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Mechanical regulation of glycolysis via cytoskeleton architecture

Nature volume 578, pages 621–626 (2020)Cite this article

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Abstract

The mechanics of the cellular microenvironment continuously modulates cell functions such as growth, survival, apoptosis, differentiation and morphogenesis via cytoskeletal remodelling and actomyosin contractility1,2,3. Although all of these processes consume energy4,5, it is unknown whether and how cells adapt their metabolic activity to variable mechanical cues. Here we report that the transfer of human bronchial epithelial cells from stiff to soft substrates causes a downregulation of glycolysis via proteasomal degradation of the rate-limiting metabolic enzyme phosphofructokinase (PFK). PFK degradation is triggered by the disassembly of stress fibres, which releases the PFK-targeting E3 ubiquitin ligase tripartite motif (TRIM)-containing protein 21 (TRIM21). Transformed non-small-cell lung cancer cells, which maintain high glycolytic rates regardless of changing environmental mechanics, retain PFK expression by downregulating TRIM21, and by sequestering residual TRIM21 on a stress-fibre subset that is insensitive to substrate stiffness. Our data reveal a mechanism by which glycolysis responds to architectural features of the actomyosin cytoskeleton, thus coupling cell metabolism to the mechanical properties of the surrounding tissue. These processes enable normal cells to tune energy production in variable microenvironments, whereas the resistance of the cytoskeleton in response to mechanical cues enables the persistence of high glycolytic rates in cancer cells despite constant alterations of the tumour tissue.

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Fig. 1: Glycolysis is mechanically modulated.
Fig. 2: F-actin bundling enhances glycolysis.
Fig. 3: TRIM21 mediates mechanically modulated PFKP degradation.
Fig. 4: The E3 ligase activity of TRIM21 is negatively regulated by sequestration on F-actin bundles.

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Data availability

Data that support the findings of this study are available within the article and its Supplementary Information. Source Data for Figs. 1–4 and Extended Data Figs 1, 3–5, 7, 9 are provided with the paper. Any additional information and related data are available from the corresponding authors upon reasonable request. No restriction on data availability applies.

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Acknowledgements

We thank D. K. Reed for technical support; H. Grossman for FACS analysis; J. Sudderth, L. Zacharias and the Children’s Research Institute’s Metabolomics Facility for metabolomics analysis; S. Barnes and M. Kim from the UT Southwestern Bioinformatics Core Facility, funded by the Cancer Prevention and Research Institute of Texas (CPRIT, RP150596) for TCGA analysis; G. DeMartino for his insight into protein degradation; the Moody Foundation Flow Cytometry Core Facility of Children’s Medical Center Research Institute at UT Southwestern for help with cell sorting; A. Lemoff from the UT Southwestern Proteomics Core Facility for mass-spectrometry analysis; and the University of Texas Southwestern Tissue Resource, a shared resource at the Simmons Comprehensive Cancer Center, which is supported in part by the National Cancer Institute under award number 5P30CA142543. L.L. and C.S.C. are supported by the NSF Science and Technology Center for Engineering Mechanobiology (CMMI: 15-48571). B.G. and J.D.M. are supported by a SPORE Grant (P50CA70907) and CPRIT Grant (RP160652). R.J.D. is supported by grants from HHMI Faculty Scholars Program, National Cancer Institute 1R35CA22044901 and Robert L. Moody, Sr. Faculty Scholar. Funding in the Danuser laboratory was provided by CPRIT (grant R1225), the Welch Foundation (grant I-1840) and National Institute for General Medical Sciences (R01GM071868).

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Authors and Affiliations

  1. Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA

    Jin Suk Park, Christoph J. Burckhardt, Tadamoto Isogai & Gaudenz Danuser

  2. Department of Cell Biology, UT Southwestern Medical Center, Dallas, TX, USA

    Jin Suk Park, Christoph J. Burckhardt, Tadamoto Isogai & Gaudenz Danuser

  3. Department of Translational Molecular Pathology, UT MD Anderson Cancer Center, Houston, TX, USA

    Rossana Lazcano & Luisa M. Solis

  4. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Linqing Li & Christopher S. Chen

  5. The Biological Design Center and Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Linqing Li & Christopher S. Chen

  6. Hamon Center for Therapeutic Oncology, UT Southwestern Medical Center, Dallas, TX, USA

    Boning Gao & John D. Minna

  7. Annette G. Strauss Center for Neuro-Oncology, UT Southwestern Medical Center, Dallas, TX, USA

    Robert Bachoo

  8. Children’s Research Institute and Department of Pediatrics, UT Southwestern Medical Center, Dallas, TX, USA

    Ralph J. DeBerardinis

  9. Howard Hughes Medical Institute, UT Southwestern Medical Center, Dallas, TX, USA

    Ralph J. DeBerardinis

  10. Eugene McDermott Center for Human Growth and Development, UT Southwestern Medical Center, Dallas, TX, USA

    Ralph J. DeBerardinis

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Contributions

J.S.P. and G.D. originally conceived the study, based on early findings. C.J.B. contributed critical insight into the biology of TRIM21 and α-actinin. J.S.P., C.J.B., T.I. and G.D. designed the experiments; J.S.P. and C.J.B. performed the experiments. J.S.P. conducted western blotting, Seahorse assays, immunofluorescence, immunoprecipitation and pharmacological perturbations, and analysed metabolomics and intensity image data. C.J.B. made constructs and cell lines, analysed filament image data and performed bioinformatics analysis. B.G. and J.D.M. provided and characterized cell lines; R.L. and L.M.S. performed histochemistry; L.L. performed rheometry; C.S.C., J.D.M., R.B. and R.J.D. advised the study; J.S.P., C.J.B. and G.D. wrote the paper with considerable edits by R.J.D. All authors edited and approved the manuscript.

Corresponding authors

Correspondence to Christoph J. Burckhardt or Gaudenz Danuser.

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Competing interests

R.J.D. is an advisor for Agios Pharmaceuticals.

Additional information

Peer review information Nature thanks Florian Bassermann, Valerie Weaver and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Mechanical modulation of cell metabolism.

a, Shear moduli of soft substrates during liquid-to-solid gelation over the course of 60 min, shown as G′ and G′′. Time = 0 min indicates the start of the time sweep. The experiment was repeated seven times over two separate days; each experiment is shown in a different colour. b, Shear moduli of preformed soft substrates over the course of an additional 60 min, shown as G′ and G′′. Time = 0 min indicates the completion of 60 min of incubation at 37 °C for gelation and the start of a second 60-min time sweep. The experiment was repeated twice; each repeat is shown in a different colour. The shear modulus increased rapidly and reached a plateau within 5 min, with a sixfold difference between G′ and G′′. Although the substrates are soft, this ratio indicates the formation of stable elastic hydrogels39. c, Left, representative images of calcein AM (green) and ethidium homodimer (red) staining of HBEC76 cells on stiff and soft substrates. Scale bar, 50 μm. Right, fraction of calcein-AM-positive cells relative to the combined count of calcein-AM-positive and ethidium-homodimer-positive cells on stiff (n = 50 images) and soft (n = 50 images) substrates from a single imaging experiment. Individual data points indicate fractions per image. d, Metabolomic profiling of HEBC76 cells on stiff and soft substrates (n = 3 independent cultures per substrate stiffness). Metabolic profiling was performed once. e, Volcano plot of metabolic shifts between substrates derived from d. Each dot shows the average ratio in metabolite concentration between stiff and soft substrates versus P values, based on three independent cultures. Vertical dotted line, log2-transformed fold change ±0.5; horizontal dotted line, P value = 0.05). f, Glycolytic rates, normalized to cell number, of HBEC30 and HBEC34 cells on stiff and soft substrates. g, Fraction enrichment of 13C-labelled lactate synthesized from uniformly labelled d-13C-glucose. M, no 13C labelling; M + 1, one 13C label; M + 2, two 13C labels; M + 3, three 13C labels. Data are shown as mean ± s.e.m. of three independent experiments. h, Glycolytic pathway illustrating enzymes tested in Fig. 1d. PFKP, PFKL and PFKM are highlighted in red. i, Verification of the specificity of PFK-isoform-targeting antibodies. GFP-tagged PFK was expressed in HEK293 cells for each isoform, and immunoblotting was performed. The experiment was performed once. j, PFK activity normalized by protein abundance on stiff and soft substrates. Data are shown as mean ± s.e.m. The experiment was performed once, with three technical repeats per substrate stiffness. k, Abundance of PFKFB2 and PFKFB3 in HBEC76 cells, per substrate stiffness. Representative data from three independent experiments. l, Normalized expression of PFKP, PFKL and PFKM in human bronchial epithelium. Data of two replicates are shown as the mean obtained from BioGPS: PFKP (no. 201037), PFKL (no. 201102) and PFKM (no. 201102). m, Abundance of PFKP on stiff and soft substrates in the head and neck epithelial cell line HHN2. The experiment was performed once. n, Normalized expression of glycolytic genes, ranked according to oncogenic enrichment. PFKP is highlighted in red; and GLUT1 (also known as SLC2A1), PFKL and PFKM shown in black. o, Effect of oncogenic transformation of HBEC3 cells on oxidative phosphorylation rates on stiff and soft substrates. Data are normalized to cell number. p, Glycolytic rates, normalized to cell number, of NSCLC cells (H2009, H1819 and HCC827 lines) on stiff and soft substrates. q, Abundance of PFKP in H2009, H1819 and HCC827 cells on stiff and soft substrates. The experiment was performed once. Data in f, o, p are from three independent experiments, shown as mean ± s.e.m. Statistical significance was assessed using two-tailed Mann–Whitney test (c), unpaired multiple t-test (e) or two-tailed Student’s t-test (f, g, j, o, p). Protein abundance was normalized to the abundance of GAPDH (k, m, q).

Source data

Extended Data Fig. 2 Analytic workflow of immunohistochemistry staining of PFKP.

a, HBEC76 cell pellets (control versus PFKP–GFP overexpression) stained with PFKP antibody used for subsequent immunohistochemistry analysis. The experiment was performed once. b, Microphotograph showing annotated areas of malignant cells (red), tumour stroma (green) and normal bronchial epithelium (yellow) using HALO v.2.3 software in a TMA core of lung-cancer tumour tissue. c, Microphotographs showing the workflow for image analysis using HALO v.2.3 software. From left to right and top to bottom: PFKP staining in a TMA core with lung adenocarcinoma and bronchial epithelium; Halo mark-up image showing the compartments analysed (bronchial epithelium in yellow, tumour stroma in green and malignant cells in red); and Halo mark-up images showing the image analysis using the Halo algorithm to detect cells with PFKP cytoplasmic immunohistochemistry (IHC) expression per bronchial epithelium, tumour stroma and malignant cells. Column charts show frequencies of different levels of expression (0, 1+, 2+ and 3+) of PFKP per tissue type.

Extended Data Fig. 3 Immunohistochemistry staining of PFKP in TMA cores of lung cancer.

a, Microphotographs showing PFKP immunohistochemistry, and their respective image analysis performed in bronchial epithelium (8 cases and 9 cores), tumour stroma (5 cases and 11 cores), non-tumoural stroma (5 cases and 7 cores) and malignant cells (6 cases and 14 cores). In non-tumoural stroma, red arrows indicate smooth muscle and a black arrow indicates bronchial epithelium. Areas indicated by black squares in the left panels are shown as enlarged images in the right panels. Single-cell colour outlines indicate Halo-analysed PFKP expression (0, white; 1+, yellow; 2+, orange; and 3+, red). Scale bars, 100 μm. The experiment was performed once. b, Frequency of cells for each PFKP staining classification (0 (low) to 3 (high)) analysed from bronchial epithelium (8 cases and 9 cores), tumour stroma (5 cases and 11 cores), non-tumoural stroma (5 cases and 7 cores) and malignant cells (6 cases and 14 cores). Individual dots represent data from a single core. Data are shown as mean ± s.d. c, Distribution score of PFKP staining analysed from b. The score was calculated for each core by subtracting the lowest frequency value from the highest frequency value. Values close to 100 (blue arrow) suggest a homogeneous (Dirac) distribution of PFKP staining whereas values close to 0 (red arrow) indicate a completely heterogeneous (uniform) distribution. Data are shown as mean ± s.d.

Source data

Extended Data Fig. 4 Quantification of F-actin organization and relations to PFKP expression.

a, Image analysis pipeline to detect F-actin bundles. The core of the pipeline is a steerable filter that enhances the contrast of curvilinear image features. White squares outline areas shown at a higher magnification. Scale bars, 20 μm (main panel), 5 μm (magnification). b–d, F-actin organization of HBEC76 cells, and bundle detection after plating cells on normal-adhesive and low-adhesive substrates (b), on stiff and soft substrates (c) or after latrunculin A (200 nM) treatment (d). Positions of magnified regions are indicated by red boxes. Scale bars, 10 μm. Representative images from a single imaging experiment. e, F-actin organization of untransformed HBECs versus NSCLC cells. Right panels show filament detection. Scale bar, 10 μm. Representative images from a single imaging experiment. f, g, Quantification of F-actin bundle length (f) or intensity (g) in HBECs (HBEC30, n = 21 cells; HBEC34, n = 15 cells; and HBEC76, n = 16 cells) versus NSCLC cells (HCC4087, n = 11 cells; H2009, n = 22 cells; and H1819, n = 11 cells). h, Effect of latrunculin A treatment on oxidative phosphorylation of HBEC76 (black), HCC4087 (red) and HCC827 (orange) cells. Mean oxidative phosphorylation rates normalized to control ± s.d. are shown for each group. Data are from three independent experiments. Data in f, g are shown as a box (median ± 25–75%) and whisker (maximum to minimum values) plot, and statistical significance was assessed using one-way analysis of variance and the Tukey test.

Source data

Extended Data Fig. 5 PFKP ubiquitination and degradation.

a, Stability of PFKP analysed by pulse-chase experiments. HEK293 cells were pulsed with AHA for 12 h, followed by a 0- or 24-h chase period. The experiment was performed once. b, Abundance of ubiquitinated proteins and PFKP expression in HEK293 cells in the presence or absence of proteasome inhibitor MG132 (10 μM). Control, DMSO. Representative data from two independent experiments. c, Abundance of polyubiquitinated PFKP upon ubiquitin pull-down using either control beads or beads conjugated to ubiquitin-binding protein using HEK293 cells. PD, pull-down. The experiment was performed once. d, Abundance of polyubiquitinated PFKP in the presence or absence of MG132 (10 μM for 3 h). Representative data from three independent experiments. e, Abundance of overexpressed PFKP–GFP containing the specified lysine-to-arginine (K-to-R) mutations on stiff and soft substrates. Data are normalized with respect to overexpressed PFKP–GFP on stiff substrate (wild type, n = 13 independent experiments; each K-to-R mutant, n = 2 independent experiments). Data are shown as mean. f–m, Abundance of PFKP–GFP containing a lysine-to-arginine (K-to-R) mutation as indicated, in cells cultured on stiff and soft substrates. Representative data from two independent experiments. n, Structure of a PFKP tetramer. Each PFKP monomer is coloured differently. Arrows point to the K281 sites in each monomer. o, Enlarged structural detail of PFKP around the K281 site. Each amino acid is shown in a different colour. p, Abundance of overexpressed PFKL(K272R)–GFP on stiff and soft substrates. The experiment was performed once. q, Abundance of overexpressed PFKM(K275R)–GFP on stiff and soft substrates. The experiment was performed once. In a, b, d, f–m, p, q, protein abundance was normalized to the abundance of GAPDH, and in c to the abundance of β-actin.

Source data

Extended Data Fig. 6 Alignment of the human PFK isoforms PFKP, PFKL and PFKM.

Conserved lysines are highlighted in green. Lysines reported as ubiquitinated in the Phosphosite database49 are labelled in cyan. PFKP K281 is highlighted in red. The previously reported PFKP ubiquitination site K10 is labelled in yellow27.

Extended Data Fig. 7 TRIM21 as a downregulated E3 ligase in lung cancer.

a, Ranking of expression change of E3 ubiquitin ligases (n = 213 ligases) in patients with lung cancer. Data were generated by normalizing individual expression of E3 ligases in tumour samples to their matched normal expression, as reported by the TCGA. Data are plotted in descending order on a log2 scale. TRIM21, red arrows. Right, a summary table of the ranked ligases is shown. Integrated from 20 datasets included in the LCE29. Genes targeted by shRNA are indicated, and TRIM21 is highlighted. b, Screening of the 18 most consistently downregulated E3 ligases, according to LCE, for their effects on PFKP expression. Abundance of PFKP relative to GAPDH is summarized as a bar chart. The experiment was performed once. TRIM21 (red) is the only one of the tested ligases, the depletion of which leads to an increase in PFKP expression. c, Abundance of TRIM21 in untransformed HBECs and NSCLC cells. Representative data from two independent experiments. d, Abundance of polyubiquinated PFKP in HEK293 cells without (control) and with overexpression of TRIM21–GFP. Representative data from two independent experiments. e, Abundance of PFKP in transformed H2009 cells upon CRISPR-based knockout (KO) of TRIM21. Representative data from two independent experiments. f, Abundance of PFKP after treating HBEC76 cells with AKT inhibitor X (AKTi) (10 μM) for 15 h. Representative data from two independent experiments. g, Abundance of PFKP(S386A)–GFP, which cannot be phosphorylated by AKT, compared to control on stiff and soft substrates. The experiment was performed once. In e, f, protein abundance was normalized to the abundance of β-actin, and in b–d, g to the abundance of GAPDH.

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Extended Data Fig. 8 Effect of expression of phosphomimetic ACTN1(Y246E) on F-actin organization and TRIM21 sequestration.

a, TIRF microscopy of TRIM21–GFP and F-actin in NSCLC cells. Scale bars, 10 μm. Representative images from a single imaging experiment. b, Fluorescence microscopy of TRIM21–GFP and F-actin in HBEC76 cells treated with lysophosphatidic acid (LPA) (20 μM) for 30 min. Scale bar, 10 μm. Representative images from a single imaging experiment. c, Epifluorescence microscopy of HBEC76 cells expressing GFP alone, ACTN1–GFP or ACTN1(Y246E)–GFP. Scale bar, 10 μm. Representative images from a single imaging experiment. d, Epifluorescence microscopy of HEK293 cells expressing GFP alone, ACTN1–GFP or mutant ACTN1(Y246E)–GFP. Scale bar, 10 μm. Representative images from a single imaging experiment. e, Epifluorescence microscopy of TRIM21–GFP and F-actin following immunofluorescence labelling of Flag-tagged ACTN1 or Flag-tagged ACTN1(Y246E) (the cell is outlined with a dotted yellow line) in HBEC76 cells. Scale bar, 10 μm. Representative images from three independent experiments. f, Epifluorescence microscopy of TRIM21–GFP and F-actin upon overexpression of ACTN1 or ACTN1(Y246E) in HEK293 cells. Scale bar, 10 μm. Representative images from a single imaging experiment. g, Abundance of PFKP on stiff and soft substrates upon overexpression of GFP alone, ACTN1–GFP or ACTN1(Y246E)–GFP in HEK293 cells. Representative data from two independent experiments. Areas indicated by red boxes in the top panels in a, b are shown magnified in the corresponding bottom panels. In all images (a–f), F-actin was stained with fluorescently conjugated phalloidin. Protein abundance was normalized to GAPDH (g).

Extended Data Fig. 9 Analysis of somatic cancer mutations in RING-domain E3 ubiquitin ligases.

a, Cancer mutations in TRIM21 RING domain found in proximity to conserved cysteines and histidines. b, Quantification of the number of genes that displayed one or more mutation at positions C1–C8 of the RING domain. c, Summary table of missense and nonsense mutations found in 118 genes at the conserved cysteine and histidine positions C1–C8. *Stop codon. d, Distribution of mutations across C1–C8. e, Frequency of missense and nonsense mutations at positions C1–C8, and overall. f, TRIM21–GFP or TRIM21(C54Y)–GFP expression in HBEC76 cells, counterstained for F-actin with phalloidin. Scale bar, 10 μm. Representative data from a single imaging experiment. g, Representative images of H2009 cells expressing TRIM21–GFP with indicated cysteine mutations, counterstained for F-actin with phalloidin. Scale bar, 10 μm. The experiment was performed once. h, Representative images of H2009 cells expressing GFP, TRIM21–GFP or GFP-tagged TRIM21 with cancer-relevant mutations, counterstained for F-actin with phalloidin. Scale bar, 10 μm. The experiment was performed once. Mutations shown in g cause protein aggregation, whereas non-cysteine mutations in h do not. i, Abundance of PFKP on stiff and soft substrates when HBEC76 cells expressed TRIM21–GFP or TRIM21(C54Y)–GFP. The experiment was performed once. j, Effect of expressing TRIM21 or TRIM21(C54Y) on glycolytic rates of HBEC76 cells, normalized to cell number. Data are from three independent experiments, shown as mean glycolytic rate ± s.e.m. Dotted line, glycolytic rates of HBEC76 cells on stiff substrates without overexpressing TRIM21, as indicated in Fig. 1c. Protein abundance was normalized to GAPDH (i).

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Supplementary information

Supplementary information

This file contains the raw, uncropped data for western blots, and Supplementary figures exemplifying the gating strategy for cell sorting.

Reporting Summary

Supplementary Tables

This file contains legends for Supplementary Tables 1-8; Supplementary Table 3: Oligonucleotide sequences; Supplementary Table 4: shRNA constructs; Supplementary Table 5: TRIM21 cancer mutations; and Supplementary Table 7: Dataset from Lung Cancer Explorer.

Supplementary Table 1

LCMS metabolomics.

Supplementary Table 2

E3 ubiquitin ligase cancer mutations.

Supplementary Table 6

TRIM21-GFP pull-down, mass spectrometry analysis.

Supplementary Table 8

Raw data of ranked glycolytic genes enriched in lung cancer.

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Park, J.S., Burckhardt, C.J., Lazcano, R. et al. Mechanical regulation of glycolysis via cytoskeleton architecture. Nature 578, 621–626 (2020). https://doi.org/10.1038/s41586-020-1998-1

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