1. Introduction

Figure 1. Schematic representation of the Warburg effect. According to this concept created by Otto Warburg, proliferating cells, including tumor cells, supply their biomass requirements by an adaptating of their metabolism. This adaptation involves the production of lactate from pyruvate even under aerobic conditions and has therefore been established as aerobic glycolysis. The intermediates of glycolysis are then utilized to provide lipids, nucleotides, and amino acids essential for high-rate cellular proliferation.

Figure 1. Schematic representation of the Warburg effect. According to this concept created by Otto Warburg, proliferating cells, including tumor cells, supply their biomass requirements by an adaptating of their metabolism. This adaptation involves the production of lactate from pyruvate even under aerobic conditions and has therefore been established as aerobic glycolysis. The intermediates of glycolysis are then utilized to provide lipids, nucleotides, and amino acids essential for high-rate cellular proliferation.

2. Immunometabolism

b. A case of metabolic-dependent activation in immune cells: monocyte and macrophage

Macrophages are CD11b⁺CD68⁺ leukocytes historically described for their anti-microbial and phagocytic activities. However, many other functions have been associated with them over the years [17]. Indeed, their participation in tissue homeostasis, as well as tissue repair in case of damage, is no longer to be proven [17]. To fulfill these essential but diverse functions, macrophages can adopt a variety of phenotypes. Given the complexity of these phenotypes, it is generally accepted to classify macrophages according to whether they have pro-inflammatory (M1) or anti-inflammatory (M2) activities [17]. Nevertheless, it is important to keep in mind that this classification greatly simplifies the repolarization capabilities of these cells, which rely on transcriptionally dynamic capacities to adopt the phenotype and functions best suited to their tissue microenvironment [17]. Moreover, macrophages are among the only cells to have so many tissue-specific counterparts (i.e., Kupffer cells in the liver, microglia in the brain, and alveolar macrophages in the lung) [17]. All these tissue-specific macrophages have an embryonic origin, unlike the monocyte lineage-macrophages, which derive from the differentiation of monocytes following their tissue infiltration. In the following, we will discuss the metabolic characteristics of macrophages according to whether they are M1 or M2 polarized in vitro and in vivo. Some differences that may occur depending on their tissue of residence, have been reviewed very recently by Wculek et al. [18].

M1 macrophages (CD11b⁺CD68⁺CD64⁺) have a predominantly pro-inflammatory activity whose activation in vitro occurs either through TLR stimulation by LPS or by cytokine stimulation with TNF or IFNγ. This activation reprograms cell metabolism in a way that supports the establishment of an oxidative burst by the production of reactive oxygen species (ROS) and nitrogen species (RNS), as well as the production of antimicrobial effectors. M1 macrophages are characterized by predominant glycolytic metabolism (Figure 2) as evidenced by their increased consumption of glucose, enabled in particular by an increase in the Glucose Type 1 receptor (GLUT1) [19,20]. Hypoxia Inducible Factor-1α (HIF-1α) was shown to be a transcription factor essential for the onset of this metabolic reprogramming [21,22]. Stabilization of HIF-1α is mediated in M1 macrophages by blocking TCA at two distinct levels. First, the inhibition of isocitrate dehydrogenase (IDH-1) leads to citrate accumulation, as well as itaconate synthesis [23]. On the one hand, the accumulated citrate is converted to acetyl-CoA, which is used for histone acetylation and inflammatory gene transcription [24]. On the other hand, itaconate inhibits the activity of the succinate dehydrogenase complex (SDH), leading to the accumulation of succinate [25]. Ultimately, the succinate generated in this way stabilizes HIF-1α [22]. Notably, this metabolic reprogramming has been shown to be essential for IL-1β production in response to LPS stimulation [22]. Mitochondrial activity in M1 macrophages is then diverted from ATP production to mitochondrial ROS production. The latter will serve within phagosomes, participating in antigen processing and presentation [26]. At this stage, ATP production is therefore mainly dependent on aerobic glycolysis, similarly to the effect described by Warburg for cancer cells [5,22]. This also supplies the necessary intermediates for the pentose phosphate pathway (PPP) leading in the end to an increase in the NADPH pool, as well as to the production of biomass (here as nucleotides) and ROS [27].

Figure 2. General metabolism involved in pro-inflammatory M1 macrophages orientation. M1 macrophages develop a glycolytic metabolism enabled by the overexpression of the glucose transporter, GLUT1, in which HIF-1α is involved. Stabilization of this factor is, on the one hand, linked to the inhibition of isocitrate dehydrogenase (IDH-1), leading to citrate accumulation and itaconate synthesis. Accumulated citrate is converted into acetyl-CoA which is used for histone acetylation and thus promotes the transcription of inflammatory genes. On the other hand, itaconate inhibits the succinate dehydrogenase complex (SDH) activity, resulting in succinate accumulation. The succinate thus produced stabilizes HIF-1α. The mitochondrial activity of M1 macrophages is dedicated to the production of mitochondrial reactive oxygen species, to be used within phagosomes. ATP production therefore relies primarily on aerobic glycolysis.

Figure 2. General metabolism involved in pro-inflammatory M1 macrophages orientation. M1 macrophages develop a glycolytic metabolism enabled by the overexpression of the glucose transporter, GLUT1, in which HIF-1α is involved. Stabilization of this factor is, on the one hand, linked to the inhibition of isocitrate dehydrogenase (IDH-1), leading to citrate accumulation and itaconate synthesis. Accumulated citrate is converted into acetyl-CoA which is used for histone acetylation and thus promotes the transcription of inflammatory genes. On the other hand, itaconate inhibits the succinate dehydrogenase complex (SDH) activity, resulting in succinate accumulation. The succinate thus produced stabilizes HIF-1α. The mitochondrial activity of M1 macrophages is dedicated to the production of mitochondrial reactive oxygen species, to be used within phagosomes. ATP production therefore relies primarily on aerobic glycolysis.

Unlike M1 macrophages, macrophages with a M2 phenotype (CD11b⁺CD68⁺CD163⁺) are obtained in vitro in response to IL-4 or IL-13 and display anti-inflammatory properties as well as involvement in tissue repair [17]. In contrast to M1, M2 macrophages have a cellular metabolism based on increased OXPHOS and mitochondrial respiration (Figure 3), as evidenced by the elevation of OCR in macrophages polarized towards an anti-inflammatory phenotype [28]. Transcriptional shifts leading to this metabolism are controlled in M2 macrophages by activation of several factors including PPARδ, PPARγ, PGC-1β, and IRF4. These factors activation depends on both STAT6 and the mTORC2 complex [28,29]. Some of these factors are associated with lipid metabolism and lead, in particular here, to the upregulation of the scavenger membrane protein CD36 that allows internalization of lipids used for fatty acid oxidation (FAO) [29,30]. While CD36 has been shown to be critical for polarization in M2 [30], the role of FAO remains ambiguous. Indeed, the use of etoxomir to inhibit FAO or genetic models that prevent it, do not appear to affect M2 polarization [31,32], but rather the ability of M2 macrophages to produce ATP. Thus, one might conclude that FAO does not appear to be essential for M2 macrophages to perform their inflammatory functions, with fatty acids serving instead as a substrate for energy production through OXPHOS [31]. In contrast, glutaminergic metabolism appears to be of paramount importance with glutamine providing one-third of the carbon derivatives of TCA [33]. Glutamine leads to the accumulation of α-ketoglutarate, which orchestrates M2 polarization by activating Jumonji domain-containing protein D3 (JMJD3) demethylases [34]. They mediate histone demethylation of M2-specific gene promoters [34]. As a result, these epigenetic modifications are partly responsible for M2 polarization.

Figure 3. General metabolism involved in anti-inflammatory M2 macrophages orientation. In M2 macrophages, activation of the PPARδ, PPARγ, PGC-1β and IRF4 factors enables transcriptional changes directing the cellular metabolism towards OXPHOS. The activation of these factors depends on both STAT6 and mTORC2. In particular, overexpression of the scavenger CD36 provides lipidic substrates required for fatty acid oxidation. Fatty acid oxidation is essential for ATP production via OXPHOS, rather than for M2 macrophage functions. On the other hand, glutamine, which supplies the Krebs cycle, is essential since it orchestrates M2 polarization through the activation of JMJD3 demethylases.

Figure 3. General metabolism involved in anti-inflammatory M2 macrophages orientation. In M2 macrophages, activation of the PPARδ, PPARγ, PGC-1β and IRF4 factors enables transcriptional changes directing the cellular metabolism towards OXPHOS. The activation of these factors depends on both STAT6 and mTORC2. In particular, overexpression of the scavenger CD36 provides lipidic substrates required for fatty acid oxidation. Fatty acid oxidation is essential for ATP production via OXPHOS, rather than for M2 macrophage functions. On the other hand, glutamine, which supplies the Krebs cycle, is essential since it orchestrates M2 polarization through the activation of JMJD3 demethylases.

As with other cells of the immune system, deficits in macrophage metabolism can occur pathologically, as in the case of atheromatous intraplaque foamy macrophages or in obesity [18].

c. The metabolic-dependent activation of tissue sentinels: dendritic cells

Dendritic cells (DCs, CD11c⁺) are antigen-presenting cells (APCs), which like macrophages, have variable origins [35]. In fact, several subtypes of DCs can be distinguished according to their origin. Conventional DCs (cDC1 and cDC2) and plasmacytoid DCs (pDCs) derived from specific progenitors, and others derived from monocytes (mo-DCs) [35]. As APCs, DCs are found in a resting state in many tissues (e.g., Langerhans cells in the dermis) where their role is to sense their environment, and then activate in case of danger. Activation occurs in response to pathogen associated molecular patterns (PAMPs) or damage associated molecular patterns (DAMPs) and results in initiating the immune response by the secretion of cytokines, as well as by the presentation of antigen in lymphoid tissues [35].

The metabolism of resting DCs has already been studied both in vitro and in vivo. Thus, it is known that the mTORC1 complex has a preponderant activity here again. Indeed, in mice, the use of rapamycin leads to the inhibition of the ex vivo growth of pDCs and cDCs from the bone marrow (BM); in vivo the pool of DCs is considerably reduced [36]. Confirming these results obtained in mouse models, the use of rapamycin on human mo-DCs results in impaired survival [37]. Conditional expression models were used to further explore the importance of mTORC1 in DCs survival. Phosphatase and TENsin homolog (Pten) deletion using a Cd11c-Cre system leads to expansion of DCs in mice. This result is consistent with the previous one, PTEN being a negative regulator of mTOR [36]. Deletion of Tsc1, another negative regulator of mTOR, using a similar system, led to an increased growth of cultured BM-DCs [38]. As one of the main factors that regulate cellular metabolism, mTORC1 directs quiescent DCs to use OXPHOS to satisfy their low energy requirement [39].

As for the other immune cells presented so far, the activation of DCs following stimulation of one of their pattern recognition receptors (PRRs) leads to a shift in metabolism with glucose becoming the main fuel for cellular metabolism. This process takes place in two steps. Indeed, shortly after the activation of DCs in a TLR-dependent manner, a rapid increase in glycolytic activity and entry of intermediates into the PPP was observed [40]. These changes are initiated by activation of AKT via TANK-binding kinase 1 (TBK1) and IκB kinase ε (IKKε) [40]. AKT phosphorylates and subsequently activates hexokinase 2 (HK2), the glycolysis-limiting enzyme whose activity increases after association with the mitochondrial surface [40]. Lately, maintenance of glycolysis within DCs relies on activation by the mTOR complex of HIF-1α [41]. Interestingly, if inhibition of HIF-1α or mTOR occurs early after TLRs stimulation, it has no impact on DCs activation, in contrast to early inhibition of AKT. This illustrates the importance of the timing of different signaling pathways in DCs activation [40]. In contrast, activation of the AMP- activated protein kinase (AMPK) pathway appears to control DCs activation, by promoting OXPHOS, in particular, through PGC1α activation, which favors mitochondrial biogenesis. In fact, AMPK knockdown has been associated with a potentiation of DCs activation [42]. Whereas its activation or that of PGC1α, leads to tolerogenic DCs [42].

Figure 4. Dendritic cell metabolism. Maintenance of dendritic cells quiescence depends on an overriding activity of mTORC1. PTEN and TSC1, negative regulators of mTOR limit dendritic cell growth. mTORC1 orientates quiescent cells towards OXPHOS utilization, with protein catabolism and triacylglycerol providing amino acids and fatty acids to supply ATP production via the Krebs cycle. Activated dendritic cells take up glucose as the main fuel for a metabolism geared towards aerobic glycolysis. Activation of mTORC1and HIF-1α, in this case, is enabled by TBK1 and IKKε activity on AKT downstream of dendritic cells PRR.

Figure 4. Dendritic cell metabolism. Maintenance of dendritic cells quiescence depends on an overriding activity of mTORC1. PTEN and TSC1, negative regulators of mTOR limit dendritic cell growth. mTORC1 orientates quiescent cells towards OXPHOS utilization, with protein catabolism and triacylglycerol providing amino acids and fatty acids to supply ATP production via the Krebs cycle. Activated dendritic cells take up glucose as the main fuel for a metabolism geared towards aerobic glycolysis. Activation of mTORC1and HIF-1α, in this case, is enabled by TBK1 and IKKε activity on AKT downstream of dendritic cells PRR.

Therefore, we have shown here that essential changes in immune cell metabolism are required during their activation to enable them to support their function, whether as a support or effector. We will now discuss the contributions of immunometabolism in certain pathological situations.

3. At the systemic level: Interplays between host metabolic status and infectious diseases

b. Exercise

Much like nutrition, physical activity has an impact on an individual's metabolic status; however, its influence on immune function remains a subject of debate (Figure 5). Epidemiological evidence suggests that regular physical activity reduces the incidence of chronic diseases and the risk of viral and bacterial infections [101]. In fact, frequent physical exercise may enhance immune competence. A prospective cohort study of 1,509 Swedish men and women aged between 20 to 60 years demonstrated that higher levels of physical activity were associated with a lower incidence of self-reported upper respiratory tract infections [102]. Acute exercise sessions of less than 60 minutes have been shown to result in a transient alteration in leukocyte counts and lymphocyte proliferation capabilities [103]. Although these changes remained modest, they could be considered as an enhancement of immunosurveillance during moderate physical activity. This type of exercise serves as a crucial adjunct to the immune system, facilitating the continuous turnover of leukocytes between circulation and tissues [104]. Moreover, physical exercise has been suggested as a valuable tool for COVID-19 prevention by improving immune function, particularly innate and adaptive immunity, and modulating the anti-inflammatory state [105].

However, prolonged and intensive exercise in athletes is associated with immune dysfunction, inflammation, and oxidative stress [106,107]. Exercise may transiently impair immune protection, thereby increasing the risk of infection [108]. Consequently, athletes may experience a temporary or sustained reactivation of viruses, such as the Epstein-Barr virus, due to the suppression of immune parameters during years of intense training [109,110,111].

Figure 5. Benefice and risk of physical activity in the context of infection. The role of physical activity on immunity in case of viral infection remains a subject of debate in the literature. The data available to date seem to point towards the description of a hormesis phenomenon. Physical activity has a beneficial effect, illustrated by a reduction in the incidence of infection and an increase in immune-surveillance, in the case of regular and moderated activity. On the other hand, when the “dose” of exercise becomes intense and prolonged, deleterious effects and a context conducive to infection are found, namely an increase of inflammation, oxidative stress and alteration of the immune system

Figure 5. Benefice and risk of physical activity in the context of infection. The role of physical activity on immunity in case of viral infection remains a subject of debate in the literature. The data available to date seem to point towards the description of a hormesis phenomenon. Physical activity has a beneficial effect, illustrated by a reduction in the incidence of infection and an increase in immune-surveillance, in the case of regular and moderated activity. On the other hand, when the “dose” of exercise becomes intense and prolonged, deleterious effects and a context conducive to infection are found, namely an increase of inflammation, oxidative stress and alteration of the immune system

Collectively, these studies demonstrate that physical exercise is beneficial for maintaining functional immunity. However, when practiced at a very high intensity, it is suggested to alter immune function and increase susceptibility to infections (Figure 5). It is also crucial to bear in mind that the connection between exercise and immune function is undoubtedly a multifactorial concept involving other aspects such as nutrition and changes in the microbiome, for example [112]. To this end, further studies using a multifactorial approach will be necessary to enhance our understanding of the immune changes induced by exercise and their underlying mechanisms.

3. At the cellular level: Metabolism involvement in antiviral innate response and sensing

a. Metabolic-dependent sensing of the viral genome and interferon secretion

During a viral infection, cells at the infection site develop an antiviral response based on the secretion of type I interferons (including IFN-α and β) and type III interferons (structurally related to the IL-10 family) [113]. About type I interferons, infected cells will predominantly secrete IFN-β, while IFN-α subtypes concern immune cells and more particularly monocytes and dendritic cells.

The secretion of IFNs and the resulting antiviral response depend on the recognition of viral PAMPs by cellular PRRs. Viral nucleic acids are the most powerful mediators among the PAMPs capable of alerting the infected cell. The PRRs involved in the detection of these viral nucleic acids vary according to their type. The recognition of viral RNA in the cytosol of infected cells is based on the RIG-I-like receptor (RLR) family, which includes RIG-I and MDA5. RIG-I has shown the ability to bind RNA with motifs that are not found in mammals, such as 5'-PPP or 5'-PP caps [114,115]. On the other hand, detection through MDA5 remains to be explored, although MDA5 has been shown to be essential as a sensor for synthetic double-stranded RNA, such as the poly:IC and the picornaviruses [116,117]. In both cases, recognition is followed by a conformational change and oligomerization of RLRs via their Caspase Activation and Recruitment Domain (CARD). Subsequently, this domain becomes available to bind to the Mitochondrial Antiviral Signaling protein (MAVS) CARD domain, localized at the external mitochondrial membrane (Figure 6) [118,119]. The RLR – MAVS interaction thus leads to the activation of IFN regulatory factors 3, 7 (IRF3, IRF7) and NF-κB. This activation occurs via a signaling cascade involving TNF3 receptor-associated factor (TRAF3), TANK-binding kinase 1 (TBK1) and IκB kinase-ε (IKKε), resulting in the secretion of type I and III IFNs (Figure 6) [120]. The detection of cytosolic DNA is based on the cGAS/STING axis, which, despite the introduction of other sensors, remains the most widely reported pathway in the literature. By detecting cytosolic dsDNA, cyclic GMP-AMP Synthase (cGAS) produces cyclic GMP-AMP (cGAMP) leading to the activation of Stimulator of Interferon Genes (STING) [121]. In turn, STING forms a signaling complex with TBK1 inducing IRF3 and the expression of type I and III IFNs (Figure 6) [121]. At the endosomal level, TLRs 3, 7/8 and 9 respectively detect double-stranded DNA, single-stranded RNA and DNA containing unmethylated CpGs [122]. Signaling by TLRs leads, via their own adaptor proteins (TIR domain Containing Adaptator Molecule 1 - TICAM1 and Myeloid Differentiation primary response 88 - MYD88), to the same transcriptional factors as for the RLR and cGAS/STING pathways, namely activation and dimerization of IRFs 3 and 7 [122]. Finally, the viral genome can also be detected via sensors associated with the inflammasome, the nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) and particularly NLRP3 NOD-Like Receptor family, Pyrin domain containing 3 (NLRP3). This detection induces MAPK-dependent signaling, associated with the implementation of unspecific inflammatory signals [123]. Additionally, signaling via MAVS leads as previously mentioned, to a more specific antiviral response [122].

Figure 6. Molecular mechanisms leading to the detection of viral genome. Detection of RNA or DNA from viral origin is dependent on several pathways acting in concert to establish a rapid and efficient antiviral response. These include the activation of RIG-I like (RLR) receptors, Toll-like (TLR) receptors but also of the inflammasome via NLRP3. In addition, cytosolic viral DNA or released DNA resulting from mitochondrial damages lead up to a detection through the cGAS/STING pathway. All these warning signals are integrated and lead to the activation of transcription factors common to these detection pathways, namely IRF3, IRF7 and NF-κB. These transcription factors interact with their promoters to enable the expression of type I and III interferons, on the one hand, and pro-inflammatory cytokines on the other.

Figure 6. Molecular mechanisms leading to the detection of viral genome. Detection of RNA or DNA from viral origin is dependent on several pathways acting in concert to establish a rapid and efficient antiviral response. These include the activation of RIG-I like (RLR) receptors, Toll-like (TLR) receptors but also of the inflammasome via NLRP3. In addition, cytosolic viral DNA or released DNA resulting from mitochondrial damages lead up to a detection through the cGAS/STING pathway. All these warning signals are integrated and lead to the activation of transcription factors common to these detection pathways, namely IRF3, IRF7 and NF-κB. These transcription factors interact with their promoters to enable the expression of type I and III interferons, on the one hand, and pro-inflammatory cytokines on the other.

Interestingly, the recent discovery of the interplay between metabolism in signaling and the detection of the viral genome has been observed. For example, the role of lactate production in IFN regulation has recently been highlighted [124]. Using a metabolomic approach in a model of human embryonic kidney cells (HEK293) stimulated with poly:IC, the down-regulation of glycolysis, illustrated by the decrease in all its intermediates, was demonstrated during the establishment of type I IFN production, dependent on the RLR pathway [124]. In particular, the reduction in glycolysis observed during RLR signaling was associated with a reduction in mitochondrial localization of hexokinase 2 (HK2), which is essential for hexokinase to exert its full functionality. These data seem to suggest an inverse relationship between glycolysis and antiviral signaling, since inhibition of glycolysis, either by HK2 knockout or pharmacologically with 2-deoxyglucose inevitably, leads to an increase in TBK1-IRF3 signaling and IFN-β production [124]. To support this idea, the same work highlighted the intrinsic inhibitory function of lactate on RLR activation. Knockdown of the PDHc subunit of pyruvate dehydrogenase led to the impairment of oxidative phosphorylation and accumulation of lactate. This lactate has been shown to interact with MAVS, limiting its mitochondrial localization, its association with RIG-I and its aggregation, all of which are essential for signaling in the IFN pathway (Figure 7) [124]. Furthermore, while lactate has a direct effect on MAVS, no deficits in the cGAS/STING pathway have been reported in these models [124]. In parallel, and supporting the idea that oxidative phosphorylation, unlike glycolysis, favors MAVS signaling, the production of mitochondrial ROS has been shown to be essential for MAVS oligomerization (Figure 7). Inhibition of mitochondrial ROS formation by MitoQ, an antioxidant that specifically targets mROS, results in a reduction in MAVS oligomerization and a subsequent lower secretion of IFN-β [125]. Mitochondrial ROS are thought to be involved in the peroxidation of mitochondrial membrane lipids, in turn, the state of the mitochondrial membrane influences MAVS oligomerization [126].

The link between metabolic pathways and viral genome detection has also been demonstrated in the cGAS/STING and TLRs pathways (Figure 7). In T cells, inhibition of mTORC1, a complex involved in the control of metabolism and cell growth, led to a drastic reduction in the production of type I IFN compared to control cells, despite stimulation with cGAMP and a co-stimulation signal [127]. The importance of mTORC1 on the activity of the cGAS/STING pathway was demonstrated pharmacologically using rapamycin but also in T cell from KO mice for Raptor, a subunit of the mTORC1, leading to a loss of activity of the complex [127]. Investigation of the mechanism by which mTORC1 influences cGAS/STING led to evidence that two downstream factors of mTORC1 (i.e., 4E-BP1 and S6K1) would play a role in the antiviral axis, with S6K1 being preponderant (Figure 7) [127]. However, it has not been explored whether this effect was due to a variation in metabolism in particular, even though mTORC1 finely regulates T cell metabolism, notably during their activation. On the other hand, it has been previously reported that specific inhibition of S6K1 by PF-4708671 resulted in exacerbated glycolysis and inhibition of mitochondrial complex I, illustrating the intimate relationship between S6K1, oxidative phosphorylation and mitochondrial metabolism [128,129]. These data again support the importance of oxidative phosphorylation in the secretion of type I IFNs. In addition to its role in cellular metabolism, S6K1 interacts directly with STING via its kinase domain to form a tripartite S6K1-STING-TBK1 complex required for IRF3 activation [130]. Similarly, the detection of the endosomal genome by TLRs appears to depend on mTORC1. In mouse pDCs, stimulation of TLR9 by its ligand in the presence of rapamycin resulted in lower type I IFN production, and this decrease was correlated with a reduction in IRF7 phosphorylation in rapamycin-treated cells [131]. As previously mentioned, the pathway involving S6K downstream of mTORC1 appears to be involved [130]. Endosomal RNA detection also seems to be affected when mTORC1 is inhibited, since immunization of mice with the Yellow Fever Virus vaccine strain (17D) in the presence of rapamycin results in a decrease in IFN-α/β secretion, this time linked to downregulation of signaling via TLR7 [131]. Interestingly, the effect of mTORC1 appears to be limited to the cGAS/STING and TLR/MYD88 axes, since the use of poly:IC as an antagonist of RLRs in the presence of rapamycin does not result in a difference in IFN production compared to the control situation [131]. Although these data were obtained in immune cells, it is reasonable to speculate that a similar function could be found in non-immune cells. Thus, the metabolic context at the time of infection influences the efficiency of interferon secretion.

Figure 7. Involvement of metabolism in viral genome detection. The study of the influence of metabolic pathways on the detection of viral pathogens reveals the intricate relationship between metabolic players and viral genome detection. The mTORC1 pathway and S6K, which is a part of this major signaling pathway, have shown their role in promoting cGAS/STING and TLR-dependent detection pathways. On the other hand, aerobic glycolysis and its final metabolite, lactate, interact negatively with the RLR-dependent detection pathway, notably by inhibiting MAVS.

Figure 7. Involvement of metabolism in viral genome detection. The study of the influence of metabolic pathways on the detection of viral pathogens reveals the intricate relationship between metabolic players and viral genome detection. The mTORC1 pathway and S6K, which is a part of this major signaling pathway, have shown their role in promoting cGAS/STING and TLR-dependent detection pathways. On the other hand, aerobic glycolysis and its final metabolite, lactate, interact negatively with the RLR-dependent detection pathway, notably by inhibiting MAVS.

b. Metabolic-dependent antiviral response

In fact, a metabolism suited to the secretion of type I and III IFNs is important, since the IFNs thus secreted, via an autocrine activation loop and a paracrine action, lead to the production of antiviral effectors [132]. Because these two types of IFNs are secreted concomitantly, it is difficult to determine their intrinsic effect on the production of interferon stimulated genes (ISGs). Type I and III IFNs bind to their respective receptors, IFNAR1/2 and IFNLR1/IL-10Rβ, resulting in the phosphorylation and homodimerization of STAT1 or Gamma interferon Activation Factor (GAF). Additionally, it results in the formation of the Interferon Stimulated Genes Factor 3 (ISGF3) complex composed of IRF9 and phosphorylated STAT1 and 2 [132]. ISGF3 then interacts with interferon-sensitive responsive element (ISRE), while GAF interacts with gamma interferon activation site (GAS), both leading to the expression of ISGs (Figure 8) [132]. ISGs include several antiviral effectors with different functions. While attachment has so far found few limiting factors, apart from heparanase [133], entry is the target of several antiviral effectors, including Myxovirus resistance genes (MX1 and MX2) or proteins of the IFN-inducible transmembrane (IFITM) and tripartite motif (TRIM) families. MX1 and MX2 appear to limit the arrival of viral components in their destination compartment [134,135], while members of the IFITM family, found mainly in late endosomes and lysosomes, inhibit the entry of viruses using these pathways [136,137,138]. However, their mechanism of action remains unclear, with some suggesting that they alter the endosomal acidification essential to the fusion process, while others argue that IFITMs may directly influence the physical properties of endosomal and lysosomal membranes [139].

Figure 8. Antiviral effectors and cell metabolism interactions. The binding of type I and III interferons (IFNs) to their respective receptors results, through the JAK/STAT pathway, in the nuclear translocation of ISGF3 and GAF factors. These factors interact with their respective promoters, ISRE and GAS, leading to the expression of interferon-stimulated genes (ISGs), which act as effectors in the antiviral response. During viral infection, several of these ISGs have exhibited antiviral activity, whether during virus entry, replication, or assembly. Other ISGs, such as ISG15, play a role in promoting the antiviral response by enhancing the activity of certain factors involved in this response. Metabolism and the antiviral response are intricately linked, particularly in the expression of antiviral effectors. Oxidative phosphorylation, for instance, tends to enhance the expression of certain interferon-stimulated genes (ISGs) like ISG15, ISG54, or ISG56. However, a duality in this response appears to exist, as some genes encoding antiviral effectors are more expressed during aerobic glycolysis, suggesting they might serve as a backup mechanism in the event of metabolic reprogramming during infection.

Figure 8. Antiviral effectors and cell metabolism interactions. The binding of type I and III interferons (IFNs) to their respective receptors results, through the JAK/STAT pathway, in the nuclear translocation of ISGF3 and GAF factors. These factors interact with their respective promoters, ISRE and GAS, leading to the expression of interferon-stimulated genes (ISGs), which act as effectors in the antiviral response. During viral infection, several of these ISGs have exhibited antiviral activity, whether during virus entry, replication, or assembly. Other ISGs, such as ISG15, play a role in promoting the antiviral response by enhancing the activity of certain factors involved in this response. Metabolism and the antiviral response are intricately linked, particularly in the expression of antiviral effectors. Oxidative phosphorylation, for instance, tends to enhance the expression of certain interferon-stimulated genes (ISGs) like ISG15, ISG54, or ISG56. However, a duality in this response appears to exist, as some genes encoding antiviral effectors are more expressed during aerobic glycolysis, suggesting they might serve as a backup mechanism in the event of metabolic reprogramming during infection.

Among the ISGs inhibiting viral entry, the cholestrol-25-hydroxylase (CH25H) has been reported for several viruses, including DENV and ZIKV [140]. The CD25H catalyzes the conversion of cholesterol into 25-hydroxy-cholesterol (25HC), an intermediate thought to be involved in the alteration of physicochemical properties of the membrane and thus, inhibiting the fusion of enveloped viruses [9]. But 25HC possibly plays a post-entry role as an inhibitor of the sterol synthesis pathway, which is essential for viral progeny [141,142,143,144]. The translation of viral proteins is also a target for antiviral effectors, including proteins of the Interferon Induced Protein with Tetratricopeptide repeats family - IFIT (ISG56/IFIT1, ISG54/IFIT2), the protein kinase R (PKR) or ISG15. PKR and IFIT proteins regulate translation initiation by interacting with the initiation factors eiF2α, eiF3C or eiF3E [145,146,147,148]. In addition, certain proteins of the IFIT family have the ability to recognize type 0 caps (without 2-O-methylation) or type 1 caps (^m7GpppA_mN) leading to inhibition of translation [149,150]. ISG15 protein, a key player in ISGylation at the post-translational stages of viral and host proteins production, has shown multiple functions depending on the ISGylated target [151,152,153]. Going from IRF3 stabilization, maintaining downstream signaling, to increasing the affinity of 4EHP, a negative regulator of translation, for the 5’ cap of messenger RNAs, promoting its ability to inhibit translation [152,153]. The establishment of the replicative complex by deformation of the endoplasmic reticulum (ER) is an essential element in flavivirus replication. This phenomenon is complicated by the expression of Interferon α inducible protein 6 (IFI-6), which prevents the ER membrane invaginations required for the replicative complex [154].

In a similar way to type I IFN secretion, we showed that the promotion of oxidative phosphorylation was favorable to the production of antiviral effectors in response to poly:IC stimulation. Indeed, the stimulation of galactose-cultured cells to increase their oxidative phosphorylation resulted in greater expression of ISG54 and ISG56, two antiviral effectors belonging to the IFIT family (Figure 8) [155]. This positive effect on ISGs expression was abolished in response to rotenone treatment, undeniably linking the observed effect to mitochondrial chain activity [155]. Recently, we showed that up-regulation of antiviral effector production in galactose-grown cells was actually associated with anaplerotic feeding of TCA and OXPHOS by L-glutamine [156]. Surprisingly, other colleagues have shown that high glucose, generally propitious to glycolysis, promotes the expression of an essential ISG for the control of Zika virus infection, namely viperin [157]. These results contrast with our own. However, in this work, the expression of other ISG was not explored [157]. We reconciled these data with our work, showing that cells cultured without L-glutamine, and therefore whose metabolism relies on glycolysis, expressed viperin and OAS1 to a greater extent in response to poly:IC. This observation was indeed related to the glycolytic metabolism of these cells, since the expression of viperin and OAS1 was reduced in the presence of 2-deoxyglucose, an inhibitor of glycolysis (Figure 8) [156]. Therefore, these two ISGs appear to be apart when compared with the other effectors we have studied. This reveals the possibility of a dichotomy in ISGs expression based on cellular metabolism, with some ISGs having greater expression in glycolytic context, while others are favored by oxidative phosphorylation and TCA. In addition, the extent to which a virus or a host relies on L-glutamine during infection remains an open and unresolved question in the literature [158]. Is L-glutamine indispensable to the virus during replication, or does the host need it to produce an effective antiviral response [158]? Our work has shown that even without a productive viral infection, L-glutamine is essential for mounting an antiviral response. However, some antiviral effectors do not require L-glutamine to be produced (such as viperin and OAS1). From a dependency point of view, it seems that viruses may have a greater requirement of L-glutamine for their progeny. It is also possible that this bivalence of the antiviral response is an evolutionary adaptation that enables a response to infection regardless of the metabolic context of the infected cell, particularly in the case of virus-induced metabolic reprogramming.

4. Concluding remarks

References

1. Berbudi, A.; Rahmadika, N.; Tjahjadi, A.I.; Ruslami, R. Type 2 Diabetes and Its Impact on the Immune System. CDR 2020, 16, 442–449. [Google Scholar] [CrossRef] [PubMed]
2. Palmer, Clovis. S. Innate Metabolic Responses against Viral Infections. Nat Metab 2022, 4, 1245–1259. [Google Scholar] [CrossRef] [PubMed]
3. Zhu, J.; Thompson, C.B. Metabolic Regulation of Cell Growth and Proliferation. Nat Rev Mol Cell Biol 2019, 20, 436–450. [Google Scholar] [CrossRef] [PubMed]
4. Thaker, S.K.; Ch’ng, J.; Christofk, H.R. Viral Hijacking of Cellular Metabolism. BMC Biol 2019, 17, 59. [Google Scholar] [CrossRef] [PubMed]
5. Warburg, O. On the Origin of Cancer Cells. Science 1956, 123, 309–314. [Google Scholar] [CrossRef] [PubMed]
6. Allen, C.N.S.; Arjona, S.P.; Santerre, M.; Sawaya, B.E. Hallmarks of Metabolic Reprogramming and Their Role in Viral Pathogenesis. Viruses 2022, 14, 602. [Google Scholar] [CrossRef] [PubMed]
7. Alivernini, S.; Tolusso, B.; Gigante, M.R.; Petricca, L.; Bui, L.; Fedele, A.L.; Di Mario, C.; Benvenuto, R.; Federico, F.; Ferraccioli, G.; et al. Overweight/Obesity Affects Histological Features and Inflammatory Gene Signature of Synovial Membrane of Rheumatoid Arthritis. Sci Rep 2019, 9, 10420. [Google Scholar] [CrossRef] [PubMed]
8. Beck, S.; Zhu, Z.; Oliveira, M.F.; Smith, D.M.; Rich, J.N.; Bernatchez, J.A.; Siqueira-Neto, J.L. Mechanism of Action of Methotrexate Against Zika Virus. Viruses 2019, 11, 338. [Google Scholar] [CrossRef] [PubMed]
9. Liu, S.-Y.; Aliyari, R.; Chikere, K.; Li, G.; Marsden, M.D.; Smith, J.K.; Pernet, O.; Guo, H.; Nusbaum, R.; Zack, J.A.; et al. Interferon-Inducible Cholesterol-25-Hydroxylase Broadly Inhibits Viral Entry by Production of 25-Hydroxycholesterol. Immunity 2013, 38, 92–105. [Google Scholar] [CrossRef] [PubMed]
10. Leone, R.D.; Powell, J.D. Metabolism of Immune Cells in Cancer. Nat Rev Cancer 2020, 20, 516–531. [Google Scholar] [CrossRef]
11. Pagès, F.; Galon, J.; Dieu-Nosjean, M.-C.; Tartour, E.; Sautès-Fridman, C.; Fridman, W.-H. Immune Infiltration in Human Tumors: A Prognostic Factor That Should Not Be Ignored. Oncogene 2010, 29, 1093–1102. [Google Scholar] [CrossRef]
12. Paijens, S.T.; Vledder, A.; De Bruyn, M.; Nijman, H.W. Tumor-Infiltrating Lymphocytes in the Immunotherapy Era. Cell Mol Immunol 2021, 18, 842–859. [Google Scholar] [CrossRef]
13. Voelxen, N.F.; Blatt, S.; Knopf, P.; Henkel, M.; Appelhans, C.; Righesso, L.A.R.; Pabst, A.; Goldschmitt, J.; Walenta, S.; Neff, A.; et al. Comparative Metabolic Analysis in Head and Neck Cancer and the Normal Gingiva. Clin Oral Invest 2018, 22, 1033–1043. [Google Scholar] [CrossRef]
14. Ottensmeier, C.H.; Perry, K.L.; Harden, E.L.; Stasakova, J.; Jenei, V.; Fleming, J.; Wood, O.; Woo, J.; Woelk, C.H.; Thomas, G.J.; et al. Upregulated Glucose Metabolism Correlates Inversely with CD8+ T-Cell Infiltration and Survival in Squamous Cell Carcinoma. Cancer Research 2016, 76, 4136–4148. [Google Scholar] [CrossRef]
15. Chang, C.-H.; Qiu, J.; O’Sullivan, D.; Buck, M.D.; Noguchi, T.; Curtis, J.D.; Chen, Q.; Gindin, M.; Gubin, M.M.; van der Windt, G.J.W.; et al. Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progression. Cell 2015, 162, 1229–1241. [Google Scholar] [CrossRef] [PubMed]
16. Ho, P.-C.; Bihuniak, J.D.; Macintyre, A.N.; Staron, M.; Liu, X.; Amezquita, R.; Tsui, Y.-C.; Cui, G.; Micevic, G.; Perales, J.C.; et al. Phosphoenolpyruvate Is a Metabolic Checkpoint of Anti-Tumor T Cell Responses. Cell 2015, 162, 1217–1228. [Google Scholar] [CrossRef]
17. Murray, P.J.; Wynn, T.A. Protective and Pathogenic Functions of Macrophage Subsets. Nat Rev Immunol 2011, 11, 723–737. [Google Scholar] [CrossRef] [PubMed]
18. Wculek, S.K.; Dunphy, G.; Heras-Murillo, I.; Mastrangelo, A.; Sancho, D. Metabolism of Tissue Macrophages in Homeostasis and Pathology. Cell Mol Immunol 2022, 19, 384–408. [Google Scholar] [CrossRef] [PubMed]
19. Freemerman, A.J.; Johnson, A.R.; Sacks, G.N.; Milner, J.J.; Kirk, E.L.; Troester, M.A.; Macintyre, A.N.; Goraksha-Hicks, P.; Rathmell, J.C.; Makowski, L. Metabolic Reprogramming of Macrophages. Journal of Biological Chemistry 2014, 289, 7884–7896. [Google Scholar] [CrossRef]
20. Rodríguez-Prados, J.-C.; Través, P.G.; Cuenca, J.; Rico, D.; Aragonés, J.; Martín-Sanz, P.; Cascante, M.; Boscá, L. Substrate Fate in Activated Macrophages: A Comparison between Innate, Classic, and Alternative Activation. The Journal of Immunology 2010, 185, 605–614. [Google Scholar] [CrossRef]
21. Palsson-McDermott, E.M.; Curtis, A.M.; Goel, G.; Lauterbach, M.A.R.; Sheedy, F.J.; Gleeson, L.E.; van den Bosch, M.W.M.; Quinn, S.R.; Domingo-Fernandez, R.; Johnston, D.G.W.; et al. Pyruvate Kinase M2 Regulates Hif-1α Activity and IL-1β Induction and Is a Critical Determinant of the Warburg Effect in LPS-Activated Macrophages. Cell Metabolism 2015, 21, 65–80. [Google Scholar] [CrossRef] [PubMed]
22. Tannahill, G.M.; Curtis, A.M.; Adamik, J.; Palsson-McDermott, E.M.; McGettrick, A.F.; Goel, G.; Frezza, C.; Bernard, N.J.; Kelly, B.; Foley, N.H.; et al. Succinate Is an Inflammatory Signal That Induces IL-1β through HIF-1α. Nature 2013, 496, 238–242. [Google Scholar] [CrossRef] [PubMed]
23. Meiser, J.; Krämer, L.; Sapcariu, S.C.; Battello, N.; Ghelfi, J.; D’Herouel, A.F.; Skupin, A.; Hiller, K. Pro-Inflammatory Macrophages Sustain Pyruvate Oxidation through Pyruvate Dehydrogenase for the Synthesis of Itaconate and to Enable Cytokine Expression. Journal of Biological Chemistry 2016, 291, 3932–3946. [Google Scholar] [CrossRef] [PubMed]
24. Lauterbach, M.A.; Hanke, J.E.; Serefidou, M.; Mangan, M.S.J.; Kolbe, C.-C.; Hess, T.; Rothe, M.; Kaiser, R.; Hoss, F.; Gehlen, J.; et al. Toll-like Receptor Signaling Rewires Macrophage Metabolism and Promotes Histone Acetylation via ATP-Citrate Lyase. Immunity 2019, 51, 997–1011.e7. [Google Scholar] [CrossRef] [PubMed]
25. Cordes, T.; Wallace, M.; Michelucci, A.; Divakaruni, A.S.; Sapcariu, S.C.; Sousa, C.; Koseki, H.; Cabrales, P.; Murphy, A.N.; Hiller, K.; et al. Immunoresponsive Gene 1 and Itaconate Inhibit Succinate Dehydrogenase to Modulate Intracellular Succinate Levels. Journal of Biological Chemistry 2016, 291, 14274–14284. [Google Scholar] [CrossRef] [PubMed]
26. Mills, E.L.; Kelly, B.; Logan, A.; Costa, A.S.H.; Varma, M.; Bryant, C.E.; Tourlomousis, P.; Däbritz, J.H.M.; Gottlieb, E.; Latorre, I.; et al. Succinate Dehydrogenase Supports Metabolic Repurposing of Mitochondria to Drive Inflammatory Macrophages. Cell 2016, 167, 457–470.e13. [Google Scholar] [CrossRef]
27. Baardman, J.; Verberk, S.G.S.; Prange, K.H.M.; Van Weeghel, M.; Van Der Velden, S.; Ryan, D.G.; Wüst, R.C.I.; Neele, A.E.; Speijer, D.; Denis, S.W.; et al. A Defective Pentose Phosphate Pathway Reduces Inflammatory Macrophage Responses during Hypercholesterolemia. Cell Reports 2018, 25, 2044–2052.e5. [Google Scholar] [CrossRef] [PubMed]
28. Huang, S.C.-C.; Smith, A.M.; Everts, B.; Colonna, M.; Pearce, E.L.; Schilling, J.D.; Pearce, E.J. Metabolic Reprogramming Mediated by the mTORC2-IRF4 Signaling Axis Is Essential for Macrophage Alternative Activation. Immunity 2016, 45, 817–830. [Google Scholar] [CrossRef]
29. Vats, D.; Mukundan, L.; Odegaard, J.I.; Zhang, L.; Smith, K.L.; Morel, C.R.; Greaves, D.R.; Murray, P.J.; Chawla, A. Oxidative Metabolism and PGC-1β Attenuate Macrophage-Mediated Inflammation. Cell Metabolism 2006, 4, 13–24. [Google Scholar] [CrossRef]
30. Huang, S.C.-C.; Everts, B.; Ivanova, Y.; O’Sullivan, D.; Nascimento, M.; Smith, A.M.; Beatty, W.; Love-Gregory, L.; Lam, W.Y.; O’Neill, C.M.; et al. Cell-Intrinsic Lysosomal Lipolysis Is Essential for Alternative Activation of Macrophages. Nat Immunol 2014, 15, 846–855. [Google Scholar] [CrossRef]
31. Namgaladze, D.; Brüne, B. Fatty Acid Oxidation Is Dispensable for Human Macrophage IL-4-Induced Polarization. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 2014, 1841, 1329–1335. [Google Scholar] [CrossRef] [PubMed]
32. Nomura, M.; Liu, J.; Rovira, I.I.; Gonzalez-Hurtado, E.; Lee, J.; Wolfgang, M.J.; Finkel, T. Fatty Acid Oxidation in Macrophage Polarization. Nat Immunol 2016, 17, 216–217. [Google Scholar] [CrossRef]
33. Jha, A.K.; Huang, S.C.-C.; Sergushichev, A.; Lampropoulou, V.; Ivanova, Y.; Loginicheva, E.; Chmielewski, K.; Stewart, K.M.; Ashall, J.; Everts, B.; et al. Network Integration of Parallel Metabolic and Transcriptional Data Reveals Metabolic Modules That Regulate Macrophage Polarization. Immunity 2015, 42, 419–430. [Google Scholar] [CrossRef] [PubMed]
34. Liu, P.-S.; Wang, H.; Li, X.; Chao, T.; Teav, T.; Christen, S.; Di Conza, G.; Cheng, W.-C.; Chou, C.-H.; Vavakova, M.; et al. α-Ketoglutarate Orchestrates Macrophage Activation through Metabolic and Epigenetic Reprogramming. Nat Immunol 2017, 18, 985–994. [Google Scholar] [CrossRef] [PubMed]
35. Cabeza-Cabrerizo, M.; Cardoso, A.; Minutti, C.M.; Pereira Da Costa, M.; Reis E Sousa, C. Dendritic Cells Revisited. Annu. Rev. Immunol. 2021, 39, 131–166. [Google Scholar] [CrossRef] [PubMed]
36. Sathaliyawala, T.; O’Gorman, W.E.; Greter, M.; Bogunovic, M.; Konjufca, V.; Hou, Z.E.; Nolan, G.P.; Miller, M.J.; Merad, M.; Reizis, B. Mammalian Target of Rapamycin Controls Dendritic Cell Development Downstream of Flt3 Ligand Signaling. Immunity 2010, 33, 597–606. [Google Scholar] [CrossRef] [PubMed]
37. Woltman, A.M.; Van Der Kooij, S.W.; Coffer, P.J.; Offringa, R.; Daha, M.R.; Van Kooten, C. Rapamycin Specifically Interferes with GM-CSF Signaling in Human Dendritic Cells, Leading to Apoptosis via Increased p27KIP1 Expression. Blood 2003, 101, 1439–1445. [Google Scholar] [CrossRef] [PubMed]
38. Pan, H.; O’Brien, T.F.; Wright, G.; Yang, J.; Shin, J.; Wright, K.L.; Zhong, X.-P. Critical Role of the Tumor Suppressor Tuberous Sclerosis Complex 1 in Dendritic Cell Activation of CD4 T Cells by Promoting MHC Class II Expression via IRF4 and CIITA. The Journal of Immunology 2013, 191, 699–707. [Google Scholar] [CrossRef] [PubMed]
39. Pearce, E.J.; Everts, B. Dendritic Cell Metabolism. Nat Rev Immunol 2015, 15, 18–29. [Google Scholar] [CrossRef]
40. Everts, B.; Amiel, E.; Huang, S.C.-C.; Smith, A.M.; Chang, C.-H.; Lam, W.Y.; Redmann, V.; Freitas, T.C.; Blagih, J.; Van Der Windt, G.J.W.; et al. TLR-Driven Early Glycolytic Reprogramming via the Kinases TBK1-IKKɛ Supports the Anabolic Demands of Dendritic Cell Activation. Nat Immunol 2014, 15, 323–332. [Google Scholar] [CrossRef]
41. Jantsch, J.; Chakravortty, D.; Turza, N.; Prechtel, A.T.; Buchholz, B.; Gerlach, R.G.; Volke, M.; Gläsner, J.; Warnecke, C.; Wiesener, M.S.; et al. Hypoxia and Hypoxia-Inducible Factor-1α Modulate Lipopolysaccharide-Induced Dendritic Cell Activation and Function. The Journal of Immunology 2008, 180, 4697–4705. [Google Scholar] [CrossRef] [PubMed]
42. Carroll, K.C.; Viollet, B.; Suttles, J. AMPKα1 Deficiency Amplifies Proinflammatory Myeloid APC Activity and CD40 Signaling. Journal of Leukocyte Biology 2013, 94, 1113–1121. [Google Scholar] [CrossRef] [PubMed]
43. Saeedi, P.; Petersohn, I.; Salpea, P.; Malanda, B.; Karuranga, S.; Unwin, N.; Colagiuri, S.; Guariguata, L.; Motala, A.A.; Ogurtsova, K.; et al. Global and Regional Diabetes Prevalence Estimates for 2019 and Projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th Edition. Diabetes Research and Clinical Practice 2019, 157, 107843. [Google Scholar] [CrossRef] [PubMed]
44. Hodgson, K.; Morris, J.; Bridson, T.; Govan, B.; Rush, C.; Ketheesan, N. Immunological Mechanisms Contributing to the Double Burden of Diabetes and Intracellular Bacterial Infections. Immunology 2015, 144, 171–185. [Google Scholar] [CrossRef] [PubMed]
45. Xu, H.; Barnes, G.T.; Yang, Q.; Tan, G.; Yang, D.; Chou, C.J.; Sole, J.; Nichols, A.; Ross, J.S.; Tartaglia, L.A.; et al. Chronic Inflammation in Fat Plays a Crucial Role in the Development of Obesity-Related Insulin Resistance. J. Clin. Invest. 2003, 112, 1821–1830. [Google Scholar] [CrossRef] [PubMed]
46. Mooradian, A.D.; Reed, R.L.; Meredith, K.E.; Scuderi, P. Serum Levels of Tumor Necrosis Factor and IL-1α and IL-1β in Diabetic Patients. Diabetes Care 1991, 14, 63–65. [Google Scholar] [CrossRef] [PubMed]
47. Ohno, Y.; Aoki, N.; Nishimura, A. In Vitro Production of Interleukin-1, Interleukin-6, and Tumor Necrosis Factor-Alpha in Insulin-Dependent Diabetes Mellitus. The Journal of Clinical Endocrinology & Metabolism 1993, 77, 1072–1077. [Google Scholar] [CrossRef]
48. Reinhold, D.; Ansorge, S. Elevated Glucose Levels Stimulate Transforming Growth Factor-Β1 (TGF-Β1), Suppress Interleukin IL-2, IL-6 and IL-10 Production and DNA Synthesis in Peripheral Blood Mononuclear Cells. Horm Metab Res 1996, 28, 267–270. [Google Scholar] [CrossRef]
49. Okada, M.; Kitahara, M.; Kishimoto, S.; Matsuda, T.; Hirano, T.; Kishimoto, T. IL-6/BSF-2 Functions as a Killer Helper Factor in the in Vitro Induction of Cytotoxic T Cells. J Immunol 1988, 141, 1543–1549. [Google Scholar] [CrossRef]
50. Hu, R.; Xia, C.-Q.; Butfiloski, E.; Clare-Salzler, M. Effect of High Glucose on Cytokine Production by Human Peripheral Blood Immune Cells and Type I Interferon Signaling in Monocytes: Implications for the Role of Hyperglycemia in the Diabetes Inflammatory Process and Host Defense against Infection. Clinical Immunology 2018, 195, 139–148. [Google Scholar] [CrossRef]
51. Kumar, M.; Roe, K.; Nerurkar, P.V.; Orillo, B.; Thompson, K.S.; Verma, S.; Nerurkar, V.R. Reduced Immune Cell Infiltration and Increased Pro-Inflammatory Mediators in the Brain of Type 2 Diabetic Mouse Model Infected with West Nile Virus. J Neuroinflammation 2014, 11, 80. [Google Scholar] [CrossRef] [PubMed]
52. Lecube, A.; Pachón, G.; Petriz, J.; Hernández, C.; Simó, R. Phagocytic Activity Is Impaired in Type 2 Diabetes Mellitus and Increases after Metabolic Improvement. PLoS ONE 2011, 6, e23366. [Google Scholar] [CrossRef] [PubMed]
53. Restrepo, B.I.; Twahirwa, M.; Rahbar, M.H.; Schlesinger, L.S. Phagocytosis via Complement or Fc-Gamma Receptors Is Compromised in Monocytes from Type 2 Diabetes Patients with Chronic Hyperglycemia. PLoS ONE 2014, 9, e92977. [Google Scholar] [CrossRef] [PubMed]
54. Chao, W.-C.; Yen, C.-L.; Wu, Y.-H.; Chen, S.-Y.; Hsieh, C.-Y.; Chang, T.-C.; Ou, H.-Y.; Shieh, C.-C. Increased Resistin May Suppress Reactive Oxygen Species Production and Inflammasome Activation in Type 2 Diabetic Patients with Pulmonary Tuberculosis Infection. Microbes and Infection 2015, 17, 195–204. [Google Scholar] [CrossRef] [PubMed]
55. Perner, A.; Nielsen, S.E.; Rask-Madsen, J. High Glucose Impairs Superoxide Production from Isolated Blood Neutrophils. Intensive Care Med 2003, 29, 642–645. [Google Scholar] [CrossRef] [PubMed]
56. Stegenga, M.E.; van der Crabben, S.N.; Blümer, R.M.E.; Levi, M.; Meijers, J.C.M.; Serlie, M.J.; Tanck, M.W.T.; Sauerwein, H.P.; van der Poll, T. Hyperglycemia Enhances Coagulation and Reduces Neutrophil Degranulation, Whereas Hyperinsulinemia Inhibits Fibrinolysis during Human Endotoxemia. Blood 2008, 112, 82–89. [Google Scholar] [CrossRef] [PubMed]
57. Joshi, M.B.; Lad, A.; Bharath Prasad, A.S.; Balakrishnan, A.; Ramachandra, L.; Satyamoorthy, K. High Glucose Modulates IL-6 Mediated Immune Homeostasis through Impeding Neutrophil Extracellular Trap Formation. FEBS Letters 2013, 587, 2241–2246. [Google Scholar] [CrossRef]
58. Berrou, J.; Fougeray, S.; Venot, M.; Chardiny, V.; Gautier, J.-F.; Dulphy, N.; Toubert, A.; Peraldi, M.-N. Natural Killer Cell Function, an Important Target for Infection and Tumor Protection, Is Impaired in Type 2 Diabetes. PLoS ONE 2013, 8, e62418. [Google Scholar] [CrossRef]
59. Mauriello, C.T.; Hair, P.S.; Rohn, R.D.; Rister, N.S.; Krishna, N.K.; Cunnion, K.M. Hyperglycemia Inhibits Complement-Mediated Immunological Control of S. Aureus in a Rat Model of Peritonitis. Journal of Diabetes Research 2014, 2014, 1–11. [Google Scholar] [CrossRef]
60. Martinez, N.; Ketheesan, N.; Martens, G.W.; West, K.; Lien, E.; Kornfeld, H. Defects in Early Cell Recruitment Contribute to the Increased Susceptibility to Respiratory Klebsiella Pneumoniae Infection in Diabetic Mice. Microbes and Infection 2016, 18, 649–655. [Google Scholar] [CrossRef]
61. Gupta, S.; Maratha, A.; Siednienko, J.; Natarajan, A.; Gajanayake, T.; Hoashi, S.; Miggin, S. Analysis of Inflammatory Cytokine and TLR Expression Levels in Type 2 Diabetes with Complications. Sci Rep 2017, 7, 7633. [Google Scholar] [CrossRef] [PubMed]
62. Vallerskog, T.; Martens, G.W.; Kornfeld, H. Diabetic Mice Display a Delayed Adaptive Immune Response to Mycobacterium Tuberculosis. J.I. 2010, 184, 6275–6282. [Google Scholar] [CrossRef]
63. Williams, N.L.; Morris, J.L.; Rush, C.; Govan, B.L.; Ketheesan, N. Impact of Streptozotocin-Induced Diabetes on Functional Responses of Dendritic Cells and Macrophages towards Burkholderia Pseudomallei. FEMS Immunol Med Microbiol 2011, 61, 218–227. [Google Scholar] [CrossRef] [PubMed]
64. Carey, I.M.; Critchley, J.A.; DeWilde, S.; Harris, T.; Hosking, F.J.; Cook, D.G. Risk of Infection in Type 1 and Type 2 Diabetes Compared With the General Population: A Matched Cohort Study. Diabetes Care 2018, 41, 513–521. [Google Scholar] [CrossRef] [PubMed]
65. Critchley, J.A.; Carey, I.M.; Harris, T.; DeWilde, S.; Hosking, F.J.; Cook, D.G. Glycemic Control and Risk of Infections Among People With Type 1 or Type 2 Diabetes in a Large Primary Care Cohort Study. Diabetes Care 2018, 41, 2127–2135. [Google Scholar] [CrossRef] [PubMed]
66. Zhang, X.; Zhu, X.; Ji, Y.; Li, H.; Hou, F.; Xiao, C.; Yuan, P. Increased Risk of Hepatitis B Virus Infection amongst Individuals with Diabetes Mellitus. Bioscience Reports 2019, 39, BSR20181715. [Google Scholar] [CrossRef] [PubMed]
67. Kulcsar, K.A.; Coleman, C.M.; Beck, S.E.; Frieman, M.B. Comorbid Diabetes Results in Immune Dysregulation and Enhanced Disease Severity Following MERS-CoV Infection. JCI Insight 2019, 4, e131774. [Google Scholar] [CrossRef] [PubMed]
68. Norouzi, M.; Norouzi, S.; Ruggiero, A.; Khan, M.S.; Myers, S.; Kavanagh, K.; Vemuri, R. Type-2 Diabetes as a Risk Factor for Severe COVID-19 Infection. Microorganisms 2021, 9, 1211. [Google Scholar] [CrossRef]
69. Reiterer, M.; Rajan, M.; Gómez-Banoy, N.; Lau, J.D.; Gomez-Escobar, L.G.; Ma, L.; Gilani, A.; Alvarez-Mulett, S.; Sholle, E.T.; Chandar, V.; et al. Hyperglycemia in Acute COVID-19 Is Characterized by Insulin Resistance and Adipose Tissue Infectivity by SARS-CoV-2. Cell Metabolism 2021, 33, 2174–2188.e5. [Google Scholar] [CrossRef]
70. Htun, N.S.N.; Odermatt, P.; Eze, I.C.; Boillat-Blanco, N.; D’Acremont, V.; Probst-Hensch, N. Is Diabetes a Risk Factor for a Severe Clinical Presentation of Dengue? - Review and Meta-Analysis. PLoS Negl Trop Dis 2015, 9, e0003741. [Google Scholar] [CrossRef]
71. Sekaran, S.D.; Liew, Z.M.; Yam, H.C.; Raju, C.S. The Association between Diabetes and Obesity with Dengue Infections. Diabetol Metab Syndr 2022, 14, 101. [Google Scholar] [CrossRef] [PubMed]
72. Després, J.-P. Body Fat Distribution and Risk of Cardiovascular Disease: An Update. Circulation 2012, 126, 1301–1313. [Google Scholar] [CrossRef]
73. World Health Organisation Obesity and Overweight Available online:. Available online: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (accessed on 30 September 2023).
74. Schwartz, M.W.; Seeley, R.J.; Zeltser, L.M.; Drewnowski, A.; Ravussin, E.; Redman, L.M.; Leibel, R.L. Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocrine Reviews 2017, 38, 267–296. [Google Scholar] [CrossRef] [PubMed]
75. Krajmalnik-Brown, R.; Ilhan, Z.-E.; Kang, D.-W.; DiBaise, J.K. Effects of Gut Microbes on Nutrient Absorption and Energy Regulation. Nutr Clin Pract 2012, 27, 201–214. [Google Scholar] [CrossRef] [PubMed]
76. Kershaw, E.E.; Flier, J.S. Adipose Tissue as an Endocrine Organ. The Journal of Clinical Endocrinology & Metabolism 2004, 89, 2548–2556. [Google Scholar] [CrossRef] [PubMed]
77. Kanneganti, T.-D.; Dixit, V.D. Immunological Complications of Obesity. Nat Immunol 2012, 13, 707–712. [Google Scholar] [CrossRef] [PubMed]
78. Weisberg, S.P.; McCann, D.; Desai, M.; Rosenbaum, M.; Leibel, R.L.; Ferrante, A.W. Obesity Is Associated with Macrophage Accumulation in Adipose Tissue. J. Clin. Invest. 2003, 112, 1796–1808. [Google Scholar] [CrossRef] [PubMed]
79. Skurk, T.; Alberti-Huber, C.; Herder, C.; Hauner, H. Relationship between Adipocyte Size and Adipokine Expression and Secretion. The Journal of Clinical Endocrinology & Metabolism 2007, 92, 1023–1033. [Google Scholar] [CrossRef] [PubMed]
80. Nguyen, M.T.A.; Favelyukis, S.; Nguyen, A.-K.; Reichart, D.; Scott, P.A.; Jenn, A.; Liu-Bryan, R.; Glass, C.K.; Neels, J.G.; Olefsky, J.M. A Subpopulation of Macrophages Infiltrates Hypertrophic Adipose Tissue and Is Activated by Free Fatty Acids via Toll-like Receptors 2 and 4 and JNK-Dependent Pathways. Journal of Biological Chemistry 2007, 282, 35279–35292. [Google Scholar] [CrossRef]
81. Shi, H.; Kokoeva, M.V.; Inouye, K.; Tzameli, I.; Yin, H.; Flier, J.S. TLR4 Links Innate Immunity and Fatty Acid–Induced Insulin Resistance. J. Clin. Invest. 2006, 116, 3015–3025. [Google Scholar] [CrossRef]
82. Zheng, C.; Yang, Q.; Cao, J.; Xie, N.; Liu, K.; Shou, P.; Qian, F.; Wang, Y.; Shi, Y. Local Proliferation Initiates Macrophage Accumulation in Adipose Tissue during Obesity. Cell Death Dis 2016, 7, e2167–e2167. [Google Scholar] [CrossRef] [PubMed]
83. Brotfain, E.; Hadad, N.; Shapira, Y.; Avinoah, E.; Zlotnik, A.; Raichel, L.; Levy, R. Neutrophil Functions in Morbidly Obese Subjects. Clinical and Experimental Immunology 2015, 181, 156–163. [Google Scholar] [CrossRef] [PubMed]
84. Lee, B.-C.; Kim, M.-S.; Pae, M.; Yamamoto, Y.; Eberlé, D.; Shimada, T.; Kamei, N.; Park, H.-S.; Sasorith, S.; Woo, J.R.; et al. Adipose Natural Killer Cells Regulate Adipose Tissue Macrophages to Promote Insulin Resistance in Obesity. Cell Metabolism 2016, 23, 685–698. [Google Scholar] [CrossRef] [PubMed]
85. Nishimura, S.; Manabe, I.; Nagasaki, M.; Eto, K.; Yamashita, H.; Ohsugi, M.; Otsu, M.; Hara, K.; Ueki, K.; Sugiura, S.; et al. CD8+ Effector T Cells Contribute to Macrophage Recruitment and Adipose Tissue Inflammation in Obesity. Nat Med 2009, 15, 914–920. [Google Scholar] [CrossRef] [PubMed]
86. Cypess, A.M. Reassessing Human Adipose Tissue. N Engl J Med 2022, 386, 768–779. [Google Scholar] [CrossRef] [PubMed]
87. Zhang, L.; Guerrero-Juarez, C.F.; Chen, S.X.; Zhang, X.; Yin, M.; Li, F.; Wu, S.; Chen, J.; Li, M.; Liu, Y.; et al. Diet-Induced Obesity Promotes Infection by Impairment of the Innate Antimicrobial Defense Function of Dermal Adipocyte Progenitors. Sci. Transl. Med. 2021, 13, eabb5280. [Google Scholar] [CrossRef] [PubMed]
88. Shipman, A.R.; Millington, G.W.M. Obesity and the Skin: Obesity and the Skin. British Journal of Dermatology 2011, 165, 743–750. [Google Scholar] [CrossRef] [PubMed]
89. Pugliese, G.; Liccardi, A.; Graziadio, C.; Barrea, L.; Muscogiuri, G.; Colao, A. Obesity and Infectious Diseases: Pathophysiology and Epidemiology of a Double Pandemic Condition. Int J Obes 2022, 46, 449–465. [Google Scholar] [CrossRef]
90. Kaspersen, K.A.; Pedersen, O.B.; Petersen, M.S.; Hjalgrim, H.; Rostgaard, K.; Møller, B.K.; Juul-Sørensen, C.; Kotzé, S.; Dinh, K.M.; Erikstrup, L.T.; et al. Obesity and Risk of Infection: Results from the Danish Blood Donor Study. Epidemiology 2015, 26, 580–589. [Google Scholar] [CrossRef]
91. Las Heras, V.; Clooney, A.G.; Ryan, F.J.; Cabrera-Rubio, R.; Casey, P.G.; Hueston, C.M.; Pinheiro, J.; Rudkin, J.K.; Melgar, S.; Cotter, P.D.; et al. Short-Term Consumption of a High-Fat Diet Increases Host Susceptibility to Listeria Monocytogenes Infection. Microbiome 2019, 7, 7. [Google Scholar] [CrossRef]
92. Mancuso, P.; Gottschalk, A.; Phare, S.M.; Peters-Golden, M.; Lukacs, N.W.; Huffnagle, G.B. Leptin-Deficient Mice Exhibit Impaired Host Defense in Gram-Negative Pneumonia. J Immunol 2002, 168, 4018–4024. [Google Scholar] [CrossRef] [PubMed]
93. Wieland, C.W.; Florquin, S.; Chan, E.D.; Leemans, J.C.; Weijer, S.; Verbon, A.; Fantuzzi, G.; van der Poll, T. Pulmonary Mycobacterium Tuberculosis Infection in Leptin-Deficient Ob/Ob Mice. International Immunology 2005, 17, 1399–1408. [Google Scholar] [CrossRef] [PubMed]
94. Louie, J.K.; Acosta, M.; Samuel, M.C.; Schechter, R.; Vugia, D.J.; Harriman, K.; Matyas, B.T. the California Pandemic (H1N1) Working Group A Novel Risk Factor for a Novel Virus: Obesity and 2009 Pandemic Influenza A (H1N1). Clinical Infectious Diseases 2011, 52, 301–312. [Google Scholar] [CrossRef] [PubMed]
95. Popkin, B.M.; Du, S.; Green, W.D.; Beck, M.A.; Algaith, T.; Herbst, C.H.; Alsukait, R.F.; Alluhidan, M.; Alazemi, N.; Shekar, M. Individuals with Obesity and COVID-19: A Global Perspective on the Epidemiology and Biological Relationships. Obesity Reviews 2020, 21. [Google Scholar] [CrossRef] [PubMed]
96. Karlsson, E.A.; Sheridan, P.A.; Beck, M.A. Diet-Induced Obesity Impairs the T Cell Memory Response to Influenza Virus Infection. J.I. 2010, 184, 3127–3133. [Google Scholar] [CrossRef] [PubMed]
97. Smith, A.G.; Sheridan, P.A.; Tseng, R.J.; Sheridan, J.F.; Beck, M.A. Selective Impairment in Dendritic Cell Function and Altered Antigen-Specific CD8 ⁺ T-Cell Responses in Diet-Induced Obese Mice Infected with Influenza Virus. Immunology 2009, 126, 268–279. [Google Scholar] [CrossRef]
98. Geerling, E.; Stone, E.T.; Steffen, T.L.; Hassert, M.; Brien, J.D.; Pinto, A.K. Obesity Enhances Disease Severity in Female Mice Following West Nile Virus Infection. Front. Immunol. 2021, 12, 739025. [Google Scholar] [CrossRef]
99. Kim, Y.-H.; Kim, J.-K.; Kim, D.-J.; Nam, J.-H.; Shim, S.-M.; Choi, Y.-K.; Lee, C.-H.; Poo, H. Diet-Induced Obesity Dramatically Reduces the Efficacy of a 2009 Pandemic H1N1 Vaccine in a Mouse Model. The Journal of Infectious Diseases 2012, 205, 244–251. [Google Scholar] [CrossRef]
100. Weber, D.; Rutala, W.; Samsa, G.; Bradshaw, S.; Lemon, S. Impaired Immunogenicity of Hepatitis B Vaccine in Obese Persons. New England Journal of Medicine 1986, 314, 1393–1393. [Google Scholar] [CrossRef]
101. Pape, K.; Ryttergaard, L.; Rotevatn, T.A.; Nielsen, B.J.; Torp-Pedersen, C.; Overgaard, C.; Bøggild, H. Leisure-Time Physical Activity and the Risk of Suspected Bacterial Infections. Medicine & Science in Sports & Exercise 2016, 48, 1737–1744. [Google Scholar] [CrossRef]
102. Fondell, E.; Lagerros, Y.T.; Sundberg, C.J.; Lekander, M.; Bälter, O.; Rothman, K.J.; Bälter, K. Physical Activity, Stress, and Self-Reported Upper Respiratory Tract Infection. Medicine & Science in Sports & Exercise 2011, 43, 272–279. [Google Scholar] [CrossRef]
103. Nieman, D.C.; Henson, D.A.; Austin, M.D.; Brown, V.A. Immune Response to a 30-Minute Walk: Medicine & Science in Sports & Exercise 2005, 37, 57–62. [CrossRef]
104. Adams, G.R.; Zaldivar, F.P.; Nance, D.M.; Kodesh, E.; Radom-Aizik, S.; Cooper, D.M. Exercise and Leukocyte Interchange among Central Circulation, Lung, Spleen, and Muscle. Brain, Behavior, and Immunity 2011, 25, 658–666. [Google Scholar] [CrossRef]
105. Fernández-Lázaro, D.; González-Bernal, J.J.; Sánchez-Serrano, N.; Navascués, L.J.; Ascaso-del-Río, A.; Mielgo-Ayuso, J. Physical Exercise as a Multimodal Tool for COVID-19: Could It Be Used as a Preventive Strategy? International Journal of Environmental Research and Public Health 2020, 17, 8496. [Google Scholar] [CrossRef] [PubMed]
106. Nieman, D.C. Immune Response to Heavy Exertion. Journal of Applied Physiology 1997, 82, 1385–1394. [Google Scholar] [CrossRef] [PubMed]
107. Northoff, H.; Berg, A. Immunologic Mediators as Parameters of the Reaction to Strenuous Exercise. Int J Sports Med 1991, 12, S9–S15. [Google Scholar] [CrossRef]
108. Nieman, D.C.; Wentz, L.M. The Compelling Link between Physical Activity and the Body’s Defense System. Journal of Sport and Health Science 2019, 8, 201–217. [Google Scholar] [CrossRef] [PubMed]
109. Clancy, R.L. Reversal in Fatigued Athletes of a Defect in Interferon Secretion after Administration of Lactobacillus Acidophilus. British Journal of Sports Medicine 2006, 40, 351–354. [Google Scholar] [CrossRef] [PubMed]
110. Gleeson, M.; Pyne, D.B.; P. Austin, J.; Lynn Francis, J.; Clancy, R.L.; Mcdonald, W.A.; Fricker, P.A. Epstein-Barr Virus Reactivation and Upper-Respiratory Illness in Elite Swimmers: . Medicine & Science in Sports & Exercise 2002, 34, 411–417. [CrossRef]
111. Reid, V.L. Clinical Investigation of Athletes with Persistent Fatigue and/or Recurrent Infections. British Journal of Sports Medicine 2004, 38, 42–45. [Google Scholar] [CrossRef]
112. Barton, W.; Penney, N.C.; Cronin, O.; Garcia-Perez, I.; Molloy, M.G.; Holmes, E.; Shanahan, F.; Cotter, P.D.; O’Sullivan, O. The Microbiome of Professional Athletes Differs from That of More Sedentary Subjects in Composition and Particularly at the Functional Metabolic Level. Gut, 2017; gutjnl-2016-313627. [Google Scholar] [CrossRef]
113. Lazear, H.M.; Schoggins, J.W.; Diamond, M.S. Shared and Distinct Functions of Type I and Type III Interferons. Immunity 2019, 50, 907–923. [Google Scholar] [CrossRef]
114. Hornung, V.; Ellegast, J.; Kim, S.; Brzózka, K.; Jung, A.; Kato, H.; Poeck, H.; Akira, S.; Conzelmann, K.-K.; Schlee, M.; et al. 5’-Triphosphate RNA Is the Ligand for RIG-I. Science 2006, 314, 994–997. [Google Scholar] [CrossRef] [PubMed]
115. Pichlmair, A.; Schulz, O.; Tan, C.P.; Näslund, T.I.; Liljeström, P.; Weber, F.; Reis E Sousa, C. RIG-I-Mediated Antiviral Responses to Single-Stranded RNA Bearing 5’-Phosphates. Science 2006, 314, 997–1001. [Google Scholar] [CrossRef] [PubMed]
116. Gitlin, L.; Barchet, W.; Gilfillan, S.; Cella, M.; Beutler, B.; Flavell, R.A.; Diamond, M.S.; Colonna, M. Essential Role of Mda-5 in Type I IFN Responses to Polyriboinosinic:Polyribocytidylic Acid and Encephalomyocarditis Picornavirus. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 8459–8464. [Google Scholar] [CrossRef]
117. Kato, H.; Takeuchi, O.; Sato, S.; Yoneyama, M.; Yamamoto, M.; Matsui, K.; Uematsu, S.; Jung, A.; Kawai, T.; Ishii, K.J.; et al. Differential Roles of MDA5 and RIG-I Helicases in the Recognition of RNA Viruses. Nature 2006, 441, 101–105. [Google Scholar] [CrossRef] [PubMed]
118. Kowalinski, E.; Lunardi, T.; McCarthy, A.A.; Louber, J.; Brunel, J.; Grigorov, B.; Gerlier, D.; Cusack, S. Structural Basis for the Activation of Innate Immune Pattern-Recognition Receptor RIG-I by Viral RNA. Cell 2011, 147, 423–435. [Google Scholar] [CrossRef] [PubMed]
119. Xu, L.-G.; Wang, Y.-Y.; Han, K.-J.; Li, L.-Y.; Zhai, Z.; Shu, H.-B. VISA Is an Adapter Protein Required for Virus-Triggered IFN-β Signaling. Molecular Cell 2005, 19, 727–740. [Google Scholar] [CrossRef]
120. Paz, S.; Vilasco, M.; Werden, S.J.; Arguello, M.; Joseph-Pillai, D.; Zhao, T.; Nguyen, T.L.-A.; Sun, Q.; Meurs, E.F.; Lin, R.; et al. A Functional C-Terminal TRAF3-Binding Site in MAVS Participates in Positive and Negative Regulation of the IFN Antiviral Response. Cell Res 2011, 21, 895–910. [Google Scholar] [CrossRef]
121. Motwani, M.; Pesiridis, S.; Fitzgerald, K.A. DNA Sensing by the cGAS–STING Pathway in Health and Disease. Nat Rev Genet 2019, 20, 657–674. [Google Scholar] [CrossRef] [PubMed]
122. Chow, K.T.; Gale, M.; Loo, Y.-M. RIG-I and Other RNA Sensors in Antiviral Immunity. Annu. Rev. Immunol. 2018, 36, 667–694. [Google Scholar] [CrossRef]
123. Swanson, K.V.; Deng, M.; Ting, J.P.-Y. The NLRP3 Inflammasome: Molecular Activation and Regulation to Therapeutics. Nat Rev Immunol 2019, 19, 477–489. [Google Scholar] [CrossRef]
124. Zhang, W.; Wang, G.; Xu, Z.-G.; Tu, H.; Hu, F.; Dai, J.; Chang, Y.; Chen, Y.; Lu, Y.; Zeng, H.; et al. Lactate Is a Natural Suppressor of RLR Signaling by Targeting MAVS. Cell 2019, 178, 176–189.e15. [Google Scholar] [CrossRef] [PubMed]
125. Buskiewicz, I.A.; Montgomery, T.; Yasewicz, E.C.; Huber, S.A.; Murphy, M.P.; Hartley, R.C.; Kelly, R.; Crow, M.K.; Perl, A.; Budd, R.C.; et al. Reactive Oxygen Species Induce Virus-Independent MAVS Oligomerization in Systemic Lupus Erythematosus. Sci. Signal. 2016, 9. [Google Scholar] [CrossRef] [PubMed]
126. Nobre, L.; Wise, D.; Ron, D.; Volmer, R. Modulation of Innate Immune Signalling by Lipid-Mediated MAVS Transmembrane Domain Oligomerization. PLoS ONE 2015, 10, e0136883. [Google Scholar] [CrossRef]
127. Imanishi, T.; Unno, M.; Kobayashi, W.; Yoneda, N.; Matsuda, S.; Ikeda, K.; Hoshii, T.; Hirao, A.; Miyake, K.; Barber, G.N.; et al. Reciprocal Regulation of STING and TCR Signaling by mTORC1 for T-Cell Activation and Function. Life Sci. Alliance 2019, 2, e201800282. [Google Scholar] [CrossRef] [PubMed]
128. Shum, M.; Bellmann, K.; St-Pierre, P.; Marette, A. Pharmacological Inhibition of S6K1 Increases Glucose Metabolism and Akt Signalling in Vitro and in Diet-Induced Obese Mice. Diabetologia 2016, 59, 592–603. [Google Scholar] [CrossRef] [PubMed]
129. Shum, M.; Houde, V.P.; Bellemare, V.; Junges Moreira, R.; Bellmann, K.; St-Pierre, P.; Viollet, B.; Foretz, M.; Marette, A. Inhibition of Mitochondrial Complex 1 by the S6K1 Inhibitor PF-4708671 Partly Contributes to Its Glucose Metabolic Effects in Muscle and Liver Cells. Journal of Biological Chemistry 2019, 294, 12250–12260. [Google Scholar] [CrossRef] [PubMed]
130. Wang, F.; Alain, T.; Szretter, K.J.; Stephenson, K.; Pol, J.G.; Atherton, M.J.; Hoang, H.-D.; Fonseca, B.D.; Zakaria, C.; Chen, L.; et al. S6K-STING Interaction Regulates Cytosolic DNA–Mediated Activation of the Transcription Factor IRF3. Nat Immunol 2016, 17, 514–522. [Google Scholar] [CrossRef] [PubMed]
131. Cao, W.; Manicassamy, S.; Tang, H.; Kasturi, S.P.; Pirani, A.; Murthy, N.; Pulendran, B. Toll-like Receptor–Mediated Induction of Type I Interferon in Plasmacytoid Dendritic Cells Requires the Rapamycin-Sensitive PI(3)K-mTOR-p70S6K Pathway. Nat Immunol 2008, 9, 1157–1164. [Google Scholar] [CrossRef]
132. Schoggins, J.W. Interferon-Stimulated Genes: What Do They All Do? Annu. Rev. Virol. 2019, 6, 567–584. [Google Scholar] [CrossRef]
133. Schoggins, J.W.; MacDuff, D.A.; Imanaka, N.; Gainey, M.D.; Shrestha, B.; Eitson, J.L.; Mar, K.B.; Richardson, R.B.; Ratushny, A.V.; Litvak, V.; et al. Pan-Viral Specificity of IFN-Induced Genes Reveals New Roles for cGAS in Innate Immunity. Nature 2014, 505, 691–695. [Google Scholar] [CrossRef]
134. Kane, M.; Yadav, S.S.; Bitzegeio, J.; Kutluay, S.B.; Zang, T.; Wilson, S.J.; Schoggins, J.W.; Rice, C.M.; Yamashita, M.; Hatziioannou, T.; et al. MX2 Is an Interferon-Induced Inhibitor of HIV-1 Infection. Nature 2013, 502, 563–566. [Google Scholar] [CrossRef] [PubMed]
135. Kochs, G.; Janzen, C.; Hohenberg, H.; Haller, O. Antivirally Active MxA Protein Sequesters La Crosse Virus Nucleocapsid Protein into Perinuclear Complexes. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 3153–3158. [Google Scholar] [CrossRef] [PubMed]
136. Feeley, E.M.; Sims, J.S.; John, S.P.; Chin, C.R.; Pertel, T.; Chen, L.-M.; Gaiha, G.D.; Ryan, B.J.; Donis, R.O.; Elledge, S.J.; et al. IFITM3 Inhibits Influenza A Virus Infection by Preventing Cytosolic Entry. PLoS Pathog 2011, 7, e1002337. [Google Scholar] [CrossRef] [PubMed]
137. Huang, I.-C.; Bailey, C.C.; Weyer, J.L.; Radoshitzky, S.R.; Becker, M.M.; Chiang, J.J.; Brass, A.L.; Ahmed, A.A.; Chi, X.; Dong, L.; et al. Distinct Patterns of IFITM-Mediated Restriction of Filoviruses, SARS Coronavirus, and Influenza A Virus. PLoS Pathog 2011, 7, e1001258. [Google Scholar] [CrossRef] [PubMed]
138. Wilkins, C.; Woodward, J.; Lau, D.T.-Y.; Barnes, A.; Joyce, M.; McFarlane, N.; McKeating, J.A.; Tyrrell, D.L.; Gale, M. IFITM1 Is a Tight Junction Protein That Inhibits Hepatitis C Virus Entry. Hepatology 2013, 57, 461–469. [Google Scholar] [CrossRef] [PubMed]
139. Wee, Y.S.; Roundy, K.M.; Weis, J.J.; Weis, J.H. Interferon-Inducible Transmembrane Proteins of the Innate Immune Response Act as Membrane Organizers by Influencing Clathrin and v-ATPase Localization and Function. Innate Immun 2012, 18, 834–845. [Google Scholar] [CrossRef] [PubMed]
140. Li, C.; Deng, Y.-Q.; Wang, S.; Ma, F.; Aliyari, R.; Huang, X.-Y.; Zhang, N.-N.; Watanabe, M.; Dong, H.-L.; Liu, P.; et al. 25-Hydroxycholesterol Protects Host against Zika Virus Infection and Its Associated Microcephaly in a Mouse Model. Immunity 2017, 46, 446–456. [Google Scholar] [CrossRef]
141. Bordier, B.B.; Marion, P.L.; Ohashi, K.; Kay, M.A.; Greenberg, H.B.; Casey, J.L.; Glenn, J.S. A Prenylation Inhibitor Prevents Production of Infectious Hepatitis Delta Virus Particles. J Virol 2002, 76, 10465–10472. [Google Scholar] [CrossRef] [PubMed]
142. Bordier, B.B.; Ohkanda, J.; Liu, P.; Lee, S.-Y.; Salazar, F.H.; Marion, P.L.; Ohashi, K.; Meuse, L.; Kay, M.A.; Casey, J.L.; et al. In Vivo Antiviral Efficacy of Prenylation Inhibitors against Hepatitis Delta Virus. J. Clin. Invest. 2003, 112, 407–414. [Google Scholar] [CrossRef]
143. Chang, T.Y.; Limanek, J.S. Regulation of Cytosolic Acetoacetyl Coenzyme A Thiolase, 3-Hydroxy-3-Methylglutaryl Coenzyme A Synthase, 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase, and Mevalonate Kinase by Low Density Lipoprotein and by 25-Hydroxycholesterol in Chinese Hamster Ovary Cells. Journal of Biological Chemistry 1980, 255, 7787–7795. [Google Scholar] [CrossRef]
144. Kandutsch, A.A.; Chen, H.W.; Heiniger, H.-J. Biological Activity of Some Oxygenated Sterols. Science 1978, 201, 498–501. [Google Scholar] [CrossRef] [PubMed]
145. Guo, J. A New Pathway of Translational Regulation Mediated by Eukaryotic Initiation Factor 3. The EMBO Journal 2000, 19, 6891–6899. [Google Scholar] [CrossRef] [PubMed]
146. Hui, D.J.; Bhasker, C.R.; Merrick, W.C.; Sen, G.C. Viral Stress-Inducible Protein P56 Inhibits Translation by Blocking the Interaction of eIF3 with the Ternary Complex eIF2·GTP·Met-tRNAi. Journal of Biological Chemistry 2003, 278, 39477–39482. [Google Scholar] [CrossRef] [PubMed]
147. Pindel, A.; Sadler, A. The Role of Protein Kinase R in the Interferon Response. Journal of Interferon & Cytokine Research 2011, 31, 59–70. [Google Scholar] [CrossRef] [PubMed]
148. Terenzi, F.; Hui, D.J.; Merrick, W.C.; Sen, G.C. Distinct Induction Patterns and Functions of Two Closely Related Interferon-Inducible Human Genes, ISG54 and ISG56. Journal of Biological Chemistry 2006, 281, 34064–34071. [Google Scholar] [CrossRef] [PubMed]
149. Fleith, R.C.; Mears, H.V.; Leong, X.Y.; Sanford, T.J.; Emmott, E.; Graham, S.C.; Mansur, D.S.; Sweeney, T.R. IFIT3 and IFIT2/3 Promote IFIT1-Mediated Translation Inhibition by Enhancing Binding to Non-Self RNA. Nucleic Acids Research 2018, 46, 5269–5285. [Google Scholar] [CrossRef] [PubMed]
150. Kumar, P.; Sweeney, T.R.; Skabkin, M.A.; Skabkina, O.V.; Hellen, C.U.T.; Pestova, T.V. Inhibition of Translation by IFIT Family Members Is Determined by Their Ability to Interact Selectively with the 5′-Terminal Regions of Cap0-, Cap1- and 5′ppp- mRNAs. Nucleic Acids Research 2014, 42, 3228–3245. [Google Scholar] [CrossRef]
151. Durfee, L.A.; Lyon, N.; Seo, K.; Huibregtse, J.M. The ISG15 Conjugation System Broadly Targets Newly Synthesized Proteins: Implications for the Antiviral Function of ISG15. Molecular Cell 2010, 38, 722–732. [Google Scholar] [CrossRef]
152. Okumura, F.; Zou, W.; Zhang, D.-E. ISG15 Modification of the eIF4E Cognate 4EHP Enhances Cap Structure-Binding Activity of 4EHP. Genes Dev. 2007, 21, 255–260. [Google Scholar] [CrossRef]
153. Shi, H.-X.; Yang, K.; Liu, X.; Liu, X.-Y.; Wei, B.; Shan, Y.-F.; Zhu, L.-H.; Wang, C. Positive Regulation of Interferon Regulatory Factor 3 Activation by Herc5 via ISG15 Modification. Molecular and Cellular Biology 2010, 30, 2424–2436. [Google Scholar] [CrossRef]
154. Richardson, R.B.; Ohlson, M.B.; Eitson, J.L.; Kumar, A.; McDougal, M.B.; Boys, I.N.; Mar, K.B.; De La Cruz-Rivera, P.C.; Douglas, C.; Konopka, G.; et al. A CRISPR Screen Identifies IFI6 as an ER-Resident Interferon Effector That Blocks Flavivirus Replication. Nat Microbiol 2018, 3, 1214–1223. [Google Scholar] [CrossRef] [PubMed]
155. Lebeau, G.; El Safadi, D.; Paulo-Ramos, A.; Hoareau, M.; Desprès, P.; Krejbich-Trotot, P.; Chouchou, F.; Roche, M.; Viranaicken, W. The Efficient Antiviral Response of A549 Cells Is Enhanced When Mitochondrial Respiration Is Promoted. Pathogens 2022, 11, 1168. [Google Scholar] [CrossRef] [PubMed]
156. Lebeau, G.; Paulo-Ramos, A.; Hoarau, M.; El Safadi, D.; Meilhac, O.; Krejbich-Trotot, P.; Roche, M.; Viranaicken, W. Metabolic Dependency Shapes Bivalent Antiviral Response in Host Cells: The Role of Glutamine, Immunology. 2023.
157. Reslan, A.; Haddad, J.G.; Desprès, P.; Bascands, J.-L.; Gadea, G. High Glucose Induces in HK2 Kidney Cells an IFN–Dependent ZIKV Antiviral Status Fueled by Viperin. Biomedicines 2022, 10, 1577. [Google Scholar] [CrossRef] [PubMed]
158. Koufaris, C.; Nicolaidou, V. Glutamine Addiction in Virus-Infected Mammalian Cells: A Target of the Innate Immune System? Medical Hypotheses 2021, 153, 110620. [Google Scholar] [CrossRef]
159. Allonso, D.; Andrade, I.S.; Conde, J.N.; Coelho, D.R.; Rocha, D.C.P.; Da Silva, M.L.; Ventura, G.T.; Silva, E.M.; Mohana-Borges, R. Dengue Virus NS1 Protein Modulates Cellular Energy Metabolism by Increasing Glyceraldehyde-3-Phosphate Dehydrogenase Activity. J Virol 2015, 89, 11871–11883. [Google Scholar] [CrossRef] [PubMed]
160. Fontaine, K.A.; Sanchez, E.L.; Camarda, R.; Lagunoff, M. Dengue Virus Induces and Requires Glycolysis for Optimal Replication. J Virol 2015, 89, 2358–2366. [Google Scholar] [CrossRef]
161. Heaton, N.S.; Perera, R.; Berger, K.L.; Khadka, S.; LaCount, D.J.; Kuhn, R.J.; Randall, G. Dengue Virus Nonstructural Protein 3 Redistributes Fatty Acid Synthase to Sites of Viral Replication and Increases Cellular Fatty Acid Synthesis. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 17345–17350. [Google Scholar] [CrossRef] [PubMed]
162. Ledur, P.F.; Karmirian, K.; Pedrosa, C.D.S.G.; Souza, L.R.Q.; Assis-de-Lemos, G.; Martins, T.M.; Ferreira, J.D.C.C.G.; De Azevedo Reis, G.F.; Silva, E.S.; Silva, D.; et al. Zika Virus Infection Leads to Mitochondrial Failure, Oxidative Stress and DNA Damage in Human iPSC-Derived Astrocytes. Sci Rep 2020, 10, 1218. [Google Scholar] [CrossRef] [PubMed]
163. Yau, C.; Low, J.Z.H.; Gan, E.S.; Kwek, S.S.; Cui, L.; Tan, H.C.; Mok, D.Z.L.; Chan, C.Y.Y.; Sessions, O.M.; Watanabe, S.; et al. Dysregulated Metabolism Underpins Zika-Virus-Infection-Associated Impairment in Fetal Development. Cell Reports 2021, 37, 110118. [Google Scholar] [CrossRef]
164. Sahoo, B.R.; Crook, A.A.; Pattnaik, A.; Torres-Gerena, A.D.; Khalimonchuk, O.; Powers, R.; Franco, R.; Pattnaik, A.K. Redox Regulation and Metabolic Dependency of Zika Virus Replication: Inhibition by Nrf2-Antioxidant Response and NAD(H) Antimetabolites. J Virol 2023, 97, e01363–22. [Google Scholar] [CrossRef]
165. Anzinger, J.J.; Butterfield, T.R.; Gouillou, M.; McCune, J.M.; Crowe, S.M.; Palmer, C.S. Glut1 Expression Level on Inflammatory Monocytes Is Associated With Markers of Cardiovascular Disease Risk in HIV-Infected Individuals. JAIDS Journal of Acquired Immune Deficiency Syndromes 2018, 77, e28–e30. [Google Scholar] [CrossRef] [PubMed]
166. Loisel-Meyer, S.; Swainson, L.; Craveiro, M.; Oburoglu, L.; Mongellaz, C.; Costa, C.; Martinez, M.; Cosset, F.-L.; Battini, J.-L.; Herzenberg, L.A.; et al. Glut1-Mediated Glucose Transport Regulates HIV Infection. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 2549–2554. [Google Scholar] [CrossRef] [PubMed]
167. Zhang, J.; Jia, L.; Lin, W.; Yip, Y.L.; Lo, K.W.; Lau, V.M.Y.; Zhu, D.; Tsang, C.M.; Zhou, Y.; Deng, W.; et al. Epstein-Barr Virus-Encoded Latent Membrane Protein 1 Upregulates Glucose Transporter 1 Transcription via the mTORC1/NF-κB Signaling Pathways. J Virol 2017, 91, e02168–16. [Google Scholar] [CrossRef] [PubMed]
168. Meade, N.; Furey, C.; Li, H.; Verma, R.; Chai, Q.; Rollins, M.G.; DiGiuseppe, S.; Naghavi, M.H.; Walsh, D. Poxviruses Evade Cytosolic Sensing through Disruption of an mTORC1-mTORC2 Regulatory Circuit. Cell 2018, 174, 1143–1157.e17. [Google Scholar] [CrossRef] [PubMed]
169. Liang, Q.; Luo, Z.; Zeng, J.; Chen, W.; Foo, S.-S.; Lee, S.-A.; Ge, J.; Wang, S.; Goldman, S.A.; Zlokovic, B.V.; et al. Zika Virus NS4A and NS4B Proteins Deregulate Akt-mTOR Signaling in Human Fetal Neural Stem Cells to Inhibit Neurogenesis and Induce Autophagy. Cell Stem Cell 2016, 19, 663–671. [Google Scholar] [CrossRef] [PubMed]
170. Angin, M.; Volant, S.; Passaes, C.; Lecuroux, C.; Monceaux, V.; Dillies, M.-A.; Valle-Casuso, J.C.; Pancino, G.; Vaslin, B.; Le Grand, R.; et al. Metabolic Plasticity of HIV-Specific CD8+ T Cells Is Associated with Enhanced Antiviral Potential and Natural Control of HIV-1 Infection. Nat Metab 2019, 1, 704–716. [Google Scholar] [CrossRef] [PubMed]
171. Chatel-Chaix, L.; Cortese, M.; Romero-Brey, I.; Bender, S.; Neufeldt, C.J.; Fischl, W.; Scaturro, P.; Schieber, N.; Schwab, Y.; Fischer, B.; et al. Dengue Virus Perturbs Mitochondrial Morphodynamics to Dampen Innate Immune Responses. Cell Host & Microbe 2016, 20, 342–356. [Google Scholar] [CrossRef]
172. Van Valen, L. A New Evolutionary Law. EVOL. THEORY 1973, 1, 1–30. [Google Scholar]