XB-ART-58621Biochemistry November 30, 2021; 60 (47): 3566-3581.
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Clustering of Aromatic Residues in Prion-like Domains Can Tune the Formation, State, and Organization of Biomolecular Condensates.
In immature oocytes, Balbiani bodies are conserved membraneless condensates implicated in oocyte polarization, the organization of mitochondria, and long-term organelle and RNA storage. In Xenopus laevis, Balbiani body assembly is mediated by the protein Velo1. Velo1 contains an N-terminal prion-like domain (PLD) that is essential for Balbiani body formation. PLDs have emerged as a class of intrinsically disordered regions that can undergo various different types of intracellular phase transitions and are often associated with dynamic, liquid-like condensates. Intriguingly, the Velo1 PLD forms solid-like assemblies. Here we sought to understand why Velo1 phase behavior appears to be biophysically distinct from that of other PLD-containing proteins. Through bioinformatic analysis and coarse-grained simulations, we predict that the clustering of aromatic residues and the amino acid composition of residues between aromatics can influence condensate material properties, organization, and the driving forces for assembly. To test our predictions, we redesigned the Velo1 PLD to test the impact of targeted sequence changes in vivo. We found that the Velo1 design with evenly spaced aromatic residues shows rapid internal dynamics, as probed by fluorescent recovery after photobleaching, even when recruited into Balbiani bodies. Our results suggest that Velo1 might have been selected in evolution for distinctly clustered aromatic residues to maintain the structure of Balbiani bodies in long-lived oocytes. In general, our work identifies several tunable parameters that can be used to augment the condensate material state, offering a road map for the design of synthetic condensates.
PubMed ID: 34784177
PMC ID: PMC8638251
Article link: Biochemistry
Species referenced: Xenopus laevis
Genes referenced: dnai1 prnp velo1
Article Images: [+] show captions
|Figure 1. Stickers-and-spacers framework that can be used to describe Velo1PLD. (A) The stickers-and-spacers framework subdivides biomolecules into sticker regions and spacer regions, whereby stickers contribute attractive interactions that drive phase transitions through multivalent interactions. (B) The Velo1 sequence architecture contains an N-terminal prion-like domain (Velo1PLD) and four fragments (F1–F4) as originally defined by Böke et al.39 The Velo1PLD sequence is shown explicitly with aromatic residues colored orange and all other residues colored black. (C) Questions of interest in this study are how sticker clustering (left) and spacer-related interactions (right) can alter the formation and equilibrium state of condensates formed by PLDs.|
|Figure 2. Velo1PLD is enriched with aliphatic and aromatic residues and depleted of small polar residues and has highly clustered aromatic residues. (A) Log2 of fractions of different amino acids in Velo1PLD divided by equivalent fractions of the same amino acid across all X. laevis IDRs. (B) Log2 of fractions of different amino acids in Velo1PLD divided by equivalent fractions of the same amino acid across human PLDs that undergo liquid–liquid phase separation. (C) Graphical definition of the aromatic clustering parameter. (D) Assessment of aromatic clustering (y-axis) compared with the fraction of aromatic or aliphatic residues (x-axis). Among those of X. laevis PLDs, the clustering score Velo1PLD (black diamond) is higher than all but one, and among those of the PLDs from PhaseSepDB, the clustering score is higher than all but two.|
|Figure 3. Sticker:spacer and spacer:spacer interaction strengths play a key role in determining the driving forces for phase separation. (A) Overview of the polymer models used in simulations. A 56-bead model is used (12 sticker beads, 44 spacer beads). Three parameters define the system: sticker:sticker, sticker:spacer, and spacer:spacer strength. (B) Summary of relative parameter ranges examined. The sticker:sticker strength is held fixed, and sticker:spacer and spacer:spacer interaction strengths are varied. (C) Simulations reveal that at a fixed starting volume fraction (ϕ) of 0.0168 the emergence of a two-phase regime is symmetrically dependent on the sticker:spacer and spacer:spacer interaction strength. As the interaction strength increases, ϕsat decreases, and in parallel ϕden (the concentration inside the droplet) increases. (D) Varying interaction strengths can be recast as modulating the effective Flory χ parameters or rescaling the critical temperature. As such, we can project the spacer:spacer interaction strength in the background of a fixed sticker:spacer interaction strength into a Flory–Huggins fit to analytically capture the interaction strength-dependent phase behavior. Points are simulation data, while lines are fits of data to Flory–Huggins theory. (E) Analogous analysis as in panel D but with a variable sticker:spacer strength in the background of a fixed spacer:spacer strength. Points are simulation data, while lines are fits of simulation data to Flory–Huggins theory.|
|Figure 4. Apparent diffusion coefficient scales with droplet density. (A) Overview of the analysis approach as applied to the polymer architecture defined in Figure 3. Individual polymers are followed as they “diffuse” within a droplet and fit to extract the diffusive scaling exponent (α) and the apparent diffusion constant (Dapp). Dapp is determined only where simple Brownian diffusion is observed (i.e., α = 1), which occurs in almost all cases (see Figures S1–S3). (B) Dapp as a function of sticker:spacer and spacer:spacer strength. (C) Graphical representation of how density and Dapp relate to one another (left) and all data from panel B plotted as a single master curve of Dapp vs dense-phase volume fraction (ϕden). The linear fit to guide the eye leads to an apparent diffusion constant of 0 when the volume fraction is 1, reflecting the limit in which every lattice site is occupied such that no free sites are available for polymers to move into.|
|Figure 5. Impact of sticker clustering can be altered by sticker:spacer strength. (A) Three polymers of equal length with equal composition but alternative sticker clustering. (B) Saturation concentration as a function of sticker clustering (x-axis) and sticker:spacer strength (top to bottom). These specific comparisons are shown also in panel D. (C) Fraction of chains in the largest cluster shown as a function of sticker:spacer, spacer:spacer, sticker clustering (top to bottom), and system temperature (left to right). (D) Saturation concentration as a function of sticker:spacer, spacer:spacer, sticker clustering (top to bottom), and system temperature (left to right). Numbers reflect the systems examined in panel B. (E) The spacer:spacer and spacer:sticker interaction strengths can determine the impact of sticker:spacer patterning by rescaling the definition of a sticker and spacer. Spacer strength here reflects the simultaneous titration of spacer:spacer and sticker:spacer interaction strength to match that of sticker:sticker strength.|
|Figure 6. Dependence of intracondensate polymer apparent diffusion on spacer-mediated interactions that depends on sticker clustering. As the level of sticker clustering increases for polymers with a strong spacer:spacer interaction, there is little to no dependence of Dapp on sticker:spacer interactions. This suggests that in the limit of (relatively) strong spacer:spacer interactions, molecular rearrangement is dominated by spacer:spacer and sticker:sticker interactions.|
|Figure 7. Sticker clustering tunes intradroplet organization. (A) Assortativity, a measure of the spatial mixing between stickers and spacers, is measured for the entire system as a function of sticker strength, spacer strength, and sticker clustering. For each system, the phase boundary is shown as a dashed line for reference. For the well-clustered sequences, significant deviations from a value of 0 are observed. (B) To understand the origins of these large assortativity values, we generated snapshots from distinct regions in panel A (orange beads are stickers, and black beads spacers). These revealed the intradroplet organization of stickers into local clusters and subdomains. For sequences with the most well-clustered stickers, we observe an assortativity of >0 at subsaturating concentrations due to the presence of small labile clusters.|
|Figure 8. Architecture of Velo1 and amino acid sequences of F1 fragment variants. (A) Relative position of the F1 fragment. (B) Amino acid sequence of the wild type sequence and rationally designed F1 variants tested.|
|Figure 9. Rationally designed Velo1 designs tune cellular localization and material state. (A) mRNAs encoding Velo1 designs and wild type Velo1 fragment F1 (Figure 8) fused to GFP were microinjected into stage I X. laevis oocytes. Oocytes were left to recover and express injected mRNAs overnight and imaged the next day. The cell membrane and nucleus are outlined in a white dashed line. (B) Internal rearrangement of fluorescent wild type or redesigned Velo1 (F1-GFP) particles after photobleaching in the Balbiani body. Note that Velo1Ali2S/Repat did not localize to the Balbiani body. (C) Overview, DIC image, and schematic of the oocyte with its Balbiani body in the Ali2S design. (D) Internal rearrangement of fluorescent redesigned Velo1 (F1-GFP) particles after photobleaching in the nucleus. Note that Velo1WT does not localize to the nucleus. (E) The fluorescent recoveries of the photobleached Velo1WT or redesigned Velo1 (F1-GFP) particles in Balbiani bodies in panel B and two other biological replicates were quantified over time. (F) The fluorescent recoveries of photobleached Velo1 designs in nuclei in panel C and two other biological replicates were quantified over time. For panels D and E, the fluorescence in the bleached region was quantified over time and normalized by an unbleached neighboring region. At least three oocytes per biological replicate were plotted. Scale bars are as indicated in the figure.|
References [+] :
Alberti, A systematic survey identifies prions and illuminates sequence features of prionogenic proteins. 2009, Pubmed