SERINC5-Mediated Restriction of HIV-1 Infectivity Correlates with Resistance to Cholesterol Extraction but Not with Lipid Order of Viral Membrane

Abstract

Serine incorporator 5 (SER5) is a protein that upon incorporation into virions inhibits HIV-1 infectivity by interfering with the ability of the Env glycoprotein to promote viral fusion. The mechanisms by which SER5 antagonizes HIV-1 fusion are not well understood. A recent study of SER5's structure revealed a lipid-binding pocket, suggesting the ability to sequester lipids. This finding, along with the well-documented modulation of HIV-1 infectivity by viral lipids, especially cholesterol, prompted our examination of SER5's effect on the general lipid order of the HIV-1 membrane. Pseudoviruses bearing the SER5-sensitive HXB2-Env and containing SER5 or SER2, a control protein that lacks antiviral activity, were analyzed using two distinct lipid-order probes. We show that SER5 incorporation does not noticeably affect the lipid order of pseudoviruses. Although viral cholesterol extraction reduces HIV-1 infectivity, SER5+ viruses are less sensitive to cholesterol extraction than the control samples. In contrast, the virus' sensitivity to cholesterol oxidation was not affected by SER5 incorporation. The hydrolytic release of sphingomyelin-sequestered cholesterol had a minimal impact on the apparent resistance to cholesterol extraction. Based on these results, we propose that a subpopulation of more stable Env glycoproteins responsible for the residual infectivity of SER5+ viruses is less sensitive to the cholesterol content of the viral membrane.

Keywords: HIV-1; SERINC; cholesterol; cyclodextrin; envelope protein stability; lipid order; restriction factors; viral fusion; virus imaging.

Figure 1

Lipid-order measurements for liposomes of different compositions using Nile Red (A,C,E) or Laurdan (B,D,F). Lipid order was measured in solution by a plate reader and presented as fluorescence spectra (A,B) or GP values (C,D) (for GP calculations, see Methods). Lo stands for liquid-ordered, and Ld stands for liquid-disordered liposomes, respectively. (E,F) Lipid order (GP) of single liposomes attached to coverglass was measured using a confocal microscope. The N representing the number of liposomes analyzed for each composition is shown above the graphs.

Figure 2

Effects of HIV-1 maturation and SERINC incorporation on the lipid order of the viral membrane. Surface-attached pseudoviruses containing either GFP-Vpr (for Nile Red imaging) or mRFP-Vpr (for Laurdan imaging) were stained with Nile Red (A,C) or Laurdan (B,D). Examples of Nile Red (A) and Laurdan (B) staining analysis on attached viruses. Horizontal black lines on the scatterplot represent 1st, 2nd, and 3rd quantiles. N, the number of viruses analyzed per experiment, is shown below the graphs. (C) Nile Red and (D) Laurdan GP changes (ΔGP) between control and +SQV-, SER5-, and SER2-incorporated pseudoviruses, where N is the number of independent measurements. ΔGP was calculated by subtracting the median of the control from the median of the +SQV-, SER5-, and SER2-containing pseudoviruses, ΔGP = X − control. n.s., p > 0.05; *, 0.05 > p > 0.01; **, 0.01 > p > 0.001. ***, p < 0.001.

Figure 3

Effects of cholesterol extraction on lipid order and infectivity of control and SERINC containing pseudoviruses. Lipid order measurements of MβCD-treated viruses stained with Nile Red (A,C) and Laurdan (B,D). Examples of Nile Red (A) and Laurdan (B) staining on attached viruses. Horizontal black lines on the scatterplot represent 1st, 2nd, and 3rd quantiles. N, the number of viruses analyzed per experiment, is shown above the graphs. Statistics analysis was conducted using Kolmogorov–Smirnov test. (C,D) Means and SD of ∆GP values of 3 independent measurements. Each ΔGP was calculated by subtracting the median of the untreated viruses from the median of the 0.05-0.25 mM MβCD-treated viruses, ΔGP = X − untreated. Each virus was compared by 2-way ANOVA repeated measures. (E) Experimental workflow of the infectivity inhibition assay with cholesterol depletion by methyl-β-cyclodextrin (MβCD). After viruses were attached and treated with MβCD, TZM-bl cells were overlayed on the viruses after PBS+/+ wash. (F) (Left) Inhibition curves of control-, SER5-, and SER2-incorporated viruses treated with MβCD. At least 3 independent virus preparations were treated with MβCD for the infectivity inhibition assay and compared by 2-way ANOVA repeated measures. Infectivity data were normalized by untreated viruses and fitted using the Hill–Langmuir equation. (Right) IC₅₀ values were derived using the fitting with an estimated 95% confidence interval within the top and bottom whiskers and compared using student’s t-test. n.s., p > 0.05; *, 0.05 > p > 0.01; **, 0.01 > p > 0.001 ***, p < 0.001.

Figure 4

Effects of cholesterol oxidation on lipid order and infectivity of pseudoviruses. (A) Lipid order measurement with Nile Red staining and infectivity measurement of cholesterol oxidase (COase)-treated viruses stained with Nile Red. Horizontal black lines on the scatterplot represent 1st, 2nd, and 3rd quantiles. N, the number of viruses analyzed per experiment, is shown above the graphs. Statistical analysis was conducted using the Kolmogorov–Smirnov test. (B) Means and SD (error bars) of ΔGP between 3 independent measurements. Each ΔGP was calculated by subtracting the median of the untreated viruses from the median of the 0.025–0.1 U/well Coase-treated viruses, ΔGP = X-untreated. Each virus was compared by 2-way ANOVA repeated measures. (C) (Left) Inhibition curves of control-, SER5-, and SER2-incorporated viruses treated with COase. At least 3 independent virus preparations were treated with COase for the infectivity inhibition assay and compared by 2-way ANOVA repeated measures. Infectivity data were normalized by untreated viruses and fitted using the Hill–Langmuir equation. (Right) IC₅₀ values were derived using the fitting with an estimated 95% confidence interval within the top and bottom whiskers and compared using student’s t-test. n.s., p > 0.05; ***, p < 0.001.

Figure 5

Assessing the effects of sphingomyelinase (SMase) treatment on virus lipid order and infectivity. (A) Nile Red staining of attached viruses (B) Means and standard deviations of ΔGP from 2 independent Nile Red measurements. Each virus was compared by 2-way ANOVA repeated measures. (C) Laurdan staining of attached viruses. Statistics analysis was conducted using Kolmogorov–Smirnov test. (D) Means and standard deviations of ΔGP from 2 independent Laurdan measurements. From (B,D), each ΔGP was calculated by subtracting the median of the untreated viruses from the median of the 0.01–0.05 U/well SMase-treated viruses, ΔGP = X-untreated. Each virus was compared by 2-way ANOVA repeated measures. For both (A,C), horizontal black lines on the scatterplot represent 1st, 2nd, and 3rd quantiles. N, the number of viruses analyzed per experiment, is shown above the graphs. (E) (Left) Inhibition of infectivity of WT-, SER5-, and SER2-incorporated viruses treated with SMase. At least 3 independent virus preparations were treated with SMase for the infectivity inhibition assay and compared by 2-way ANOVA repeated measures. Infectivity data were normalized by untreated viruses and fitted using the Hill–Langmuir equation. (Right) IC₅₀ values were derived using the fitting with an estimated 95% confidence interval within the top and bottom whiskers and compared using student’s t-test. n.s., p > 0.05; ***, p <0.001.

Figure 6

Inhibition of infectivity assays first treated (0.05 U/well) using SMase and then MβCD. (Left) 3 independent virus preparations were sequentially treated with SMase and MβCD for the infectivity inhibition assay and compared by 2-way ANOVA repeated measures. Infectivity data were normalized by SMase treatment only and fitted using the Hill–Langmuir equation. (Right) IC50 values were derived using the fitting with an estimated 95% confidence interval within the top and bottom whiskers and compared using student’s t-test. n.s., p > 0.05; *, 0.05 > p > 0.01; **, 0.01 > p > 0.001.

Figure 7

Proposed model for SER5-mediated restriction of HIV-1 Envs. We propose that HIV-1 pseudoviruses are decorated with both stable (represented by blue) and unstable (represented by grey) subpopulations of Envs. SER5 can preferentially inactivate the unstable subpopulation with minimal effect on the stable Envs. Stable Envs are likely more resistant to cholesterol extraction, resulting in the apparent resistance to MβCD observed in SER5 viruses.