Pattern Recognition Receptor-Mediated Regulatory T Cell Functions in Diseases

Abstract

The advent of new technologies in gene expression, immunology, molecular biology, and computational modeling studies has expedited the discovery process and provided us with a holistic view of host immune responses that are highly regulated. The regulatory mechanisms of the immune system lie not only in weakening the attacker directly but also in fortifying the defender for the development of an efficient adaptive immune response. This chapter reviews a comprehensive set of experimental and bioinformatic studies designed to deepen the current knowledge on the regulatory T cells (Tregs) in the context of Pattern Recognition Receptors (PRRs). Initially, we examined both membrane-bound Toll-like Receptors (TLRs) and C Type Lectin Receptors (CLRs); and cytosolic NOD-like Receptors (NLRs) and RIG-I like Receptors (RLRs) in Tregs. Then, we revisited the disease conditions associated with regulatory T cells by emphasizing the essential roles of PRRs. Expanding our knowledge and strategies on the regulatory mechanisms are likely to provide our best chances for long-term disease control and maintenance of homeostasis.

1. Introduction

Regulatory T cells (Tregs) are a subtype of T cells that are responsible for the maintenance of homeostasis and tolerance to self-molecules. They mediate their action by suppressing the T cell proliferation, and cytokine productions; thereby preventing autoimmunity [1]. In this sense, Tregs can be both helpful to the host by alleviating the immunopathology, and immune system related tissue damages and unfortunately can also be harmful to the host by sabotaging the properly induced immune responses against pathogens [2]. Therefore, harnessing Treg mechanisms could be an efficient therapeutic approach to treat some distinct diseases, including infectious diseases, asthma and allergies, and cancer [3].

It is established that immune cells rely on the germ-line encoded pattern recognition receptors (PRR) that recognize common structural motifs shared by pathogens called pathogen-associated molecular patterns (PAMPs), and also recognize cellular stress and death via molecules known as damage-associated molecular patterns (DAMPs) to initiate inflammation and activate tissue repair mechanisms [4]. As much as the efficacy of PRRs in executing an immune response is critical for the host, it can also be the reason for unintended responses. Fortunately, understanding of how PRRs drive these responses has expanded enormously in the last few decades. In this chapter, we compile the available data using the open-source databases and the current knowledge in an attempt to discern the layers of complex mechanisms from a regulatory T cell standpoint.

2. The expression profiles of Pattern Recognition Receptors (PRRs) in regulatory T cells (Tregs)

2.1 Membrane-bound Toll-Like Receptors (TLRs)

Toll-like receptors (TLRs) have critical roles in the initial defense of innate and adaptive immunity [5]. TLRs which are type I integral membrane receptors have three domains: The N-terminal domain (NTD), which is located either on the outside of the cell membrane or in endosomes, a single helix transmembrane domain that is in the center, and the C-terminal domain (CTD), which is located in the cytoplasm. The N-terminal ectodomains contain a conserved 19–25 tandem leucine-rich repeat (LRR) region leading to the recognition of PAMPs and DAMPs. NTD also contains glycan moieties to bind ligands from different pathogens. On the other hand, the CTD contains the toll-IL-1 receptor (TIR) homologous domain, which enables the interaction with downstream adaptor proteins for signal transduction and thus, activation of the signaling pathway [6, 7, 8]. To date, 13 members of the mammalian TLRs have been identified. 10 members of this receptor family are expressed in humans (TLR1–10), while 12 members are expressed in mice (TLR1–9, TLR11–13). Each TLR can recognize different PAMPs from various pathogens. TLRs divided into two classes according to their localization: cell surface and intracellular [4, 5]. TLR1/TLR2 (triacyl lipopeptides), TLR4 (lipopolysaccharide), TLR5 (flagellin), TLR2/TLR6 (lipoproteins), TLR10 (bacterial 23S rRNA) are expressed in the cell membrane and TLR3 (dsRNA), TLR7/TLR8 (ssRNA), TLR9 (unmethylated CpG DNA), TLR11 (flagellin or profilin-like molecule from T. gondii), TLR12 (profilin from T. gondii), and TLR13 (bacterial 23S rRNA) are expressed in endosomal membranes [4]. TLRs are important for the proper functioning of Tregs because they directly mediate the pathogen sensing. Tregs have higher expression levels of TLR4, TLR5, TLR7, and TLR8 as compared to the effector T cells in humans; however, especially TLR2, TLR4, TLR5, and TLR8 activation has different effects on the differentiation, expansion, and proliferation of Tregs [9]. Although their numbers increased, Tregs lost their suppressive function when treated with PAM3Cys to activate TLR2 signaling, along with T cell receptor (TCR) and interleukin 2 (IL2) stimulation in order for Treg differentiation and function [10, 11]. In this study, the immune response was suppressed neither in vitro nor in vivo in mice who underwent acute infection, as Tregs were not activated by the induction of the TLR2 signaling pathway [9]. However, when the TLR2 ligand was removed, Tregs’ suppressive functions were recovered [11]. MyD88 is an adaptor protein located downstream of TLR2 signaling. Additionally, the effects of TLR2-MyD88 signaling pathway were examined in Tregs isolated from MyD88 deficient mice, and a reduction in suppressive functions of Tregs in the absence of MyD88 was reported [5, 9]. Suppressive functions of Tregs were induced by cell-contact mechanisms and secretion of TGFβ and IL10, which are immunosuppressive cytokines, without an increase in the number of Tregs [11].

Unlike TLR2, LPS indued TLR4 enhanced the suppression effect owing to the increase in FOXP3 expression in both human and murine Tregs. In fact, LPS not only increased the number of Tregs, but also increased the expression of activation markers in cells [12]. As for TLR5, the suppression capacity of CD4 + CD25+ Tregs increased as a result of TLR5 activation by flagellin [13]. TLR8, on the other hand, has been shown to abundantly express in Tregs and upon stimulation with TLR8 ligand, the suppressive function of Tregs was abolished but it had no effect on the Tregs proliferation [14]. Lastly, studies have revealed that TLR9 ligand CpG oligodeoxynucleotide induced proliferation of both effector T cells (Teff) and Tregs and partly inhibits the suppressive activity of regulatory T cells in rats. This combined effect of TLR9 ligand is likely to reinforce the adaptive immunity by not only expanding effector cells but also by mitigating the suppressive activity of regulatory T cells [15]. Taken together, multiple studies suggest that the suppressive properties of regulatory T cells with respect to their proliferation and cytokine production capacities may differ depending on the induction and the differential expression of different TLRs in regulatory T cells (Figure 1).

Figure 1.

TLR expression profile in naive Tregs and memory Tregs: Expression of TLRs in A: naive (CD3+, CD4+, CD25 high, CD45RA+, CD127low); B: memory (CD3+, CD4+, CD25 high, CD45RA-, CD127low) regulatory T cells. C: Differentially expressed genes in between two Treg populations. Expression of genes reported as TPM was normalized to β-actin (TPMgene/TPMβ-actin) for individual representation of naive and memory Tregs. Results are depicted as bar plots in GraphPad Prism.

By examining the open-source databases of immune cell-specific gene expression profiles, we evaluated the results from published literature for TLRs in Tregs and compared their expressions among all available immune cell types. TLRs did not display a Treg specific high expression across the datasets we examined [16, 17, 18]. The DICE database generated by Schmiedel et al contains RNA-seq data of 13 immune cells including naive and memory Tregs (and two ex vivo activated cell types) collected from peripheral blood mononuclear cell fraction of 91 healthy human donors. We used this dataset for a detailed search in Tregs. Expression patterns of TLRs in Tregs are shown as boxplots (Figure 1A and B). Although TLRs had relatively low expression profiles, TLR1, TLR2, TLR5, and TLR6 are the ones with the most prominent representation and differential expression in Tregs (Figure 1C and Table 1). Interestingly, TLR1, TLR2, and TLR5 had higher expression in naive Tregs than memory Tregs, whereas TLR6 expression was higher in memory Tregs than naïve Tregs. Additionally, this dataset presents the sex-biased transcripts for immune cell types. For example, TLR1 in naive Tregs were revealed as one of genes having female bias [16].

	Biotype	Naive treg mean expression (TPM)	Memory treg mean expression (TPM)	Log 2 fold change	Adjusted p-value
TLR1	Protein coding	7.18	6.1	0.16	0.017
TLR2	Protein coding	14.71	2.27	2.66	6.90E−233
TLR5	Protein coding	15.46	6.59	1.12	6.90E−35
TLR6	Protein coding	1.08	6.31	−2.78	6.50E−251

Table 1.

Differentially expressed toll-like receptor family members in naïve and memory tregs, p-values (https://dice-database.org/).

2.2 Membrane-bound C-type lectin (CTLs) receptors in tregs

C-type lectin receptors (CLRs) also belong to the family of pattern recognition receptors (PRRs) which recognize PAMPs and induce innate immune responses [19]. CLRs comprise a variety of receptors including selectins, collectins, proteoglycans, and lymphocyte lectins. This receptor family possesses at least one structurally homologous carbohydrate recognition domain (CRD), also known as C-type lectin-like domain (CTLD), that determines the carbohydrate specificity [20]. Based on the protein location site on the cell membrane, CLRs are categorized as transmembrane receptors and secretory receptors [21]. Upon ligand recognition by CLRs, most of them are able to induce intracellular pathways and caspase-recruitment domain-containing domain protein 9 (CARD9) pathway, which are vital, and their dysregulation or malfunctioning may result in critical infections in humans and mice [22, 23].

CLRs are expressed on antigen presenting cells (APCs), such as dendritic cells (DCs) and macrophages, and play essential roles in antigen uptake and presentation. In this regard, they are divided mainly into two subgroups: type I and type II CLRs. Two subsets of transmembrane CLRs can be classified based on their CRDs; type I and II. Type I CLRs are mannose receptor family (MR) and DEC-205, whereas type II CLRs are sialoglycoprotein receptor family, DC-associated C-type lectin 1 (dectin-1) and macrophage galactose C type lectin (MGL) [24]. MGLs are able to recognize particularly terminal α and β N-acetylglactoseamine (GalNAc or Tn) residues from filovirus, helminths, bacteria, and tumor-associated antigens in humans [25]. Human counterparts of MGL in mice are MGL1 and MGL2, which are expressed on DCs [26] and activated macrophages [27]. The potency of human MGL was shown by Napoletano et al. as an adjuvant for designing novel anticancer vaccines because MGL engagement led to the increased antigen presenting potential in DCs and enhancement in antigen-specific CD8⁺ cell activation [28]. Dectin-1 is another type II C-type lectin receptor, which is involved in the antifungal immunity by recognizing β-1,3-glucans in the cell wall of several pathogenic fungi. Dectin-1 is able to induce several responses including phagocytosis, through spleen tyrosine kinase (Syk)/CARD9 pathway, which results in the cytokine production [29, 30]. Besides innate immunity, dectin-1 is also capable of triggering adaptive immune responses. For instance, curdlan activated dectin-1 in DCs, skewed the T cell polarization into Th17 and Th1 CD4⁺ T cell subsets in mice in vitro [31]. Finally, C-type lectin receptor CD69 have been shown to control T cell development and homeostasis in mice along with miR155 as both CD69 and miRNA155 were simultaneously regulated to ensure a balanced immunity [32].

Due to lack of studies focusing on CLRs in Treg populations, we used the DICE database to evaluate the expression profiles of CLRs in naive and memory Tregs collected from PBMC fractions of healthy donors. As described in Figure 2A and B, among all CLRs with low expressions, CLEC4A (DICR) is the one with higher representation in both cell types. Even though CLEC7A (Dectin 1) had low expression in both cell types, it was the only differentially expressed gene (Figure 2C and Table 2). Taken together, CLRs did not have a notable expression profile in Tregs, thus our analysis is in agreement with previously reported data [16, 17, 18].

Figure 2.

C type lectin expression in naive Tregs and memory Tregs: Expression of CLRs in A: naive (CD3+, CD4+, CD25 high, CD45RA+, CD127low); B: memory (CD3+, CD4+, CD25 high, CD45RA-, CD127low) regulatory T cells. C: Expression of genes reported as TPM was normalized to β-actin (TPMgene/TPMβ-actin) for individual representation of naive and memory Tregs. Results are depicted as bar plots in GraphPad Prism.

	Biotype	Naive treg mean expression (TPM)	Memory treg mean expression (TPM)	Log 2 fold change	Adjusted p-value
CLEC7A	Protein coding	2.05	2.44	−0.42	0.031

Table 2.

Differentially expressed C type lectin receptor family members in naïve and memory tregs, p-values. (https://dice-database.org/).

2.3 Cytosolic NOD-like receptors (NLRs) in regulatory T cells

Nucleotide binding oligomerization domain (NOD)–like receptors (NLRs) are a family of cytoplasmic PRRs that are known to drive the initial innate immune responses. There are 22 NLR members in human and 34 in mice [33], and they are characterized by a C-terminal domain of leucine rich repeats (LRRs) which senses PAMPs and danger molecules (DAMPs); a central NACHT domain that facilitates NLR oligomerization; and an N-terminal signaling domain [34]. The NLR members have been classified in 5 subfamilies based on their N-terminal domain: i) NLRA (CIITA), which contains acidic transactivation domain; ii) NLRB (NAIP) subfamily having an N-terminal baculovirus inhibition of apoptosis repeat (BIR) domain; iii) NLRC subfamily that contains caspase activation and recruitment domain (CARD) and allows direct interaction of NLR family members; iv) NLRP subfamily that bears a pyrin domain (PYD); and v) NLRX subfamily that has a mitochondria-targeting sequence required for its trafficking [34]. Some of the NLR members including NLRP1, NLRP3, NLRP6, NLRP7, NLRP12, and NLRC4 are reported to assemble large multimeric protein complexes called “Inflammasomes” which regulate the activation of caspases-1 [35, 36]. The signaling pathway where the assembled inflammasome activates pro-caspase-1 into its catalytically active form is generally referred to as the canonical inflammasome whose activation requires two steps: transcription and oligomerization. The first step is regulated by innate immune signaling, primarily by TLR signaling, and/or cytokine receptors such as TNF which leads to the production of biologically inactive pro interleukin-1β (IL1β), IL18, and NLR transcription via nuclear factor-κB (NF-κB) activation. The second step leads to inflammasome oligomerization and eventually caspase-1 activation which, in turn, results in IL1β and IL18 processing and secretion [37]. Biologically active IL1β and IL18 promote inflammatory and antimicrobial responses and activate different helper T cell subsets such as Th1 and Th17 cells [38]. Although NLRs activation leads to numerous signaling cascades which subsequently initiate the appropriate immune responses including the regulation of B and T cell functions [39], studies focusing on NLRs especially in Tregs are limited. Hence, utilizing open-source datasets, we evaluated the expression patterns of NLRs in Treg populations. Firstly, to address whether there is a Treg specific NLR expression, we compared 28 and 29 cell types studied by Ota et al., and Monaco et al., respectively and showed that there is no NLR specifically expressed in Tregs [17, 18]. Based on the current database, NLRC5 and NLRP1 appear to have higher expression levels in memory Tregs among 13 immune cells included by the DICE database. Next, we examined the data obtained from this database for NLR expression by focusing on Tregs separately (Figure 3). As detailed in Figure 3A and B, most NLR family members have low expressions in both I and memory Treg populations. NLRC3, NLRC5, and NLRP1 have higher expression levels than the rest of the NLRs in both cells. Interestingly, Schmiedel et al. listed NLRP2 transcripts as sex-biased (toward females) in both Treg populations [16]. Several NLRs are detected to be differentially expressed naive and memory Tregs are compared with one another (Figure 3C and Table 3). CIITA, NOD2, NLRC5 expressions were significantlyIr in naive Tregs, while NLRP6 has a relatively higher differential expression profile than the remaining NLRs in memory Tregs. Critical roles, if any, of NLRs’ expressional diversity within and in between Treg populations might need further investigation.

Figure 3.

NLR expression profile in naive Tregs and memory Tregs: Expression of NLRs in A: naive (CD3+, CD4+, CD25 high, CD45RA+, CD127low); B: memory (CD3+, CD4+, CD25 high, CD45RA-, CD127low) regulatory T cells. C: Expression of genes reported as TPM was normalized to β-actin (TPMgene/TPMβ-actin) for individual representation of naive and memory Tregs. Results are depicted as bar plots in GraphPad Prism.

	Biotype	Naive TREG mean expression (TPM)	Memory treg mean expression (TPM)	Log 2 fold change	Adjusted p-value
CIITA	Protein coding	1.6	16.65	−3.89	8.50E−262
NAIP	Protein coding	20.72	13.32	0.62	1.10E−09
NOD1	Protein coding	16.61	26.42	−0.79	2.90E−116
NOD2	Protein coding	3.1	11.2	−2.1	5.90E−169
NLRC3	Protein coding	168	237.03	−0.61	7.40E−50
NLRC4	Protein coding	1.39	0.96	0.5	4.40E−09
NLRC5	Protein coding	222.01	418.41	−1.03	1.50E−81
NLRP1	Protein coding	272.66	376.61	−0.57	6.90E−54
NLRP2	Protein coding	8.35	14.42	−0.88	7.40E−08
NLRP3	Protein coding	1.33	1.81	−0.59	7.00E−08
NLRP6	Protein coding	3.59	1.33	1.21	5.90E−17
NLRX1	Protein coding	6.78	9.8	−0.65	2.20E−19

Table 3.

Differentially expressed NOD like receptor family members in naïve and memory tregs, p-values (https://dice-database.org/).

Apart from these, studies concentrating on NLRP3 and NOD2 roles in directing (Treg) differentiation and function demonstrated that NLRP3 negatively regulates Treg differentiation in an inflammasome-independent manner via translocation to the nucleus and subsequently interacting with Kpna2 [40]. Of note, immunoprotective roles were reported for NLRP3 inflammasome in controlling the Th1/Th17 immunity against fungal infection of pulmonary paracoccidioidomycosis by suppressing the expansion and migration of Tregs in mice [41]. In addition to NLRP3, NOD2 has been shown to get activated by muramyl dipeptide (MDP), resulting in NF-κB translocation to nucleus in primary human FOXP3+ T cells thereby protecting from death receptor Fas-mediated apoptosis [42]. Finally, MDP-stimulated migration of Tregs has been shown to suppress the Th17 cells in the lungs of influenza A virus-infected mice [43]. Together, these results suggest that the control of inflammation during fungal and viral infections is mediated by Tregs, with the contribution of NLRs.

2.4 Cytosolic retinoic acid-inducible Gene (RIG-i) like receptor

Type I interferons are proven to be indispensable during viral infections for their ability to generate an antiviral state. Although they are expressed at low levels, they are induced during the course of an infection which is detected by the presence of the foreign nucleic acids [44]. One example for such sensors is retinoic acid-inducible gene (RIG) I like receptors (RLR) whose activation results in type I interferons [45, 46]. RLRs sense viral RNA in the cytosol. The RLR family comprises three proteins: i) RIG-I; ii) melanoma differentiation-associated antigen 5 (MDA-5); and iii) laboratory of genetics and physiology 2 (LGP2) [47]. All these RLRs share a common structure including a central helicase domain responsible for ATP hydrolysis to unwind RNA and a C-terminal regulatory (CTR) domain adjacent to the helicase core. The CTR domain aids to distinguish the self RNAs from the foreign RNA fragments within the cellular environment. Added to these domains, RIG-I and MDA-5 have N-terminal caspase activation and recruitment domains (CARDs) that are required for downstream signaling through the interaction with CARDs of CARD containing adaptor proteins. Dissimilar to RIG-I and MDA5, LGP2 lacks the CARD domain. Instead, LGP2 is of importance to regulate the RIG-I and MDA5 directed antiviral responses [48, 49, 50].

To investigate the RLR expressions in Treg cells, we used the expression data from the DICE database (Figure 4). Similar to other PRRs, we did not observe a Treg specific expression of RLRs. As depicted in Figure 4A and B, RLRs have similar expression patterns among their members, DDX58 (RIG1) having a relatively higher expression trend as compared to IFIH1 (MDA5) andI8 in both naive and memory Tregs. Next, we listed the differentially expressed genes (Figure 4C and Table 4). Interestingly, Schmiedel et al. identified IFIH1 (MDA5) transcripts to have female-biased sex ratios. Data analysis indicates that all RLRs are expressed in Tregs from healthy donors [16].

Figure 4.

RIG-I expression profile in naive Tregs and memory Tregs: Expression of RLRs in A: naive (CD3+, CD4+, CD25 high, CD45RA+, CD127low); B: memory (CD3+, CD4+, CD25 high, CD45RA-, CD127low) regulatory T cells. C: Expression of genes reported as TPM was normalized to β-actin (TPMgene/TPMβ-actin) for individual representation of naive and memory Tregs. Results are depicted as bar plots in GraphPad Prism.

	Biotype	Naive treg mean expression (TPM)	Memory treg mean expression (TPM)	Log 2 fold change	Adjusted p-value
DDX58	Protein coding	65.92	58.75	0.09	0.026
IFIH1	Protein coding	29.18	29.88	−0.13	0.0012

Table 4.

Expression levels of mRNA for each RIG-I like Receptor family member naïve and memory Tregs, p-values (https://dice-database.org/).

RLRs, MDA-5, and RIG-I are ubiquitously expressed in the cytoplasm of immune cells including Tregs. Although exhaustion of Tregs following bacterial ligand treatments has been demonstrated [14, 51, 52], the impact of viral infection on Treg derived suppression remained elusive. A study by Anz et al. suggested a direct suppression mechanism through the activation of RLRs. In this particular study, regulatory and effector T cells from wild-type and MDA-5 deficient mice were cocultured and infected with encephalomyocarditis virus. Results suggested that MDA-5 deficient Tregs lost their ability to suppress the immune responses when compared with wild type counterparts during the viral infection [53].

Because IFN-beta promoter stimulator (IPS-1) is the main adaptor protein of RLR signaling [54], its influence on the RLR signaling during West Nile Virus (WNV) infection were studied with respect to Tregs using IPS-1 deficient mice. Conceivably, uncontrolled inflammatory responses including the more pronounced immune cell responses and failure in virus neutralization were identified with the lack of Tregs expansion which is a characteristic of WNV infection [55]. Moreover, Xu et al. showed the intrinsic suppression ability of RNA stimulated RIG-I in Treg differentiation in a IFN regulatory transcription factor (IRF)-3 dependent manner [56]. Another study investigated the role of RNA-unprimed RIG-I (apo RIG-I) in Treg differentiation. In contrast to the suppression of Treg differentiation by RNA ligand primed RIG-I, this study showed that apo-RIG-I maintains the Treg/Th17 cell balance [57]. Taken together, ever expanding novel findings from large datasets and state-of-the-art approaches may change the way we evaluate PRRs of Tregs in most bewildering ways.

3. Functionality of regulatory T cells in disease in the context of PRRs

3.1 Regulatory T cells in infections

Immune system as a whole represents a quite complex and interacting vast network of cells and biochemical signals circulating in blood and tissues. Therefore, this complexity necessitates a tight regulation. Tregs maintain the homeostasis by suppressing the immune response after the infection is resolved. As discussed earlier, Tregs can be activated by a variety of pathogens and their suppressive functions may differ depending on the pathogen and the progression of the infection. Not only pathogens, but also non-pathogenic environments with endogenous proteins are essential in regulating Treg responses. Heat shock protein gp96 is a chaperone for several TLRs including TLR4 and acts as a ligand as well [58, 59]. Tregs suppress the T cell proliferation and cytokine release to protect the host against excessive immune response. There are few aspects still being investigated, especially which receptors are expressed in Tregs and regulate Tregs during viral, bacterial, and fungal infections [60]. In this part of the chapter, we will continue to discuss the Tregs in terms of infectious and non-infectious disease conditions.

3.1.1 Regulatory T cells in bacterial infections

Helicobacter pylori is a Gram-negative bacterium that infects the stomach and is a highly contagious pathogen [61]. Currently, studies covering PRRs in the context of Tregs are rather sparse when it comes to bacterial infections. However, TLR4 inhibition following H. pylori has been shown to increase the Foxp3 expression and the CD4⁺CD25⁺Foxp3⁺ Treg cell numbers in the gastric mucosa and enhance the bacterial colonization. However, blocking Tregs led to the limited colonization of H. pylori, resulting in the reduced inflammatory responses. This study indicated that the crosstalk between TLR4 signaling pathway and Tregs is crucial for limiting H. pylori colonization and suppressing the inflammation of infected mice [62].

3.1.2 Regulatory T cells in fungus infections

Paracoccidioidomycosis (PCM) is an endemic disease caused by the fungus Paracoccidioides brasiliensis [63]. In PCM disease, regulation of Treg functions is mediated by PRRs such as TLRs, CLRs, and NLRs and downstream proteins like MyD88 [41, 64, 65, 66, 67]. In one study, TLR2-deficient mice had a reduced number of Tregs along with an excessive immune response, suggesting that TLR2 is required for Treg expansion to control the inflammatory response [67]. The study utilized MyD88-deficient mice to further analyze the effect of the downstream effectors of TLR2 signaling pathway in Tregs. The absence of MyD88 resulted in the impaired T cell responses and uncontrolled spread of fungal infection in the murine model of PCM infection [65]. Another study showed that Treg proliferation is decreased in WT mice compared to TLR4 deficient mice, therefore, the level of infiltration of activated T cells and macrophages into the lung increased, resulting in severe infection [64]. Dectin-1 is a CTL receptor involved in the antifungal immune response [68]. In Dectin-1-deficient mice with P. brasiliensis, low levels of activated effector/memory CD4+ and CD8+ T cells along with increased CD4 + CD25 + Foxp3+ Treg levels let the infection spread to tissues, eventually controlling the severity of the disease and causing the increased mortality in mice [66]. Besides the membrane bound Dectin-1 receptor, NLRP3 inflammasome complex mediated the activation and secretion of IL1β and IL18 have been reported in P. brasiliensis infected mice. NLRP3 deficient mice infected with P. brasiliensis had an increased Th1/Th17 immune response and reduced Treg response [41].

During candidiasis, a fungal infection mediated by Candida albicans, TLR2 is of great importance to control Treg survival. In this study, Netea et al. reported that TLR2-deficient mice, but not TLR4, had increased Th1 immune response and a reduced IL10 producing CD4 + CD25+ Treg population as compared to the WT mice infected with C. albicans. Tregs were further stimulated with a TLR2 ligand, peptidoglycan, to assess the significance of TLR2 signaling. Findings from this study highlights the importance of TLR2 signaling in the maintenance of survival, expansion and suppression capacity of Treg and IL10 production in candidiasis [69].

3.1.3 Regulatory T cells in viral infections

One of the immune system mechanisms that are used to protect the host from viral infections is the recognition of viral nucleic acids by PRRs such as TLRs and RLRs [70]. The human genome encodes 10 different TLRs, four of which are responsible for the recognition of viral genome, and these are TLR3, TLR7, TLR8, and TLR9. Interestingly, unlike other TLRs located on the outer cell membrane, they are located in the endosomal membranes and induce downstream molecules through adaptor proteins [4]. RLRs sense viral RNAs in the cytosol. Among RLRs, RIG-I, and MDA5 have RNA helicase activity which give them the ability to bind viral RNA and induce immune response [46]. It is well established in the literature that innate immune cells are activated through these PRRs as the first line of host defense against viral attacks. Activated innate immune cells then phagocytose and process the virus to present it to naive T cells in the draining lymph nodes. Primed T cells eventually differentiate into different types of helper T cells including Tregs [60].

TLR2 and TLR4 have been the focus of numerous studies which emphasized the effects on regulatory T cells in the course of viral infections. During hepatitis C virus infection, Tregs were suggested to suppress the HCV-specific antiviral responses resulting in viral persistence [71, 72]. In a different study by Zhai et al., the core protein of hepatitis C virus (HCVc) in the blood of HCV-infected patients has been shown to induce the proliferation of Tregs which subsequently hampered the CD4+ T cell proliferation and IFN-γ production. Since HCVc binds TLR2 on Kupffer cells and dendritic cells, just like the TLR2 agonist lipoteichoic acid, which leading to the similar processes, they proposed that HCVc mediated Treg expansion was TLR2 dependent [73]. In a separate study, the number of Tregs, TLR2 and TLR4 expression levels in the peripheral blood monocytes of chronic hepatitis C patients have been shown to elevate in parallel to viral load [74]. To this end, literature on TLRs seem to report consistent results with respect to Treg regulation.

Additionally, cytosolic RLRs have roles during viral infections. Amphiregulin, known as EGFR ligand, is produced mainly by Tregs in lungs during influenza A virus infection and it is important for tissue protection [75, 76, 77]. Interestingly, EGFR signaling has been suggested to suppress RIG-I signaling during viral infections [78, 79]. Thus, amphiregulin produced by regulatory T cells may reduce RIG-I signaling to increase survival during viral infections.

3.2 Regulatory T cells in autoimmune diseases

Autoimmunity can be defined as immunologic aberrations which exclusively exhibit abnormal self-antigen tolerance. PRRs can govern autoimmunity by playing pivotal roles in distinct immunological mechanisms [80, 81]. Autoimmune diseases have been associated with viral, bacterial and, more recently, fungal infections after detection by PRRs because of the reduced number of Treg cells and increased proinflammatory cytokines, such as IL17, IL22 and IL23, which drive the differentiation into CD4 Th17 T cells [82].

As we discussed previously in this chapter (Figure 1), TLRs are expressed and have functions in adaptive immune cells such as TCR alpha beta cells, TCR gamma beta T cells and regulatory T cells [83]. LPS induced TLR4 in CD4⁺CD25⁺ T cells have been shown to lead to activation and proliferation of Treg cells [12]. Although controversial, other TLRs including TLR5, TLR7, and TLR8 have been shown to express in human and murine CD4⁺CD45⁺ Tregs [11]. With regards to autoimmunity, using a cohort of MS patients who were helminth-infected or non-infected, Correale and Farez investigated the roles of retinoic acid (RA) and TLR2 in parasite mediated protection in MS patients. Helminth-activated DCs not only inhibited IL-17 and IFN-γ production via autoreactive T cells but also led to the immunoprotection which was attributed to the involvement of TLR2 and RA and the augmentation of CD4⁺CD25⁺FOXP3⁺ Treg cells [84].

Multiple sclerosis (MS) is a central nervous system autoimmune disease [85] which is characterized by demyelination [86]. When healthy individuals were compared to MS patients, it was found that Tregs of the healthy group displayed higher TLR2 expression. Furthermore, the PBMC samples from these two separate groups were stimulated with an agonist of TLR1/2, Pam3Cys, which lowered Treg functions and induced Th17 in MS groups samples [87]. Another example of autoimmune disease is type 1 diabetes mellitus (T1DM) that is associated with pancreatic β cell deficiency which results in abnormal sugar level [88]. High mobility-group box (HMGB) proteins have a role to induce the innate immunity by interacting with nucleic acids and recruiting them to PRRs and they engage receptors for advanced glycation end products (RAGE) [89]. Wild et al. showed that HMGB1 enhanced IL-10 levels and prolonged survival of Treg cells [90]. Furthermore, the inhibition of HMGB1 during beta cell mass turnover at an early stage in NOD mice is followed by reduced incidence of diabetes. Additionally, TLR4 and RAGE were shown to be predominant HMGB1 receptors in Treg cells and blockade of either one diminished Treg instability whilst stimulation with recombinant HMGB1 remarkably increased the amount of phosphorylated downstream targets including PI3K, Akt and mTOR in Tregs [91]. Overall, PRRs and Tregs axis in certain immune conditions has been pending to be investigated more elaborately.

3.3 Regulatory T cells in asthma and allergy

Persistent inflammation with the excessive production of cytokines by the immune cells can be harmful which is associated with numerous diseases including asthma and allergy [92]. Asthma is an inflammatory disease of airways which is linked to excessive T helper cell type-2 (Th2) immunity. Both allergic and non-allergic stimuli including house dust mites (HDM), pollens, viral infections and tobacco smoke trigger a cascade of events resulting in chronic airway inflammation which then leads to the airway hyperresponsiveness (AHR) [93]. Th2 cells in the airway release specific cytokines including IL4, IL5, IL9, and IL13; thereby promoting eosinophilic inflammation and immunoglobulin E (IgE) production which in turn, triggers the release of other inflammatory mediators, such as leukotrienes and histamines [94]. One of the hallmarks of asthma pathogenesis is the enhanced Th2 response and the inadequate differentiation and functional defects of Tregs. Baatjes et al. has reported that CD4⁺CD25^highFoxp3⁺ Tregs were lower in the peripheral blood of the asthma patients than non-asthmatic individuals [95]. In vivo animal model studies have shown that IL10 and TGFβ secreted by Treg remarkably suppressed the airway inflammation and AHR [96] while blocking IL10 and TGFβ worsened the airway inflammation and AHR [97].

Several studies also revealed the involvement of PRRs, especially TLRs and NLRs in asthma susceptibility. Simpson et al. firstly discovered the upregulation of TLR2, TLR4, and pro-inflammatory cytokines, IL1β and IL18, in neutrophilic asthma [98]. Another study reported that TLR2 activation induces Treg and long-term suppression of asthma symptoms in OVA-sensitized mice [99]. Moreover, the important roles of TLR7 in alleviation of airway inflammation, promoting Th1 immune responses and reversing AHR have been shown [100]. Meng et al. reported that TLR7 stimulation suppressed eosinophilic inflammation by reducing Th2 cytokines IL4 and IL5, eotaxin and IgE in numerous animal models of asthma [101]. Yet another study added that treatment with TLR7 agonist R848 induced Treg cell-mediated suppression of asthma symptoms in OVA-sensitized and challenged mice [102].

Although limited, the involvement of inflammasome activation in asthmatic airway inflammations has been studied as well. The prolonged administration of IL1β, an inflammasome dependent cytokine, has been shown to induce AHR [103]. Also, increased levels of IL1β in the serum and BALF of asthmatic patients were decreased after glucocorticoids inhalation [104]. Significantly higher inflammasome depent IL18 levels in the serum of asthma patients were detected [105]. Moreover, Simpson et al. reported the elevated expression of the NLRP3 inflammasome in patients with neutrophilic asthma [106]. Another recent study using HDM-induced mouse models of allergic airway inflammation reported elevated expression of NLRP3, NLRC4, NLRC5, and caspase-1 genes as well as pro- IL1β levels in the lungs, while mature IL1β was not observed which suggested that inflammasome components are upregulated even if they do not form functional inflammasome complexes [107]. Unfortunately, studies as to the effects of inflammasomes on Treg functions in allergy and asthma conditions are limited. One study demonstrated that intranasal stimulation with NOD2 ligand disrupted the generation of Tregs and subsequently induced the development of eosinophil-associated airway inflammation [108]. Altogether, targeting NLRs and inflammasome components can be another potential therapeutic approach for the control of airway inflammation.

3.4 Regulatory T cells in cancer

Countless pathological conditions involve infections and tissue damage leading to chronic inflammation after the activation of PRRs. Innate immunity and PRRs in cancer initiation and progression are extensively studied because PRRs are expressed in different tumor tissues, such as lung, breast, colon, gastric cancer, and melanoma [21, 109]. The PRR activation in cancer cells can stimulate the production of many cytokines, chemokines, hormones, and vascular-promoting factors to induce the formation of an inflammatory tumor microenvironment that promotes the tumor progression [110]. The activation of PRRs on antigen presenting immune cells can induce dendritic cells, tumor-associated macrophages, and B cells for the generation of tumor-specific T cell responses. Tregs are found in tumor microenvironment and are able to suppress anti-tumor immune responses which is required for escaping immune system thereby cancer progression.

Signaling through PRRs results in robust pro-inflammatory responses by promoting antigen presenting cells and orchestrating adaptive immunity against tumor associated antigens [110]. Indeed, PRR ligands can both stimulate tumors and tumor-infiltrating immune cells to secrete cytokines and chemokines which modulate immune cell polarization and reprogramming the tumor microenvironment to reinforce innate and adaptive anti-tumor immunity [111]. Even though the roles of NLRs and RLRs in tumor immunity still largely unknown, TLRs have significant roles in stimulating DC maturation, antigen uptake and presentation, and the differentiation of CD4⁺ T cells. Additionally, Nyirenda et al. have reported the reversion of immunosuppressive skills of Tregs upon TLR stimulation and this is especially interesting for cancer research due to their activity in tumor microenvironment [112]. Given what we know about TLRs and their ligands currently, different TLR agonists have been used in anticancer therapies. Studies on TLR8 have demonstrated that adoptive transfer of TLR8 ligand-stimulated Treg cells reduced the tumor growth in mice [14] by reprogramming Treg glucose metabolism [113]. On the contrary, peritumoral administration of TLR5 ligand flagellin did not affect the growth of murine breast carcinoma D2F2 [114].

Several inflammasome forming NLRs including NLRP1, NLRP3, NLRP6, and NLRC4 may both have protective and detrimental roles in tumor development by their modulation of innate and adaptive immunity, apoptosis and differentiation [115]. On one hand, Janowski et al. has reported the protective role of NLRC4 in melanoma progression independent of the inflammasome components ASC and caspase-1 [116]. On the other hand, a recent study has shown that in metastatic melanoma and sarcoma models, NLRP3 inflammasome activation increased Treg population while inhibiting both NK and T-cell mediated anti-tumor immunity [117]. Additionally, recent studies demonstrated that NLRP3 inflammasome inhibitor (MCC950) reduced IL1β production and Tregs in head and neck squamous cell carcinoma mouse model [118]; and NLRP3 inhibitor (OLT1177) reduced melanoma growth and Foxp3⁺ cells in tumor microenvironment, and when given in combination with anti-PD-1 therapy, its efficacy increased as compared to monotherapy [119].

Even though CLRs are expressed by dendritic cells, they trigger distinct signaling pathways which induce the expression of cytokines and ultimately determine the T cell differentiation. There are several CLR agonists or antagonists that can be used as anti-cancer drugs, such as β-glucan as dectin-1 agonists [120]. With this, Osorio et al. have shown that a bacterial β-glucan, curdlan, skewed Tregs toward Th17 cells both in vitro and in vivo using 4 T1 mouse mammary tumor models [121]. Also, another study has demonstrated that LSECtin, a type II transmembrane protein, which belongs to the C-type lectin receptor superfamily inhibited the proliferation of tumor-specific effector T cells and induce more IL10 production from Treg cells [122].

In addition to these, cancer cells may mimic viral infections to activate interferon response pathway, and activation of RLR signaling in cancer cells may trigger cell death, activation of innate immune cells in tumor microenvironment or increased recruitment of adaptive immune cells into poorly immunogenic tumors [123]. RLRs could inhibit growth or induce apoptosis of different types of cancer cells upon recognition of RNA ligands. Jiang et al. has noted that intratumoral delivery of SLR14, RIG-I agonist induced strong anti-tumor immune responses through the reduction of CD4 + FoxP3+ Treg cells and induction of CD8⁺ T lymphocytes and NK cells [124]. In another study, high RIG-I expression in ovarian cancer was associated with increased FoxP3 expression and enriched PD-L1 and PD-1 mRNA expression [125]. Taken together, it is noteworthy that even though cancer disease and tumor microenvironment are highly heterogeneous, findings from completed and ongoing research raise the possibility of new targets for the treatment of cancer.

4. Conclusions

To study infectious and immune system related diseases is specifically difficult due to the genetic diversity of hosts and pathogens, the ever-changing nature of infection as it progresses, and the secession of host responses during the course of infection and the disease progression. Despite these challenges, utilization of more sophisticated, contemporary immunogenetic methods and tools such as single cell sequencing, high throughput screenings, computational modeling along with the availability of novel in vivo disease models, 3D organoid cultures perhaps lead to exciting outcomes for the long-term control of both infectious diseases and non-infectious immune system related diseases. These approaches will eventually lay the foundation of a framework to understand the interactive relationship between PRRs and regulatory T cells.

Acknowledgments

Authors thank ITU Scientific Research Projects Department Project No 43336 and 40713.

Conflict of interest

The authors declare no conflict of interest.