www.cell.com/structure/pdf/S0969-2126(19)30445-9.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5442197/

Maybe there are more…

6) The same comment goes for the ThT/TEM results. You need to cite previous publications showing that and compare.

7) Figure 2E – Specify in the legend what the y-axis represents (CD signal at 220 nm?) Is this really the right wavelength to compare? It Is only relevant If this was the major minimum in the RT/37C spectra. Since the stability of PSMα3 is specifically discussed, monitoring the spectra at 222mm is more relevant. Also, I am not sure if I understand the interpretation here. PSMα3 is stable as a helix in the monomeric and fibril forms, thus how does monitoring the secondary structure changes reflects fibril stability? If the fibrils disassemble into monomers, then it is still helical. The same goes for the Urea experiment (Figure 2—figure supplement 1D). The results in my opinion indicate on the stability of the helical structure, regardless of fibril formation. I would support the stability analyses with micrographs of the heated fibrils. (This will be a best option for the other PSMs as well).

8) The Results section indicates use synchrotron CD, but the methods also indicate regular CD (Jasco instrument). Which one is presented where?

9) In Figure 2—figure supplement 1 – methods indicate that the samples were freshly dissolved and pelleted, while the sup was transferred to a separate tube, and the pellet was resuspended. But which one was measured and shown in Figure 2—figure supplement 1 and Figure 2A? And what was the treatment to obtain the fibrils that were measured in synchrotron CD shown in Figure 2A? This should also be indicated in the legend.

10) FTIR – the Materials and methods section does not indicate clearly how was the sample treated and what was actually measured, nor the figure legend.

11) Figure 2—figure supplement 1C – what does the y-axis represent?

12) Figure 4 – controls with no seeds are needed to appreciate the effect, especially since Figure 1 doesn't necessarily shows the same concentration, and it is not in the same scale. Zoom-in view into the flat graphs is needed as well (Similar to Figure S7, but for all of them). This is a major results in this paper and needs to be made clear.

13) Concentration of the seeds should be uniformed in the units reported (concentration in nM or %) to better compared graphs in Figure 1 and Figure 4.

14) The schematic representation in Figure 4E is not intuitive (arrows need to be replaced or explained).

15) Subsection “PSMα1 display promiscuous cross-seeding while other PSMs display selective cross-seeding abilities”: " Like PSMα3, PSMβ1 is only capable of accelerating the aggregation of PSMα1". – Both also accelerated PSMβ2 if I understand correctly (hard to appreciate from the graphs, as I indicated).

16) Subsection “PSMα1 display promiscuous cross-seeding while other PSMs display selective cross-seeding abilities”: "None of the other PSM peptides were able to induce aggregation of these two PSM peptides which also do not aggregate on their own under conditions" – add except from PSMα1.

17) Discussion section: "Further, in vitro studies also confirmed that contrary to what previously thought (Schwartz et al., 2012), not all PSMs forms amyloid structures even at higher concentrations." – should be mentioned that only in the condition tested, and moreover, the cross-seeding results actually show that given the right conditions, all tested PSMs can aggregate into ThT-binding species.

Conceptual comments:

18) Is it possible that the drastic effect of PSMα1 on self- and cross-seeding lies in the preparation of the seeds themselves? Were the seeds tested by TEM? Dissolving and measuring concentration? Since this is the major result of the paper, I feel that more validations are needed for the properties of the seeds.

19) How does the primary nucleation and elongation mechanism for PSMβ2 reconciles with the cross-seeding effect by PSMα1, PSMα3 and PSMβ1?

20) In the Discussion section, PSMa3 is suggested to "..sustain the integrity of biofilms". But it is yet unclear if this peptide is actually a part of the biofilm (it was not found in a MS analysis). It is also not clear if it is present in the same vicinity as the other PSMs after secretion.

21) Since ThT/TEM/CD/FTIR measurements were already performed for most PSMs (citations and comparisons are missing here), a clear statement of what is especially novel here is required. Overall, a more focused and specific interpretation of the results is needed, while conclusions about the in-vivo setting should be tuned down.

Reviewer #3:

The authors describe in vitro aggregation studies of the phenol-soluble modulins (PSMs) from S. aureus. The aggregation kinetics and molecular mechanisms of PSM amyloid formation are detailed. Importantly, the manuscript also describes work on co-aggregation or cooperation between the different PSMs. The authors take the observations on how individual PSMs aggregate and speculate on how the PSMs collectively contribute during the biofilm formation process. I have some comments and suggestions that could be addressed that might strengthen the manuscript.

Essential revisions:

1) Figure 1G - PSMβ2 seems to be following the nucleation-elongation model at the concentrations tested. It would be interesting to see if this model is still followed at lower concentrations like the concentrations used of PSMβ1 (Figure 1C). Also, it would be helpful to have the units added to each of the panels in Figure 1. That might also involve altering the panels a bit so that it is clear on visual inspection which curves are seeded reactions and which are unseeded.

2) Figure 1H- Self-seeding of PSMβ2 at the low concentrations of seed almost seems to flatten the slope of the curve (or slightly increase the lag phase). According to the nucleation-elongation model, no kinetic changes should occur in presence of low amounts of seeds. Is this reproducible?

3) Figure 1—figure supplement 4 - The figure legend mentions "three" straight line plots. I think it should be four.

4) Subsection “Secondary structure analysis confirm α-helical structure of PSMα3 and β-sheet structure for other PSMs” – Instead of "Figure 3B" it should be "Figure 2D".

5) Figure 2—figure supplement 1D - How old were the PSMα3 fibers that were subjected to chemical denaturation? According to heat denaturation of PSMα3 fibers (Figure 2E), 7-day old fibers are more resistant to denaturation as compared fibers that are only 1 hour old.

6) Figure 4 – It would be helpful to show individual graphs of the cross-seeding reaction be shown i.e. seeded vs unseeded? This way it might be easier to compare the effect of various seeds on the aggregation kinetics.

7) Figure 4 – At 0.25mg/ml concentration the lag time for PSMβ1 becomes independent of the monomer concentration (Figure 1—figure supplement 3). However, in Figure 4, the concentration of PSMβ1 used is 0.25mg/ml, how was this done?

8) Figure 4E – The biofilm formation model suggests that PSMα3 form unstable fibrils which are the accelerated by stable PSMα1 fibrils. PSMα1 fibrils are also suggested to be accelerating the fibril formation by other PSMs. However, the data supporting this model comes from Figure 4A-D where sonicated fibers were added as seeds. Do the authors think that unsonicated fibers will also cross-seed? Can the authors teste this as in nature sonication is not possible?

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

Thank you for resubmitting your work entitled "Cross-talk between individual phenol soluble modulins in S. aureus biofilm enables rapid and efficient amyloid formation" for further consideration by eLife. Your revised article has been evaluated by Dominique Soldati-Favre (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) Regarding the significance of self-seeding – authors please insert a statement in the manuscript text like: "The seeding effect is more clearly visible in PSMα1 in comparison to other two peptides. However, if we do the comparison through raw data we have found that almost 50% reduction in lag phase was observed in presence of low regime of the preformed seeds in PSMβ1."

2) The authors should discuss in the text why many of the curves in Figure 4 do not start at 0. Is the initial ThT fluorescence due to ThT binding to the seeds or due to rapid reaction kinetics?

https://doi.org/10.7554/eLife.59776.sa1

Author response

Reviewer #1:

In this study, Zaman et al., specifically address the basic aggregation mechanism of individual phenol-soluble modulins (PSMs), the structural scaffold of the pathogen S. aureus biofilms. The aggregation kinetics of individual PSMs (PSMα1-4, PSMβ1-2 and δ-toxin) was determined using the Thioflavin T (ThT) assay. By fitting the aggregation kinetics into pre-defined equations, the authors deduce the aggregation mechanism of the individual PSMs. They find that PSMα1, PSMα3 and PSMβ1 aggregate via secondary nucleation (mechanism for disease related amyloid fibrils – nucleus formation catalysed by existing aggregates), while PSMβ2 aggregates via primary nucleation-elongation (mechanism linked to functional amyloids). They further analysed and compared the elongation rates in the presence of high concentrations of preformed fibril seeds (20-50%), and found that PSMα3 fibrils had the strongest effect on elongation, suggesting that interaction of PSMα3 fibrils contributes to the assembly process during biofilm formation. Secondary structural analysis by SRCD and FTIR revealed that PSMα3 aggregates have a significantly higher content of α-helical structure and are thermally unstable compared to other PSM peptide fibrils. TEM analysis of the aggregates supported the kinetics and secondary structural analysis of the individual PSMs. Finally, the authors performed cross-seeding experiments using 20% preformed fibrils to investigate the interplay between the PSMs. They show that fibrillation of the different PSMs is coordinated, enabling the rapid formation of an initially unstable amyloid structure into a very stable biofilm structure. The interplay between PSMs in biofilm formation offers new insights into the higher-order peptide-peptide interactions and may be useful for drug design towards treatment strategies of infections with biofilm-forming pathogens.

Essential revisions:

1) The idea of cross-talk between individual PSMs, the main message of the story, in the context of biofilm formation is attractive. However, the conclusion that PSMα1 seeds accelerate the aggregation of all other PSM peptides, based on data shown in Figure 4A, is overstated. The amount of ThT binding is really low and the extent of aggregation with PSMα1 seeds appears not more prominent than the aggregation of the individual PSMs (Figure 1—figure supplement 5).

We have altered the presentation of our results in Figure 4 to better show the acceleration of aggregation in the presence of seeds compared to the non-seeded data. The ThT signal in the plate reader assay is highly dependent on the gain setting in the plater reader. Hence a low gain setting will result in a low signal which is not equivalent to low amount of aggregation. Another factor to consider is the fact that some aggregates show low ThT signal despite the presence of high amounts of aggregates due to other factors such as lateral association which can render ThT binding sites unavailable due to steric hindrance. Hence, we are confident that despite low ThT signal for some of the aggregates our results are not overstated but do indeed reflect the molecular interactions between individual PSM peptides.

2) The statement "Pre-formed seeds of PSMβ2 were able to accelerate the aggregation of PSMα1 and PSMβ1 while also inducing aggregation of PSMα2 and δ-toxin, Figure 4D", is not convincing based on the present figure display. The authors need to better display their data to convince the reader of the cooperation between individual PSMs. Except for PSMα1 being the promiscuous aggregator, it is difficult to see the interplay between the individual PSM peptides.

We thank the reviewer for this comment as we realized that Figure 4D in its present form is not convincingly displaying our results. For further clarification, we have modified our figure display so that the figure now displays the individual PSM peptide monomers in the absence and presence of preformed fibril seeds rather than focusing on the seed type. This allows us to better display the results and hence emphasize the cooperation taking place between PSM peptides. As a consequence of this we have also modified the text describing the panels of Figure 4.

3) To establish a biologically relevant cross-talk, is it possible to verify the initial interaction of PSMα3 with PSMα1 by generating PSM mutants?

We agree with the reviewer that this would be an interesting analysis of the molecular basis of the cross-talk between the PSM peptides. However, this type of mutational study would be a complete and comprehensive study on its own and hence we consider it to be outside the scope of the current study.

4) The model in Figure 4E suggests that the fast aggregating PSMα3 initiates biofilm formation by forming unstable fibrils, which accelerates the aggregation of PSMα1. Is the resulting stable fibril consisting of both PSMα3 and PSMα1?

The fibrils formed when seeding PSMα1 with PSMα3 are formed from PMSα1 monomers since these are the only monomeric species present along with the PSMα3 seeds. However, the resulting fibrils display α-helical structural signature observed for aggregates of PSMα3. We therefore assume that the pre-formed fibrils seeds of PSMα3 template the α-helical fibrillary structure similar to that seen published in (Tayeb-Fligelman et al., 2017) formed by elongating the PSMα3 seeds with PSMα1 monomers hence we speculate that the resulting fibrils formed when cross-seeding PSMα1 with PSMα3 consists of bot peptide. However, the sable PSMα3 fibrillar aggregates observed in Figure 2E is only composed of PSMα3 since this is the only peptide present during the formation of these.

Reviewer #2:

In the paper, the authors assessed the biophysical properties and fibrillation of seven PSM family members, which plays a major role in S. aureus virulence.

The main findings include:

1) PSMs show varied aggregation kinetics:

a) PSMα1, PSMα3, PSMβ1 and PSMβ2 showed reproducible ThT aggregation kinetics, while PSMα2, PSMα4 and δ-toxin did not. This partially overlaps with previous reports on PSMs fibrillation.

b) PSMα3 shows the fastest aggregation kinetics.

c) PSMs reach saturation in aggregation kinetics in different concentrations, with PSMβ1 showing saturation in relatively low concentration compared to other PSMs.

2) PSMs seeds (of the same peptide) show varied effects on aggregation lag time:

PSMα1>PSMα3>PSMβ1, while PSMβ2 shows no seeding effects. Thus,

3) PSMs show varied aggregation mechanisms:

a) A secondary nucleation mechanism for PSMα1, PSMα3 and PSMβ1, and a primary nucleation and elongation mechanism for PSMβ2.

b) PSMα1 and PSMβ2 show a similar elongation rate constant. PSMβ1, which aggregates at very low monomeric concentrations, showed a smaller elongation rate constant by a factor of 1000. In contrast, PSMα3 shows a higher elongation rate constant (few dozen fold) compared to PSMα1 and PSMβ2.

4) PSMs show varied cross-seeding effects (See notes – this is not obvious from the figures, as controls and zoom-in view into the graphs are needed).

a) PSMα1 seeds promoted self-aggregation and cross-seeded (accelerated/induced aggregation of) all other PSMs (PSMα2, PSMα3, PSMα4, PSMβ1, PSMβ2 and δ-toxin)

b) PSMα3 seeds promoted self-aggregation and cross-seeded (accelerated aggregation of) PSMα1 and PSMβ2.

c) PSMβ1 accelerated the aggregation of PSMα1 (and PSMβ2?).

d) PSMβ2 accelerated the aggregation of PSMα1 and PSMβ1, and induced aggregation of PSMα2 and δ-toxin. No effect on PSMα3 and PSMα4.

In the Conclusions the authors suggested a model in which the different properties of the PSMs allow regulation of biofilm formation and other functions in the bacteria.

In terms of impact, the PSMs play a major role in S. aureus virulence and are promising anti-virulence drug targets. Therefore, understanding their Interplay and properties can advance therapeutic approaches. In addition, they provide an excellent model system for understanding properties and mechanisms of functional amyloids, by providing a simplified system of naturally produced short peptides. The complexity in their fibrillation and functional properties and interplay, as also shown here, is fascinating considering their short sequences.

Essential revisions:

1) Figure 1 – In my opinion, seeding did not produce a significant effect on the rate of aggregation of PSMβ1. The effect on PSMα3 is present, but also not very significant. The differences might be related to the fibrillation lag time of the different PSMs. For PSMα1, with a long lag time, the seeding will be more significant.

We agree with the reviewer that longer lag-times gives rise to a more significant reduction in the lag-time upon seeding. To determine whether or not fragmentation or secondary nucleation is active, we performed this experiment within a low regime of preformed seeds (nM range) to find generation of new aggregates. This strategy is used to distinguish fragmentation or secondary nucleation from potentially very complex and poorly understood primary nucleation processes (Cohen et al., 2012). It also confers which mechanism is dominated as preformed seeds accelerate the reaction such as it reaches completion before the equivalent reaction without preformed seeds. The seeding effect is more clearly visible in PSMα1 in comparison to other two peptides. However, if we do the comparison through raw data we have found that almost 50% reduction in lag phase was observed in presence of low regime of the preformed seeds in PSMβ1. In the absence of preformed seeds, aggregates are only generated at a slow rate during the first 8 hours (Figure 1C) of the time course of the reaction, as indicated by the constant (approximately zero) slope of the rate profile by primary or secondary nucleation. When preformed seeds are added at the beginning of the reaction, the kinetic profile reaches saturation before the corresponding reaction without preformed seeds. The rapid increase in the slope after 3 hours (Figure 1F) indicates rapid creation of new aggregates. It means that there will be more number of growing ends in presence of low amount of seeds, which accelerates the reaction of PSMβ1. The kinetic data without preformed seeds shows that primary nucleation is not rapidly creating new aggregates at this time, and by definition, the addition of seeds cannot affect primary nucleation, pinpointing the origin of the new aggregates as the effect of secondary pathways (Cohen et al., 2012).

2) Table 1 and figures – "MRE" needs to be explained.

As per reviewer’s suggestion, we have explained it in our revised manuscript.

3) Figure 1—figure supplement 5 – I wonder if controls at the same PSM concentration with no seeds should be added to the extrapolation.

We thank the reviewer for this suggestion. We have changed the layout of the data presentation in Figure 4 from focusing on seed type to now focus on the individual PSM monomeric peptide type. While changing the Figure we have also added the non-seeded aggregation data at the same concentration for all the individual PSM peptides. This allows for better comparison of the effects of the heterogeneous seeds in the cross-seeding.

4) Subsection “Elongation rates differ by a factor of 1000 between fastest and slowest PSM” – “The stronger effect of elongation of PSMα3 in comparison with other three peptides suggests that interactions with the fibrils increases the importance of PSMα3 fibrils in the assembly reactions compared with the assembly of free monomers of other peptides in the solution." Sentence is not completely clear.

We have clarified the sentence and changed it to “The stronger effect of elongation of PSMα3 in comparison with the other three peptides suggests that interactions with the fibrils of PSMα3 could be important during the assembly reactions in biofilm formation compared with the assembly of free monomers of other peptides into fibrils”.

5) Figure 2 – Solution CD is not the best way to look at secondary structures of fibrils, which are mostly insoluble. The CD spectra after incubation show that PSMβs had a higher helical content than β, while FTIR shows differently. This is probably related to the measurement in solution, which still contains high level of soluble species. At any case, this discrepancy needs to be acknowledged. Also, individual CDs for each peptide before and after Incubation will better show the trend of change (also in Figure 2—figure supplement 1B). This point becomes less critical since CD spectra of some PSMs were already shown in other papers. In that respect, you seriously need to cite, and compare results with at least some of the papers that show CD/FTIR of PSMs:

http://dx.doi.org/10.1021/acs.biochem.6b00615

https://www.sciencedirect.com/science/article/pii/S0005273614003149

https://www.nature.com/articles/srep34552

https://www.cell.com/structure/pdf/S0969-2126(19)30445-9.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5442197/

Maybe there are more…

We thank the reviewer for pointing out the missing information with regards to the sample preparation for the CD analysis. In order to eliminate a possible contribution to the CD signal of the fibrils of PSM peptides from remaining monomeric or non-aggregated species all fibril samples were centrifuged and the supernatant with remaining non-aggregated peptide were discarded before structural analysis. Hence the signal obtained from CD analysis of the PSM samples were originating from the fibrillary structures resuspended in the absence of non-aggregated species. We have clarified this in the Materials and methods section of the revised manuscript. As per reviewers request, we have also included further references in the revised manuscript in order to compare our results with those previously published.

6) The same comment goes for the ThT/TEM results. You need to cite previous publications showing that and compare.

As suggested, we have included more references in the revised manuscript in order to better compare our results to previously published results.

7) Figure 2E – Specify in the legend what the y-axis represents (CD signal at 220 nm?) Is this really the right wavelength to compare? It Is only relevant If this was the major minimum in the RT/37C spectra. Since the stability of PSMα3 is specifically discussed, monitoring the spectra at 222mm is more relevant. Also, I am not sure if I understand the interpretation here. PSMα3 is stable as a helix in the monomeric and fibril forms, thus how does monitoring the secondary structure changes reflects fibril stability? If the fibrils disassemble into monomers, then it is still helical. The same goes for the Urea experiment (Figure 2—figure supplement 1D). The results in my opinion indicate on the stability of the helical structure, regardless of fibril formation. I would support the stability analyses with micrographs of the heated fibrils. (This will be a best option for the other PSMs as well).

We thank the reviewer for pointing out the lack of information with regards to the y-axis in Figure 2E. We have added this information in the figure.

While the both the monomeric form and the aggregated form of PSMα3 are α-helical in structure the CD signal for the two species are different. In the aggregated form the minima at approx. 222 nm becomes the most predominant minima compared to the minima at 208 nm. This is probably due to a shift in the contacts stabilizing the helical structure from intramolecular one in the monomeric species to intermolecular contacts in the aggregates species. Along with this the minimum shifts to a slightly higher wavelength. We chose to monitor the changes in CD signal at 220 nm since this wavelength would report on changes to both the β-sheet structure seen for the other PSM peptides along with the changes in the α-helical spectra of PSMα3 when changing from the monomeric α-helical to the aggregated α-helical structure.

8) The Results section indicates use synchrotron CD, but the methods also indicate regular CD (Jasco instrument). Which one is presented where?

We have used both instruments i.e., synchrotron CD as well as JASCO instrument. The fibrillary structures were studied using synchrotron CD. However, JASCO CD was used to monitor spectra of monomeric peptide along with thermal and chemical stability of fibrils. We have specified in the text and figure legend which technique has been used to obtain specific results.

9) In Figure 2—figure supplement 1 – methods indicate that the samples were freshly dissolved and pelleted, while the sup was transferred to a separate tube, and the pellet was resuspended. But which one was measured and shown in Figure 2—figure supplement 1 and Figure 2A? And what was the treatment to obtain the fibrils that were measured in synchrotron CD shown in Figure 2A? This should also be indicated in the legend.

We thank the reviewer for pointing this out. We have added this in the paper and also clarified this in the figure legend.

10) FTIR – the Materials and methods section does not indicate clearly how was the sample treated and what was actually measured, nor the figure legend.

We have made the methods section more descriptive of how the samples were treated and what was measured make it easier for the readers to follow the sample preparation.

11) Figure 2—figure supplement 1C – what does the y-axis represent?

We thank the reviewer for noticing that this bit of information was missing. The y-axis represents CD ellipticity at 220 nm. We have clarified this in the revised manuscript.

12) Figure 4 – controls with no seeds are needed to appreciate the effect, especially since Figure 1 doesn't necessarily show the same concentration, and it is not in the same scale. Zoom-in view into the flat graphs is needed as well (Similar to Figure S7, but for all of them). This is a major results in this paper and needs to be made clear.

We thank the reviewer for this comment. We have added the non-seeded aggregation data to Figure 4 while also changing the layout of Figure 4 to focus on the individual PSM monomeric type.

13) Concentration of the seeds should be uniformed in the units reported (concentration in nM or %) to better compared graphs in Figure 1 and Figure 4.

We thank the reviewer for noticing this. We have added the concentration of seeds in nM/µM along with the information of the % of seeds to make it more uniform.

14) The schematic representation in Figure 4E is not intuitive (arrows need to be replaced or explained).

As per reviewer’s suggestion we have replaced the curved arrow with normal arrows to indicate which way the reaction is proceeding.

15) Subsection “PSMα1 display promiscuous cross-seeding while other PSMs display selective cross-seeding abilities”: "Like PSMα3, PSMβ1 is only capable of accelerating the aggregation of PSMα1". – Both also accelerated PSMβ2 if I understand correctly (hard to appreciate from the graphs, as I indicated).

We thank the reviewer for allowing us to specify this. As mentioned previously we have changed the way the data in Figure 4 is presented. This makes it easier to apprehend the cross-seeding capacity. As a consequence, we have also made changes to the text where the results are presented. Hence this has been clarified in the revised text.

16) Subsection “PSMα1 display promiscuous cross-seeding while other PSMs display selective cross-seeding abilities”: "None of the other PSM peptides were able to induce aggregation of these two PSM peptides which also do not aggregate on their own under conditions" – add except from PSMα1.

We thank the reviewer for catching this typo. It has been corrected in the revised manuscript.

17) Discussion section: "Further, in vitro studies also confirmed that contrary to what previously thought (Schwartz et al., 2012), not all PSMs forms amyloid structures even at higher concentrations." – should be mentioned that only in the condition tested, and moreover, the cross-seeding results actually show that given the right conditions, all tested PSMs can aggregate into ThT-binding species.

As per reviewers request we have add a comment on this in the revised manuscript.

Conceptual comments:

18) Is it possible that the drastic effect of PSMα1 on self- and cross-seeding lies in the preparation of the seeds themselves? Were the seeds tested by TEM? Dissolving and measuring concentration? Since this is the major result of the paper, I feel that more validations are needed for the properties of the seeds.

We thank the reviewer for giving us the opportunity to clarify this. All seeded were prepared using the same protocol namely collecting aggregates from a plate reader experiment followed by pelleting the aggregates using centrifugation where the supernatant with any remaining non-aggregate species is removed. The aggregates are then resuspended in the original volume of buffer and sonicated using a probe sonicator. Hence any differences in the seeds are not from the preparation of the seeds but from the aggregates/seeds themselves. This protocol for seed preparation is commonly used to study seeding in protein aggregation and has not previously been observed to alter the aggregates/seeds (Ohhashi et al., 2005 and Pfamatter et al., 2017). We therefore feel confident that the effects seen during cross-seeding experiments are due to the interactions between the PSM peptides and the seeds and are not caused by differences in the seed preparation protocol.

19) How does the primary nucleation and elongation mechanism for PSMβ2 reconciles with the cross-seeding effect by PSMα1, PSMα3 and PSMβ1?

We thank the reviewer for allowing us to clarify this point. The presence or absence of secondary nucleation dictates which aggregation mechanism the dominating one during the aggregation of the peptide when starting from a homogeneous monomeric population of the peptides. Hence it only gives information on whether the surface of the aggregates formed are capable of acting as a catalyst for the production of new aggregation nuclei from monomers of the same peptide. We see no apparent connection between the dominant aggregation mechanism and the cross-seeding. Not all peptides that aggregate via a secondary nucleated dominated mechanism can cross-seed each other (PSMβ1 does not accelerate the aggregation of PSMα3). The peptides that aggregate via a secondary nucleated dominated mechanism can be cross-seeded by PSMβ2 which on its own aggregates in a primary nucleation and elongation dominated mechanism (PSMα1 aggregation is seed by PSMβ2). Furthermore, PSMβ2 cannot be cross-seeded by all the peptides that aggregate via a secondary nucleated dominated mechanism (PSMα3 does not accelerate the aggregation of PSMβ2). The same goes for the induction of aggregation in the PSMs that do not aggregate on their own (PSMβ2 does not induce aggregation in PSMα2 and PSMα4 but does induce aggregation in δ-toxin.

We have added a few lines in the revised paper to point this out.

20) In the Discussion section, PSMa3 is suggested to "..sustain the integrity of biofilms". But it is yet unclear if this peptide is actually a part of the biofilm (it was not found in a MS analysis). It is also not clear if it is present in the same vicinity as the other PSMs after secretion.

While we agree we agree with the reviewer that until now it is not clear that this peptide is actually a part of biofilm and is present in the same vicinity as the other PSMs after secretion. However, earlier studies demonstrated that S. aureus produces amyloid-like fibers that contribute to biofilm integrity (Schwartz et al., 2012; Marinelli et al., 2016). Further, they do not observe PSMα3 aggregates during LC-MS/MS analysis after formic acid treatment. We make a general hypothesis that those PSMs that form fibers may provide structural integrity to biofilms. However, at the same time some in vivo studies should be performed to make a solid conclusion for PSMα3 role in biofilm integrity. To address this, we have corrected it in our revised manuscript and made a more general statement by avoiding the name of specific PSMs, which play a significant role in biofilm structural integrity.

21) Since ThT/TEM/CD/FTIR measurements were already performed for most PSMs (citations and comparisons are missing here), a clear statement of what is especially novel here is required. Overall, a more focused and specific interpretation of the results is needed, while conclusions about the in-vivo setting should be tuned down.

We agree with the reviewer’s point that various studies have performed for most of the PSMs using biophysical techniques. We have incorporated more literature references in in our manuscript to relate our results to those already published. However, there have been no efforts to date to establish a general picture for the self-assembly mechanism of the individual PSMs peptides nor has the interactions between PSM during aggregation been established. We have put further emphasis on the novel results obtained here while making a more general hypothesis on how they aggregate and communicate with each other hence tuning down the conclusions on the in vivo settings.

Reviewer #3:

The authors describe in vitro aggregation studies of the phenol-soluble modulins (PSMs) from S. aureus. The aggregation kinetics and molecular mechanisms of PSM amyloid formation are detailed. Importantly, the manuscript also describes work on co-aggregation or cooperation between the different PSMs. The authors take the observations on how individual PSMs aggregate and speculate on how the PSMs collectively contribute during the biofilm formation process. I have some comments and suggestions that could be addressed that might strengthen the manuscript.

Essential revisions:

1) Figure 1G - PSMβ2 seems to be following the nucleation-elongation model at the concentrations tested. It would be interesting to see if this model is still followed at lower concentrations like the concentrations used of PSMβ1 (Figure 1C). Also, it would be helpful to have the units added to each of the panels in Figure 1. That might also involve altering the panels a bit so that it is clear on visual inspection which curves are seeded reactions and which are unseeded.

We thank the reviewer for allowing us to elaborate on this point. We did test lower concentrations of PSMβ2 in the same range as those tested for PSMβ1. However, at concentrations below 50 µg/mL of PSMβ2 the ThT fluorescence signal become low to the point where the data becomes non-reproducible and below 30 µg/mL we see no ThT signal above the background signal. We therefore strongly believe that the aggregation models described in the paper represents the actual aggregation mechanism.

2) Figure 1H - Self-seeding of PSMβ2 at the low concentrations of seed almost seems to flatten the slope of the curve (or slightly increase the lag phase). According to the nucleation-elongation model, no kinetic changes should occur in presence of low amounts of seeds. Is this reproducible?

We agree with the reviewer that the slope of the curve for PSM β2 in the presence of law amounts of seeds is flattened slightly compare to the non-seeded aggregation curve. However, this is due to the fact that the two experiments are conducted with peptide from two different productions batches. We have consistently observed batch to batch variations in the aggregation behavior especially with the longer peptides PSMβ1 and PSMβ2. This is something which is often seen when working with synthetically produced peptides. The aggregation kinetics are reproducible within each batch and the aggregation data from different bathes can be described by the same aggregation mechanism which is why we feel confident in our results.

3) Figure 1—figure supplement 4 - The figure legend mentions "three" straight line plots. I think it should be four.

We thank the reviewer for pointing out this typo. It has been corrected as per reviewer’s suggestion in the revised manuscript.

4) Subsection “Secondary structure analysis confirm α-helical structure of PSMα3 and β-sheet structure for other PSMs” – Instead of "Figure 3B" it should be "Figure 2D".

As the reviewer pointed out this has been corrected in the revised manuscript.

5) Figure 2—figure supplement 1D - How old were the PSMα3 fibers that were subjected to chemical denaturation? According to heat denaturation of PSMα3 fibers (Figure 2E), 7-day old fibers are more resistant to denaturation as compared fibers that are only 1 hour old.

We used the 1 hour PSMα3 fibril samples for the chemical denaturation analysis. As per reviewer’s request we have added this information in the methods section in the revised manuscript.

6) Figure 4 – It would be helpful to show individual graphs of the cross-seeding reaction be shown i.e. seeded vs unseeded? This way it might be easier to compare the effect of various seeds on the aggregation kinetics.

We thank the reviewer for the suggestion. We have modified the representation of the data in Figure 4 from focusing on seed type to focusing on the individual PSM peptide monomers. We have also included the non-seeded aggregation data to better compare the effects of the individual seeds.

7) Figure 4 – At 0.25mg/ml concentration the lag time for PSMβ1 becomes independent of the monomer concentration (Figure 1—figure supplement 3). However, in Figure 4, the concentration of PSMβ1 used is 0.25mg/ml, how was this done?

We apologize for typological error. We have used 0.025 mg/ml (PSMβ1) for cross-seeding experiments and this has been corrected in the revised manuscript.

8) Figure 4E – The biofilm formation model suggests that PSMα3 form unstable fibrils which are the accelerated by stable PSMα1 fibrils. PSMα1 fibrils are also suggested to be accelerating the fibril formation by other PSMs. However, the data supporting this model comes from Figure 4A-D where sonicated fibers were added as seeds. Do the authors think that unsonicated fibers will also cross-seed? Can the authors teste this as in nature sonication is not possible?

We agree with the reviewer that we are looking at conditions which do not occur in vivo by adding sonication to the aggregated species. However, in order to investigate the elongation or the cross-seeding capacity of the peptides we find that this is a necessary technique to use. It has been shown that the sonication of fibrillates proteins do not alter the structure of the fibrils but only acts to break the fibrils into shorter pieces (Ohhashi et al., 2005 and Stathopulos et al., 2008). This in turn produces more growing ends from which the fibrils can elongate. We therefore strongly believe that even unsonicated fibrils are capable of acting as seeds also in a cross-seeding setting between different PSM peptides however this seeding will happen only at the growing ends and with less growing ends the seeding will happen in a slower manner than in our experiments.

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

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) Regarding the significance of self-seeding – authors please insert a statement in the manuscript text like: "The seeding effect is more clearly visible in PSMα1 in comparison to other two peptides. However, if we do the comparison through raw data we have found that almost 50% reduction in lag phase was observed in presence of low regime of the preformed seeds in PSMβ1."

We have inserted a statement similar to that suggested by the editor.

2) The authors should discuss in the text why many of the curves in Figure 4 do not start at 0. Is the initial ThT fluorescence due to ThT binding to the seeds or due to rapid reaction kinetics?

We have added the following to subsection “PSMα1 display promiscuous cross-seeding while other PSMs display selective cross-seeding abilities” to address this concern: “Due to the presence of 20% preformed fibril seeds the initial ThT fluorescence signal is higher for the seeded experiments compared to the unseeded experiments for all the different PSM peptides. This effect is due to binding of ThT to the preformed fibril seeds at the beginning of experiment.”

https://doi.org/10.7554/eLife.59776.sa2

Article and author information

Author details

  1. Masihuz Zaman

    Aarhus University, Department of Biomedicine, Aarhus, Denmark
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared

  2. Maria Andreasen

    Aarhus University, Department of Biomedicine, Aarhus, Denmark
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing - review and editing
    For correspondence
    mariaj@biomed.au.dk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6096-2995

Funding

Aarhus Universitets Forskningsfond (AUFF-E-2017-7-16)

  • Maria Andreasen

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

Acknowledgements

This work was supported by Aarhus University Research Foundation. MA is the recipient of a Starting Grant from Aarhus University Research Foundation. Furthermore, we acknowledge the award of beam time on the AU-CD beam line at ASTRID2, under project number ISA-20–1013.

Senior Editor

  1. Dominique Soldati-Favre, University of Geneva, Switzerland

Reviewing Editor

  1. Manajit Hayer-Hartl, Max Planck Institute of Biochemistry, Germany

Reviewers

  1. Meytal Landau, Technion - Israel Institute of Technology, Israel
  2. Matthew Chapman, University of Michigan, United States

Version history

  1. Received: June 8, 2020
  2. Accepted: November 30, 2020
  3. Accepted Manuscript published: December 1, 2020 (version 1)
  4. Version of Record published: December 11, 2020 (version 2)

Copyright

© 2020, Zaman and Andreasen

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

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  1. Masihuz Zaman
  2. Maria Andreasen
(2020)
Cross-talk between individual phenol-soluble modulins in Staphylococcus aureus biofilm enables rapid and efficient amyloid formation
eLife 9:e59776.
https://doi.org/10.7554/eLife.59776

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    Rui-Qiu Yang, Yong-Hong Chen ... Cheng-Gang Zou
    Research Article Updated

    An imbalance of the gut microbiota, termed dysbiosis, has a substantial impact on host physiology. However, the mechanism by which host deals with gut dysbiosis to maintain fitness remains largely unknown. In Caenorhabditis elegans, Escherichia coli, which is its bacterial diet, proliferates in its intestinal lumen during aging. Here, we demonstrate that progressive intestinal proliferation of E. coli activates the transcription factor DAF-16, which is required for maintenance of longevity and organismal fitness in worms with age. DAF-16 up-regulates two lysozymes lys-7 and lys-8, thus limiting the bacterial accumulation in the gut of worms during aging. During dysbiosis, the levels of indole produced by E. coli are increased in worms. Indole is involved in the activation of DAF-16 by TRPA-1 in neurons of worms. Our finding demonstrates that indole functions as a microbial signal of gut dysbiosis to promote fitness of the host.

    1. Biochemistry and Chemical Biology
    2. Structural Biology and Molecular Biophysics

    The neuronal calcium sensor NCS-1 regulates the phosphorylation state and activity of the Ga chaperone and GEF Ric-8A

    Daniel Muñoz-Reyes, Levi J McClelland ... Maria Jose Sanchez-Barrena
    Research Article

    The Neuronal Calcium Sensor 1, an EF-hand Ca2+ binding protein, and Ric-8A coregulate synapse number and probability of neurotransmitter release. Recently, the structures of Ric-8A bound to Ga have revealed how Ric-8A phosphorylation promotes Ga recognition and activity as a chaperone and guanine nucleotide exchange factor. However, the molecular mechanism by which NCS-1 regulates Ric-8A activity and its interaction with Ga subunits is not well understood. Given the interest in the NCS-1/Ric-8A complex as a therapeutic target in nervous system disorders, it is necessary to shed light on this molecular mechanism of action at atomic level. We have reconstituted NCS-1/Ric-8A complexes to conduct a multimodal approach and determine the sequence of Ca2+ signals and phosphorylation events that promote the interaction of Ric-8A with Ga. Our data show that the binding of NCS-1 and Ga to Ric-8A are mutually exclusive. Importantly, NCS-1 induces a structural rearrangement in Ric-8A that traps the protein in a conformational state that is inaccessible to Casein Kinase II-mediated phosphorylation, demonstrating one aspect of its negative regulation of Ric-8A-mediated G-protein signaling. Functional experiments indicate a loss of Ric-8A GEF activity towards Ga when complexed with NCS-1, and restoration of nucleotide exchange activity upon increasing Ca2+ concentration. Finally, the high-resolution crystallographic data reported here define the NCS-1/Ric-8A interface and will allow the development of therapeutic synapse function regulators with improved activity and selectivity.

    1. Biochemistry and Chemical Biology
    2. Cell Biology

    Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications

    Riham Ayoubi, Joel Ryan ... Carl Laflamme
    Research Advance

    Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50–75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.