IFNγ Increases Intracellular Amino Acid Content in Human Alveolar Epithelial Cells: Role of the STAT/IRF1 Axis in the Stimulation of Transmembrane Transport

Abstract

Interferon-γ (IFNγ), a key inflammatory cytokine that orchestrates immune responses, also emerges as a regulator of cellular metabolism; however, in alveolar epithelial cells its impact on amino acid homeostasis remains poorly defined. Here, we investigated the effects of IFNγ on intracellular amino acid content and transmembrane transport in human alveolar epithelial A549 cells, focusing on the contribution of the JAK/STAT/IRF1 signaling axis. To this end, A549 WT and IRF1 knockout (IRF1 KO) cells were used to investigate IRF1 contribution, and baricitinib to evaluate the role of the JAK/STAT pathway. HPLC analysis reveals that in WT, but not in IRF1 KO cells, IFNγ markedly increases the intracellular concentration of many amino acids, including glutamine, glutamate, and several neutral and cationic amino acids, without affecting the cell volume, thus indicating true metabolic accumulation. The measurement of the transmembrane uptake of specific radiolabeled amino acids demonstrates a concomitant increase in transport Systems ASC, A, L, and y⁺ activity; an upregulation of the related transporters ASCT2, SNAT2, LAT1, and CAT1 has also been observed by means of qPCR analysis. Moreover, conditioned medium from SARS-CoV-2 spike-activated macrophages recapitulates IFNγ-driven amino acid remodeling in a JAK/STAT/IRF1-dependent manner. Overall, our findings identify IFNγ as a potent regulator of intracellular amino acid availability in alveolar epithelial cells through the modulation of the activity of membrane transporters. The observed IFNγ-reprogramming is IRF1 dependent, ascribing a crucial role to this transcription factor in linking inflammation and amino acid metabolism.

1. Introduction

Pro-inflammatory cytokines released during hosts’ defenses to pathogens, including tumor necrosis factor-α (TNFα), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interferon-γ (IFNγ), act not only as mediators of immune signaling, but also as modulators of cellular metabolism to prevent secondary harmful hyperinflammation, as well as to support stress adaptation, and tissue remodeling [1,2].

IFNγ, in particular, is a pleiotropic cytokine that plays a central role in the regulation of immune and inflammatory responses, particularly at barrier tissues such as the lungs [3,4,5]. On the other hand, the overproduction of pro-inflammatory cytokines, including IFNγ, has been linked to the exacerbation of conditions like chronic obstructive pulmonary disease (COPD) and severe pneumonia [6]. Originally characterized for its antiviral and immunomodulatory activities, IFNγ is now widely recognized as a key regulator of cellular metabolism, capable of reshaping nutrient utilization to support cell-specific functional programs during inflammation and infection [7,8,9].

IFNγ signaling is primarily mediated through the activation of the JAK/STAT pathway, which leads to the induction of interferon-stimulated genes via STAT1 and downstream transcription factors, such as interferon regulatory factor 1 (IRF1). While IFNγ-driven metabolic reprogramming has been investigated in immune cells [10], much less is known about how this cytokine affects metabolic pathways in epithelial cells, which are increasingly appreciated as active participants in immune responses, rather than as passive structural components.

Among the different metabolic pathways involved in cellular responsens to inflammation, amino acids represent critical metabolic and signaling molecules, serving not only as building blocks for protein synthesis, but also as regulators of redox balance, epigenetic modifications, and signal transduction pathways [2,11]. Glutamine, in particular, is described as the most versatile intracellular amino acid, as it connects metabolism, signaling, redox control, and gene regulation [12]. Inside cells, indeed, glutamine can be used for the biosynthesis of nucleotides, glutathione (GSH), and nonessential amino acids, and it participates in tricarboxylic acid (TCA) cycle supplementation; moreover, stress conditions, such as disease, poor nutrition, or intense exercise, increase the body’s demand for glutamine [13].

Amino acid intracellular availability is tightly controlled by the coordinated activity of plasma membrane transporters endowed with significant structural and functional heterogeneity, belonging to the solute carrier (SLC) superfamily. This latter encompasses 66 subfamilies (http://slc.bioparadigms.org/, accessed on 10 January 2026), including the following: (i) the “neutral systems” transporting neutral amino acids, such as Systems A, ASC, L and B⁰; (ii) the “basic systems” for cationic amino acids and cystine, i.e., Systems y⁺, y⁺L, B^0,+ and b^0,+; and (iii) the “acidic systems” transporting glutamate and aspartate, i.e., Systems X⁻_AG and x⁻_c [14]. Thus far, the extent to which cytokines modulate amino acid transport and intracellular amino acid pools in human alveolar epithelial cells, and the molecular mechanisms underlying this regulation remain largely unexplored.

In this study, we investigated the effects of IFNγ on intracellular amino acid content and transmembrane amino acid transport in human alveolar epithelial A549 cells, along with the contribution of the STAT/IRF1 axis to IFNγ-induced metabolic remodeling. Furthermore, by using conditioned medium from SARS-CoV-2 spike-activated macrophages, we assessed the relevance of the cytokine in reshaping intracellular amino acids in a more complex model of inflammation.

2. Results

In order to assess the effect of IFNγ on amino acid transport and metabolism, A549 wild-type (WT) and IRF1-deficient (IRF1 KO) cells were treated with this cytokine and the intracellular amino acid pool was first measured. Figure 1 shows that IFNγ induces in WT cells a marked remodeling of intracellular amino acid pool, characterized by significant increases in several amino acids. Although the most impressive changes concern glutamate and glutamine, the intracellular content of many other amino acids increases, in particular the neutral threonine, serine, asparagine, proline, glycine, alanine and leucine. Additionally, the cationic amino acids lysine and arginine increase, while no change is observed for taurine and aspartate. Since the overall increase in the intracellular amino acid content (middle panels) is not paralleled by changes in cell volume (right panels), the incubation with IFNγ in WT cells results in an increase in the total concentration of intracellular amino acids from 39.3 ± 2.9 mM to 71.6 ± 7.9 mM. In contrast, A549 IRF1 KO cells show a blunted metabolic response to IFNγ, with minimal changes in individual amino acid levels, total amino acid content, or cell volume.

Trying to elucidate the mechanism underlying these changes, we next addressed the effect of IFNγ on the transmembrane transport of selected amino acids. As far as L-glutamine is concerned, the treatment with the cytokine significantly increases the total uptake in WT but not in IRF1 KO cells (Figure 2A). In both cell models, the transport of the amino acid is sodium dependent and is completely inhibited by threonine, a specific substrate of System ASC [15]. These findings demonstrate that glutamine uptake in airway epithelial cells is almost completely mediated by the ASC transport System. Upon IFNγ incubation, the activity of this System is significantly stimulated in WT cells, but not in IRF1 KO cells. Consistently, this functional increase correlates with elevated expression of SLC1A5/ASCT2 in WT cells, whereas no induction is observed in IRF1 KO cells, indicating a critical role for IRF1 signaling in this response.

To explain the IFNγ-dependent increase in intracellular proline, a preferential substrate of System A [16], we next addressed the activity of this System by measuring the uptake of the specific amino acid analog methylaminoisobutyric acid (MeAIB) [17]. As shown in Figure 2B, IFNγ significantly increases MeAIB uptake in WT cells and strongly induces the expression of the System A-related SLC38A2/SNAT2 transporter. In contrast, IRF1 KO cells fail to respond to IFNγ stimulation, again supporting an essential role for the IRF1 transcription factor in the IFNγ-mediated regulation of System A transport activity.

Then, the analysis of L-leucine uptake, a specific substrate of System L under sodium-free conditions, reveals a robust IFNγ-dependent increase in WT cells (Figure 2C), which is associated with the increased expression of SLC7A5/LAT1. This induction is significantly blunted in IRF1 KO cells, both at the functional and transcriptional levels, indicating that IRF1 signaling is also required for the IFNγ-driven activation of System L.

Collectively, these data demonstrate that IFNγ enhances neutral amino acid uptake through the coordinated induction of Systems ASC, A, and L in an IRF1-dependent manner, highlighting IRF1 as a central regulator of amino acid metabolic reprogramming in response to IFNγ.

As for cationic amino acids, IFNγ significantly increases the total L-arginine uptake in WT cells compared with control conditions. Competition experiments shown in Figure 3A reveal that arginine transport is largely sensitive to leucine, and is further reduced by the combined presence of leucine and lysine, indicating the involvement of both y⁺L and y⁺ transport Systems. The functional analysis of each transport component (Figure 3B) shows that System y⁺ activity is significantly enhanced by IFNγ in WT cells, whereas no induction is observed in IRF1 KO cells. In contrast, System y⁺L activity is only modestly affected by the cytokine and does not display significant differences between WT and IRF1 KO cells, suggesting a selective regulation of cationic amino acid transport through System y⁺. Consistent with these functional data, IFNγ strongly upregulates SLC7A1/CAT1 expression in WT and not in IFR1 KO cells (Figure 3C). In contrast, the expression levels of y⁺L-related transporters SLC7A6/y+LAT2 and SLC7A7/y+LAT1 are not significantly modulated by IFNγ in either model. Overall, these results demonstrate that IFNγ enhances cationic amino acids’ uptake predominantly through an IRF1-dependent induction of System y⁺ activity and CAT1 expression, while System y⁺L-mediated transport remains largely unaffected.

Finally, we addressed the effects of IFNγ treatment on anionic amino acid uptake. As shown in Figure 4A, under basal conditions, the total uptake of L-glutamic acid is inhibited in both models by L-aspartic acid, substrate specific for System X⁻_AG, and, to a much higher extent by threonine, a substrate of ASC System; moreover, a significant portion of glutamate uptake is detectable in Na⁺-free medium, indicating that also System x⁻_c is operative in these cells. These findings collectively demonstrate that in A549 cells a relevant quote of glutamate uptake is ascribable to Systems ASC and x⁻_c, with only a minor contribution of System X⁻_AG (Figure 4B). The incubation with IFNγ causes a slight increase in glutamate uptake only in WT cells, which is specifically referable to the induction of System ASC activity; no increase is, instead, observed in the activity of Systems x⁻_c and X⁻_AG, with the latter that is even lower than in control cells. Consistently, IFNγ treatment in WT cells results in a marked reduction in the mRNA levels for the glutamate transporter SLC7A1/EAAT3, responsible for System X⁻_AG activity; a similar although less evident effect is observed in IRF1 KO cells (Figure 4C). Under the same conditions, a significant induction of the expression of the cystine/glutamate antiporter SLC7A11/xCT, related to System x⁻_c, is observed in WT but not in IRF1 KO cells. As far as ASC-related genes are concerned, a significant induction of the expression of ASCT2 upon incubation with the cytokine has already been described for WT cells in Figure 2.

We have previously demonstrated that IFNγ is among the inflammatory mediators released from human macrophages upon incubation with spike S1 of SARS-CoV-2 [18]. Thus, to explore the effects of the cytokine in a more complex inflammatory milieu, we have addressed here the effects of conditioned medium from untreated (CM_cont) or S1-treated macrophages (CM_S1) on the intracellular amino acid content and amino acid transporter expression in both WT and IRF1-KO cells; baricitinib, a well-known inhibitor of the JAK/STAT axis, has been employed to define the role of this pathway in the effects observed. The results in Figure 5 show that the exposure of WT cells to CM_S1 significantly alters the intracellular levels of several amino acids compared with the control medium (CM_cont). In particular, similarly to what is observed upon IFNγ treatment, CM_S1 induces a marked increase in glutamate and glutamine levels, along with significant changes in threonine, serine, asparagine, glycine, leucine, and arginine. Treatment with baricitinib partially reverses CM_S1-induced changes for most of these amino acids, although significant inhibition is observed only for glutamine. Consistently, in WT cells, CM_S1 strongly upregulates the mRNA expression of the amino acid transporters ASCT2, SNAT2, LAT1, CAT1, and xCT, while baricitinib significantly hampers these inductions, bringing the transporters levels closer to basal values (Figure 5; right). Conversely, no change in the glutamate content is observed in IRF1 KO cells, where only a modest, not significant increase in glutamate occurs upon incubation with IFNγ. Under the same conditions the other amino acids also show limited or no significant variation, and baricitinib treatment exerts no additional effects. Similarly, the induction of amino acid transporter genes by CM_S1 is strongly blunted in IRF1 KO cells compared to WT cells. These data indicate that metabolic amino acid reprogramming induced by conditioned medium from activated macrophages is mostly ascribable to the activation of the JAK/STAT/IRF1 axis by IFNγ, although a small contribution to the overall stimulation is provided by a STAT-independent mechanism.

3. Discussion

Interferon-γ (IFNγ) is a key cytokine orchestrating immune and inflammatory responses, and is increasingly recognized as a major driver of cellular metabolic reprogramming. In this study, we demonstrate that, in human alveolar epithelial cells, it also profoundly reshapes intracellular amino acid homeostasis by coordinately enhancing amino acid transport and accumulation, with mechanisms that are dependent on the activation of the STAT/IRF1 pathway. Indeed, all the effects observed are almost completely abolished in IRF1 knockout A549 cells, thus pointing to the central role of IRF1 as a transcriptional regulator of IFNγ-induced amino acid metabolism in alveolar epithelial cells. Although we are aware that A549, as a cancer-derived cell line, possess limitations in accurately mimicking a normal lung epithelium, it represents a valuable experimental model for studies requiring stable gene-knockout approaches.

Interestingly, the marked increase in total intracellular amino acid content induced by IFNγ in A549 WT cells occurs independently of changes in cell volume; whether this increase generates a real hypertonic intracellular milieu, or, rather, is accompanied by the loss of other osmolytes or ions such as potassium remains to be investigated. Although higher levels of intracellular amino acids usually associate to a response aimed at promoting anabolic processes and cell proliferation, we have recently demonstrated that in A549 WT, and not in IRF1 KO cells, IFNγ causes an evident growth arrest [19]. This apparent dissociation between amino acid accumulation and cell proliferation reflects an adaptive metabolic response rather than a pro-anabolic program. In fact, IFNγ is known to induce STAT1/IRF1-dependent growth arrest, while simultaneously promoting metabolic remodeling aimed at maintaining cellular homeostasis and survival under inflammatory stress. In this context, intracellular amino acids likely act as metabolic buffers, supporting energy production, redox balance and stress-related functions. On the other hand, it is also well-known that IFNγ triggers conflicting signaling pathways able to inhibit or promote cell proliferation [20], with the activation of STAT1 and the expression of IRF1 implicated in the inhibition of cell proliferation [21,22].

Mechanistically, we show here that the intracellular accumulation of amino acids observed upon incubation with IFNγ is ascribable to an IRF1-depedent increase in the expression and activity of transmembrane transporters. Indeed, the comparison of the findings obtained in WT and IRF1 KO cells demonstrates the correspondence between the intracellular content of specific amino acid and the activity of the related transporters. Among transport Systems for neutral amino acids, System ASC likely accounts for most of the increase in intracellular glutamine content through the induction of the related transporter SLC1A5/ASCT2. Similarly, recent evidence has also highlighted the central role of ASCT2 in the IFNγ-mediated induction of glutamine uptake in breast cancer cell lines [23]. Besides glutamine, other substrates of ASC System are increased by the cytokine in A549 WT cells, such as alanine, serine, threonine and asparagine; moreover, the same transporter also accounts for the marked increase in glutamate content in IFNγ-treated WT cells, pointing to ASCT2 as the main transporter responsible for the increased amount of intracellular amino acids in A549 cells. Similarly, IFNγ also induces System A activity through the transcriptional upregulation of SLC38A2/SNAT2, consistent with previous reports describing SNAT2 as a stress- and cytokine-responsive transporter [24]. The concomitant activation of System L, via increased SLC7A5/LAT1 expression, further supports the enhanced exchange of intracellular amino acids with extracellular essential amino acids, reinforcing metabolic flexibility and biosynthetic capacity of alveolar epithelial cells in response to inflammatory conditions.

IFNγ also selectively enhances the uptake of cationic amino acids, mainly arginine, through the upregulation of System y⁺ activity and the induction of SLC7A1/CAT1. Notably, System y⁺L activity remains unaffected, suggesting that IFNγ selectively targets the CAT1 transporter, a high-capacity unidirectional System. Given the pivotal role of arginine in many physio-pathological processes, such as nitric oxide production, polyamine synthesis, and immune signaling [25], it is conceivable that this induction is required to meet the increased intracellular demand of arginine to support epithelial response to inflammatory stress. Actually, we have previously demonstrated that IFNγ, in association with TNFα and IL-1β, upregulates in A549 WT cells the expression of the inducible nitric oxide synthase (NOS2) [18].

Finally, peculiar attention has to be paid to the transporters involved in anionic amino acids’ uptake. Regarding this concern we have observed that the treatment of WT cells with IFNγ significantly increases the intracellular content of glutamate but not of aspartate, although the two amino acids typically share the high-affinity Na⁺-dependent transport System X⁻_AG [26]. Consistently, the activity of this latter transport System is not stimulated by IFNγ, while the mRNA level of the SLC1A1/EAAT3 transporter is even lowered. Moreover, despite the marked induction of the mRNA for xCT by the cytokine, the activity of the Na⁺-independent cystine-glutamate antiporter System x⁻_c, does not change. Interestingly, however, we show here that glutamate in A549 WT cells also interacts with System ASC, as has already been described in relation to cultured human fibroblasts, where we demonstrated that this System also accepts, besides neutral amino acids, glutamate but not aspartate [27]. As a result, we can conclude that also the IFNγ-driven increase in glutamate intracellular content in WT cells is ascribable to the stimulation of System ASC activity, as already described for the uptake of neutral amino acids.

Notably, the conditioned medium from SARS-CoV-2 spike S1-activated macrophages, which recapitulates a complex inflammatory milieu [19], mirrors the effects of IFNγ alone, inducing amino acid accumulation and transporter expression in A549 WT cells. The partial reversal of these effects by the JAK/STAT inhibitor baricitinib, together with their strong attenuation in IRF1 knockout cells, indicates that IFNγ is a major, but not exclusive, mediator of macrophage-induced metabolic reprogramming. These data, moreover, suggest that epithelial metabolic adaptation during inflammation arises from the integration of IFNγ-dependent signaling with additional cytokine- or stress-activated pathways.

Collectively, our results identify IFNγ as a potent regulator of amino acid transport and intracellular availability in alveolar epithelial cells and establish IRF1 as a key transcriptional hub linking inflammatory signaling to metabolic remodeling in this cell model. This coordinated induction of multiple amino acid transport Systems likely supports epithelial survival, biosynthesis, and redox balance under inflammatory conditions. Future studies should address how IFNγ-driven amino acid reprogramming impacts epithelial–immune cell interactions and whether targeting specific transporters may represent a viable strategy to modulate inflammatory lung pathology.

4. Materials and Methods

4.1. Cell Models and Experimental Treatments

Human A549 wild-type cells (A549 WT; ab255450) and IRF1 knockout A549 cells (IRF1 KO, ab267042) were obtained from Abcam (Prodotti Gianni S.r.l., Milano, Italy). Cells were cultured in RPMI1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin at 37 °C in a humidified incubator with 5% CO₂. For experimental assays, cells were seeded in multi-well plates at a density of 50 × 10³ cells/cm²; after 24 h, cells were treated as required by the experimental plan, either with RPMI1640 containing 50 ng/mL IFNγ (R&D Systems, Bio-Techne, Milano, Italy) or with conditioned medium from macrophages left untreated (CM_cont) or activated with SARS-CoV-2 Spike protein (CM_S1), obtained as previously described [28]; the protocol was approved by the local ethics committee (460/2021/TESS/UNIPR) and conducted according to the Declaration of Helsinki (1964). Where indicated, cells were pre-incubated with baricitinib (1 µM) for 1 h before cell treatment with CM_S1, and the inhibitor was maintained in the culture medium throughout the experiment. As shown in Figure S1, the transduction pathway activated by IFNγ is preserved in IRF1 KO cells, which display the cytokine-induced phosphorylation of STAT1 similarly to A549 WT cells. As expected, no expression of IRF1 is detected in IRF1 KO cells.

4.2. Determination of the Intracellular Amino Acid Content

Intracellular amino acid levels were quantified by means of high-performance liquid chromatography using a Biochrom 30 amino acid analyzer (Biochrom, Cambridge, UK). Briefly, A549 cells were cultured in 12-well plates and, after the treatment, intracellular metabolites were extracted by incubation in ethanol for 10 min at 4 °C; samples were then lyophilized and reconstituted in 150 μL of Lithium Loading Buffer. Individual amino acids were separated using a high-resolution lithium column and lithium-based elution buffers (Biochrom). Post-column derivatization was carried out with the EZ Nin Reagent Kit (Biochrom); the reaction mixture was passed through a high-temperature coil and detected photometrically at 570 and 440 nm. Amino acid concentrations were normalized to protein levels, measured using a Lowry assay, and expressed as nmol/mg protein.

4.3. Amino Acid Uptake

Amino acid uptake was determined in A549 cells seeded onto 96-well trays. The activity of transport Systems was determined by measuring the uptake of selective or preferential amino acids under specific characterizing conditions (see below). Briefly, after two rapid washes in a pre-warmed transport buffer [Earle’s Balanced Salt Solution (EBSS) containing (in mM) 117 NaCl, 1.8 CaCl₂, 5.3 KCl, 0.9 NaH₂PO₄, 0.8 MgSO₄, 5.5 glucose, and 26 Tris/HCl, adjusted to pH 7.4], the cells were incubated in the same solution containing the labeled amino acid; when required, Na⁺-free EBSS was obtained via the substitution of Na⁺ with N-methyl D-glucamine in Na⁺ salts. The experiment was terminated by two rapid washes (<10 s) in an ice-cold 300 mM urea. The ethanol-soluble pool was extracted in 0.1 mL ethanol and radioactivity was measured with the MicroBeta^2® liquid scintillation spectrometer (PerkinElmer, Milano, Italy). The monolayers were then dissolved in 1 N NaOH containing 0.5% sodium deoxycholate and assayed for protein content via a Lowry procedure, so as to express the amino acid uptake as nmol/mg of protein/min.

L-glutamine uptake: [³H]Glutamine 50 µM and 1 µCi/mL for 30 sec in EBSS in the absence or in the presence of 5 mM L-threonine or in Na⁺-free EBSS. The activity of the ASC System was calculated as the difference between the total uptake and the uptake in the presence of threonine [15].

Methylaminoisobutyric acid (MeAIB) uptake for System A activity: [¹⁴C]MeAIB 100 μM and 1 μCi/mL for 5 min in EBSS in cells depleted of intracellular amino acids through incubation for 90 min in EBSS with the addition of 3% dialyzed FBS [16,17].

L-leucine uptake for System L activity: [³H]Leucine 100 μM and 1 μCi/mL for 1 min in Na⁺-free EBSS [29].

L-arginine uptake: [³H]Arginine 50 μM and 2 μCi/mL for 1 min in the absence or in the presence of 2 mM L-leucine or 2 mM L-leucine + 2 mM L-lysine. The activity of System y⁺L was calculated as the leucine-inhibitable component from the difference between the total influx and the influx measured in the presence of leucine; the activity of System y⁺ was the quote further inhibited by lysine, corresponding to the difference between the influx measured in the presence of leucine and lysine [30].

L-glutamic acid uptake: [³H]Glutamic acid 100 µM and 3 μCi/mL for 1 min in EBSS in the absence (total) or in the presence of 5 mM L-aspartic acid (as an inhibitor of System X⁻_AG) or 5 mM threonine (as an inhibitor of ASC System), or in Na⁺-free EBSS [26,27]. The activity of System X⁻_AG was calculated as the difference between the total uptake and the uptake measured in the presence of L-aspartic acid; the activity of the ASC System was calculated as the difference between the total uptake and the uptake in the presence of threonine. The uptake of glutamic acid measured in Na⁺-free EBSS is referable to System x⁻_c [31].

4.4. Cell Volume

Cell volume was estimated as the urea distribution space in 24 well trays, as already described [32]. To this end, [¹⁴C]urea (1.5 μCi/mL, 0.5 mM final concentration) was added to the cell culture during the last 10 min of incubation. The incubation was terminated by two rapid washings (<5 s) with an ice-cold solution of 300 mM urea; the cells were then covered with 0.2 mL of ethanol, and the radioactivity in cell extracts was counted using the liquid scintillation spectrometer (MicroBeta^2®). The values of cell volume are expressed as microliters per milligram of protein.

4.5. RT-qPCR Analysis

For gene expression analysis, cells were seeded in 24-well plates and treated according to the experimental design. After 24 h, total RNA was extracted using the GeneJET RNA Purification Kit (Thermo Fisher Scientific, Monza, Italy) and reverse transcribed with the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). Quantitative PCR was carried out on a StepOnePlus Real-Time PCR System (Thermo Fisher Scientific) using gene-specific forward and reverse primers (

Table 1. Sequences of the primer pairs employed for RT-qPCR analysis.

Gene/Protein Name (Gene ID)	Forward Primer	Reverse Primer
SLC1A1/EAAT3 (6505)	CAGAAAGTGGGTGAAATTGCG	TCACGCTTAGTTTTGTACTGCTG
SLC1A5/ASCT2 (6510)	GAGCTGCTTATCCGCTTCTTC	GGGGCGTACCACATGATCC
SLC7A1/CAT1 (6541)	CTTCATCACCGGCTGGAACT	GGGTCTGCCTATCAGCTCGT
SLC7A5/LAT1 (8140)	GAACAGGGACCCATTGACGG	GAACAGGGACCCATTGACGG
SLC7A6/y+LAT2 (9057)	Hs00187757_m1 (Applied Biosystems)
SLC7A7/y+LAT1 (9056)	Hs00374417_m1 (Applied Biosystems)
SLC7A11/XCT (23657)	TGTCCACAAGCACACTCCTC	CTGCCAGCCCCATAAAAAGC
SLC38A2/SNAT2 (54407)	ATGAAGAAGGCCGAAATGGGA	TGCTTGGTGGGGTAGGAGTAG
RPL15/RPL (6138)	GCAGCCATCAGGTAAGCCAAG	AGCGGACCCTCAGAAGAAAGC

) in combination with SYBR™ or TaqMan Gene Master Mix (Thermo Fisher Scientific). Relative mRNA expression levels were determined using the ΔΔCt method, following normalization to the housekeeping gene RPL15.

4.6. Western Blot Analysis

For the analysis of protein expression, cell lysates were prepared using LDS sample buffer (Thermo Fisher Scientific) and analyzed as already described [33]. Anti-phospho-STAT1 (Tyr705) and anti-IRF1 rabbit polyclonal antibodies (1:2000, Cell Signaling Technology, Euroclone, Milano, Italy) were employed; anti-vinculin mouse monoclonal antibody (1:2000, Merck) was employed as the loading control. Western blot images were captured using an iBright FL1500 Imaging System (Thermo Fisher Scientific) and analyzed with iBright Analysis Software (version 5.0).

4.7. Statistical Analysis

The statistical analyses were performed using GraphPad Prism 9 (GraphPad Software, CA, USA). The differences between groups were assessed using Student’s or the one-sample t test, as indicated in the figures’ legends. A p-value < 0.05 was considered statistically significant [34].

4.8. Materials

Endotoxin-free fetal bovine serum of South American origin, compliant with EU regulations, was obtained from Euroclone (Milano, Italy). Radiolabeled amino acids (L-[2,3,4-³H]-monohydrochloride arginine (48.8 Ci/mmol), L-[4,5-³H]-leucine (162 Ci/mmol), L-[3,4-³H]-glutamic acid (48 Ci/mmol), L-[3,4-³H]-glutamine (48.2 Ci/mmol), and [1-¹⁴C]methylaminoisobutyric acid (50.5 mCi/mmol), as well as [¹⁴C]urea (58 mCi/mmol), were supplied by PerkinElmer (Milano, Italy). Baricitinib was from Vinci-Biochem; all other chemicals, unless otherwise stated, were purchased from Merck (Milano, Italy).