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. 2024 Feb 8;25(4):2086.
doi: 10.3390/ijms25042086.

SARS-CoV-2 Infection Alters the Phenotype and Gene Expression of Adipocytes

Paola Quaranta  1   2 , Gaia Scabia  3   4 , Barbara Storti  5 , Alessia Dattilo  4 , Lara Quintino  3 , Paola Perrera  1 , Cristina Di Primio  2 , Mario Costa  2 , Mauro Pistello  1   6 , Ranieri Bizzarri  5   7 , Margherita Maffei  3   4   8
Affiliations

SARS-CoV-2 Infection Alters the Phenotype and Gene Expression of Adipocytes

Paola Quaranta et al. Int J Mol Sci. .

Abstract

Epidemiological evidence emphasizes that excess fat mass is associated with an increased risk of severe COVID-19 disease. Nevertheless, the intricate interplay between SARS-CoV-2 and adipocytes remains poorly understood. It is crucial to decipher the progression of COVID-19 both in the acute phase and on long-term outcomes. In this study, an in vitro model using the human SGBS cell line (Simpson-Golabi-Behmel syndrome) was developed to investigate the infectivity of SARS-CoV-2 in adipocytes, and the effects of virus exposure on adipocyte function. Our results show that SGBS adipocytes expressing ACE2 are susceptible to SARS-CoV-2 infection, as evidenced by the release of the viral genome into the medium, detection of the nucleocapsid in cell lysates, and positive immunostaining for the spike protein. Infected adipocytes show remarkable changes compared to uninfected controls: increased surface area of lipid droplets, upregulated expression of genes of inflammation (Haptoglobin, MCP-1, IL-6, PAI-1), increased oxidative stress (MnSOD), and a concomitant reduction of transcripts related to adipocyte function (leptin, fatty acid synthase, perilipin). Moreover, exogenous expression of spike protein in SGBS adipocytes also led to an increase in lipid droplet size. In conclusion using the human SGBS cell line, we detected SARS-CoV-2 infectivity in adipocytes, revealing substantial morphological and functional changes in infected cells.

Keywords: COVID-19; SGBS; inflammation; lipid droplet.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Phenotypes of SGBS pre-adipocytes and adipocytes, as revealed by confocal microscopy. (A,B) One SGBS pre-adipocyte (A) and one SGBS adipocyte (B) stained by Hoechst and Oil red, and confocally imaged at the medial plane by collecting fluorescence in the blue and far-red channels. (C,D) One SGBS pre-adipocyte (C) and one SGBS adipocyte (D) immunostained for ACE2 (primary Ab: rabbit antiACE2, secondary Ab: αr488) confocally imaged at the membrane plane by collecting fluorescence in the green channel. Fluorescence intensity was coded by a pseudo-color FireHot scale to pinpoint the strong difference in expression levels between the two cell types. In all cases, the cell’s contours were highlighted by a white line. Scale bar: 10 μm.
Figure 2
Figure 2
Differentiated SGBS cells are permissive for SARS-CoV-2 infection. (A) ACE2 protein expression in SGBS adipocytes (black label) and pre-adipocytes (blue label); (B) RNA-dependent RNA polymerase Ct in SGBS adipocytes (left, white bars) and pre-adipocytes (right, black bars), with respect to undiluted virus titer (striped bars). Cts are significantly decreased after 48 h in PID10 cells (SGBS adip) in respect to T0 (red asterisk); (C) Nucleocapsid protein expression in infected VERO E6 cells (positive control, left lane) and not infected (negative control, middle lane) and infected (right lane) SGBS adipocytes. One-way analysis of variance followed by Dunnett’s multiple comparison test: **** p < 0.0001; ** p < 0.01; * p < 0.05.
Figure 3
Figure 3
SARS-CoV-2 infection of SGBS pre-adipocytes and adipocytes, as revealed by confocal and super-resolution microscopy. (A–D) Confocal imaging of SGBS adipocytes exposed (A,C) and not exposed (B,D) to SARS-CoV-2 and immunostained for the S protein ((A,B), green) and the N protein ((C,D), yellow). Red signal: lipid droplets (Oil red); blue signal: Hoechst. (E,F) Super-resolution (Image Scanning Microscopy/Airyscan) images of infected adipocytes (green: Spike protein, red: lipid droplets). The portion of the image surrounded by the white square in (E) is magnified in (F). Scale bar: 10 μm.
Figure 4
Figure 4
SARS-CoV-2 infection of SGBS adipocytes induces enlargement of LDs, as revealed by confocal microscopy. (A,B) Transmitted light microscopy images of SGBS adipocytes not exposed (A) and exposed (B) to SARS-CoV-2 stained with Oil red O (20× magnification; scale bar: 50 μm). (C,D): Confocal imaging of SGBS adipocytes exposed (C) and not exposed (D) to SARS-CoV-2 and immunostained for the S protein (green); red signal: lipid droplets (Oil red); blue signal: Hoechst; scale bar: 10 μm. (E) Column bar graph showing mean and SEM of LD areas in CTRL and infected cells (CTRL: 4.8 ± 0.2 μm2, #1182 LDs; infected: 8.1 ± 0.4 μm2. #1325 LDs); ****: p < 0.0001 (t-test). (F,G) Frequency distributions (F) and cumulative distributions (G) of LD areas in CTRL and infected cells; the cumulative distributions were statistically different (p < 0.0001), as assessed by the Kolmogorov–Smirnov test. (H,I) CTRL and infected SGBS adipocytes’ expression of terminal adipocyte differentiation markers (H) (Lep, Adipoq, PPARγ, FASN, ap2/FABP4 and PLIN1 and inflammatory and oxidative stress markers (I) (Hp, IL-6, MCP1/CCL2, PAI-1 and MnSOD). Student’s t-test: *** p < 0.001; ** p > 0.01; * p < 0.5.
Figure 5
Figure 5
Exogenous expression of Spike protein in SGBS adipocytes induces enlargement of LDs as revealed by confocal microscopy. (A,B) Immunostaining followed by confocal imaging of SGBS adipocytes positive (Spike(+), (A)) or negative (Spike(−), (B)) for Spike protein (green). Red signal: lipid droplets (Oil red); blue signal: nuclei (Hoechst). Scale bar: 10 μm. (C) Column bar graph showing mean and SEM of LD areas in Spike(−) and Spike(+) cells (CTRL: 7.2 ± 0.1 μm2, #10106 LDs; infected: 8.7 ± 0.2 μm2, #7951 LDs. ****: p < 0.0001 (t-test). (D) Frequency cumulative distribution of LD areas in Spike(−) and Spike(+) cells; the cumulative distributions resulted statistically different (p < 0.0001) as assessed by the Kolmogorov-Smirnov test.
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Grants and funding

This work was supported by the following funding agencies: Piano Nazionale di Ripresa e Resilienza (PNRR) Missione 4, Componente 2, Investimento 1.4 “Centro Nazionale per lo sviluppo di terapia genica e farmaci con tecnologia a RNA”, Spoke 3–“Neurodegeneration” to M.P.; EU funding within the NextGeneration EU-MUR PNRR TUSCANY HEALTH Ecosystem—THE (Project no. ECS_00000017)” spoke 1 to M.M. and M.C. and 8 to C.D.P.; EU funding within the NextGenerationEU—MUR PNRR Extended Partnership initiative on Emerging Infectious Diseases (Project no. PE00000007, INF-ACT) to M.C. and C.D.P.; Ricerca Salute 2018 “Tuscany Antiviral Research Network (TUSCAVIR.NET)” to M.P.; COVID-19 Toscana 2020 “Suppression of Airborne Viral Epidemic Spread by Ultraviolet light barriers (SAVES-US)” to M.P.
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