Female C3H/HeJ mice 6-8-week-old were purchased from The Jackson Laboratory. All mice were housed in specific pathogen–free conditions at the Association for Assessment and Accreditation of Laboratory Animal Care Internationally Accredited University of South Carolina, School of Medicine, Animal Resource Facility and were kept under 12 light/12 dark cycles at a temperature of ∼18°C–23°C and 40%–60% humidity. Food and water were available ad libitum. All experiments performed using mice in this study were approved by the Institutional Animal Care and Use Committee, University of South Carolina under animal use protocol (AUP2363).

Resveratrol (RES) was purchased from Supelco (MO, United States) and prepared freshly in an appropriate vehicle (VEH) consisting of 1% carboxyl methyl cellulose (CMC, Sigma-Aldrich, United States). SEB was obtained from Toxin Technology Inc. (FL, United States) and aliquoted in sterile phosphates buffer saline (PBS) at a stock concentration of 2 μg/μL prior to storage at −20°C until being used.

We used a previously described method to induce ARDS by SEB involving double sensitization with SEB in C3H/HeJ mice which leads to 100% mortality and treatment with RES reverses this action. Briefly, C3H/HeJ mice pretreated orally with two doses of either 100 mg/kg resveratrol or VEH at 24 h intervals. After the second dose of RES, intranasal administration of 5µg/mouse of SEB was given and then after 90 min from the first SEB dose, a second dose of i. p 2µg/mouse of SEB was given as a fatal model of dual-dose SEB exposure (Rao et al., 2015b; Alghetaa et al., 2018). Endpoint of the experiment was 48 h post first SEB exposure at which time the mice were euthanized and the infiltrating mononuclear cells in the lungs were collected by gradient separation (Elliott et al., 2016).

Mononuclear cells (MNCs) from the lungs were prepared as described previously (Alghetaa et al., 2018; Mohammed et al., 2020b; Alghetaa et al., 2021; Sultan et al., 2021). Briefly, excised lungs were subjected to a mechanical tissue homogenizer (Seward, England). The tissue suspension was filtered and the single-cell mixture was suspended in cold fluorescence-activated cell sorting buffer (FACS) prepared from PBS enriched with 10% fetal bovine serum protein (FBS). RBC-lysis buffer (Sigma-Aldrich, United States) was used to remove the RBC from the mixture before being filtered with a 100 micron-strainer (Fisher Scientific, China). The filtered suspension was layered on Ficoll gradient, Histopaque-1077 (Sigma-Aldrich, United States) to separate MNCs. All MNC preparations were finally suspended in FACS and cell counts were measured by using trypan blue dye via auto cell counter 2000 (Bio-Rad, United States). Spleen cell preparations were carried out in a similar fashion except that they were not subjected to Ficoll gradient separation.

miRNA analysis was carried out as described previously (Alharris et al., 2018; Neamah et al., 2019). Briefly, total RNA, including miRNA, was isolated from lung mononuclear cells using the miRNeasy kit from QIAGEN and following the protocol of the company. Microarray was performed using Affymetrix miRNA Array (version 4.1). Raw files generated from the miRNA microarray were uploaded to Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and deposited under accession number GSE220159. By using Transcriptome Analysis Console (TAC, ThermoFisher, United States), Log2 fold change of more than 3,000 miRNAs was detected from the raw array data, and only those miRNAs that were altered more than 2-fold were considered for further analysis. Filtered miRNAs were analyzed for their role in various biological pathways using Ingenuity Pathway Analysis (IPA) software http://www.ingenuity.com (Qiagen, Germany). Also, microRNA.org database was used to examine the sequence alignment regions between miR-100-5p and its targeted genes.

Naïve spleens were excised from C3H/HeJ mice and processed to prepare a single cells suspension. The cells were seeded in U-bottom 96-well plates (Corning, United States) at a concentration of 5 × 10⁵ cells/well in complete DMEM medium (Sigma, United States) supplemented with 10% FBS (Atlanta for Biologicals, United States) 1% penicillin/streptomycin cocktail (Sigma, United States) and 50 µM mercaptoethanol (2-ME, Sigma, United States). Seeded cells were pretreated with either 50 µM RES or VEH for one hour before adding 1 μg/mL SEB to the culture. Activated cultures were incubated for 48 h at 37°C with 5% CO2 and the culture supernatants were collected for further analysis. Collected cultured cells were washed with phosphate buffer saline (PBS) twice before being suspended in FACS buffer. PE-conjugated anti-CD3 antibody (Biolegend, United States) was used to stain all harvested cells and then purified positively by using EasySep™ PE Positive Selection Kit II magnetic microbeads (STEMCELL, United States).

To evaluate the proliferative capacity of activated immune cells, SEB-activated MNCs from lungs or in vitro SEB-activated splenocytes were treated with thymidine (methyl-3H; PerkinElmer Health Sciences, United States) isotope at a concentration of 1µCi/well and incubated for 16 h to and then radioactivity was measured using a liquid-scintillation counter (MicroBeta TriLux; PerkinElmer, United States).

To determine the expression of Mtor, Pkm, Prkaa1, Rac1 and Rptor in the lung MNC or PE-selected T cells post-RES or vehicle treatment, RT-PCR was performed. The primer sequence of these genes was determined from the Primer Bank database and then built using Integrated DNA Technologies tools (Table 1). RT-PCR was performed for 40 cycles using the following conditions: 98°C (denaturing temperature) for 30 s, 98°C (annealing temperature) for 10 s, and 60°C (extension temperature) for 30 s. The expression of the above genes was normalized against mouse housekeeping gene Gapdh expression (internal control).

Transfection of SEB-activated splenocyte cultures with miR-100-5p mimic and inhibitor was performed as previously described (Alharris et al., 2018). Briefly, C3H/HeJ naïve splenocytes were cultured in 24-well plates (2 × 10⁵ cells per well) and activated with 1 μg/mL of SEB for 24 h. Transfections were performed with Qiagen HiPerfect Transfection Reagent and 20 nmol/L of either syn-mmu-miR-100-5p mimic (Qiagen, MSY0000098), anti-mmu-miR-100-5p inhibitor (Qiagen, MIN0000098) or transfection reagent alone (mock, Qiagen). Transfection efficiency was validated with qRT-PCR for miR-100 (Qiagen, MS00031234), and expression of mTOR, Rac1, and Rptor after transfection was determined with RT-PCR (Mohammed et al., 2020a).

Total RNA was isolated from lung mononuclear cells, cultured splenocytes, and miR-100-5p-transfected cells and then converted into cDNA by using miScript RT II kit (Qiagen, Germany) to prepare reverse transcription reaction component. After that, miR-100-5p-specific miScript Primer Assays (Qiagen, MS00031234, forward primer) were mixed with Universal Primer (reverse primer) and QuantiTect SYBR Green PCR Master Mix to quantify the enrichment of miR-100 in these prepared cDNAs. For accurate and reproducible results in miRNA quantification by real-time PCR, the amount of miR-100 was normalized by using an endogenous reference RNA consisting of Snord96A. The cycling conditions for RT-PCR were listed in Table 2.

Single cell suspension of mononuclear cells was isolated from the lungs by using the gradient Ficoll layering method and prepared as described previously (Alghetaa et al., 2018). To avoid false positive or false negative immunofluorescence results, all cells were suspended in FACS buffer and incubated with FcγR blocker (Biolegend, United States). Then these cells were permeabilized (Pharmingen BD, United States) and stained with Biolegend anti-CD3, CD4, CD8, T-bet and IFN-γ antibodies conjugated with APC, BV786, PE, BV605 and APC-R700 fluorochromes respectively, which were used according to the manufacturer’s protocols.

To validate the PCR results of upregulated mTOR gene expression, we performed immunofluorescent staining of lung tissue. We calculated the corrected total cell fluorescence (CTCF) for mTOR-stained cells in the lung tissue of naïve, SEB + VEH, and SEB + RES groups using ImageJ software from NIH, we performed immunofluorescent staining as described earlier (Alharris et al., 2018; Sarkar et al., 2019; Alharris et al., 2022). Briefly, the slides with a section of lung tissue were first deparaffinized according to the standard protocol, antigen retrieval was done using an antigen-retrieval solution from Abcam (Cambridge, MA, United States). The slides were then washed with PBS twice, permeabilized with 0.01% Triton X-100 (Sigma) for 15 min, washed three times with PBS for 5 min each, and then incubated overnight at 4°C with primary antibody (mouse-specific mTOR) from Abcam (Cambridge, MA, United States) diluted in 1% FBS in PBS. Next, the slides were washed with PBS three times for 10 min each and incubated with anti-mouse secondary antibody diluted in 1% FBS in PBS for 1 h at 37°C followed by washing the slides three times with PBS. The slides were then stained with DAPI to show the nuclei of the cells and washed three times with PBS and finally mounted with Antifade Mounting Medium from Vector Labs (Burlingame, CA). The tissue was visualized and imaged by usa ing Confocal microscope (Leica, United States).

Naïve splenocytes were prepared and suspended in 10 µM CSFE staining protein-free medium for 20 min, followed by staining the cells. The cells were washed twice before grouping them according to the treatment conditions and suspended in complete DMEM medium and seeded them at a concentration of 5 × 10⁵ cell/well in 96-well plates. Treated cells were then incubated for 48 h post 1 μg/mL SEB-activation at 37°C with 5% CO2 and then purified as described above to measure the cell division of T cells.

Mitochondrial respiration, glycolytic rate, ATP rate, and fuel substrate dependency were measured based on the quantification of oxygen consumption rates (OCR), extracellular acidification rate, and proton efflux rate (PER). In all of these assessments, initially 2.5 × 10⁵ cells/well of purified T cells were seeded in XFp culture plates (Agilent, United States) by using CellTak cell tissue adhesive (Corning, United States). According to the assay recommendations, the cells were suspended in Agilent Seahorse XF DMEM Medium pH 7.4 (Agilent, United States) supplemented with 10 mM glucose, 1 mM sodium pyruvate, and/or 2 mM glutamine. Mitochondrial stress test, glycolysis stress test, ATP production rate test, glucose dependency test, and fatty acid dependency test were performed according to the manufacturer’s protocols. To achieve these measurements, a combination of 1 mM oligomycin, 1 mM fluorocarbonyl-cyanide-phenylhydrazone (FCCP), 0.5 mM rotenone, and antimycin-A and/or 2-deoxy-D-glucose (2-DG) were used according to the assay protocol. Once the assay was finished, all the cells in the run plate were stained with 2 µM final concentration of Hoechst 33,342 dye to quantify the live cell numbers by Cytation5 Imaging Cytometer (BioTek, United States) and to normalize the acquired data accordingly. All acquired data generated by Seahorse XFp were normalized and interpreted kinetically with Seahorse Wave Desktop Software (SWD) (Agilent, United States). Kinetic data of glycolytic and mitochondrial ATP production were generated by using Seahorse XF Real-Time ATP Rate Assay Kit according to the manufacturer’s protocol. Briefly, the alterations of concentrations of oxygen and proton in the culture medium due to the mitochondrial and glycolysis processes in living cells was detected via the chemical sensors. Oligomycin, an inhibitor of mitochondrial ATP synthase and rotenone/antimycin A chemicals were injected into the microplate to challenge the cellular respiration events by which the sensors calculate the delta of OCR and ECAR. All acquired data were then interpreted by using the SWD into bioenergetic kinetics. For detecting what type of fuel is utilized by mitochondria, Agilent Seahorse XF Mito Fuel Flex Test Kits was used. The assay medium consisting of DMEM for this test was supplemented with 1 mM pyruvate, 2 mM glutamate and 10 mM glucose and the medium pH was adjusted to be 7.4 at the time of analysis. The sensor cartridge was loaded with 30 µM bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES), an inhibitor of glutamine oxidation pathway, 40 µM Etomoxir, an inhibitor of long chain fatty acid oxidation, and 20µM UK5099, an inhibitor of glucose oxidation pathway. Afterwards, SWD was used to calculate the dependency, capacity and flexibility of T cells in switching among different sources of energy fuels.

The TC20 Automated Cell Counter (BioRad) was used to measure the cell count and viability of isolated lung cells. The cells were next loaded onto the Chromium Controller (10x Genomics). Following the manufacturer’s protocol, the Chromium single cell 5′ reagent kits (10x Genomics) were used to process samples into single-cell RNA-seq (scRNAseq) libraries. Sequencing of those libraries was performed using the NextSeq 550 instrument (Illumina) with a depth of 40k–60k reads per cell. The base call files generated from sequencing the libraries were then processed in the 10x Genomics Cell Ranger pipeline (version 2.0) to create FASTQ files. The FASTQ files were then aligned to the mm10 mouse genome and the read count for each gene in each cell was generated (Mohammed et al., 2020c). Downstream analysis was completed using Seurat suite version 3.0 within R studio (Butler et al., 2018; Stuart et al., 2019). Data were integrated into Seurat using anchor and integration functions. The integrated data were scaled and Principal-component analysis (PCA) was completed for dimensionality reduction. Clusters were made following PCA analysis by adjusting the granularity resolution to 0.25. We determined the number of principal components (PCs) to utilize post-JackStraw analysis within Seurat to determine PCs with the lowest p-value. Differential expressions were determined for each cluster to determine cluster biomarkers, and between the vehicle and resveratrol-treated samples using the default Wilcoxon rank sum test.

In this study, we used groups of five mice treated with SEB + VEH or SEB + RES unless stated differently. The metabolome data were log2-transformed and normalized with internal standards on a per-sample, per-method basis. Statistical analyses were performed with a t-test in R Studio (R Studio Inc., Boston, MA). Differential metabolites were identified by adjusting the p-values for multiple testing at an FDR (Benjamini Hochberg method) threshold of <0.25. The ANOVA test was applied whenever there were more than two group comparisons with multiple Tukey’s corrections. The T-test was applied with the Holm-Sidak correction method to compare two groups. p < 0.05 was considered as significant threshold. Significant differences were depicted as *p < 0.05, **p < 0.01, ***p < 0.001, #p < 0.0001. Each experiment was performed three independent times unless stated differently.

Previous studies from our laboratory showed that RES is highly effective in preventing SEB-mediated ARDS (Alghetaa et al., 2018; Alghetaa et al., 2021). To investigate the potential role of miRNA in the amelioration effects of RES, we performed microRNA arrays. The majority of miRNAs (miR) in the lung’s MNCs showed altered expression when the mice were exposed to SEB and this effect was reversed significantly when RES was used (Figure 1A). When we considered those miRs with a >2-fold change (Figure 1A), we identified 21 miRNAs that were down-regulated and 44 miRs that were upregulated in the SEB + RES group in comparison to the SEB + VEH group (Figure 1B). By using IPA software to predict the target genes of these miRs, we were able to identify pathways that regulated cellular metabolism and proliferation and found that miR-100-5p was of interest because it was down-regulated by SEB exposure and reversed by RES treatment (Figure 1C). Interestingly, the pathway analysis suggested that miR-100-5p may target the mTOR signaling pathway as well as the Regulatory-associated protein of mTOR (RPTOR), and Rac family small GTPase 1 (Rac1). To further confirm the target genes, we used the bioinformatic tool, microrna.org, and found that miR-100-5p did target RPTOR, and Rac1 (Figure 1D). We also studied the mTOR protein expression in the lungs using the immunofluorescence technique and found that mTOR expression was significantly upregulated following SEB treatment when compared to the naïve group and furthermore, RES treatment caused a significant decrease in mTOR expression (Figures 1E,F).

Next, we directly examined the expression of mTOR and miR-100-5p in isolated lung MNCs and found that the SEB + RES group expressed higher levels of miR-100-5p and decreased levels of mTOR when compared to the SEB + VEH group (Figure 2A). Similar observations were made using SEB-activated in vitro cultures (Figure 2B). Transfection studies using mimic and inhibitor of miR-100-5p revealed the inverse relationship between the expression of miR-100-5p and mTOR gene expression (Figure 2C). The metabolic kinetics showed that the basal respiration rate, non-mitochondrial oxygen consumption rate, and ATP production rate were significantly higher in T cells of SEB + VEH than in the SEB + RES group (Figures 2D,E).

scRNA-seq was used to unveil the lung cellular components. We found that the majority of resident and infiltrating immune cells were natural killer T cells (NKT), macrophages (Mac), alveolar macrophages (AM), and myeloid-derived suppressor cells (MDSCs) where each cell phenotype was clustered based on their similarity of transcriptomic profile (Figure 3A). Also, the T-distributed Stochastic Neighbor Embedding (tSNE) plot was used to visualize the different cell phenotypes in each study group colored by group ID (Figure 3B). The violin plots showed that the mTOR was highly expressed in macrophages (Mac), alveolar macrophages (AM), and endothelial cells (EC) in the SEB + VEH group and it was significantly decreased in the SEB + RES group (Figure 3C). Furthermore, violin plots also showed other genes related to energy metabolism such as Rac1 (Figure 3D) and Akt2 (Figure 3E), in which Rac1 was increased while Akt2 was decreased in some of the cells examined from the SEB + RES group when compared to the controls. There was also a significant reduction in hexokinase two enzyme (Hk2), a key enzyme of glucose metabolism, in T cells in general and NKT populations of the SEB + RES group in comparison with the SEB + VEH group (Figure 3F).

The findings that Hk2 is inhibited led us to examine the glycolysis rate of glucose in SEB-activated T cells treated with RES or VEH by using the Seahorse system, a real-time metabolism analyzer. Interestingly, we found that RES-treated T cells significantly lowered their glycolysis capacity measured by extracellular acidification rate (ECAR) before and after being challenged with glucose (Figure 4A). According to the kinetic calculation of the metabolic behavior of study groups, the glycolysis rate and glycolytic capacity of SEB-activated T cells treated with VEH were significantly higher than in of SEB + RES group (Figure 4B). All the above changes drove us to examine the cell division of SEB-activated T cells in presence of RES or VEH by using CSFE, a permeable dye. We found that SEB-activated T cells were able to continue in the biological cell divisions and divided for up to four new generations within 48 h into SEB + VEH treated cells (Figures 4C,D), while SEB + RES treatment blocked the cell division (Figures 4C,D).

We also tested the levels of mTOR signaling pathway molecules and found that Rptor and Rac1 were found to be significantly suppressed in lung MNCs in SEB + RES-treated mice when compared to the SEB + VEH group (Figures 5A,B). Because mTOR is the major regulator of energy production in mitochondria and cell proliferation (Morita et al., 2015), we also studied this effect. We noted that there was a significant reduction in the total production of ATP molecules either via glycolysis or mitochondrial oxidation (Figure 5C). We found that inhibiting glucose mitochondrial oxidation by using UK5099, a selective mitochondrial pyruvate carrier (MPC) inhibitor, did not change the mitochondrial glucose oxidation rate in T cells from the SEB + RES group, while it caused a significant drop in the mitochondrial oxidative respiration in SEB + VEH, which suggested that these cells were glucose-dependent on their energy production in SEB-activation model (Figure 5D). Furthermore, efforts to detect the alternative fuel substrate utilized by RES-treated SEB-activated T cells led to the finding that fatty acid β-oxidation may be the main pathway of mitochondrial energy production in this group when compared to the SEB + VEH group (Figure 5E). Finally, we found that metabolic reprogramming of infiltrating and resident mononuclear immune cells by RES treatment was associated with a significant reduction in the number of these cells in the lungs of SEB-injected mice (Figure 5F). This reduction in the number of mononuclear cells was accompanied also by a statistical reduction in the effector T cell populations in the lungs of SEB-injected mice treated with RES such as the CD3⁺CD8⁺ (Figure 5G) and inflammatory IFN-γ⁺Th1⁺ cells (Figure 5H).

To further characterize the metabolic pathways, we used LC-MS to determine glucose-derived metabolites in the serum. Interestingly, we found significant upregulation of glucose, α-ketoglutarate, lactate, malate, and pyruvate in the sera of the SEB + VEH group when compared to the SEB + RES group (Figure 6A). The elevated levels of glucose metabolism intermediates were associated with a significant increase in the gene expression of pyruvate kinase enzyme (Pkm) in the lung’s infiltrating mononuclear cells (MNC) when compared to naïve or SEB + RES groups (Figure 6B). To detect if this downregulation of glucose metabolism by RES could have an impact on the adenosine triphosphate production, we looked at the expression of AMP-activated protein kinase (Ampk), the gene that regulates cellular energy homeostasis, largely to activate glucose and fatty acid uptake and oxidation when cellular energy is low. We found that the gene expression of Ampk was significantly higher in the MNC of the SEB + RES group than in naïve or SEB + VEH groups (Figure 6C). It is known that when Ampk is highly expressed this will arrest most of the cellular biological functions in these affected cells. We found that RES treatment in vitro caused a significant reduction in the proliferative capacity of SEB-activated MNCs when compared to the SEB + VEH group (Figures 6D,E).