The SARS-CoV-2 spike S1 protein induces global proteomic changes in ATII-like rat L2 cells that are attenuated by hyaluronan

Abstract

The COVID-19 pandemic continues to impose a major impact on global health and economy since its identification in early 2020, causing significant morbidity and mortality worldwide. Caused by the SARS-CoV-2 virus, along with a growing number of variants, COVID-19 has led to 651,918,402 confirmed cases and 6,656,601 deaths worldwide (as of December 27, 2022; https://covid19.who.int/). Despite advances in our understanding of COVID-19 pathogenesis, the precise mechanism by which SARS-CoV2 causes epithelial injury is incompletely understood. In this current study, robust application of global-discovery proteomics identified highly significant induced changes by the Spike S1 protein of SARS-CoV-2 in the proteome of alveolar type II (ATII)-like rat L2 cells that lack ACE2 receptors. Systems biology analysis revealed that the S1-induced proteomics changes were associated with three significant network hubs: E2F1, CREB1/RelA, and ROCK2/RhoA. We also found that pretreatment of L2 cells with high molecular weight hyaluronan (HMW-HA) greatly attenuated the S1 effects on the proteome. Western blotting analysis and cell cycle measurements confirmed the S1 upregulation of E2F1 and ROCK2/RhoA in L2 cells and the protective effects of HMW-HA. Taken as a whole, our studies revealed profound and novel biological changes that contribute to our current understanding of both S1 and hyaluronan biology. These data show that the S1 protein may contribute to epithelial injury induced by SARS-CoV-2. In addition, our work supports the potential benefit of HMW-HA in ameliorating SARS CoV-2-induced cell injury.

INTRODUCTION

Coronavirus disease 2019 (COVID-19) is caused by a new β-coronavirus, SARS-CoV-2; its epicenter was in Wuhan, China. COVID-19 was identified as a pandemic by the World Health Organization (WHO) on March 11, 2020. As of December 27, 2022, there have been 651,918,402 cases worldwide resulting in 6,656,601 deaths (https://covid19.who.int/). This far exceeds the total deaths caused by both SARS CoV-1 and Middle Eastern respiratory syndrome (MERS). A large fraction of patients with COVID-19 develop acute respiratory distress syndrome (ARDS) within 24–48 h after onset of symptoms with more than a 50% mortality rate (1).

COVID-19 is caused by an enveloped, nonsegmented, positive-sense RNA new β-coronavirus, SARS-CoV-2; it belongs to the sarbecovirus, orthornaviridae subfamily, which is broadly distributed in humans and other mammals (2). It contains four main structural proteins including three glycoproteins [spike (S), envelope (E), and a membrane (M)], a nucleocapsid (N) protein, and several accessory proteins (3). The S glycoprotein is a transmembrane protein with a molecular weight of ∼150 kDa, found in the outer portion of the virus. It forms homotrimers protruding in the viral surface and facilitates binding of SARS CoV-2 to human ACE2 and CD147 receptors of host cells (4). The SARS CoV-2 S protein has significant homology with the SARS-CoV-1 (5), but binds with higher affinity to ACE2 receptors (6) due to a furin-like cleavage site (⁶⁸²RRAR/S⁶⁸⁶) inserted in the S1/S2 protease cleavage site (7). The S1 region of the Spike protein is responsible for binding to the host cell ACE2 receptor, whereas the S2 region is responsible for fusion of the viral RNA and cellular membrane. SARS-CoV-2 infects several different animal species like rats, ferrets, hamsters, nonhuman primates, minks, tree shrews, raccoon dogs, fruit bats, and rabbits (8).

Despite the introduction of several effective vaccines, the mutagenicity and transmissibility of SARS-CoV-2, as evidenced by the emergence of several variants, make the understanding of the pathophysiological mechanism involved and the identification of therapeutic agents an extremely high priority (9). COVID-19 is mainly a respiratory disease but it can lead to other systemic effects and eventually to a systemic disorder (10, 11). SARS-CoV-2 exhibits a very broad tissue tropism as it can infect and affect a variety of different organs and systems, including the musculoskeletal (12), nervous (13), renal (14), and cardiovascular systems (11).

The pathophysiology and immune response to SARS-CoV-2 infection are broad and involve signaling pathways from several cell and tissue types (15). In particular, although it is understood that SARS-CoV-2 used S-protein binding to ACE2 to enter cells, it is now increasingly reported that it binds to other receptors such as neurophilin-1, (16), CD147 (17), tyrosine-protein kinase receptor UFO (AXL) (18, 19), ASGR1, KREMEN1 (20), and others. Viral genetic material activates TLR7, and there have been several reports linking TLR7 deficiency with severe outcomes in COVID-19 (21–23). Less well understood is the role that S-protein-induced inflammation and cell injury may play in this disease. There is increasing evidence that the S-protein can activate innate immunity, including Toll-like receptors TLR2 and TLR4 (24–27). In addition, instillation of the SARS-CoV-2 protein in K18-hACE2 mice caused marked inflammatory response and pulmonary edema (28). Thus, it is important to understand the inflammatory effect of S1 on epithelial cells, independent of viral replication, if we are to grasp the full spectrum of SARS-CoV-2 effects on mammalian biology.

Hyaluronan or hyaluronic acid (HA) is a linear polymer formed by a repeating disaccharide structure of glucuronic acid and N-acetyl-glucosamine consisting of up to 25,000 disaccharide units (MW = 1–10 million Da). High molecular weight hyaluronan (HMW-HA) is a major structural component of the extracellular matrix; it promotes cell survival, has antiangiogenic properties, and anti-inflammatory effects on immune cells, some of these mediated via binding to its receptors CD44, TLR2, and TLR4 (29–32).

Dysregulation of HA expression is relevant in disease and injury (33). Reactive species fracture HMW-HA to lower fragments (LMW-HA ∼300 kDa) that act as endogenous innate immune ligand, and promote inflammatory responses, angiogenesis, and epithelial to mesenchymal transition (30, 31, 33–35). LMW-HA increases vascular permeability by activating RhoA and ROCK (its downstream kinase), inducing cytoskeletal reorganization and inhibiting cell-cell contacts (36, 37). LMW-HA is increased in severe COVID-19 (38–40). On the contrary, administration of HMW-HA protects from the development of lung injury in ozone exposure (41), bleomycin administration (42), smoke inhalation, and sepsis (43, 44) and has been used in the treatment of COVID-19 (clinicaltrials.gov, NCT04830020). A pharmaceutical preparation of HMW-HA (Yabro; ∼1,000 kDa) has been shown to prevent exercise-induced bronchoconstriction in patients with asthma (45). No protective effects were seen following administration of LMW-HA (46). Several studies indicate that the relevant factor for HMW-HA protection is their size and not the source from which HMW-HA is derived (31, 43, 44). Thus, HMW-HA can be a useful anti-inflammatory agent in SARS-CoV-2-induced cell injury and inflammation. To better understand both organ-specific and global effects of COVID-19, in addition to better drug targets, a growing number of bioinformatics studies include the mining of public data repositories containing –omics datasets derived from patients with COVID-19 and translational models (13, 14).

Herein, we used an unsupervised global proteomics analysis followed by systems biology analysis to identify changes in the proteome of an ATII-like rat lung epithelial cell line (L2) following incubation with SARS-CoV-2 Spike S1 recombinant protein, independent of the S-protein-ACE2 interaction, since rat ACE2 has very low affinity to S1. AT2 cells, even though less numerous than AT1 cells, play a crucial role in lung physiology, as surfactant producers, orchestrators of the immune response, and AT1 progenitors. AT2 cells also stabilize the alveolar-capillary barrier and are involved in lung fluid homeostasis and prevention of pulmonary edema through sodium transport (47). It is also believed that the inflammatory response in ATII cells plays an outsized role in COVID-19 and that ATII cells may be the main target of SARS-CoV-2 and a major hub for severe COVID-19 development (48). Therefore, we believe that our choice of L2 cells make sense in the biological context of severe COVID-19. Our findings showed the S1 treatment alone resulted in significant modification to the rat L2 cellular proteome. Network analysis indicated increased activation of E2F1, CREB1, and RhoA/ROCK2 networks, which were confirmed by Western blot studies. Conversely, preincubation of L2 cells with HMW-HA before addition of S1 prevented these changes. Previous protein-protein interaction studies along with analysis of differentially expressed genes identified within RNA sequencing datasets from patients with COVID-19 highlighted the transcription factor E2F1 as an important player (49).

MATERIALS AND METHODS

Materials

SARS-CoV-2 (2019-nCoV) Spike S1-His recombinant protein (Cat. No 40591-V08H) was purchased from Sino Biological Inc. Yabro (pure, pharmaceutical-grade HMWHA, MW, 800,000–1,200,000, at a concentration of 3 mg/mL) was provided by IBSA Farmaceutici Italia srl, Italy. The chemical reagents dithiothreitol (Cat. No. D9779) and iodoacetamide (Cat. No. I1149) were purchased from Sigma-Aldrich (St. Louis, MO). Acetonitrile (ACN) was purchased from Thermo Fisher Scientific (Cat. No. A996SK; St. Louis, MO). All other disposables are referenced within the manuscript.

Cell Culture and Exposure to S1 Protein

Rat lung type II-like epithelial cells (L2) were provided to us by Dr. Judith Creighton (deceased). Their origin was characterized and verified using the presence of human and rat genes using PCR, immunohistochemistry, and Western blots of appropriate markers (Ref. 50; Supplemental Fig. S1). Cells were cultured in DMEM media supplemented with 10% FBS and 1% antibiotics in a humidified incubator with 95% air and 5% CO₂ at 37°C until they became confluent as determined by light microscopy examination. For the experiment, cells were seeded in six-well plates at 5 × 10⁴ cells/cm² and incubated at 37°C until they became confluent in a humidified atmosphere of 95% air and 5% CO₂. On the day of the experiment, some wells were pretreated with HMW-HA (200 ng/mL) for 1 h, and after that, cells were treated with S1 protein (100 ng/0.5 mL) for 24 h, in the presence of HMW-HA, at which time they were used for various experiments.

Proteomics Analysis

Proteomics analysis was carried out as previously described (51). The protein fractions of L2 cells were quantified using an 8-point Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, Cat. No. PI23225) and 20 µg of protein per sample diluted to 35 µL using NuPAGE LDS sample buffer (1x final conc., Invitrogen, Cat. No. NP0007). Proteins were then reduced with dithiothreitol (DTT) and denatured at 70°C for 10 min before loading onto Novex NuPAGE 10% Bis-Tris Protein gels (Invitrogen, Cat. No. NP0315BOX) and separated half way (15 min at 200 constant voltage). The gels were stained overnight with Novex Colloidal Blue Staining kit (Invitrogen, Cat. No. LC6025). Following destaining, each lane was partitioned into three separate molecular weight fractions and equilibrated in 100 mM ammonium bicarbonate (Millipore SIGMA; CAS No. 1066-33-7). Each gel plug was then digested overnight with Trypsin Gold, Mass Spectrometry Grade (Promega, Cat. No. V5280) following manufacturer’s instruction. Peptide extracts were reconstituted in 0.1% formic acid and double distilled H₂O at 0.1 µg/µL.

nLC-ESI-MS2 Analysis and Database Searches

Peptide digests (8 µL each) were injected onto a 1260 Infinity nHPLC stack (Agilent Technologies) and separated using a 100 µm ID × 13.5 cm pulled tip C-18 column (Jupiter C-18 300 Å, 5 micron, Phenomenex, Torrance, CA). This system runs in-line with a Thermo Orbitrap Velos Pro hybrid mass spectrometer, equipped with a nanoelectrospray source (Thermo Fisher Scientific), and all data were collected in collision-induced dissociation (CID) mode. The nHPLC was configured with binary mobile phases that included solvent A [0.1% formic acid (FA) in double distilled H₂O] and solvent B (0.1% FA in 15% double distilled H₂O-85% acetonitrile), programmed as follows: 10 min @ 5% solvent B (2 µL/min, load), 90 min @ 5%–40% solvent B (linear: 0.5nL/min, analyze), 5 min @ 70% solvent B (2 µL/min, wash), and 10 min @ 0% solvent B (2 µL/min, equilibrate). Following each parent ion scan (300–1,200 m/z @ 60k resolution), fragmentation data (MS2) were collected on the top most intense 15 ions. For data-dependent scans, charge state screening and dynamic exclusion were enabled with a repeat count of two, repeat duration of 30 s, and exclusion duration of 90 s.

The XCalibur*.raw files were collected in profile mode, centroided, and converted to MzXML using ReAdW v. 3.5.1. The data were searched using SEQUEST, which was set for two maximum missed cleavages, a precursor mass window of 20 ppm, trypsin digestion and variable modifications of cysteine residues involved in disulfide bridges capped/alkylated by iodoacetamide treatment in in-gel digestion process, resulting in carbamidomethylation with 57.0293 Da shift and methionine residues oxidized with a mass shift of 15.9949 Da. Searches were performed separately with both human and rat species-specific subsets of the UniProtKB databases. Following this analysis, we used the high-confidence Rattus norvegicus to human tryptic peptide homolog data to avoid confusion for downstream Systems Analysis.

Peptide Filtering, Grouping, and Quantification

The list of peptide IDs generated based on SEQUEST (Thermo Fisher Scientific) search results were filtered using Scaffold (Protein Sciences, Portland, OR). Scaffold filters and groups all peptides to generate and retain only high-confidence IDs while also generating normalized spectral counts (N-SC’s) across all samples for the purpose of relative quantification. The filter cut-off values were set with minimum peptide length of >five amino acids (AAs), with no (m/z + 1) charge states, with peptide probabilities of >80% confidence intervals (CIs), and with the number of peptides per protein ≥2. The protein probabilities were then set to a >99.0% CI and a false discovery rate (FDR) <1.0. Scaffold incorporates the two most common methods for statistical validation of large proteome datasets, the false discovery rate (FDR), and protein probability (52–54). Relative quantification across experiments were then performed via spectral counting (55), and when relevant, spectral count abundances were then normalized between samples (56).

Statistical and Systems Biology Analysis

For the proteomic data generated, two separate nonparametric statistical analyses were performed between each pair-wise comparison. These nonparametric analyses include 1) the calculation of weight values by significance analysis of microarray (SAM; cutoff >|0.6|) combined with 2) t test (single tail, unequal variance, cutoff of P < 0.05), which then were sorted according to the highest statistical relevance in each comparison. For SAM (57), whereby the weight value (W) is a statistically derived function that approaches significance as the distance between the means (μ1 − μ2) for each group increases, and the SD (δ1 − δ2) decreases using the formula, W = (μ1 − μ2)/(δ1 − δ2). For protein abundance ratios determined with N-SC’s, we set a 1.5–2.0 fold change as the threshold for significance, determined empirically by analyzing the innerquartile data from the control experiment indicated above using ln-ln plots, where the Pierson’s correlation coefficient (R) was 0.98, and >99% of the normalized intensities fell between ±1.5 fold. In each case, any two of the three tests (SAM, t test, or fold change) had to be satisfied.

Gene ontology assignments and pathway analysis were carried out using MetaCore (GeneGO Inc., St. Joseph, MI). Interactions identified within MetaCore are manually correlated using full-text articles. Detailed algorithms have been described previously (58).

Measurement of RhoA Activity

The Rho activity was measured and quantified by using the RhoA Activation Assay Biochem Kit (Cat. No. BK036; Cytoskeleton) based on the Rhotekin pull-down assay as per the manufacturer’s instructions as described previously (36). In brief, L2 cells (n = 10) were cultured in six-well culture plates and few wells were pretreated with HMW-HA (200 ng/mL) for 1 h and then treated with or without spike S1 (100 ng) protein for 30 min at 37°C. The cell lysate was prepared in radioimmunoprecipitation assay (RIPA) buffer containing protease inhibitors (Cat. No. 78425; Thermo Fisher Scientific) immediately after treatment and protein estimation was done, and equal amounts of protein were incubated with Rhotekin-RBD beads (Cat. No. RT02) for 1 h at 4°C. After the beads were washed with wash buffer, proteins were removed from the beads with Laemmli buffer and then subjected to Western blotting.

Measurement of E2F1 and ROCK2 Phosphorylation

In brief, L2 cells (n = 10) were cultured in six-well culture plates and few wells were pretreated with HMW-HA (200 ng/mL) for 1 h and then treated with or without spike S1 (200 ng/mL; 100 ng total) protein for 24 h at 37°C. The cell lysates were prepared in RIPA buffer containing protease inhibitors (Thermo Fisher Scientific, Rockford, IL) immediately after treatment. Protein estimation was carried out using the BCA assay. Equal amounts of protein in Laemmli buffer were loaded in 4%–20% gradient Tris·HCl Criterion precast gels, and proteins were transferred to PVDF membranes. Membranes were probed with p-E2F1 (1:1,000 dilution; Cat. No. MABE1782; MilliporeSigma) or E2F1 (1:1,000 dilution; Cat. No. 3742S; Cell Signaling Technology, Beverly, MA) and ROCK2 (1:1,000 dilution; Cat. No. 8236; Cell Signaling Technology, Beverly, MA) or pTyr722-ROCK2 antibody (Cat. No. SAB4301564; MilliporeSigma). Bands were detected by the chemiluminescent horseradish peroxidase substrate. Protein loading was normalized by reprobing the membranes with an antibody specific to β-actin.

Cell Cycle Analysis by Flow Cytometry

Cell cycle was analyzed using staining with DAPI nucleic acid stain (ThermoFisher, Waltham, MA) in cells following dissociation and fixation in single-cell suspension. DAPI fluorescence was detected using flow cytometry in the UAB Comprehensive Flow Cytometry Core facility using an LSR II Flow Cytometer (Beckton Dickinson, Franklin Lakes, NJ) and cell cycle was determined based on nucleic acid content/cell. Cells associated with the peak at the lower fluorescence intensity were deemed to be G0/G1 cells, cells in the peak at the higher fluorescence were deemed G2M phase, and cells with intermediate fluorescence were determined to be S phase.

Statistical Analysis for Nonproteomics Studies

Figures were generated and statistics were performed using GraphPad Prism version 8 for Windows (GraphPad Software, San Diego, CA). The mean ± SEM were calculated in all experiments, and statistical significance was determined by one-way ANOVA followed by the Tukey’s multiple comparisons test.

Pathway Associations with Human Cell Lines Exposed to SARS-CoV-2

To determine whether the associated functions observed in the S1-Tx proteomics L2 cells are correlated with the transcriptomic data, the RNA-seq transcriptomic data of five human lung cell lines that were exposed to SARS-CoV-2 were downloaded from public domain (Supplemental Table S2). The Gene Set Enrichment Analysis (GSEA v4.0.3; https://www.gsea-msigdb.org/gsea/index.jsp) was conducted against the Hallmark and C2 curated canonical pathways datasets in the Molecular Signatures Database (MSigDB v7.4) with 1,000 permutations (min size 15, max size 500). Gene sets with adjusted FDR q-value less than 0.25 were considered as significant.

RESULTS

Spike S1 Effects on the ATII-Like Rat L2 Proteome

We tested whether treatment of ATII-like rat L2 cells with SARS-CoV-2 Spike S1-recombinant protein for 24 h causes significant changes to its proteome (Tx-S1 vs. vehicle control/C, n = 4). The proteomics analysis revealed 1,678 altered proteins, 730 of which had <1% false discovery rate (FDR) with at least 3 out of 4 replicates measuring nonzero values across all groups tested (Supplemental Table S1). Ninety-nine proteins were significantly changed in abundance by S1 treatment, 51 of which increased (Table 1) and 48 decreased (Table 2). This dataset was best visualized using a volcano plot (Fig. 1), in addition to a two-dimensional (2-D) hierarchical heat map (HCA-HM, Fig. 2A) followed by principal component analysis (PCA, Fig. 2B). All statistical analysis and visualizations were carried out using the data matrix found in Supplemental Table S1.

Table 1.

List of 51 proteins of rat L2 cells that were increased significantly 24 h post-S1

				Statistics			Metacore Definitions
UniProtKB Name	UniProt ID	Entrez ID	Network ID	Sam	t-Test (P Value)	Fold Δ (S1/C)	Object Type	Fig. 1	Fig. 5
Protein diaphanous homolog 1	O60610	1729	DIA1	2.67	0.00548	3.4	Generic binding protein	X	X
Structural maintenance of chromosomes protein 3	Q9UQE7	9126	SMC3	1.18	0.02235	3.2	Generic binding protein		X
Leukotriene A-4 hydrolase	P09960	4048	LTA4H	1.42	0.01370	3.2	Generic enzyme
ATP-citrate synthase	P53396	47	ACLY	1.57	0.00275	3.0	Generic enzyme		X
C-Jun-amino-terminal kinase-interacting protein 4	O60271	9043	SPAG9	1.82	0.00251	2.7	Generic binding protein
Splicing factor 3 A subunit 1	Q15459	10291	SF3A1	1.00	0.03406	2.7	Generic binding protein		X
Alcohol dehydrogenase [NADP(+)]	P14550	10327	ALDX	0.79	0.05316	2.7	Generic enzyme
Poly(rC)-binding protein 1	Q15365	5093	PCBP-1	1.55	0.00584	2.5	Generic binding protein	X	X
Vacuolar protein sorting-associated protein 26 A	O75436	9559	Vps26A	1.16	0.08734	2.5	Generic binding protein		X
α-Crystallin B chain	P02511	1410	α Crystallin B	1.32	0.00562	2.4	Generic binding protein		X
Dopamine receptor interacting protein 4	Q4W4Y1			1.12	0.03228	2.3
Rab GDP dissociation inhibitor α	P31150	2664	RABGDIA	1.64	0.01578	2.3	Regulators (GDI, GAP, GEF)	X	X
Protein DJ-1	K7ELW0	11315	DJ-1	0.98	0.01989	2.2	Generic enzyme	X
ATP-binding cassette subfamily E member 1	P61221	6059	RLI	1.35	0.00696	2.2	Generic binding protein		X
Galectin-1	P09382	3956	Galectin-1	0.81	0.07362	2.2	Receptor ligand
Exportin-2	P55060	1434	CSE1L	1.18	0.00793	2.2	Transporter		X
Phosphoglycerate kinase 1	P00558	5230	PGK1	1.93	0.00097	2.2	Generic kinase	X	X
Protein FAM49B	Q9NUQ9	51571	FAM49B	0.95	0.07184	2.1	Protein		X
S-phase kinase-associated protein 1	P63208	6500	SKP1	0.85	0.06385	2.1	Generic binding protein
Adapter molecule crk	P46108	1398	CRK	1.19	0.00888	2.1	Generic binding protein
Similar to 60S ribosomal protein L7	O95036			2.11	0.00063	2.1			X
Vinculin	P18206	7414	Vinculin	1.33	0.00801	2.0	Generic binding protein	X
Heat shock protein β-1	P04792	3315	HSP27	0.79	0.07920	2.0	Generic binding protein		X
Rab GDP dissociation inhibitor β	P50395	2665	GDI2	0.81	0.05170	2.0	Generic binding protein		X
Ephrin type-A receptor 2	P29317	1969	Ephrin-A receptor 2	0.84	0.04238	2.0	Receptor with enzyme activity		X
Transgelin-2	P37802	8407	TAGLN2	1.02	0.02116	2.0	Protein
Proteasome subunit β type-5	P28074	5693	PSMB5	0.91	0.03810	2.0	Generic protease
S-methyl-5′-thioadenosine phosphorylase	Q13126	4507	MTAP	0.77	0.04806	2.0	Generic enzyme
Golgin subfamily A member 3	Q08378	2802	Golgin-160	1.11	0.05072	1.9	Generic binding protein		X
Regulator of nonsense transcripts 1	Q92900	5976	RENT1	0.79	0.04820	1.9	Generic binding protein		X
26S proteasome non-ATPase regulatory subunit 12	O00232	5718	PSMD12	1.33	0.01118	1.9	Generic binding protein		X
Fructose-bisphosphate aldolase C	P09972	230	ALDOC	1.50	0.01419	1.8	Generic enzyme	X	X
Pyruvate dehydrogenase E1 component subunit β	P11177	5162	PDH	0.82	0.03006	1.8	Generic enzyme		X
60S ribosomal protein L6	Q02878	6128	RPL6	0.98	0.01818	1.8	Generic binding protein		X
Peroxiredoxin-6	P30041	9588	NSGPeroxidase	0.74	0.04281	1.8	Generic phospholipase
COP9 signalosome complex subunit 4	Q9BT78	51138	COPS4	0.81	0.04235	1.8	Generic binding protein		X
Heat shock 70 kDa protein 4	P34932	3308	HSP70	0.95	0.02048	1.7	Generic binding protein		X
Peptidyl-prolyl cis-trans isomerase D	Q08752	5481	PPID	0.83	0.05670	1.7	Generic enzyme
40S ribosomal protein S26	P62854	6231	RPS26	0.95	0.02206	1.7	Generic binding protein		X
Heat shock protein 105 kDa	Q92598	10808	HSP105	0.90	0.06162	1.7	Generic binding protein		X
Drug-sensitive protein 1	Q9NZ23			1.77	0.00557	1.7			X
Desmoplakin	P15924	1832	Desmoplakin	1.33	0.01192	1.7	Generic binding protein		X
Actin-related protein 2/3 complex subunit 4	P59998	10093	ARPC4	0.97	0.03172	1.7	Generic binding protein
Transcription elongation factor B polypeptide 2	Q15370	6923	Elongin B	0.84	0.08657	1.6	Generic binding protein		X
Proteasome subunit α type-4	P25789	5685	PSMA4	0.94	0.04583	1.6	Generic protease
Elongation factor 1-α 1	P68104	1915	eEF1A	0.86	0.02515	1.6	Generic binding protein		X
Plectin	Q15149	5339	Plectin 1	0.94	0.01884	1.5	Generic binding protein		X
Tubulin β-2A chain	Q13885	7280	Tubulin β	1.86	0.00221	1.5	Generic binding protein		X
Kinesin-1 heavy chain	P33176	3799	KIF5B	0.78	0.03425	1.5	Generic binding protein		X
60S ribosomal protein L5	P46777	6125	RPL5	1.07	0.02048	1.5	Generic binding protein	X	X

X denotes whether a protein was included in the indicated analysis and whether its increase by S1 (Fig. 1) was prevented by pretreatment of L2 with HMW-HA (Fig. 5). HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

Table 2.

List of 48 proteins of rat L2 cells that were significantly decreased 24 h post-S1

				Statistics			Metacore Definition
UniProtKB Name	UniProt ID	Entrez ID	Network ID	SAM	t Test (P Value)	Fold Δ (S1/C)	Object Type	Fig. 1	Fig. 5
Trifunctional enzyme subunit α, mitochondrial	P40939	3030	HADHA	1.18	0.06945	−4.6	Generic enzyme		X
Basement membrane-specific heparan sulfate proteoglycan core protein	P98160	3339	Perlecan	1.54	0.00765	−3.7	Generic binding protein		X
Nuclear mitotic apparatus protein 1	Q14980	4926	NUMA1	1.99	0.00737	−3.2	Generic binding protein
Signal recognition particle 54 kDa protein	P61011	6729	SRP54	1.26	0.04139	−3.0	Generic binding protein		X
Ras-related protein Rab-5B	P61020	5869	RAB-5B	0.97	0.02599	−2.8	RAS - superfamily	X	X
Clathrin light chain A	P09496	1211	Clathrin light chain	0.96	0.04513	−2.6	Generic binding protein
NAD-dependent malic enzyme, mitochondrial	P23368	4200	ME2	0.86	0.03056	−2.4	Generic enzyme
Glycerol-3-phosphate dehydrogenase, mitochondrial	P43304	2820	GPD2	1.33	0.01184	−2.3	Generic enzyme
Pyridoxal-dependent decarboxylase domain-containing protein 1	Q6P996	23042	LP8165	1.42	0.03052	−2.3	Generic binding protein		X
Heterogeneous nuclear ribonucleoprotein M	P52272	4670	hnRNP M	1.36	0.00843	−2.3	Generic binding protein		X
Prostacyclin synthase	Q16647	5740	PTGIS	0.88	0.04089	−2.3	Generic enzyme		X
Tumor protein D54	O43399	7165	D54	1.16	0.01435	−2.3	Generic binding protein	X	X
Nucleobindin-1	Q02818	4924	Nucb1	0.98	0.03256	−2.3	Generic binding protein
Vacuolar protein sorting-associated protein 13 A	Q96RL7	23230	VPS13A	1.28	0.04082	−2.3	Transporter		X
Cleavage and polyadenylation specificity factor subunit 5	O43809	11051	CPSF5	1.02	0.03985	−2.2	Generic enzyme		X
Pre-mRNA-processing factor 19	Q9UMS4	27339	NMP200	1.24	0.00673	−2.2	Generic binding protein	X	X
La-related protein 4	Q71RC2	113251	LARP4	2.21	0.00918	−2.2	Generic binding protein	X
Lanosterol 14-α demethylase	Q16850	1595	CYP51A1	0.79	0.05002	−2.1	Generic enzyme		X
RNA-binding protein FUS	P35637	2521	FUS	0.91	0.04299	−2.1	Generic binding protein		X
10 kDa heat shock protein, mitochondrial	P61604	3336	HSP10 (mitochondrial)	1.27	0.00704	−2.0	Generic binding protein		X
UDP-glucose:glycoprotein glucosyltransferase 1	Q9NYU2	56886	UGCGL1	1.89	0.00092	−2.0	Generic enzyme
Hypoxia upregulated protein 1	Q9Y4L1	10525	HYOU1	0.91	0.02828	−2.0	Generic binding protein		X
Staphylococcal nuclease domain-containing protein 1	Q7KZF4	27044	SND1	1.80	0.01223	−2.0	Generic binding protein		X
ELAV-like protein 1	Q15717	1994	ELAVL1 (HuR)	0.93	0.02877	−2.0	Generic binding protein		X
B-cell receptor-associated protein 31	P51572	10134	BCAP31	1.02	0.02750	−1.9	Generic binding protein
26S proteasome non-ATPase regulatory subunit 7	P51665	5713	PSMD7	0.79	0.04622	−1.9	Generic binding protein		X
Elongation factor Tu, mitochondrial	P49411	7284	TUFM	0.85	0.02811	−1.9	Generic binding protein
Prolyl 4-hydroxylase subunit α-2	O15460	8974	P4HA	0.82	0.03704	−1.9	Generic enzyme		X
P37 AUF1	Q12771			0.71	0.04582	−1.8
Ornithine aminotransferase, mitochondrial	P04181	4942	OAT	0.81	0.05087	−1.8	Generic enzyme
Actin-related protein 2/3 complex subunit 1B	O15143	10095	ARPC1	0.93	0.01943	−1.7	Generic binding protein		X
Utrophin	P46939	7402	Utrophin	1.48	0.00458	−1.7	Generic binding protein	X	X
Vesicle-associated membrane protein-associated protein A	Q9P0L0	9218	VAPA	1.11	0.01762	−1.7	Generic binding protein		X
Ras-related protein Rab-1A	P62820	5861	Rab-1A	1.08	0.01950	−1.7	RAS - superfamily
Calnexin	P27824	821	Calnexin	0.79	0.04528	−1.7	Generic binding protein		X
α-Aminoadipic semialdehyde dehydrogenase	P49419	501	AL7A1	1.82	0.03742	−1.7	Generic enzyme		X
Ras-related protein Rab-1B	Q9H0U4	81876	Rab-1B	0.99	0.01658	−1.7	RAS - superfamily
Calreticulin	P27797	811	Calreticulin	1.55	0.00609	−1.7	Generic binding protein		X
Polymerase I and transcript release factor	Q6NZI2	284119	PTRF	1.73	0.01208	−1.7	Generic binding protein	X	X
Neutral α-glucosidase AB	Q14697	23193	GANAB	1.30	0.00599	−1.6	Generic enzyme
Pyruvate dehydrogenase E1 component subunit α	P08559	5160	PDH	0.88	0.07592	−1.6	Generic enzyme
Coatomer subunit α	P53621	1314	COPA	1.06	0.01301	−1.6	Generic binding protein	X	X
Heterogeneous nuclear ribonucleoprotein F	P52597	3185	hnRNP F	0.75	0.04082	−1.6	Generic binding protein
Serpin H1	P50454	871	HSP47	1.25	0.02301	−1.6	Generic binding protein		X
Nischarin	Q9Y2I1	11188	IRAS	1.03	0.02231	−1.6	Generic binding protein
Neuroblast differentiation-associated protein AHNAK	Q09666	79026	AHNAK	1.38	0.00585	−1.6	Generic binding protein		X
Endoplasmin	P14625	7184	Endoplasmin	1.21	0.02263	−1.5	Generic binding protein		X
60 kDa heat shock protein, mitochondrial	P10809	3329	HSP60	1.05	0.03181	−1.5	Generic binding protein
Malate dehydrogenase	Q6FHZ0	4191	MDH2	1.15	0.01381	−1.5	Generic enzyme		X

X denotes whether a protein was included in the indicated analysis and whether its decrease by S1 was prevented by pretreatment of L2 cells with HMW-HA. HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

Figure 1.

Volcano plot of proteins increased or decreased in abundance following addition of S1 vs. vehicle 24 h later. Ninety-nine proteins were significantly changed in abundance by S1 treatment, 51 of which increased (Table 1), and 48 decreased (Table 2). The y-axis is the negative log (base 10) of the P value. This results in highly significant points with low P values appearing toward the top of the plot. The log of the fold change between the two conditions is plotted on the x-axis in base 2 (ex: Log2 Fold 35 = 5). Points found toward the top of the plot that are either on the left- or right-hand sides represent values that display large-magnitude fold changes as well as high statistical significance. Values are means of four different experiments.

Figure 2.

Two-dimensional (2-D) HCA heat map (A) and PCA plot (B) of the 36 most significantly changed proteins from rat L2 cells 24 h following S1 treatment vs. control. Each column in the HCA heat map and each circle in the PCA plot represent different experiments. HCA, hierarchical heat map; PCA, principal component analysis.

Spike S1 Associated Biological Processes and Pathways via Systems Biology

To identify key biological processes altered following treatment of L2 cells with S1 proteins, we performed in-depth systems biology analysis using the data listed in Tables 1 and 2. Based on the results of this analysis, we generated a pie chart to better visualize the more common Gene Ontology (GO)-based process networks that the most changed proteins are associated with (Fig. 3). This analysis suggests that treatment of L2 cells with the S1 protein alters numerous cellular functions, including catabolic and glucose metabolism, endoplasmic reticulum (ER) stress, cell migration, and signal transduction. We then performed unbiased network analysis using Metacore’s Systems Biology software (https://www.nihlibrary.nih.gov/about-us/news/metacore-enabling-systems-biology-research-through-pathway-analysis). This analysis identified three important network hubs that cross correlate with the E2F1 pathway (Fig. 4A), CREB/RelA (Fig. 4B), and ROCK2/RhoA (Table 3). To better understand the network and pathway figures, we have included a complete explanation of the various symbols in Supplemental Fig. S1 As illustrated in Fig. 4A, nearly the entire list of significantly changed proteins after S1 addition (listed as network IDs in Tables 1 and 2) is associated with E2F1 signaling. Similarly, as shown in Fig. 4B, both CREB1 and RelA transcription factors are highlighted as central network hubs. Further analysis illustrated in Table 3 highlights networks such as cell adhesion, cytoskeleton associated (actin filaments, rearrangement, spindle and cytoplasmic tubules), in addition to cell cycle mitosis and meiosis. Also of interest, there was an apparent pronounced effect on cellular MHC class I immune response as the top pathway (Fig. 4C).

Figure 3.

Top process networks pie chart from systems biology analysis carried out on the most significantly changed proteins (Tables 1 and 2), from rat L2 cells 24 h after treatment with SARS-CoV-2 S1 or vehicle. Highlighted segments contain proteins associated with ROCK2/RhoA (cell migration, motility, and locomotion), CREB1/RelA (MAPK cascade, signal transduction), and E2F1 (catabolic and metabolic processes).

Figure 4.

Top network map with the use of the 99 most significantly changed proteins at 24 h post-S1 identified in Tables 1 and 2, using Metacore’s Systems Biology software to carry out network analysis. E2F1 (A) and ReA/p65, NF-κB (B) were identified as a major centralized network hub. Nearly the entire list of proteins identified were shown to interact/associate in some way with E2F1 as a direct results of treatment of L2 cells with SARS-CoV-2 S1 protein for 24 h. Top network map for SPIKE S1 treatment; RelA and CREB1 presented as minor and major network hubs, respectively. Blue circles indicate a decrease, whereas red circles indicate an increase in protein abundance at 24 h postincubation with S1 as compared to vehicle. Detailed description of all symbols is shown in Supplemental Fig. S1. C: top pathway map for SPIKE S1 treatment: immune response-antigen presentation by major histocompatibility (MHC) class I, classical pathway. Blue bars indicate a decrease and red bars indicate an increase in protein abundance 24 h post-S1 incubation compared to vehicle control.

Table 3.

Enrichment by process networks (S1 treated vs. vehicle)

#	Network	Total	P Value	FDR	In Data	Network Objects from Active Data
1	Protein folding_Response to unfolded proteins	71	1.1E-11	8.8E-10	12	HSP90, HSP27, HSP47, Endoplasmin, HSP70, HSP60, Calreticulin, HYOU1, HSP105, HSP10 (mitochondrial), HSPA4, Calnexin
2	Protein folding_Folding in normal condition	119	5.5E-09	2.1E-07	12	HSP90, HSP27, HSP47, α crystallin B, Endoplasmin, HSP70, HSP60, HYOU1, HSP105, HSP10 (mitochondrial), HSPA4, PPID
3	Protein folding_ER and cytoplasm	44	4.4E-07	1.1E-05	7	HSP90, HSP47, HSP70, Calreticulin, UGCGL1, HYOU1, Calnexin
4	Immune response_Phagosome in antigen presentation	241	6.3E-05	1.2E-03	11	HSP90, PSMA4, CRK, Endoplasmin, PSMD7, HSP70, Calreticulin, Vinculin, PSMB5, Calnexin, PSMD12
5	Cytoskeleton_Regulation of cytoskeleton rearrangement (RhoA)	183	1.8E-04	2.8E-03	9	ARPC1B, CRK, ARPC1, Vinculin, ARPC4, Plectin 1, Tubulin β, DIA1, Tubulin β 2
6	Cytoskeleton_Intermediate filaments (RhoA)	81	2.6E-04	2.9E-03	6	KIF5B, Kinesin heavy chain, Plectin 1, Tubulin β, Tubulin β 2, Desmoplakin
7	Immune response_Antigen presentation	193	2.6E-04	2.9E-03	9	HSP90, PSMA4, Endoplasmin, PSMD7, HSP70, Calreticulin, PSMB5, Calnexin, PSMD12
8	Cytoskeleton_Actin filaments (RhoA)	176	7.0E-04	6.8E-03	8	Utrophin, ARPC1B, CRK, ARPC1, Vinculin, ARPC4, Plectin 1, DIA1
9	Proteolysis_Ubiquitin-proteasomal proteolysis	166	2.3E-03	2.0E-02	7	HSP90, PSMA4, PSMD7, HSP70, PSMB5, PSMD12, Elongin B
10	Protein folding_Protein folding nucleus	58	3.8E-03	3.0E-02	4	HSP90, HSP70, HYOU1, HSPA4
11	Transcription_mRNA processing	160	8.5E-03	6.0E-02	6	PCBP-1, SF3A1, hnRNP M, CPSF5, NMP200, hnRNP F
12	Cell adhesion_Integrin-mediated cell-matrix adhesion (RhoA)	214	9.4E-03	6.1E-02	7	Galectin-1, CRK, Vinculin, Tubulin β, DIA1, Tubulin β 2, IRAS
13	Signal transduction_Insulin signaling	171	4.1E-02	2.4E-01	5	PDH, PDH β, ACLY, PDH α, PDHA (somatic)
14	Translation_Elongation-Termination	232	4.3E-02	2.4E-01	6	eEF1A, RPS26, eEF1A1, SRP54, RPL6, RPL5
15	Cell cycle_Mitosis	179	4.8E-02	2.5E-01	5	NUMA1, CRK, Tubulin β, SMC3, CSE1L
16	Cell cycle_Meiosis	106	1.1E-01	5.3E-01	3	HSP70, Tubulin β, SMC3
17	Cytoskeleton_Spindle microtubules (RhoA)	109	1.2E-01	5.3E-01	3	NUMA1, Tubulin β, SMC3
18	Translation_Translation initiation	171	1.2E-01	5.3E-01	4	RPS26, RPL6, RENT1, RPL5
19	Cytoskeleton_Cytoplasmic microtubules (RhoA)	115	1.4E-01	5.6E-01	3	KIF5B, Kinesin heavy chain, Tubulin β
20	Signal transduction_Androgen receptor nuclear signaling	126	1.6E-01	6.4E-01	3	HSP90, Calreticulin, ELAVL1 (HuR)
21	Apoptosis_Endoplasmic reticulum stress pathway	89	2.6E-01	9.3E-01	2	Endoplasmin, BCAP31

HMW-HA Pretreatment Attenuates Spike S1-Induced Changes to the Proteome of Rat L2 Cells

As shown in the heat map of Fig. 5A, incubation of L2 cells with HMW-HA for 1 h before the addition of S1 reversed most of the S1 changes to their proteome. PCA analysis of the data from L2 cells treated with vehicle, S1, and preincubated with HMW-HA showed tight clustering and clear separation among groups (Fig. 5B).

Figure 5.

Heat map (A) and three-dimensional (3-D) PCA plot of control (vehicle; B) vs. S1 (SPIKE), and HMW-HA pretreatment prior to S1 treatment of rat L2 cells, stemming from the most significant protein differences by ANOVA (analysis of variance) using the data matrix from Table 4. Each column represents a different experiment. Note the tight grouping of the various experiments in the 3-D PCA plot (each point represents a different experiment). HA, hyaluronan (hyaluronic acid); HMW, high molecular weight; PCA, principal component analysis.

HMW-HA Effects on the Rat L2 Proteome and Associated Pathways

In a separate follow-up proteomics experiment, we found that the addition of HMW-HA to rat L2 cells for 24 h (in the absence of the S1 protein) caused significant changes in 75 proteins (37 increased and 38 decreased, HMW-HA vs. C; Table 4). A 2 D-HCA heat map of the top most significantly changed 37 proteins (Fig. 6A) is illustrated along with a separate PCA plot (Fig. 6B).

Table 4.

Seventy-five most significantly changed proteins in HMW-HA v vehicle groups (37 increased and 38 decreased)

		Vehicle				HMW-HA Treated				Stats (HMW-HA vs. Vehicle)
UniProtKB Name	Acc#	C1	C2	C3	C4	HA1	HA2	HA3	HA4	SAM	t Test	Fold (HMW-HA vs. Contr.)
Epididymis luminal protein 215	Q6PUJ7	2.0	1.1	1.1	3.8	5.3	10.7	5.6	8.2	1.44	0.007	3.7
116 kDa U5 small nuclear ribonucleoprotein component	Q15029	5.0	1.1	2.2	3.8	10.6	7.1	19.6	4.1	0.87	0.057	3.4
Endothelin-1	P05305	4.0	4.3	3.4	2.8	6.3	19.6	4.7	9.2	0.86	0.077	2.7
Eukaryotic translation initiation factor 2 subunit 1	P05198	3.0	1.1	4.5	1.9	5.3	4.5	9.3	9.2	1.10	0.015	2.7
Coiled-coil domain-containing protein 47	Q96A33	2.0	1.1	1.1		6.3	1.8		3.1	0.81	0.111	2.7
RNA-binding protein 3	P98179	1.0	2.1		1.9		3.6	4.7	5.1	1.99	0.005	2.7
Clathrin interactor 1	Q14677	1.0	3.2	1.1	1.9	3.2	7.1	3.7	5.1	1.07	0.017	2.7
Drug-sensitive protein 1	Q9NZ23	4.0	12.9	3.4	3.8	17.9	14.3	18.7	9.2	1.01	0.014	2.5
Prohibitin-2	Q99623	2.0	2.1		1.9	7.4	4.5	4.7	3.1	1.49	0.024	2.4
Lon protease homolog, mitochondrial	P36776	3.0	2.1	3.4	0.9		5.4	6.5	5.1	1.80	0.002	2.4
Rho GDP-dissociation inhibitor 1	P52565	5.0	3.2	8.9	3.8	10.6	9.8	16.8	12.3	1.24	0.006	2.4
Heparin-binding protein HBp15	Q7Z4W8	2.0	2.1	2.2	0.9	1.1	6.2	4.7	5.1	0.86	0.057	2.3
LDLR chaperone MESD	Q14696		3.2	2.2	1.9	6.3	5.4	7.5	3.1	1.21	0.018	2.3
Proliferation-associated protein 2G4	Q9UQ80	4.0	2.1	4.5	1.9	6.3	8.0	5.6	8.2	1.52	0.002	2.2
Cysteine and glycine-rich protein 1	P21291	3.0	2.1	2.2	0.9	3.2	7.1	3.7		0.86	0.080	2.2
Glycylpeptide N-tetradecanoyltransferase 1	P30419	4.0	1.1	2.2		7.4		3.7	5.1	0.89	0.049	2.2
60S ribosomal protein L7	P18124	4.0	8.6	4.5	6.6	16.9	7.1	8.4	19.4	0.86	0.049	2.2
RNA-binding motif protein, X chromosome	P38159	1.0	1.1	3.4	4.7	5.3	6.2	6.5	4.1	1.03	0.018	2.2
Proteasome subunit β type-7	Q99436	2.0		1.1	0.9		2.7	3.7	2.0	1.02	0.039	2.1
Histone H2B type 1-M	Q99879	3.0	2.1	4.5	1.9	4.2	5.4	7.5		1.00	0.038	2.0
Calpain small subunit 1	P04632	2.0	1.1		0.9	2.1	2.7	3.7	2.0	0.95	0.026	2.0
Histone H4	P62805	5.0	3.2	2.2	3.8	7.4	8.9	4.7		1.03	0.045	2.0
B-cell receptor-associated protein 31	P51572		3.2	2.2	1.9	2.1	5.4	6.5	5.1	0.91	0.042	2.0
Ubiquitin-conjugating enzyme E2 N	P61088	2.0		1.1	1.9	3.2	2.7	4.7	2.0	0.92	0.037	1.9
l-lactate dehydrogenase A chain	P00338	6.0	5.4	4.5	2.8	8.4	6.2	12.1	7.2	0.96	0.026	1.8
C-Jun-amino-terminal kinase-interacting protein 4	O60271	1.0	1.1	1.1		2.1	0.9	2.8		0.85	0.129	1.8
Phospholipase D3	Q8IV08	2.0	1.1	2.2	3.8	4.2	3.6	5.6	3.1	0.83	0.028	1.8
Similar to 60S ribosomal protein L7	O95036	6.0	13.9	7.8	5.7	16.9	11.6	11.2	20.4	0.81	0.032	1.8
Adenylyl cyclase-associated protein 1	Q01518	2.0	3.2	2.2	3.8		5.4	4.7	5.1	1.91	0.003	1.8
Src substrate cortactin	Q14247	4.0	4.3		2.8		7.1	4.7	8.2	1.15	0.044	1.8
Dihydropyrimidinase-related protein 2	Q16555	9.0	5.4	3.4	4.7	10.6	8.0	11.2	9.2	1.07	0.016	1.7
40S ribosomal protein S6	P62753	8.0	8.6	7.8	9.4	7.4	13.4	15.0	20.4	0.92	0.064	1.7
Destrin	P60981	2.0	2.1	3.4	3.8	6.3	4.5	4.7	3.1	0.82	0.035	1.6
Prolyl 4-hydroxylase subunit α-1	P13674	9.0	8.6	4.5	10.4	15.8	15.2	13.1	9.2	0.94	0.019	1.6
Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit β-2	P62879	5.0	5.4	2.2	4.7	6.3	6.2	9.3	6.1	0.90	0.022	1.6
Small nuclear ribonucleoprotein Sm D2	P62316	2.0		1.1	1.9	3.2	2.7		2.0	0.92	0.045	1.6
Ribosomal protein L19	J3QR09	9.0	11.8	6.7	5.7	12.7	12.5	10.3	15.3	0.92	0.022	1.5
Poly(rC)-binding protein 1	Q15365	23.1	16.1	16.8	19.8	12.7	8.9	10.3	16.4	1.06	0.011	−1.6
Coatomer subunit α	P53621	23.1	21.4	20.1	25.5	20.1	14.3	9.3	13.3	1.23	0.012	−1.6
Insulin-like growth factor 2 mRNA-binding protein 3	O00425	3.0	4.3		3.8	2.1		2.8	2.0	1.29	0.021	−1.6
26S proteasome non-ATPase regulatory subunit 14	O00487	14.1	9.6	8.9	8.5	8.4	7.1	6.5	3.1	0.82	0.030	−1.6
60S ribosomal protein L23a	P62750	18.1	16.1	22.3	9.4	6.3	12.5	10.3	11.2	0.80	0.046	−1.6
SAP domain-containing ribonucleoprotein	P82979	2.0	3.2		2.8		1.8	0.9	2.0	0.92	0.044	−1.7
Ran-specific GTPase-activating protein	P43487	4.0	4.3		5.7	4.2	1.8	2.8	2.0	0.98	0.024	−1.7
DNA replication licensing factor MCM4	P33991	7.0	7.5	7.8	4.7	3.2	5.4	3.7	3.1	1.19	0.008	−1.8
Endoplasmic reticulum resident protein 29	P30040	7.0	7.5	6.7	5.7	4.2	1.8	3.7	5.1	1.38	0.007	−1.8
Ras-related protein Rab-5C	P51148	13.1	9.6	6.7	11.3	8.4	7.1	4.7	2.0	0.83	0.028	−1.8
Small ubiquitin-related modifier 1	P63165	2.0	3.2	2.2	1.9	1.1	1.8	0.9		1.02	0.021	−1.9
Heme oxygenase 2	P30519	1.0	1.1	2.2	2.8	1.1	0.9	0.9		0.84	0.081	−1.9
E3 ubiquitin-protein ligase HUWE1	Q7Z6Z7	14.1	7.5	6.7	14.2	6.3	5.4	3.7	7.2	0.90	0.043	−1.9
Synaptic vesicle membrane protein VAT-1 homolog	Q99536	12.1	10.7	8.9	16.0	2.1	5.4	7.5	10.2	0.88	0.024	−1.9
Serine/arginine-rich splicing factor 5	Q13243	5.0	3.2		5.7	3.2	2.7	1.9	2.0	1.18	0.039	−1.9
40S ribosomal protein S23	P62266	7.0	5.4	8.9	6.6	6.3	2.7	4.7	1.0	0.87	0.030	−1.9
U2 small nuclear RNA auxiliary factor 2 isoform b	B5BU25	2.0	3.2	5.6	3.8	1.1	2.7	1.9	2.0	0.80	0.048	−1.9
Vigilin	Q00341	19.1	9.6	14.5	11.3	4.2	9.8	8.4	6.1	0.98	0.022	−1.9
Protein disulfide-isomerase A6	Q15084	33.2	19.3	26.8	25.5	17.9	12.5	15.0	9.2	1.33	0.006	−1.9
40S ribosomal protein S8	P62241	24.1	22.5	32.4	31.1	19.0	12.5	11.2	13.3	1.61	0.002	−2.0
Small glutamine-rich tetratricopeptide repeat-containing protein α	O43765	4.0		3.4	2.8	1.1	1.8	0.9	3.1	1.07	0.018	−2.0
NADH-cytochrome b5 reductase	Q6ZVI6	8.0	13.9	8.9	9.4	3.2	1.8	8.4	6.1	0.93	0.019	−2.1
Proteasome subunit β type-3	P49720	6.0	3.2	4.5	7.5	3.2	3.6	1.9	1.0	0.95	0.023	−2.2
Serine-protein kinase ATM	Q13315	2.0	1.1	3.4			0.9	0.9	1.0	0.99	0.106	−2.3
Endoplasmic reticulum resident protein 44	Q9BS26	3.0	5.4	2.2			1.8	1.9	1.0	0.95	0.080	−2.3
10 kDa heat shock protein, mitochondrial	P61604	20.1	22.5	7.8	11.3	6.3	8.9	4.7	7.2	0.99	0.042	−2.3
60S ribosomal protein L38	P63173	3.0	5.4	6.7	7.5	3.2	1.8	2.8	2.0	1.22	0.020	−2.3
Atlastin-3	Q6DD88	8.0	10.7	7.8	8.5	3.2	6.2	1.9	3.1	1.62	0.002	−2.4
DNA replication licensing factor MCM2	P49736	5.0	8.6	10.1	15.1	1.1	3.6	4.7	6.1	0.92	0.030	−2.5
ER membrane protein complex subunit 8	O43402		3.2	1.1	2.8		0.9	0.9	1.0	1.22	0.077	−2.5
α-Aminoadipic semialdehyde dehydrogenase	P49419	2.0	4.3	2.2	3.8	1.1	1.8	0.9	1.0	1.24	0.019	−2.6
Far upstream element-binding protein 2	Q92945	15.1	8.6	11.2	11.3	1.1	8.0	3.7	5.1	1.26	0.005	−2.6
Proteasome (Prosome, macropain) 26S subunit, non-ATPase, 13, isoform CRA_d	B4DJ66	3.0	4.3	2.2	4.7	2.1	0.9	0.9		1.23	0.011	−2.7
Dehydrogenase/reductase SDR family member 9	Q9BPW9	4.0	2.1	2.2	2.8	1.1		0.9	1.0	1.95	0.012	−2.8
Talin-2	Q9Y4G6		27.9	13.4	18.9	10.6	3.6		7.2	1.20	0.036	−2.8
Coatomer subunit gamma-1	Q9Y678	5.0	4.3	12.3	9.4	2.1	1.8	0.9	6.1	0.82	0.036	−2.8
Spectrin-like protein of the nuclear envelope and Golgi	Q7RTM4	2.0	1.1	6.7	2.8	1.1	0.9		1.0	0.85	0.089	−3.2
Serine/arginine repetitive matrix protein 2	Q9UQ35	3.0	2.1	6.7	2.8	1.1	0.9		1.0	1.25	0.039	−3.7

HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

Figure 6.

Multivariate analysis carried out on the 37 top most significantly changed proteins between HMW-HA vs. vehicle (C) using the data from Table 4. Two-dimensional (2-D) HCA heat map (A) and three-dimensional (3-D) PCA plot (B) of control (C) vs. HMW-HA-treated (HA) rat L2 cells. Each column of the heat map and each point of the PCA represents a different experiment. HA, hyaluronan (hyaluronic acid); HMW, high molecular weight; PCA, principal component analysis.

Together these data indicate that HMW-HA induced a profound effect on the rat L2 proteome. The top 75 significantly differential protein list was then used for systems biology analysis. This analysis led to the identification of a number of significant Network Hubs and GO Biological Processes that include strong associations with E2F1, E2F5, and MCM-complex-associated cellular response to stress, DNA replication, and cell cycle regulation (Table 5). In addition, p53, NF-κB, and RAD54B-associated protein SUMOylation and DNA repair were also significantly affected by HMW-HA.

Table 5.

Top network list (HMW-HA vs. vehicle)

#	Network	GO Processes	Total Nodes	Seed Nodes	P Value	z Score	G Score
1	E2F5, Histone H1, G0/G1switch 2, CDK3, Ebp1	Cellular response to stress (38.4%; 1.909e-13), nucleic acid metabolic process (41.4%; 3.344e-13), establishment of localization (58.6%; 1.255e-12), cellular macromolecule catabolic process (25.3%; 4.243e-12), cellular localization (45.5%; 9.177e-12)	100	81	6.1E-284	440.04	440.04
2	MCM4, MCM2, E2F1, MCM10, Geminin	G1/S transition of mitotic cell cycle (20.2%; 2.689e-20), cell cycle G1/S phase transition (20.2%; 3.197e-20), DNA replication initiation (11.7%; 6.811e-18), DNA replication (19.1%; 1.542e-17), nucleic acid metabolic process (45.7%; 1.169e-15)	95	77	2.9E-268	429.18	429.18
3	Histone H2B, RBP-J kappa (CBF1), c-Jun, NF-κB, REV-ERBα	Negative regulation of gene expression (37.6%; 5.810e-10), cellular macromolecule catabolic process (23.5%; 2.256e-09), macromolecule catabolic process (23.5%; 4.291e-08), positive regulation of cellular process (61.2%; 4.466e-08), viral process (21.2%; 7.526e-08)	85	65	1.0E-219	382.99	382.99
4	HIP1, VAMP2, Synaptotagmin I, Caspase-3, Clathrin	Response to abiotic stimulus (45.0%; 3.968e-30), regulation of transport (50.7%; 9.340e-29), response to organonitrogen compound (42.9%; 2.997e-28), response to nitrogen compound (43.6%; 1.069e-27), positive regulation of transport (39.3%; 2.645e-27)	154	63	4.3E-188	280.3	280.3
5	MAP-1B, ERK1/2, WNT, L-Glutamic acid extracellular region, BDNF	Establishment of localization in cell (51.8%; 2.860e-17), establishment of localization (68.7%; 6.767e-17), transport (66.3%; 7.749e-16), localization (73.5%; 2.307e-15), cellular localization (54.2%; 2.654e-15)	93	56	1.4E-178	318.87	318.87
6	NF-κB1 (p50), DLC1 (Dynein LC8a), MTA1, ESR1 (nuclear), PR (nuclear)	Nucleic acid metabolic process (48.1%; 2.864e-10), cellular nitrogen compound metabolic process (57.7%; 5.046e-10), nucleobase-containing compound metabolic process (50.0%; 4.050e-09), organic cyclic compound metabolic process (53.8%; 4.856e-09), metabolic process (84.6%; 4.918e-09)	53	44	7.9E-149	328.32	328.32
7	NF-κB1 (p50), DLC1 (Dynein LC8a), REA, ESR1 (nuclear), PR (nuclear)	Nucleic acid metabolic process (48.1%; 2.864e-10), cellular nitrogen compound metabolic process (57.7%; 5.046e-10), nucleobase-containing compound metabolic process (50.0%; 4.050e-09), organic cyclic compound metabolic process (53.8%; 4.856e-09), metabolic process (84.6%; 4.918e-09)	53	44	7.9E-149	328.32	328.32
8	Histone H2, Testosterone intracellular, Activin A, Inhibin, NCOA4 (ARA70)	Positive regulation of transmembrane receptor protein serine/threonine kinase signaling pathway (21.2%; 1.831e-14), activin receptor signaling pathway (13.5%; 1.389e-12), response to organic substance (65.4%; 3.507e-12), regulation of transmembrane receptor protein serine/threonine kinase signaling pathway (23.1%; 7.720e-12), positive regulation of follicle-stimulating hormone secretion (9.6%; 3.467e-11)	56	36	6.6E-115	266.1	266.1
9	p53, NF-κB, MDM2, BRIP1, RAD54B	Protein sumoylation (17.2%; 2.807e-15), double-strand break repair (22.4%; 1.676e-14), cellular response to stress (48.3%; 3.211e-13), positive regulation of protein metabolic process (46.6%; 2.677e-12), positive regulation of cellular protein metabolic process (44.8%; 4.875e-12)	58	34	1.8E-105	242.48	242.48
10	MAT2A, Bcl-XS, IGF-2, Ethanol intracellular, IGF-1	Phosphatidylinositol 3-kinase signaling (23.5%; 2.938e-15), phosphatidylinositol-mediated signaling (26.5%; 5.712e-14), response to peptide (47.1%; 6.707e-14), inositol lipid-mediated signaling (26.5%; 8.453e-14), response to peptide hormone (44.1%; 1.502e-13)	50	19	1.4E-55	157.34	157.34
11	ERP5, ATF-4, GRP78, 19S regulator, JIK	Response to endoplasmic reticulum stress (45.7%; 9.974e-21), cellular response to stress (74.3%; 6.606e-19), intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress (25.7%; 7.354e-17), response to stress (88.6%; 7.790e-17), regulation of endoplasmic reticulum unfolded protein response (22.9%; 1.856e-16)	39	13	2.7E-36	113.01	113.01

GO, Gene Ontology; HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

S1 Increased E2F1, RhoA, and ROCK2 Phosphorylation, Which Were Prevented by Preincubation with HMW-HA

Western blot analysis carried out on L2 cells treated with S1 protein for 24 h resulted in a significant increase of E2F1 phosphorylation as compared with controls (Fig. 7A). Pretreatment with HMW-HA before S1 addition prevented E2F1 phosphorylation when normalized to either actin or total E2F1 (Fig. 7A). There were no changes in total E2F1 as compared with actin for any condition (data not shown). Similarly, and in agreement with the proteomics outcome, as illustrated in Fig. 7A, incubation of L2 cells with the S1 protein induced phosphorylation (activation) of both RhoA (Fig. 7B) and ROCK2 (Fig. 7C) within 30 min following incubation, whereas pretreatment with HMW-HA significantly inhibited these S1-associated effects.

Figure 7.

Western blots of confluent rat L2 cells, treated with PBS (Cont), HMW-HA (200 ng/mL) for 24 h, S1 (100 ng) for 24 h or HMW-HA for 1 h followed by S1 for 24 h. A: immunoblotting was performed using specific antibodies against E2F1, its phosphorylated form and actin (top). The intensities of the shown bands were quantified and the ratios of pE2F1 to E2F1 are shown at bottom. B and C: immunoblotting was performed using specific antibodies against phosphRhoA (active RhoA), total RhoA, phosphoROCK2, or total ROCK2 (top). The intensities of the various bands were quantified and the ratios of the phosphorylated to total forms are shown (bottom). Incubation of rat L2 cells with S1 upregulated the activities of the phosphorylated forms of E2F1, RhoA, and its downstream kinase, ROCK2. Preincubation with HMW-HA prevented these effects. Values are means with standard error of the means and individual points from three different batches of cells. One-way analysis of variance followed by the Tukey’s t test for multiple comparisons. HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

As E2F1 plays an important role in cell cycle regulation, we assessed changes in cell cycle by S1 and its reversal by HMW-HA. As shown in Fig. 8, treatment of L2 cells with the S1 protein for 24 h resulted in a significant increase in S phase accumulation with a concomitant decrease in the G2 phase as compared with the nontreated control, and both of these changes were either reversed or blocked with 1 h pretreatment with HMW-HA.

Figure 8.

Cell cycle analysis of confluent rat L2 cells incubated with S1 protein (100 ng) for 24 h or pretreated with HMW-HA (200 ng/mL) for 1 h prior to incubation with S1 protein. Cell cycle analysis was conducted as described in the materials and methods. Values are means with standard error of the means and individual values. One-way analysis of variance followed by the Tukey’s t test for multiple comparisons. Data from two different batches of cells. HA, hyaluronan (hyaluronic acid); HMW, high molecular weight.

Bioinformatic Networks Correlation of Spike S1-Exposed Rat L2 Proteome Data to SARS-CoV-2-Exposed Human Cells Transcriptome

The list of 99 proteins stemming from statistically significant changes induced by 24 h Tx-S1 in rat L2 cells at the beginning of these studies (Tables 1 and 2) was strongly correlated with a number of pathways, including E2F1, cell cycle, Rock2/RhoA, and CREB1. Following our validation, we were interested to see if there would be further correlation of these pathways at transcriptomic level using RNA-seq data from human lung cell lines exposed to live SARS-CoV-2. The Gene Set Enrichment Analysis (GSEA) results revealed that E2F signaling, E2F-mediated DNA metabolism, G2M checkpoint, and/or cell cycle were significantly correlated in three of the cell lines, and CREB was also correlated in two of these lung-derived human cell lines (Table 6).

Table 6.

Rat L2 proteomic S1-Tx cross correlated with pathway associations using transcriptomic data from five human cell lines exposed to SARS-CoV-2 virus

Geneset	Pathway	#Targets	NES	P Value	q Value
1. NHBE (Normal Human Bronchial Epithelial)
HALLMARK	E2F	195	−1.4	0.0091	0.2044
2. A549 (adenocarcinoma human alveolar basal epithelial cells)
HALLMARK	E2F	197	−3.1	0.0000	0.0000
HALLMARK	G2M checkpoint	194	−2.7	0.0000	0.0000
HALLMARK	Fatty acid metabolism	150	−1.5	0.0105	0.0320
3. A549-ACE2
HALLMARK	Fatty acid metabolism	150	−2.3	0.0000	0.0000
HALLMARK	E2F	196	−1.8	0.0000	0.0002
REACTOME	E2F-mediated DNA replication	22	−1.9	0.0056	0.0051
BIOCARTA	CREB	22	−1.6	0.0295	0/0661
4. HAE (Human Airway Epithelial)
HALLMARK	Fatty acid metabolism	151	−1.6	0.0000	0.0177
BIOCARTA	CREB	21	−1.7	0.0037	0.1270
5. Calu (nonsmall-cell lung cancer)
HALLMARK	E2F	197	−3	0.0000	0.0000
HALLMARK	G2M checkpoint	194	−2.3	0.0000	0.0000
HALLMARK	Fatty acid metabolism	147	−2.0	0.0000	0.0000
REACTOME	E2F-mediated DNA replication	22	−1.9	0.0030	0.0119
KEGG	Cell cycle	124	−1.6	0.0081	0.0510

DISCUSSION

In this study, we addressed two key questions: 1) does human SARS-CoV-2 Spike S1 protein elicit a global proteomic effect on a rat lung-derived ATII-like L2 cell line that is expected to be primarily rat-ACE2 independent when exposed to S1 protein (59) and 2) does HWW-HA attenuate these effects? Our findings support a novel proposed model of S1-protein-induced cell injury (Fig. 9). Beyond the well-described interaction of the S-protein with host alveolar epithelia via the receptor human angiotensin-converting enzyme 2 (hACE2) (5, 60, 61), the S1-protein also exerts pathological effects (changes in proteome, E2F1, CREB, p65 activation) independent of ACE2. Furthermore, HMW-HA, which is naturally found in mammalian airways (29, 62), and can also be given pharmacologically (63), attenuates the deleterious effects of S1-protein (Fig. 9). Thus, our findings suggest a novel injury pathway and a novel treatment agent for COVID-19-associated lung injury.

Figure 9.

Experimental workflow. Similar to other coronaviruses, the makeup of SARS-CoV-2 is composed of four structural proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins. S, E, and M proteins are glycosylated, and the N protein is phosphorylated. The S protein is involved in the interaction with the host receptor human angiotensin-converting enzyme 2 (hACE2), which is also heavily glycosylated. The S protein makes up two parts, the S1 and S2 spike proteins, which are heavily glycosylated, and S1 contains the receptor-binding domain (RBD). S1 is known to present with biological activities that are presumed largely to be due to interactions with hACEII receptors in human ATII cells (gray background). However, previously unreported, S1 also induces significant changes in ATII-like rat L2 cells that appear independent of hACEII as measured via proteomics analysis (green background). Similarly, and also previously unreported, HA induces significant changes in L2 cells (blue background) that greatly attenuate the deleterious effects of S1-Tx (turquois; bottom right). We have reported these observations here.

It is becoming increasingly evident that S-protein may impact cellular biology independent of interactions with the ACE2 receptor. Recently, there have been reports that the SARS-CoV-2 S protein interacts with other receptors such as neurophilin-1, (16), CD147 (17), tyrosine-protein kinase receptor UFO (AXL) (18, 19), ASGR1 and KREMEN1 (20), and others. In addition, the S-protein induces innate immune activation via Toll-like receptors TLR2 and TLR4 (24, 25, 27, 64) and in silico modeling suggests that it may direct interact with TLRs (65). TLR expression levels correlate with markers of organ injury in COVID-19 (66) while genetic polymorphisms in TLR2 and TLR4 are associated with COVID-19 outcomes (67, 68). Thus, the TLR axis may contribute significantly to the inflammation observed with SARS-CoV2 infection. Our results support this conclusion. Our unbiased analysis suggested p65, a central downstream mediator of TLR activation (69), as a major hub of our proteomic changes. Furthermore, other major hubs like CREB1 and E2F are modulators of TLR signaling (70, 71).

E2F is mostly described for its role in regulation of the cell cycle, with additional roles in metabolism and cell fate determination. However, there is an emerging pattern of E2F playing a role in inflammation as well (70, 72). In particular, recent papers suggest that the E2F activation may play a role in COVID-19 inflammatory cascade and cytokine storm (73). Our unbiased approach supports this conclusion and further suggests that this effect may be impacting the innate immune pathway, at least partly downstream of the S1-protein. It is also interesting that we identified ROCK/RhoA as a top enriched gene pathway. Interestingly, a recent report showed that the S1 protein caused a concentration-dependent decrease in transendothelial electrical resistance of human lung microvascular cells, an event that is associated with activation of the RhoA/ROCK pathway (74). In addition, recent papers highlight a role for these kinases in cellular injury and disruption of the cell barrier in COVID-19 (75, 76), thus supporting that our findings have biological relevance. Importantly, we have shown in several papers that RhoA/ROCK mediate noninfectious lung injury (36, 77–81), a process which can be inhibited by HMW-HA, a striking parallel to the finding in this work.

In aggregate, our proteomic results support that S-protein induces a robust innate immune activation cascade that can be a target of therapeutic interventions. HMW-HA is such a potential intervention. It is naturally present in mammalian airways (29, 62) and has been extensively studied for its ability to modulate TLR signaling (82). Importantly, therapeutic application of HMW-HA has been shown to have beneficial effects, including in infectious and inflammatory lung disease (63, 78, 79, 81). Given that it is a cheap, natural, biological compound with essentially no adverse effects, HMW-HA is therefore attractive as a first-line agent in emerging infectious diseases like COVID-19.

The addition of HMW-HA to L2 cells, even in the absence of S-protein, resulted in significant changes to the proteome. Systems biology analysis led to the identification of a number of significant Network Hubs and GO Biological Processes that include strong associations with E2F1, E2F5, DNA replication, cell cycle regulation, p53, NF-κB, and DNA repair. Given that a number of reports have presented beneficial effects of HMW-HA on pulmonary pathologies (36, 37, 41, 81), it is possible that some of the effects of HMW-HA in our model are mediated by a pro-homeostatic effect of HMW-HA that is not specific to S-protein. Indeed, HMW-HA is known to ameliorate inflammation downstream of LPS/TLR4 signaling (83), thus again supporting a global activity pattern as an anti-inflammatory mediator. In further support of this interpretation, instillation of S1 protein in K18-hACE2 mice that overexpress human ACE2 receptors resulted in robust activation of STAT3 and NF-κB inflammatory response, suggesting that modulation of the innate immune response may indeed significantly alter the inflammatory response in COVID-19 (74).

In conclusion, this work demonstrates that S-protein exposure in epithelial cells leads to a robust proteomic response, even in the absence of ACE2 engagement. We identify several novel signaling hubs, including E2F1, CREB, and ROCK/RhoA, and demonstrate that HMWHA, a natural component of airway lining, can ameliorate these changes and promote cellular homeostasis. Our findings shed new light on the pathogenesis of COVID-19 and suggest novel angles of treatment that will ameliorate cell injury and lung dysfunction in this devastating disease.

DATA AVAILABILITY

The mass spectrometry proteomics data will be made available upon request.

SUPPLEMENTAL DATA

Supplemental Tables S1 and S2 and Supplemental Fig. S1: https://doi.org/10.6084/m9.figshare.21785384.v1.

GRANTS

Funding was provided by the CounterACT Program, National Institutes of Health Office of the Director, the National Institute of Neurological Disorders and Stroke, and the National Institute of Environmental Health Sciences, Grants 5UO1 ES026458, 3UO1 ES026458 03S1, and 1R21 ES032956 to S. Matalon; UAB Comprehensive Cancer Center, agency: National Institutes of Health, institute: National Cancer Institute, Project no. P30CA013148 to J. A. Mobley; and a REINVENT grant from the Department of Anesthesiology and Perioperative Medicine to S. Matalon and ES102605, ES103342 to S. Garantziotis.

DISCLOSURES

Sadis Matalon receives an honorarium from American Physiological Society for his service as Editor of Physiological Reviews. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.

AUTHOR CONTRIBUTIONS

J.A.M., T.J., J.-L.L., S.G., and S.M. conceived and designed research; J.A.M., A.M., K.K., I.A., and S.M. performed experiments; J.A.M., K.K., I.A., J.-L.L., and S.M. analyzed data; J.A.M., I.A., T.J., J.-L.L., S.G., and S.M. interpreted results of experiments; J.A.M., J.-L.L., and S.M. prepared figures; J.A.M., I.A., S.G., and S.M. drafted manuscript; J.A.M., A.M., K.K., T.J., J.-L.L., S.G., and S.M. edited and revised manuscript; J.A.M., A.M., K.K., T.J., J.-L.L., S.G., and S.M. approved final version of manuscript.