Proteolethargy is a pathogenic mechanism in chronic disease

Abstract

The pathogenic mechanisms of many diseases are well understood at the molecular level, but there are prevalent syndromes associated with pathogenic signaling, such as diabetes and chronic inflammation, where our understanding is more limited. Here, we report that pathogenic signaling suppresses the mobility of a spectrum of proteins that play essential roles in cellular functions known to be dysregulated in these chronic diseases. The reduced protein mobility, which we call proteolethargy, was linked to cysteine residues in the affected proteins and signaling-related increases in excess reactive oxygen species. Diverse pathogenic stimuli, including hyperglycemia, dyslipidemia, and inflammation, produce similar reduced protein mobility phenotypes. We propose that proteolethargy is an overlooked cellular mechanism that may account for various pathogenic features of diverse chronic diseases.

Keywords: chronic disease; cysteine; protein mobility; proteolethargy; reactive oxygen species; signaling.

Figure 1.. Mobility of diverse proteins in cells. See also Figures S1–S2, and Table S1.

(A) Cellular compartments, biological processes and proteins examined in this study. (B, C) Live-cell imaging of HepG2 cells expressing HaloTag (B) or green fluorescent protein (GFP) -tagged (C) versions of the indicated proteins. Dashed lines show outline of nucleus. Scale bars are indicated. (D) Representative tracks for movement of individual molecules as determined by single particle tracking (SPT) of HaloTag versions of the indicated proteins. Dashed magenta lines represent outline of the plasma membrane. Dashed blue lines represent outline of the nucleus. (E) Complementary cumulative distribution function (CCDF) graphs of apparent diffusion coefficients as determined by SPT of the indicated proteins (n = 294, 1751, 2591, 2855, 5458 molecules for insulin receptor (IR), MED1, HP1α, FIB1, and SRSF2, respectively). (F) Representative images of FRAP of HepG2 cells expressing GFP-tagged versions of the indicated proteins. Images before (Before), immediately following (Bleach), and after recovery (Post) are shown. Scale bars are indicated. (G) Quantification of FRAP experiments of the indicated proteins (n=10, 11, 15, 15, 15 cells for IR, MED1, HP1α, FIB1, SRSF2, respectively). Data shown as mean (blue line) ± standard error of the mean (SEM) (light blue).

Figure 2.. Protein mobility decreases in a model of pathogenic signaling. See also Figures S2, S3 and S4 and Table S2 and S3.

(A, B) Model for protein mobility in pathogenic signaling: individual molecules move at fast or slow speeds (A), depending on exposure to normal or pathogenic signaling (B). (C) Schematic representation of cell treatments. (D) Representative individual protein tracks as determined by SPT for the indicated proteins and experimental treatments. Scale bars are indicated. (E) CCDF graphs of apparent diffusion coefficients as determined by SPT for the indicated proteins and experimental treatments (Normal, n = 357, 5719, 5199, 153, 3399 molecules for IR, MED1, HP1α, FIB1, and SRSF2, respectively; Pathogenic, n = 154, 2227, 3529, 146, 2872 molecules for IR MED1, HP1α, FIB1, and SRSF2, respectively). Mann-Whitney test was used for statistical analysis. (F) Representative FRAP images for the indicated proteins and experimental treatments. Images before (Before), immediately following (Bleach), and after recovery (Post) are shown. Scale bars are indicated. (G) Quantification of FRAP experiments for the indicated proteins and experimental conditions (Normal and Pathogenic, n = 16, 10, 14, 10, 20 cells each condition for IR, MED1, HP1α, FIB1, SRSF2, respectively). Data shown as mean (Normal, blue line; Pathogenic, red line) ± SEM (Normal, light blue; Pathogenic, light red). t-test was used for statistical analysis. Cohen’s d = 0.9, 0.4, 1.2, 0.9, and 0.0 for IR, MED1, HP1α, FIB1, SRSF2, respectively.

Figure 3.. Oxidative environment affects protein mobility. See also Figures S4 and Table S3.

(A) Increased reactive oxygen species (ROS) in pathogenic signaling. (B) Relative ratio of oxidized to reduced glutathione (GSSG/GSH) in cells treated as indicated. Data shown as mean ± SEM. T-test was used for statistical analysis. (C) Relative GSSG/GSH ratio in cells treated with different hydrogen peroxide (H₂O₂) concentrations. Data shown as mean ± SEM. H₂O₂ concentration expected to phenocopy pathogenic signaling is indicated. (D, G) Schematic representation of cell treatments. (E, H) Representative FRAP images for the indicated proteins and experimental treatments. Images before (Before), immediately following (Bleach), and after recovery (Post) are shown. (F, I) Quantification of FRAP experiments for the indicated proteins and experimental conditions. For (F), 0mM and 7.5mM, n = 10 cells each condition for each protein. Data shown as mean (0mM, blue line; 7.5mM, red line) ± SEM (0mM, light blue; 7.5mM, light red). For (I), (Pathogenic, n = 16, 10, 15, 10, 20 for IR, MED1, HP1α, FIB1, and SRSF2, respectively; Pathogenic + NAC, n = 16, 10, 15, 20, 20 for IR, MED1, HP1α, FIB1, and SRSF2. Data shown as mean (Pathogenic, red line; Pathogenic + NAC, purple line) ± SEM (Pathogenic, light red; Pathogenic + NAC, light purple). t-test was used for statistical analysis (F, I). For (F), Cohen’s d = 0.7, 0.7, 1.2, 1.0, and 0.0 for IR, MED1, HP1α, FIB1, SRSF2, respectively. For (I), Cohen’s d = 0.5, 0.8, 0.9, 0.6, and 0.2 for IR, MED1, HP1α, FIB1, SRSF2, respectively.

Figure 4.. Surface-exposed cysteines sensitize proteins to oxidation-driven decrease in protein mobility. See also Figures S5 and S6 and Table S4.

(A) Rendering of the crystal structure of indicated proteins showing cysteines in red. (B) Diverse models for decreased protein mobility, including change in effective protein mass, protein conformation, interaction with immobile protein, interaction with a protein that facilitates transport, cellular viscosity increasing resistance to movement. (C) Predicted normalized diffusion coefficient from simulations of a mixture of proteins with (red) and without (gray) surface-exposed cysteines as a function of the ratio of oxidized (GSSG) to reduced (GSH) glutathione. The diffusion coefficient was normalized to the mean of all simulated data points for GSSG/GSH<10⁻³ (see Methods). (D) Quantification of FRAP data for insulin receptor (7.5 mM H₂O₂ n=16 cells, 7.5mM H₂O₂ + NEM n=16 cells), MED1 (7.5 mM H₂O₂ n=29 cells, 7.5mM H₂O₂ + NEM n=15 cells), HP1α (7.5 mM H₂O₂ n=14 cells, 7.5mM H₂O₂ + NEM n=13 cells), FIB1 (7.5 mM H₂O₂ n=24 cells, 7.5mM H₂O₂ + NEM n=24 cells) and SRSF2 (7.5 mM H₂O₂ n=12 cells, 7.5mM H₂O₂ + NEM n=12 cells) in HepG2 cells treated with 0mM or 7.5mM of H₂O₂ after pre-treatment with 10 μM N-ethyl maleimide. Data are plotted as means ± SEM. (E, F) Top: representation of SRSF2 fusion proteins with an added serine or cysteine-containing rigid linker. Bottom: quantification of FRAP data for SRSF2 fusion proteins in cells treated with the indicated experimental conditions (SRSF2-Ser, 0mM H₂O₂, n=13 cells, 7.5mM H₂O₂, n=12, Normal, n=10 cells, Pathogenic, n=10 cells; SRSF2-Cys, 0mM H₂O₂, n=13 cells, 7.5mM H₂O₂, n=13, Normal, n=10 cells, Pathogenic, n=10 cells). Data are plotted as mean ± SEM. (G) Representation of wildtype and mutant IR fusion proteins. (H) Quantification of FRAP data for wildtype (IR WT, n=15 cells) or Y1361C mutant IR (IR Y1361C, n=15 cells). Data are plotted as mean ± SEM. (I) Quantification of FRAP data for Y1361C mutant IR in cells treated with (n=15 cells) or without (n=15 cells) N-acetyl cysteine. Data are plotted as mean ± SEM. T-test was used for statistical analysis (D-I).

Figure 5.. Diverse pathogenic factors decrease protein mobility. See also Figure S7.

(A) Representations of HaloTag fusion protein (HaloTag-Cys). (B) Apparent diffusion coefficient of HaloTag-Cys as determined by SPT in cells treated as indicated (n = 245, 316, 428, 560, 305 molecules for 0, 1, 3, 8 or 20mM H₂O₂, respectively). (C) Apparent diffusion coefficient of HaloTag-Cys as determined by SPT in cells treated as indicated (n = 446, 173 molecules for normal and pathogenic, respectively). (D) Cartoon depicting pathogenic stimuli. (E) ROS quantification in cells treated as indicated. Data are plotted as mean ± SEM. Numbers of cells: normal glucose (77) vs. high glucose (67); BSA (115) vs. high fat (171); BSA (150) vs. TNFa (91); DMSO (152) vs. Etoposide (ETO, 83); control (82) vs. lipopolysaccharide (LPS, 78). (F) Apparent diffusion coefficient of HaloTag-Cys as determined by SPT in cells treated as indicated. Numbers of molecules: normal glucose (1001) vs. high glucose (582); BSA (126) vs. high fat (101); BSA (265) vs. TNFa (363); DMSO (1718) vs. ETO (1804); control (1456) vs. LPS (1327). Cohen’s d = 0.1, 0.2, 0.1, 0.2, and 0.1 for hyperglycemia, dyslipidemia, inflammation, genotoxic stress and endotoxin, respectively. Data are plotted as means ± SEM. Mann-Whitney test was used for statistical analysis (C,F). t-test was used for statistical analysis (E).

Figure 6.. Protein mobility affects function. See also Figure S7.

(A-C) Cartoons depicting relationship between protein mobility, functional output and collision frequency (A), models and assays used to study IRS phosphorylation (B) and the phosphorylation of IRS1 by a kinase. (D) Second-order rate constant from simulations of IRS1 phosphorylation as a function of diffusion coefficient. (E) Immunoblot for phosphorylated IRS1 (pIRS1) and IRS1 (left). IRS1 phosphorylation assay was performed in solutions containing 5%, 15% or 30% glycerol. Quantification of relative pIRS1 amount (right) (n = 3 biological replicates). T-test was used for statistical analysis. (F) Immunoblot for phosphorylated IRS1 (pIRS1) and IRS1 (left). IRS1 phosphorylation assay was performed in solutions containing 0% or 15% glycerol with agitation (1200 RPM) or without agitation (0 RPM). Quantification of relative pIRS1 amount (right) (n = 2 biological replicates). (G) Cartoon depicting biotinylation assay. (H) Cartoon depicting high mobility in normal conditions and low mobility in pathogenic conditions. (I) Schematic representation of cell treatments. (J) Representative tracks for movement of individual molecules as determined by single particle tracking (SPT) of the indicated proteins (left). Apparent diffusion coefficient of the indicated proteins in cells treated with normal or pathogenic insulin (right). Numbers of molecules: BirA-SNAP normal (1003) vs. pathogenic (865); AviTag-Halo-Cys normal (1022) vs pathogenic (1067). Mann-Whitney test was used for statistical analysis. (K) Immunoblot for biotinylated and unbiotinylated AviTag-Halo-Cys. (L) Cartoon depicting function decreases in diseased cells (left). Quantification of relative pIRS1 determined by immunoblotting (t-test was used for statistical analysis), Log₂(fold change) of gene expression for genes whose promoter is occupied or not occupied by MED1, and Log₂(fold change) of expression of protein-coding genes or repetitive elements.

Figure 7.. Proteolethargy is a pathogenic mechanism in chronic disease.

(A) Diverse pathogenic factors lead to oxidative stress via multiple cellular pathways and mechanisms. (B) Proteins with surface-exposed cysteines suffer reduced mobility in high ROS environments due to their sensitivity to oxidation. (C) Alterations in plasma membrane and cytoplasmic fluidity can also occur in high ROS environments. (D) Mobility is decreased in pathogenic signaling, thereby reducing rates of particle collision and leading to reduced functional output for diverse cellular processes.