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Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis

Nature Methods volume 16, pages 743–749 (2019)Cite this article

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A Publisher Correction to this article was published on 12 August 2019

This article has been updated

Abstract

Cellular behavior is controlled by the interplay of diverse biomolecules. Most experimental methods, however, can only monitor a single molecule class or reaction type at a time. We developed an in vitro nuclear magnetic resonance spectroscopy (NMR) approach, which permitted dynamic quantification of an entire ‘heterotypic’ network—simultaneously monitoring three distinct molecule classes (metabolites, proteins and RNA) and all elementary reaction types (bimolecular interactions, catalysis, unimolecular changes). Focusing on an eight-reaction co-transcriptional RNA folding network, in a single sample we recorded over 35 time points with over 170 observables each, and accurately determined five core reaction constants in multiplex. This reconstruction revealed unexpected cross-talk between the different reactions. We further observed dynamic phase-separation in a system of five distinct RNA-binding domains in the course of the RNA transcription reaction. Our Systems NMR approach provides a deeper understanding of biological network dynamics by combining the dynamic resolution of biochemical assays and the multiplexing ability of ‘omics’.

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Fig. 1: Molecules and reactions of the target co-transcriptional network.
Fig. 2: NMR observables, data and mathematical model fits used for network parameterization.
Fig. 3: Validation of Systems NMR-derived constants.
Fig. 4: Monitoring multiple proteins in one NMR sample employing selective labeling.

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Data availability

Raw NMR data from the main experiments was deposited in Zenodo (10.5281/zenodo.2554066). Source data for the main figures are included in the online article. SRM-MS-Proteomics data was deposited in PeptideAtlas (PASS01365). The ODE network model was deposited in BioModels54 (MODEL1812270001). RNA-encoding plasmids were deposited in AddGene (ID nos. 126040, 126041, 126042, 126043). Further data and code are available from the corresponding authors upon request.

Code availability

Main code with examples and a protocol for the Systems NMR setup is available at github.com/systemsnmr/ivtnmr.

Change history

  • 12 August 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank J. Vollmer and G. Fengos for the help with network modeling. We acknowledge G. Wider and all members of the ETH BNSP platform for excellent maintenance of the NMR infrastructure, S. Heinrich for the help with microscopy and E. Lehmann for providing T7 polymerase. We thank all members of the Allain Laboratory, in particular F. Damberger, and the Parpan retreat participants for helpful discussions. This work was supported by the Promedica Stiftung, Chur (Grant no. 1300/M to Y.N.), Novartis Foundation and Krebsliga Zurich (Y.N.), NCCR RNA and Disease by the Swiss National Science Foundation (F.A.).

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Authors and Affiliations

  1. Department of Biology, Institute of Molecular Biology & Biophysics, ETH Zurich, Zurich, Switzerland

    Yaroslav Nikolaev, Nina Ripin & Frédéric H.-T. Allain

  2. Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland

    Martin Soste & Paola Picotti

  3. Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland

    Dagmar Iber

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  1. Yaroslav Nikolaev
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  2. Nina Ripin
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Contributions

Y.N. conceived the idea, obtained initial funding, performed experiments, data analysis and network modeling. F.H.-T.A. supervised the project, provided infrastructure and financial support. D.I. supervised the network modeling part of the project. N.R. expressed and purified selectively labeled proteins and contributed to the transcription-NMR experiments. P.P. and M.S. supervised and provided technical and analytical support on mass spectrometry analyses. Y.N. wrote the manuscript with input from all of the authors.

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Correspondence to Yaroslav Nikolaev or Frédéric H.-T. Allain.

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Integrated supplementary information

Supplementary Figure 1 Systems NMR data and parameter uncertainty analysis with replicate experiments.

(a) NMR-based concentrations for different molecular observables (in columns), and independent replicate experiments (n = 3 (for RNA0, SMN1) or n = 4 (SMN2, EV2)), as a function of reaction time. Data for four RNAs is shown in rows. Final fits of the ODE network models shown in thin black lines. Part of differences in the rate of RNA synthesis is likely due to the changes in activity of RNA Polymerase stock over time – some replicates were done more than 18 months apart. The origin of sporadic secondary burst of T7 RNA Pol activity (visible in one of RNA0 replicates) requires further investigation. (b) Concentrations of bound protein for His33 and Arg75 observables as a function of RNA concentration, determined from time-resolved data. (c) Combined plots of experimental data for all RNAs and all replicates – as a function of time for first six observables, and as a function of RNA concentration – for the two last, protein, observables. (d) Summary of the main ODE-network-derived reaction constants. RNA folding ∆G was derived independently by imino signal lineshape analysis.

Supplementary Figure 2 Systems NMR applications: RNA network perturbations by adding UP1 protein at different time points of transcription reaction.

The assays were performed under two conditions: “co-transcriptionally” – UP1 protein added from reaction start, and “post-transcriptionally” – UP1 added near end of reaction, when most of the RNA had been synthesized and folded. The effect of protein on folded RNA was observed by time-resolved changes in linewidth of selected imino signals. Color code: green – free RNA before addition of the protein, blue – after addition of the protein after 13 h delay, red – when protein was added co-transcriptionally from reaction start. (a-c) Imino regions of 1D 1H spectra for SMN1, SMN2 and EV2 RNAs ~13 h after transcription start. Intensities of the co-transcriptional imino spectra were scaled to correct for differences in the total RNA amount. Quantified iminos are highlighted as orange circles in the RNA stems in the insets. (d-f) Linewidths of SMN1 U5, SMN2 U5 and EV2 U4 imino signals resolved over transcription time. Post-transcriptional reactions with SMN1 and SMN2 were acquired once (n = 1); all other reactions were repeated twice (n = 2 independent experiments) with similar results. Black dotted lines show the 41/π Hz linewidth increase expected from increased R2 relaxation rate upon formation of a 1:1 RNA:UP1 protein complex. For SMN RNAs, imino linewidth broadening was similar in co- and post-transcriptional conditions (d,e). For SMN1 the broadening matched what would be expected from increased R2 relaxation rate upon 1:1 binding to a 22.5 kDa UP1. For SMN2 the broadening was larger than expected just from increased R2 relaxation rate upon 1:1 complex formation, suggesting potential unwinding of the SMN2 stem by UP1. For EV2 RNA, a 2-fold larger imino signal broadening was observed upon post-transcriptional addition of UP1 (f). This suggests that under these conditions, a 2:1 UP1:EV2 complex is formed when the protein is added post-transcriptionally, whereas only a 1:1 complex is formed in co-transcriptional setup. The formation of 2:1 complex is consistent with the reports of a second, weak UP1 binding site in the EV2 RNA (Levengood et al., RNA Biology, 10:7, 1136–1145 (2013)). The inability of UP1 to form 2:1 complex under co-transcriptional conditions seems best explained by the reduced concentration of free UP1, due to RNA-driven partial sequestration of UP1 into phase-separated droplets during transcription (discussed in main text in context with Fig. 4).

Supplementary Figure 3 Isothermal Titration Calorimetry (ITC) determination of UP1-RNA affinities.

ITC was performed in conditions approximating those at the end of transcription-NMR reaction: 40 mM Tris-HCl, 0.01% Triton-X100, 2.5 mM β-ME, pH 7.5, 37 mM NaPO4, 2.6 mM NTP, 24 mM MgCl2, 303K. For each RNA the reference RNA-to-buffer titration was performed and subtracted from the RNA-to-protein data. In all fits the data from the first injection point was discarded. To more accurately represent the pool of unspecific RNAs in the transcription reaction, RNA0 for ITC experiments was purified and used as a combined pool of 2-10 nt RNAs from transcription reaction. The final concentration of this RNA0+aborts pool was normalized by the average size of 5.8 nts based on HPLC-UV-weighted composition of the corresponding transcription mixture. For RNA0, SMN1 and SMN2 RNAs the ITC data was fitted to one-site interaction model using MicroCal Origin (a-c). A one-site KD was needed for all RNAs to compare it with the one-site binding model used in the ODE analysis. The standard MicroCal Origin’s fitting protocol ran into local minima when fitting one-site model into EV2-UP1 ITC data (likely due to the known bi-modal binding of UP1 to this RNA (Levengood et al., RNA Biology 10 (7): p1136 (2013)). Therefore for EV2-UP1 the apparent one-site KD constant (d) was derived as a mean of three different fitting protocols: (e) high-affinity KD from two-site model in MicroCal Origin, (f) one-site KD fitted using stoichiometric 1:1 equilibrium model in Affinimeter software (affinimeter.com), and (g) one-site KD fitted using general ligand:target equilibrium binding isotherm (Pollard TD., Molecular Biology of the Cell 21: p4061 (2010)), which parameterizes only KD constant (without considering ∆H) and assumes stoichiometry N=1. All three fitting procedures yielded comparable KD values for EV2-UP1 binding (4.8, 7.1 and 3.3 µM). Short RNA (RNA0, SMN1, SMN2) experiments were repeated once, EV2 RNA experiments were repeated twice with similar results (n = 2 independent experiments). Reported KD values correspond to: the optimal parameters ± s.d. uncertainties from isotherm data fits (for RNA0, SMN1, SMN2); and a mean ± s.d. values from three different KD fitting procedures (for EV2 RNA).

Supplementary Figure 4 Affinity (as dissociation constant, KD) of UP1-NTP interaction based on NMR titration and HN chemical shift perturbation data.

(a) Overlay of 2D-HN spectra – following titration of 150 µM UP1 with 0-82.5 mM equimolar mixture of four NTPs used in the in vitro transcription reaction (ATP, GTP, CTP, UTP). Buffer was pH 7.7, 40 mM Tris-HCl, 0.01% Triton-X100, 5 mM DTT, 37 mM NaPO4, 4 mM MgCl2, 5% D2O, 50 µM DSS. NTPs were added in lyophilized form, prepared as equimolar mixture with MgCl2 to match near-equimolar NTP-Mg used in the transcription reaction. Titrations were pH-adjusted to maintain the initial titration conditions, stability of pH was verified from 1H signal of Tris buffer. Overall experiment was repeated once (n = 1) using ten titration points (n = 10). (b) Global 1:1 binding KD fit for 5 most strongly perturbed 15N-UP1 protein HN resonances. HN peaks were traced in CARA and fitted in Graph Pad Prism. KD value shows the optimal parameter ± s.d. uncertainty from the global fit of chemical shift perturbation data.

Supplementary Figure 5 Systems NMR applications: RNA network perturbations by small molecules.

The SMN RNAs used here correspond to the splicing enhancer 1 (ESE1) sequences from exon 7 of human SMN1 and SMN2 genes. Aberrant splicing of this exon leads to Spinal Muscular Atrophy (SMA), the leading genetic cause of infant mortality, treatment of which is sought in the correction of splicing aberrations (Sivaramakrishnan et al., Nat. Commun. 8, 1476 (2017)). To test the utility of our assay in drug mode-of-action analysis, we perturbed the SMN2 network using three candidate modifiers of the exon 7 splicing, at final 200 µM and 1% DMSO each. (a, b) Network scheme highlighting the perturbed reactions (top), and example 31P and 1H spectra (at 12 h) used for quantification (bottom, n = 2 experimental replicates). (c) Positions of the observed imino protons within SMN2 hairpin. (d) Time-resolved RNA concentrations. (e-h) Linewidths of different imino signals. High variance of linewidths at the start of reaction is due to the low signal-to-noise ratio of imino signals at low RNA concentrations. (i-l) Raw data for individual imino signals at 12 h. In (d-l) plots for six transcription samples are overlaid: free SMN2 (two replicates – black and grey lines), SMN2 with DMSO, SMN2 with small molecules NVS-SM1, smn-C5, smn-C7. The 2.5-fold reduction of RNA transcription rate in presence of smn-C7 is indicated in the inset next to time-resolved 31P data in (d). The apparent 1.5-fold broadening of G2/G3 imino proton signal in presence of smn-C5 is indicated next to the time-resolved 1H data in (f), manifesting a perturbation of SMN2 RNA in presence of smn-C5, but not in presence of the other molecules.

Supplementary Figure 6 Phase-Separation of proteins triggered by in vitro transcription of RNA.

Microscope (60x) images of in vitro transcription mixture of SMN2 exon 7 ESE1 RNA synthesized in presence of (a) 5 protein constructs (Tra2-β1, SRSF1-RRM1, SRSF1-RRM2, UP1, hnRNP G) - each at 83 µM concentration (415 µM total); (b) Only UP1 protein at 413 µM (5×83) concentration; (c) Only RRM1 of UP1 at 415 µM concentration. Transcription reaction conditions match those described in Methods. Single-protein experiments were repeated once for each protein, five-protein experiments were repeated twice (n = 2 independent experiments) with similar results.

Supplementary Figure 7 Quantification of protein concentrations in nuclear extracts of HEK293 cells by targeted Mass-Spectrometry proteomics.

(a) Chromatographic traces of peptide elution and detection by Selected Reaction Monitoring showing relative signal intensities of exemplary peptide precursor ions for pure 15N labelled NMR protein preparations (blue) spiked into extracts containing endogenous 14N proteins (red). Experiments were repeated twice with similar results (n = 2 experimental replicates). Mean values from the replicates are used in the 14N/15N ratios. c.p.s., counts per second. (b) Sequences, 14N/15N ratios and the resulting concentrations of peptides used in quantification.

Supplementary Figure 8 Estimation of the UP1 HN reporter signal positions in the fully saturated protein state.

(a) Raw NMR data showing positions of His33 and Arg75 HN signals at the start of reaction (HNfree), and at the end of reaction – for 150, 30 and 20 µM protein concentrations. Final concentration of EV2 RNA at the end of all reactions was ~120 µM. Splitting of R75 peak in 20 and 30 µM protein samples is due to multiple binding sites in EV2 RNA, which start to become occupied at high RNA:protein ratios. In case of peak splitting the HN peak position was taken as the centre of mass for the overall peak integral. At least two equivalent NMR spectra were recorded at the end points of transcription experiments (n = 2 technical replicates). In addition, independent experimental replicates of the complete transcription reaction were performed one to four times (n = 1 for 20 µM, n = 2 for 30 µM, n = 4 for 150 µM UP1 samples). Both technical and experimental replicates showed reproducible results. (b) UP1-EV2 KD fits for His33 and Arg75 HN residues using 1:1 ligand:target binding equation (Pollard TD., Mol. Biol. Cell 21: p4061 (2010)). The HN data is normalized by the total protein concentration, converting the independent variable (x-axis) from RNA concentration to [RNA]/[protein] ratio, thus allowing to express the data from three different protein concentration datasets (150, 30 and 20 µM) within the same coordinate system. Dashed lines show the final value of the “max shift” parameter used for the final ODE model fitting. Error bars in 150 µM experiments reflect standard deviations from replicate experiments. For 30 and 20 µM datasets the data was recorded at “equilibrium” conditions at the end of transcription, ~30-50 hours after the reaction start. The imperfect fit of the HN shift data from datasets with high (150 µM) and low (20-30 µM) protein concentrations appears either (1) due to additional weak binding sites in the EV2 RNA, which become populated at low 20-30 µM protein concentrations, as suggested by Arg75 peak splitting; or (2) due to competitive binding reactions with RNA abortive products and free NTPs, which cannot be accurately approximated by 1:1 ligand:target binding isotherm.

Supplementary Figure 9 Parameter (reaction constants) uncertainty analysis by bootstrap fitting of the ODE network model to NMR data.

For each of the RNAs, bootstrap analysis of one of the experimental replicates is shown. (a) NMR-based concentrations for different observables as a function of reaction time (x-axis) overlaid with fitted functions from 200 bootstrap model fits (black). (b) Observed chemical shift perturbations for His33 and Arg75 residues, and resulting concentration of bound protein, as a function of RNA concentration determined from time-resolved data. (c) Summary of bootstrap statistics for three ODE network constants used in validation. (d) Combined plot of NMR-based data and ODE fitting results for UP1 protein binding to four different RNAs. Bootstrap procedure was repeated 3 times with similar results (n = 3 in silico runs with 20 to 200 model optimizations in each run).

Supplementary information

Supplementary Information

Supplementary Figures 1–9, Supplementary Notes 1 and 2, Supplementary Tables 1–3 and Supplementary Protocol.

Reporting Summary

Supplementary Video 1: Time-resolved animation of data from a co-transcriptional Systems NMR experiment

Video shows the reconstructed eight-reaction co-transcriptional network (as static background), and the dynamic time-resolved NMR spectra—1D 31P, 1D 1H and 2D 1H15N—used to quantify the key molecules and constants in the network. 1D 1H spectrum shows individual imino signals of the folded RNA stem. 2D 1H15N spectrum shows signals from individual residues of the protein backbone, one of which is zoomed. Parts of the network related to metabolites, RNA and proteins are colored pale yellow, grey and red, respectively. Blue text highlights three core reactions—RNA transcription (catalysis), RNA folding (unimolecular state changes), RNA-protein binding (bimolecular interactions)—which reflect the three elementary reaction types generally found in biomolecular networks

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Nikolaev, Y., Ripin, N., Soste, M. et al. Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis. Nat Methods 16, 743–749 (2019). https://doi.org/10.1038/s41592-019-0495-7

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