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Molecular interactions underlying liquid−liquid phase separation of the FUS low-complexity domain

Nature Structural & Molecular Biology volume 26, pages 637–648 (2019)Cite this article

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Abstract

The low-complexity domain of the RNA-binding protein FUS (FUS LC) mediates liquid−liquid phase separation (LLPS), but the interactions between the repetitive SYGQ-rich sequence of FUS LC that stabilize the liquid phase are not known in detail. By combining NMR and Raman spectroscopy, mutagenesis, and molecular simulation, we demonstrate that heterogeneous interactions involving all residue types underlie LLPS of human FUS LC. We find no evidence that FUS LC adopts conformations with traditional secondary structure elements in the condensed phase; rather, it maintains conformational heterogeneity. We show that hydrogen bonding, π/sp2, and hydrophobic interactions all contribute to stabilizing LLPS of FUS LC. In addition to contributions from tyrosine residues, we find that glutamine residues also participate in contacts leading to LLPS of FUS LC. These results support a model in which FUS LC forms dynamic, multivalent interactions via multiple residue types and remains disordered in the densely packed liquid phase.

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Fig. 1: NMR characterization of condensed FUS LC.
Fig. 2: Lack of evidence for structured conformations in condensed phase FUS LC.
Fig. 3: Nonspecific intermolecular contacts between all residue types underlie FUS LC LLPS.
Fig. 4: Interactions in the condensed phase are dispersed throughout the entire sequence.
Fig. 5: Hydrogen bonding interactions and glutamines contribute to stabilizing FUS LC LLPS.
Fig. 6: Hydrophobic and π/sp2 contacts contribute to FUS LC LLPS.
Fig. 7: Interactions between FUS LC molecules that stabilize LLPS are structurally heterogeneous.

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Data availability

Chemical shift assignments and relaxation parameters for the condensed phase can be accessed using the BMRB accession 27887. All other data are available from the corresponding author upon reasonable request.

Code availability

Simulation software described in Methods section are publicly available and can be found at http://www.gromacs.org/ for the atomistic resolution simulations and (http://glotzerlab.engin.umich.edu/hoomd-blue/) for coarse-grained simulations HOOMD.

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Acknowledgements

We thank M. Naik for helpful advice and assistance with NMR spectroscopy and W. Shing Tang for assistance with NOE calculations from simulated ensembles. Research was supported in part by NIGMS R01GM118530 (to N.L.F.) and Human Frontier Science Program RGP0045/2018 (to S.H.P. and N.L.F.), and NSF 1845734 (to N.L.F.). A.C.M. was supported in part by NIGMS training grant to the MCB graduate program at Brown University (T32GM007601) and NSF graduate fellowship (1644760, to A.C.M.). G.L.D., G.H.Z., and J.M. are supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Division of Material Sciences and Engineering under Award DESC0013979 (to J.M.). Y.K. and S.H.P. are supported by the Deutsche Forschungsgemeinschaft (DFG) PA-252611-1 and Marie Curie Foundation CIG322284 (to S.H.P.). Use of the high-performance computing capabilities of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the NSF grant TG-MCB-120014, is gratefully acknowledged in addition to resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-05CH11231. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

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Authors and Affiliations

  1. Graduate Program in Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA

    Anastasia C. Murthy

  2. Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA, USA

    Gregory L. Dignon, Gül H. Zerze & Jeetain Mittal

  3. LUT School of Engineering Science, LUT University, Lappeenranta, Finland

    Yelena Kan

  4. Department of Molecular Spectroscopy, Max Planck Institute for Polymer Research, Mainz, Germany

    Yelena Kan & Sapun H. Parekh

  5. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

    Gül H. Zerze

  6. Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA

    Sapun H. Parekh

  7. Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI, USA

    Nicolas L. Fawzi

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Contributions

A.C.M and N.L.F. designed and performed experiments and analyzed data for NMR spectroscopy, phase separation assays, and microscopy. Y.K. and S.H.P. designed, performed, and analyzed data for CARS. G.L.D., G.H.Z., and J.M. designed and performed simulation experiments and analyzed the resulting data. A.C.M. and N.L.F. wrote the manuscript with comments from all authors.

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Correspondence to Jeetain Mittal or Nicolas L. Fawzi.

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Integrated supplementary information

Supplementary Figure 1 NMR characterization of conformational exchange processes within the condensed phase.

A) Chemical shift differences between the dispersed and condensed phase of FUS LC are small. B) Additional dark-state exchange saturation transfer profiles of condensed FUS LC as a function of the offsets from the 15N carrier frequency and applied saturation field. The differences between profiles can be explained by the differences in value of the transverse relaxation rate constant, 15N R2, of an individual residue. Data are plotted as mean ± s.d. (approximately the size of the circles) propagated from best-fit parameter confidence interval equal to 1 s.d. in the representative data set of two independent experiments. C) 15N CPMG 1D signal intensity profiles of the amide backbone for the reference experiment (τ=0, no relaxation) and as a function of refocusing pulses field (τ=60 ms, νCPMG as listed) indicate that there is no evidence for exchange on the μs-ms timescale for all backbone positions. D) Reduced spectral density mapping for 15N relaxation parameters measured at 850 MHz (black) and 500 MHz (red) 1H Larmor frequencies. Data represent values of J(ω) at frequencies of 0, 86.2, 740 and 0, 50.7, 435 MHz. Data are plotted as mean ± propagated best-fit parameter confidence interval equal to 1 s.d. in the representative data set of two independent experiments.

Supplementary Figure 2 Intermolecular contacts in the condensed phase.

A) Comparison of the glutamine/asparagine side chain plane of the 3D (1H)15N-HSQC-NOESY-1H13C-HSQC between a 1:1 15N/12C and 14N/13C sample (blue) and a 1:1 15N/12C + 14N/12C sample (red). Decreased intensity of the natural abundance control (red) suggests a low contribution of intramolecular artifact to the NOEs. B) 13C/12C filtered-edited NOESY-HSQC on a 1:1 15N/12C and 15N/13C sample. This experiment shows NOEs to all the different residue types. Effective NOE amplitudes calculated from a two-chain simulation of FUS120-163 for serine/threonine backbone (C), glutamine/tyrosine backbone (D), glutamine/asparagine side chains (E), and glycine backbone (F). The overall trend of interaction is similar to experimental NOE profiles, but slight differences in the amplitudes of the NOEs exist, possibly due to the absence of dynamical information in the simulation. Predicted intermolecular NOEs from the FUS 37-97 fibril structure (PDB ID: 5W3N) for serine/threonine backbone (G), glutamine/tyrosine backbone (H), glutamine/asparagine side chains (I), and glycine backbone (J). The overall pattern of NOEs in the fibril structure differs from the condensed phase. The difference between the observed and predicted random coil Cα and Cβ chemical shift values for condensed FUS LC and fibrillar FUS 37-97. Condensed FUS LC values are between -1 and 1, indicating that disorder is preserved in the condensed phase, while chemical shifts in the fibrillar form are closer to values associated with β-sheet structure.

Supplementary Figure 3 Validation of all-atom simulations of FUS120-163.

A) Comparison of experimental dispersed FUS LC and simulated NMR parameters. The simulated dataset is qualitatively similar to the experimental NMR observables, indicating that the all-atom simulation is capturing the molecular details of the 120-163 region. Differences in the N-terminal region between the simulation and experimental dataset arise from the use of a short 44 residue fragment in the simulation. As a result, the N-terminus of the simulated fragment is free to move, while the same region in the experimental dataset is constrained due to being in the middle of the chain. Simulation data are plotted as mean ± s.e.m of n=5 equal divisions of the total data. B) Comparison of secondary shifts between experimental dispersed FUS LC and simulated datasets. Simulation data are plotted as mean ± s.e.m of n=5 equal divisions of the total data. Experimental data are plotted as mean ± estimated uncertainty in peak position. C) Starting configurations of the two-chain simulation of the 120-163 region. This diverse array of configurations was used to avoid bias in the resulting conformations.

Supplementary Figure 4 Paramagnetic relaxation enhancement within the condensed phase.

A) 1H transverse relaxation rates of condensed FUS LC containing ~1% Ac-MTSL labeled A16C, S86C, and S142C. Labeling at position S86C was repeated (S86C #2) and shows a similar relaxation rate as the original experiment (S86C #1). Data are plotted as mean ± best-fit parameter confidence interval equal to 1 s.d. in one data set, with two data sets plotted for S86C. B) 1H transverse relaxation rates of condensed FUS LC containing ~1% MTSL labeled A16C, S86C, and S142C. C-E) Signal intensity ratios of the condensed FUS LC diamagnetic MTSL and paramagnetic MTSL. The global decrease in signal intensity indicates labeling with paramagnetic MTSL. Data are plotted as peak height ratio mean ± propagated uncertainty equal to r.m.s.d. of baseline noise in the representative data set of two experiments.

Supplementary Figure 5 Hydrogen bonding, π/sp2 and hydrophobic interactions calculated from two-chain simulations of FUS120-163.

A) Comparison of the amino acid frequencies found within the low complexity domain of FUS and average disordered and globular proteins24. B) Per-residue hydrogen bonds between each residue of the sequence and different residue types in the other chain. Error bars represent the standard error of the mean for five equal blocks. Tyrosine and glutamine residues are highlighted with gray and red bars respectively. C) Average number of hydrogen bonds formed by each residue of the FUS120-163 sequence in simulation. For the two-chain simulation (dimer) case, only intermolecular hydrogen bonds are considered, and for the single chain simulation (monomer), neighbors of sequence separation of 5 residues or fewer are not counted. D) Average number of hydrophobic contacts formed by each residue of the FUS120-163 sequence in simulation. For the two-chain (dimer) case, only intermolecular hydrogen bonds are considered, and for single chain (monomer), neighbors of sequence separation of 5 residues or fewer are not counted. E) Average number of intermolecular hydrogen bonds, sp2 or hydrophobic contacts between all residues of chain 1 with each residue of chain 2. F). Average total number of hydrogen bonds, sp2 or hydrophobic contacts between residues of type X in chain 1 with type Y in chain 2. Simulation data are plotted as mean ± s.e.m of n=5 equal divisions of the total data.

Supplementary Figure 6 Hofmeister salts do not change the structure of dispersed FUS LC.

A) 1H-15N HSQC spectral overlay of dispersed FUS LC in the presence of the Hofmeister salts.

Supplementary Figure 7 Substitution of tyrosines with phenylalanine in FUS LC leads to aggregation.

A) DIC microscopy of a FUS LC variant in which all 24 tyrosines are mutated to phenylalanine (FUS LC YF). This variant forms aggregates instead of spherical droplets. Phase separation as monitored by turbidity of MBP-FUS FL WT (B) or YF (C). The increased turbidity and light scattering and long incubation time in the YF variant indicates the presence of stable aggregates. Data are plotted as mean ± s.d. of n=3 technical replicates.

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Murthy, A.C., Dignon, G.L., Kan, Y. et al. Molecular interactions underlying liquid−liquid phase separation of the FUS low-complexity domain. Nat Struct Mol Biol 26, 637–648 (2019). https://doi.org/10.1038/s41594-019-0250-x

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