Type 1 diabetes (T1D) and Hashimoto's thyroiditis (HT) are the two most common autoimmune endocrine diseases that have rising global incidence. These diseases are caused by the immune-mediated destruction of hormone-producing endocrine cells, pancreatic beta cells and thyroid follicular cells, respectively. Both genetic predisposition and environmental factors govern the onset of T1D and HT. Recent evidence strongly suggests that the intestinal microbiota plays a role in accelerating or preventing disease progression depending on the compositional and functional profile of the gut bacterial communities. Accumulating evidence points towards the interplay between the disruption of gut microbial homeostasis (dysbiosis) and the breakdown of host immune tolerance at the onset of both diseases. In this review, we will summarize the major recent findings about the microbiome alterations associated with T1D and HT, and the connection of these changes to disease states. Furthermore, we will discuss the potential mechanisms by which gut microbial dysbiosis modulates the course of the disease, including disruption of intestinal barrier integrity and microbial production of immunomodulatory metabolites. The aim of this review is to provide broad insight into the role of gut microbiome in the pathophysiology of these diseases.

Introduction

Over the past few decades, the incidence of autoimmune thyroid diseases (AITD) and that of type 1 diabetes (T1D) have each increased dramatically [1,2]. Recent research suggests a possible link with these trends, in that immune disorders, including autoimmunity, are intimately connected to imbalances in bacterial gut communities (dysbiosis). This review will provide broad insights into the role of the gut microbiome in the pathophysiology of T1D and Hashimoto's thyroiditis (HT, the most common AITD). Both diseases are governed by cellular autoimmune responses rather than humoral autoimmunity as in Graves’ AITD. Although these diseases affect different glands, they share common pathogenetic pathways dictated by the interplay between genetic susceptibility, altered gut microbiome, and loss of self-immunotolerance (Figure 1). Insights into this nexus may offer novel therapeutic opportunities, which are greatly needed considering the unavailability of effective therapies other than continuous hormone treatment and the risk of co-morbidities.

Simplified overview of the role of gut microbiome in endocrine diseases.

Figure 1.
Simplified overview of the role of gut microbiome in endocrine diseases.

An ever growing body of evidence have illuminated the intertwined relationship between indigenous bacteria and host immunity. In this complex interplay, environmental factors (1) are known determinants of microbiota composition (2). There are several potential mechanisms by which gut microbial dysbiosis modulate the course of the disease, including disruption of the intestinal barrier leading to a ‘leaky gut’ (3). Together with genetic susceptibility (4), this plays an important role in the etiology of both type 1 diabetes and Hashimoto's thyroiditis (5).

Figure 1.
Simplified overview of the role of gut microbiome in endocrine diseases.

An ever growing body of evidence have illuminated the intertwined relationship between indigenous bacteria and host immunity. In this complex interplay, environmental factors (1) are known determinants of microbiota composition (2). There are several potential mechanisms by which gut microbial dysbiosis modulate the course of the disease, including disruption of the intestinal barrier leading to a ‘leaky gut’ (3). Together with genetic susceptibility (4), this plays an important role in the etiology of both type 1 diabetes and Hashimoto's thyroiditis (5).

Pathogenesis

T1D and HT are, respectively, characterized by progressive destruction of insulin-producing beta cells and thyroid hormone-producing thyrocytes leading to an absolute hormone deficiency, for which the only current treatment is life-long hormone supplementation. The overriding feature of T1D and HT consist in breakdown of immunotolerance to autoantigens derived from pancreatic beta cells and follicular thyroid cells, respectively, resulting in circulating autoantibodies, lymphocyte infiltration in the targeted glands and ultimately T cell-mediated destruction of hormone-producing endocrine cells. The latter events culminate in the clinical manifestation of the diseases owing to decline and, at late stages, deficiency in circulating insulin in T1D or thyroxine (T4) and tri-iodothyronine (T3) in HT cases. During the pancreatic- or thyroid–homing of leukocytes (normally referred as insulitis or thyroiditis), activated cytotoxic CD8T cells account for the direct destruction of beta or follicular cells, whereas other immune cells (macrophages, effector CD4T cells and B lymphocytes) endorse tissue damage and inflammation by secreting chemokines, inflammatory cytokines and sustaining CD8T cell immunity. Importantly, self-tolerance is normally ensured by regulatory lymphocytes (Treg and Breg) which suppress the function of effector CD4T helper cell [3–5]. Dysfunction of Treg cells or aberrant Th responses can cause inflammation to ‘go haywire’ with consequent breakdown of immune tolerance. As T cells display high plasticity in lineage differentiation and cytokine profile, shifts towards other Th phenotypes are regarded as prominent features in these autoimmune disorders [3,5].

Both T1D and HT are characterized by production of antibodies against autoantigens derived from beta cells, such as glutamic acid decarboxylase 65 (GAD 65), islet cell, insulin (IAA, IA-2A), and Zinc transporter 8 (ZnT8), or from thyrocytes, such as enzyme thyroid peroxidase (TPO) and thyroglobulin (Tg) in the case of HT. Notably, the production of autoantibodies precedes the clinical manifestation of disease and may be used as prognostic markers [3,6–8].

Genetic susceptibility and environmental components play an important role in the etiology of both diseases. Genetic susceptibility is mainly accounted by the carriage of high-risk class II human leukocyte antigen (HLA) haplotypes as well as polymorphisms in genes encoding cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and protein tyrosine phosphatase, non-receptor type 22 (PTPN22), both regulators of T cell activation. However, the steep increase in the incidence of both T1D and HT in the Western world cannot be explained solely by genetic variants as shown by the discordant rate in the lifetime risk of monozygotic twins [9–11]. A plethora of environmental triggers have been identified as risk factors for the development of autoimmune disease [12–14]. Recent evidence suggests the importance of the gut microbiome as an environmental risk factor [15–18]. Table 1 shows an overview of the common hallmarks of both endocrine diseases. Of note, many environmental risk factors are known determinants of microbiota composition: diet, drugs, infections (Figure 1).

Table 1.
Common hallmarks of type 1 diabetes (T1D) and Hashimoto's thyroiditis (HT): autoimmunity involves both genetic and environmental factors
DiseaseIncidenceAge onsetGenetic associationEnvironmental factorsAuto-antibodies
T1D Per 100.000 person-years; in children 0–19 years; The Netherlands: 11.1–21.4 [19]; Finland: 64.3 [20]; United States: 13.1–25.9 [21]; Global estimate: 4.9 [22Early in life HLA-DR3 [23]; HLA-DR4 [23]; PTPN22 [24]; CTLA-4 [25]; IFIH1 [26]; INS [23]; Probandwise concordance rate in MZ: 6% [9]–42.9% [10]1 Infections: Coxsackie A [27]; Coxsackie B [27]; Cytomegalovirus [28]; Rotavirus [29]; Enterovirus [30,31]; Dietary components; Vitamin D deficiency [32]; Early exposure to Cow's milk [33anti-GAD [34]; anti-IA-2 [35]; IAA [35]; anti-ZnT8A [8]; ICA [35
HT Per 1000 persons; The Netherlands [36]; F: 2.1 per 1000; M: 0.4 per 1000; U.K. [37]; F: 4.75 per 1000; M: 1.09 per 1000; United states [38]; F: 3.5 per 1000; M: 0.8 per 1000 45–65 years HLA-DR3 [39]; HLA-DR5 [39]; CD40 [39]; CTLA-4 [39,40]; PTPN22 [41]; FOXP3 [39]; CD25 [39]; Probandwise concordance rate in MZ: 55% [11Infections: Hepatitis C [42,43]; Helicobacter pylori [44]; Yersinia enterolitica [45]; Borrelia burgdorferi [45]; Dietary components: Mineral deficiency: iodide, selenium, iron, zinc deficiency [46]; Vitamin D deficiency [47]; Medication: Amiodaron [48]; Lithium [49]; IFN-α [43anti-TPO [6]; anti-Tg [6
DiseaseIncidenceAge onsetGenetic associationEnvironmental factorsAuto-antibodies
T1D Per 100.000 person-years; in children 0–19 years; The Netherlands: 11.1–21.4 [19]; Finland: 64.3 [20]; United States: 13.1–25.9 [21]; Global estimate: 4.9 [22Early in life HLA-DR3 [23]; HLA-DR4 [23]; PTPN22 [24]; CTLA-4 [25]; IFIH1 [26]; INS [23]; Probandwise concordance rate in MZ: 6% [9]–42.9% [10]1 Infections: Coxsackie A [27]; Coxsackie B [27]; Cytomegalovirus [28]; Rotavirus [29]; Enterovirus [30,31]; Dietary components; Vitamin D deficiency [32]; Early exposure to Cow's milk [33anti-GAD [34]; anti-IA-2 [35]; IAA [35]; anti-ZnT8A [8]; ICA [35
HT Per 1000 persons; The Netherlands [36]; F: 2.1 per 1000; M: 0.4 per 1000; U.K. [37]; F: 4.75 per 1000; M: 1.09 per 1000; United states [38]; F: 3.5 per 1000; M: 0.8 per 1000 45–65 years HLA-DR3 [39]; HLA-DR5 [39]; CD40 [39]; CTLA-4 [39,40]; PTPN22 [41]; FOXP3 [39]; CD25 [39]; Probandwise concordance rate in MZ: 55% [11Infections: Hepatitis C [42,43]; Helicobacter pylori [44]; Yersinia enterolitica [45]; Borrelia burgdorferi [45]; Dietary components: Mineral deficiency: iodide, selenium, iron, zinc deficiency [46]; Vitamin D deficiency [47]; Medication: Amiodaron [48]; Lithium [49]; IFN-α [43anti-TPO [6]; anti-Tg [6

HLA, human leukocyte antigens, PTPN22, encodes the lymphoid protein tyrosine phosphatase important for regulating T-cell receptor signaling; CTLA-4, cytotoxic T-lymphocyte-associated molecule-4; IFIH1, interferon-induced helicase; INS, insulin; MZ, monozygotic twins; anti-GAD, antibodies to glutamic acid decarboxylase; anti-IA-2A, anti-tyrosine phosphatase-like insulinoma antigen 2; IAA, antibodies to insulin; anti-ZnT8, antibodies against Zinc transporter 8; ICA, islet-cell antibodies; FoxP3, forkhead box P3; CD40, cluster of differentiation 40; CD25, cluster of differentiation 25; anti-TPO, antibodies to thyroid peroxidase; anti-Tg, antibodies to thyroglobulin; F, female; M, male.

1

Dependent on age of diagnosis.

The gut microbiome

The human is the host of hundreds of trillions of microorganisms, consisting of commensal bacteria, archaea, viruses, fungi and yeasts, all living in a symbiotic state [50]. Prior studies often reported that in the human body bacteria outnumber the human cells by an estimated 10-fold. More recently, however, this has been lowered to a more equal ratio of 3 : 1 or 1 : 1 [51].

The recent introduction of new molecular techniques including high-throughput technology to sequence the bacterial 16S rRNA genes has allowed new insights into bacterial communities in health and disease. Moreover, the advent of metagenomics has allowed to address open questions on functional and strain-specific differences in healthy and ‘disease’ microbiomes. Depicting the microbiome composition revealed that, despite the interpersonal variation, only a limited number of phyla are dominant in the intestinal microbial community: the Gram-negative phyla Bacteroidetes and Proteobacteriae and the Gram-positive phyla Firmicutes, Actinobacteria and Verrucomicrobia [52].

Colonization of the microbiome essentially begins at birth with mode of delivery (caesarian section versus vaginal birth) and diet during infancy (formula feeding or breast milk) as major colonization pattern determinants [53,54]. During life a variety of factors can regulate the composition: (prior) use of medication (especially antibiotics), smoking, diet, gender and even ethnicity, geographical regions and cultural differences play an important role [55–57], which challenges the reproducibility of the results of studies reporting a link between the gut microbiome and disease. These patients’ characteristics as well as differences in the study methodologies and data analysis could explain the differences found in in the microbiome composition across studies.

‘All disease begins in the gut.’—Hippocrates, 400 years BC

Although a universal characterization of ‘healthy microbiota’ has not yet been defined, a key accepted feature of healthy microbiota is microbial diversity, a high richness of different taxa, renders the microbiome resistant to environmental perturbations. When the gut microbiota composition is disrupted and the microbial ecosystem becomes imbalanced, that can be defined as the occurrence of dysbiosis. Across all metabolic and inflammatory disorder (obesity, diabetes, inflammatory bowel disease, autoimmune diseases) currently linked to altered gut microbiota composition, dysbiosis is commonly characterized by the loss of diversity (reduction in alpha-diversity) with concomitant reduction in (beneficial) commensals and an overgrowth of pathogenic bacterial strains [58]. This results in reduced resistance against microbial and inflammatory imbalance, and failure to maintain of immune homeostasis, a form of resilience.

A growing body of evidence has illuminated the complex interplay of environmental factors and intestinal characteristics, including immune status and host genotype, together modulate the composition of commensal communities [59–61]. Particular microbial lineages provide beneficial tolerogenic signaling, while others induce and/or amplify inflammation. In addition, the microbiota may control host negative regulatory mechanisms that reduce the antimicrobial responses and could contribute to dietary, commensal and self-antigen immunotolerance (‘balanced signal hypothesis’) [62]. Therefore, dysbiosis may increase susceptibility to autoimmunity or alter the trajectory of an established disease and may interfere with both the process of innate immune receptor activation and the production of microbial-derived immunomodulatory metabolites (such as short chain fatty acids and tryptophan derivatives).

However, only associations and correlations between gut microbiota and disease pathogenesis have been shown for most dysbiosis-related diseases; causality has not been demonstrated. The dysbiosis may be driving the illness, may result from the illness, or may reflect medications used to treat the illness; in fact these are not exclusive categories, which makes analysis more difficult.

Alterations in gut microbial composition in T1D and HT

In the following paragraphs, we will explore current knowledge about the microbiome alterations associated with T1D/HT and the potential mechanisms linking dysbiosis to disease onset and progression.

Table 2 shows the concordant and discordant changes found in the gut microbiome signatures in T1D and HT reported by different human studies performed in different geographic regions.

Table 2.
Overview of taxonomic gut microbiota signatures in T1D and HT
Taxonomic levelOrganismT1DHT1Possible functional effects
Phylum Actinobacteria Discordant results [63–65Increased [NS] [66 
Phylum Bacteroidetes Increased [64,67–69Decreased [66,70 
Phylum Firmicutes Decreased [63–65,71Discordant results [66,70Comprise conversion to secondary BA [72
Phylum Fusobacteria Discordant results [67,73Increased [NS] [66 
Phylum Proteobacteria Discordant results [64,65Increased [NS] [66 
Phylum Verrucomicrobia Decreased [73Decreased [NS] [66 
Family Bacteroidaceae Increased [64,68Discordant results [66,70 
Family Enterococcaeae Decreased [53,67Increased [NS] [66 
Family Lachnospiraceae Decreased [35,64,65,73–75Increased [66,70 
Family Peptostreptococcaceae Decreased [75Increased [70 
Family Prevotellaceae Discordant results [53,73Decreased [66,70 
Family Ruminococcaceae Decreased [53,64,67Decreased [NS] [66 
Family Streptococcaceae Not assessed Increased [70 
Family Veillonellaceae Decreased [64,67Decreased [NS] [66 
Genus Akkermansia Discordant results [53,73,75Not assessed Inducing Treg [15
Genus Alistipes Discordant results [76,77Increased [NS] [66Promote mucus production [78
Genus Bacteroides Increased [63–65,67,68,75,77,79Discordant results [66,70Inducing Treg [15]
SCFA producer [70
Genus Bifidobacterium Discordant results [63,65,73,75,77Decreased [66,70Regulating translocation of intestinal bacteria [80]
Possible antigenic in HT [81
Genus Blautia Increased [35,65,74,75Increased [70 
Genus Clostridium Discordant results [63,71,82Not assessed Potent driver of Treg expansion and differentiation [83
Genus Dialister Discordant results [35,53,67,71,73,84Decreased [NS] [66 
Genus Dorea Decreased [75Increased [70 
Genus Escherichia-Shigella Increased [71,73Increased [NS] [66 
Genus Faecalibacterium Decreased [75Decreased [70 
Genus Fusicatenibacter Increased [NS] [53Increased [70 
Genus Lachnoclostridium Increased [NS] [53Decreased [70 
Genus Lactobacillus Decreased [63,73,76Decreased [66Inducing Treg [15]
Possible antigenic in HT [81
Genus Prevotella Discordant results [63–65,73,77,79Decreased [66,70 
Genus Romboutsia Decreased [NS] [53Increased [70 
Genus Roseburia Discordant results [65,73,75,85Increased [70 
Genus Ruminococcus Discordant results [53,65,74,75Discordant results [66,70 
Genus Streptococcus Discordant results [53,65,73,74Not assessed Inducing Treg [15
Genus Subdoligranulum Decreased [73Increased [NS] [66 
Species Alistipes shahii Increased [85Not assessed  
Species Bacteroides clarus Increased [86Increased [NS] [66 
Species Bacteroides dorei Discordant results [69,82,86Increased [NS] [66 
Species Bacteroides fragilis Decreased [64Increased [NS] [66Induce IL-10 secretion [87]
Enhance bacterial translocation [68
Species Bacteroides vulgatus Discordant results [69,86Increased [NS] [66 
Species Bifidobacterium adolescentis Decreased [68,82Not assessed SCFA producer (acetate and lactate) [68] Induce Th17 cell response [15
Species Bifidobacterium longum Increased [82,84,86Decreased [NS] [66 
Species Bifidobacterium pseudocatenulatum Discordant results Decreased [68,85Not assessed  
Species Dialister invisus Discordant results [74,84Not assessed  
Species Escherichia coli Increased [64Increased [NS] [66Induce Th17 cell response [15
Species Eubacterium hallii Decreased [NS] [68Increased [70 
Species Faecalibacterium prausnitzii Discordant results [64,68,82,86Not assessed Regulating Th17 cell [70
Species Lactobacillus gasseri Not assessed Decreased [66 
Species Lactobacillus lactis Decreased [85Not assessed  
Species Olsenella sp. SK9K4 Not assessed Decreased [66 
Species Roseburia faecis Decreased [68Not assessed  
Species Roseburia hominis Increased [85Not assessed  
Species Ruminococcus flavefaciens Not assessed Increased [66 
Species Ruminococcus gnavus Decreased [64,74Not assessed Inducer of proinflammatory polysaccharide [88
Species Streptococcus mitis/oralis/pneumonia Increased [85Not assessed  
Species Streptococcus thermophilus Decreased [85Not assessed  
General characteristics 
α-diversity  Decreased [64,74Discordant results [66,70 
Bacterial richness  Decreased [35Discordant results [66,70 
F/B ratio  Decreased [63,74Increased  
Taxonomic levelOrganismT1DHT1Possible functional effects
Phylum Actinobacteria Discordant results [63–65Increased [NS] [66 
Phylum Bacteroidetes Increased [64,67–69Decreased [66,70 
Phylum Firmicutes Decreased [63–65,71Discordant results [66,70Comprise conversion to secondary BA [72
Phylum Fusobacteria Discordant results [67,73Increased [NS] [66 
Phylum Proteobacteria Discordant results [64,65Increased [NS] [66 
Phylum Verrucomicrobia Decreased [73Decreased [NS] [66 
Family Bacteroidaceae Increased [64,68Discordant results [66,70 
Family Enterococcaeae Decreased [53,67Increased [NS] [66 
Family Lachnospiraceae Decreased [35,64,65,73–75Increased [66,70 
Family Peptostreptococcaceae Decreased [75Increased [70 
Family Prevotellaceae Discordant results [53,73Decreased [66,70 
Family Ruminococcaceae Decreased [53,64,67Decreased [NS] [66 
Family Streptococcaceae Not assessed Increased [70 
Family Veillonellaceae Decreased [64,67Decreased [NS] [66 
Genus Akkermansia Discordant results [53,73,75Not assessed Inducing Treg [15
Genus Alistipes Discordant results [76,77Increased [NS] [66Promote mucus production [78
Genus Bacteroides Increased [63–65,67,68,75,77,79Discordant results [66,70Inducing Treg [15]
SCFA producer [70
Genus Bifidobacterium Discordant results [63,65,73,75,77Decreased [66,70Regulating translocation of intestinal bacteria [80]
Possible antigenic in HT [81
Genus Blautia Increased [35,65,74,75Increased [70 
Genus Clostridium Discordant results [63,71,82Not assessed Potent driver of Treg expansion and differentiation [83
Genus Dialister Discordant results [35,53,67,71,73,84Decreased [NS] [66 
Genus Dorea Decreased [75Increased [70 
Genus Escherichia-Shigella Increased [71,73Increased [NS] [66 
Genus Faecalibacterium Decreased [75Decreased [70 
Genus Fusicatenibacter Increased [NS] [53Increased [70 
Genus Lachnoclostridium Increased [NS] [53Decreased [70 
Genus Lactobacillus Decreased [63,73,76Decreased [66Inducing Treg [15]
Possible antigenic in HT [81
Genus Prevotella Discordant results [63–65,73,77,79Decreased [66,70 
Genus Romboutsia Decreased [NS] [53Increased [70 
Genus Roseburia Discordant results [65,73,75,85Increased [70 
Genus Ruminococcus Discordant results [53,65,74,75Discordant results [66,70 
Genus Streptococcus Discordant results [53,65,73,74Not assessed Inducing Treg [15
Genus Subdoligranulum Decreased [73Increased [NS] [66 
Species Alistipes shahii Increased [85Not assessed  
Species Bacteroides clarus Increased [86Increased [NS] [66 
Species Bacteroides dorei Discordant results [69,82,86Increased [NS] [66 
Species Bacteroides fragilis Decreased [64Increased [NS] [66Induce IL-10 secretion [87]
Enhance bacterial translocation [68
Species Bacteroides vulgatus Discordant results [69,86Increased [NS] [66 
Species Bifidobacterium adolescentis Decreased [68,82Not assessed SCFA producer (acetate and lactate) [68] Induce Th17 cell response [15
Species Bifidobacterium longum Increased [82,84,86Decreased [NS] [66 
Species Bifidobacterium pseudocatenulatum Discordant results Decreased [68,85Not assessed  
Species Dialister invisus Discordant results [74,84Not assessed  
Species Escherichia coli Increased [64Increased [NS] [66Induce Th17 cell response [15
Species Eubacterium hallii Decreased [NS] [68Increased [70 
Species Faecalibacterium prausnitzii Discordant results [64,68,82,86Not assessed Regulating Th17 cell [70
Species Lactobacillus gasseri Not assessed Decreased [66 
Species Lactobacillus lactis Decreased [85Not assessed  
Species Olsenella sp. SK9K4 Not assessed Decreased [66 
Species Roseburia faecis Decreased [68Not assessed  
Species Roseburia hominis Increased [85Not assessed  
Species Ruminococcus flavefaciens Not assessed Increased [66 
Species Ruminococcus gnavus Decreased [64,74Not assessed Inducer of proinflammatory polysaccharide [88
Species Streptococcus mitis/oralis/pneumonia Increased [85Not assessed  
Species Streptococcus thermophilus Decreased [85Not assessed  
General characteristics 
α-diversity  Decreased [64,74Discordant results [66,70 
Bacterial richness  Decreased [35Discordant results [66,70 
F/B ratio  Decreased [63,74Increased  

T1D, type 1 diabetes; HT, Hashimoto's thyroiditis; [NS] [89], FDR adjusted P-value (Q-value) was not significant; α-diversity is the variety of microorganisms within a single sample; bacterial richness is the total number of different species; F/B ratio is the Firmicutes to Bacteroidetes ratio.

1

To date, only two studies have analyzed the gut microbiome composition in HT patients. Discordant results observed in these two studies may be due to the different thyroid functional status of the patients involved (euthyroid HT patients in study of Zhao et al. and hypothyroid HT patients in the study of Ishaq et al.).

Type 1 diabetes

The relationship between gut microbiome dysbiosis and T1D has been extensively studied in past decades and these studies have been summarized elsewhere [61,90,91]. The role of the gut microbiome as a regulator of T1D progression is strongly supported by evidence from murine studies with non-obese diabetic (NOD) mice, a polygenic model for spontaneous autoimmune diabetes, in which T1D incidence strongly depends on environmental/microbial exposure [92–95]. In support of the ‘balanced signal hypothesis’, Burrows et al. [94] elegantly showed that T1D development depends on microbiota-induced signaling through TLRs and the adaptor signaling molecule MyD88. Specifically, deletion of MyD88 protected mice from T1D development under specific pathogen-free (SPF) conditions, but not in germ-free (GF) vivaria. Such data provide evidence that host interactions with the commensal bacteria occurring through MyD88 produces pathogenic signals. Colonization of GF NOD.MyD88−/− mice with a probiotic mix containing Lactobacillaceae suppressed insulitis [94]. This can be interpreted as showing that the probiotics have disease-reducing interactions that do not require the MyD88 pathway related to TLR signaling. Analysis of SPF/GF NOD mice lacking TLRs or the downstream adaptor TRIF indicated that TLR4 and TRIF signaling act as microbiota-induced tolerizing pathways, whereas TLR2 mediates (indirectly) microbial pro-diabetic signals [94]. The protective phenotype of MyD88 knockout mice was associated with an increased intestinal abundance of Lactobacillaceae (Firmicutes), Rikenellaceae and Porphyromonadaceae (both Bacteroidetes). The protective effect was successfully transmitted to GF NOD mice that were exposed to microbiota from SPF NOD.MyD88−/− mice, as assessed by reduced insulitis and increased proportion of intact islets compared with the uncolonized GF NOD mice [92]. Thus, the unperturbed microbiota has a tolerizing effect on the disease phenotype. Overall, these studies define important roles of microbial-immune cross-talks on the progression of T1D.

While comparing taxonomic changes of gut microbiome in T1D patients with those in healthy controls has revealed some concordant microbial signatures (Table 2), other studies have observed contradictory trends. Common features to the diabetogenic human gut microbiome are lower Firmicutes to Bacteroidetes (F/B) ratio [63,74], as well as decreased diversity [64,74] and richness [35]. Importantly, the existence of diabetogenic microbiota appears to precede disease onset. In an American study, Alkanani et al. [76] reported that the microbiota composition of seropositive and seronegative first-degree relatives was similar but distinct from that of unrelated healthy controls or new-onset T1D subjects. Strikingly, the authors found an increase in the relative abundance of Bacteroides and a decreased abundance of Prevotella in seropositive subjects with multiple versus one autoantibody. In a European longitudinal study examining the microbiota of children from birth until 3 years of age, a marked drop in alpha-diversity was found after seroconversion in patients that progressed to T1D [74]. The reduced microbiota diversity was accompanied by the outgrowth of Blautia, the Rikenellaceae, and the Ruminococcus and Streptococcus genera. In addition, the microbiota characteristics of seroconverters, in terms of T1D-associated phylogenies and metabolic pathway carriage, were found to be at an intermediate level between non-converters and T1D cases. Both studies indicate that altered microbiota composition may contribute to T1D development and also may serve as a marker for differentiation between rapid and slow progressors to T1D [74,76].

Accordingly, in a large longitudinal study examining the taxonomic and functional microbiota profile in children with HLA-conferred T1D susceptibility (the TEDDY study), distinct microbiota features were observed at different pre-clinical and clinical stages [53,85]. Applying metagenomics sequencing analysis of stool samples from 783 children (seroconverter and/or confirmed T1D cases with their time-matched controls) collected from 3 months of age until clinical end-point (seroconversion or T1D), Vatanen et al. showed that healthy controls contained higher levels of Lactobacillus rhamnosus and Bifidobacterium dentium, whereas children with autoimmunity had higher abundance of Streptococcus group mitis/oralis/pneumoniae species. Notably, the progressors to T1D contained higher levels of Bifidobacterium pseudocatenulatum, Roseburia hominis and Alistipes shahii species, and non-progressors had more Streptococcus thermophilus and Lactococcus lactis species instead [85]. S. thermophilus may be a marker for probiotic exposure.

Due to the complexity of microbial communities, it remains difficult to identify specific beneficial or detrimental lineages and to determine whether the altered microbiota contributes to or results from compromised immune function. In this regard, Akkermansia muciniphila was identified as a protective symbiont against T1D onset in NOD mice as it was abundant in T1D low-incidence colonies (NOD/MrkTac) and absent from high-incidence (NOD/Jax) colonies. In addition, oral transfer of Akkermansia to NOD/Jax mice delayed diabetes onset, reduced insulitis severity and promoted intestinal barrier function. Similarly, it induced remote protective effects in pancreatic islet with an increased Foxp3+ Treg cell count, elevated expression of the anti-inflammatory cytokines IL-10 and TGF-β, reduced infiltration by mononuclear leukocytes, diminished TLR2 and TLR4 levels, and in total, delayed T1D onset [96]. Since A. mucinophila is regarded as an organism that increases in abundance opportunistically, it is a marker of compromised microbiota; its association with protection is consistent with the maxim with NOD mice that ‘dirty protects’.

Notably, two separate studies found A. muciniphila levels to be associated with diminished risk of developing T1D-associated autoantibodies in children at risk [73,97] indicating consistency with the studies in NOD mice [73,97]. Several studies have reported correlations between significantly different fecal taxa and clinical parameters of glycemic control, mainly reported by HbA1c levels [35,67,75]. As such, T1D patients with reduced abundance of beneficial microbes Bacteroidetes and Lactobacillales tend to have higher levels of HbA1c [77]. Furthermore, Subdoligranum was associated with poorer metabolic control [98], the abundance of Blautia positively correlated with HbA1c and T1D-associated antibodies [35], and the F/B ratio inversely correlated with the plasma glucose level [63]. It should be noted that, similar to the discordant results of taxonomic signatures shown in Table 2, conflicting results of possible functional effects of bacteria have been reported. For instance, while Salamon et al. [75] found a negative correlation between the abundance of the family Erysipelotrichaceae and HbA1C levels, other studies have suggested a highly immunogenic role and positive association with T1D onset for this bacterial family [53,99].

A role for the microbiota in protecting from or inducing T1D has been indicated in multiple studies in which antibiotics have been used in NOD mice [100–104]. Studies have shown both enhanced induction of T1D by certain antibiotics (notably broad spectrum combinations, macrolides, and vancomycin) and protection by others. This point to multiple immunological mechanisms. Loss of small intestinal Treg cell populations and alterations in other T helper cells is one attractive mechanism for the altered diathesis [100,102,103]. Studies of intestinal epithelial gene expression show that the antibiotic-perturbed microbiota differentially induces important innate immune pathways, including those related to TLRs, SAA and NO, as well as adaptive immune effects as well (e.g. Th17, and Tregs) [100,101]. Importantly, timing of the antibiotic exposure is important with effects seen prenatally [102], or in early life [100,101]. Such results are consistent with the hypothesis that the early life microbiota affects immunological development, and the nature of the immunological tone, which affects the development of autoimmunity in the NOD mouse. Transfer of the antibiotic-altered microbiota resulted in similar phenotypes [100,102], indicating that it is the antibiotic-altered microbiota per se that is having the effect. Such studies move the question of microbiome effects on T1D development in human children to the earliest months of life, in many cases years before the development of T1D.

Hashimoto's thyroiditis

Although fewer studies have addressed the link between microbiome and HT, the topic is receiving growing attention [46,60]. An association between the gut microbiome and thyroid function was already postulated in murine models by the 1970s. Modifying the microbiome in rats by exposing them to antibiotics led to reduced thyroid gland function, measured by the uptake of radioactive iodine [105]. Another study showed a 25% increase in TSH levels in GF mice compared with conventionally raised mice with normal intestinal microbiota [106]. Similarly to the NOD model, early-life environmental exposures influence the susceptibility to the disease. Using thymectomy and irradiation to induce experimental autoimmune thyroiditis (EAT), Penhale et al. [107] discovered that maintenance of female PVG/c rats under SPF conditions until weaning conferred resistance to AITD, whereas antibiotic treatment and microbiota transfer from conventionally reared rats into newly weaned SPF rats increased the autoimmune susceptibility of the latter. On the contrary, daily administration of isolated ‘probiotic’ strains Lactobacillus rhamnosus HN001(HN001) and Bifidobacterium lactis HN019 (HN019) had no impact on autoimmune responses, assessed by autoantibody levels, spleen weight and lymphocyte infiltration into thyroid glands, after inducing EAT in CBA/CaH (H-2k) mice by consecutive injections of mouse thyroglobulin [108].

More recently, two Chinese studies have compared the fecal microbiota of HT patients with matched healthy controls, and described a HT-associated dysbiosis [66,70]. Ishaq et al. [66] included hypothyroid HT patients, whereas Zhao et al. [70] studied the fecal samples of euthyroid HT patients [70]. This difference in thyroid functional status, and treatments are major confounding factors possibly affecting the gut microbiome composition and may explain some of the discordant observations in fecal microbiome characteristics shown in Table 2. Notably, independent of the thyroid functional state, both reports identified a reduction in abundance of the species Prevotella_9 in subjects with HT. The Zhao study found no significant increase in microbiota species richness and diversity in HT patients, but taxon-dependent analysis showed an overall different population structure, with 27 genera found significantly altered between the HT and healthy microbiomes, and the effects markedly associated with clinical parameters of thyroid disease (anti-TPO, anti-Tg, fT4 and TSH) [70]. The genera enriched in HT patients that were positively correlated with the thyroid antibodies anti-TPO and anti-Tg mostly belong to the phylum Firmicutes, whereas the genera depleted in HT patients that were inversely correlated with these antibodies mostly were in the phylum Bacteroides. The genus Alloprevetolla was positively correlated with FT4 levels, whereas an inverse correlation was observed between the genera Fusicatenibacter and Romboutsia and FT4 and TSH levels. Prediction modeling selected 10 of the 27 species as biomarkers of HT-associated microbiome in both the exploratory cohort and in a second validation cohort of HT patients and healthy controls [70]. Interestingly, as reported in the diabetogenic microbiome [35], the microbiota of HT patients was enriched in Blautia genera, belonging to the order Clostridiales, which are important mediators of intestinal homeostasis and tolerogenic immunity [83,109].

Overall these data indicate that the characteristics of the gut microbiota may be associated with disease progression; however, further investigations in EAT models are needed to uncover the possible mechanisms linking dysbiosis and the growth of specific bacteria to the development of HT.

Mechanisms linking the gut microbiome to autoimmune T1D and HT

Microbiota influence on immune system: from development to function

In the evolutionary process of animal life on earth, the gut microbiome and the immune system have co-evolved profoundly leading to a reciprocal microbiome-immune system interplay. Previous studies with GF mice [110,111] have shown extensive deficits in the development of gut-associated lymphoid tissue (GALT), a secondary lymphoid organ present throughout the gastro-intestinal tract. These deficits include fewer and smaller mesenteric lymph nodes with lower numbers of CD4+CD25+Foxp3T-lymphocytes [112] and IgA-secreting plasma cells [113]. Notably, these immunologic deficiencies reversed within a few weeks after colonization of GF mice, and the normal development and maturation of the GALT was followed [111,112]. Commensal bacteria are important for both immune system development and function. Previous studies have demonstrated that microbiota composition influences the balance between two major effector T cell populations, IL-17+ Th17 and CD25+ Foxp3+ Treg. Ivanov et al. [114] reported that intestinal colonization with a single commensal microbe-segmented filamentous bacterium (Savagella) was sufficient to induce Th17 cells in the intestinal lamina propria. Intestinal colonization by other mucosal-associated bacteria (Escherichia coli, Bifidobacterium adolescentis, Staphylococcus aureus) also have been shown to induce Th17 cell responses, although with distinct cytokine profiles [15].

Administration of Bacteroides fragilis-derived polysaccharide A (PSA) restored immunologic deficits in GF mice by inducing CD4+ T cell expansion systemically and by correcting the Th1/Th2 imbalance [115]. B. fragilis-PSA exerts systemic anti-inflammatory activities by enhancing activated T cell-induced IL-10 production and by promoting the frequency and function of IL-10+Foxp3+ Treg cells [89,115,116]. A mixture of Clostridia strain, isolated from human microbiota, was identified as potent driver of Treg expansion and differentiation in GF mice [83]. Other microbiota members capable of inducing Treg include Escherichia, Akkermansia, Bacteroides, Lactobacillus, and Streptococcus strains, as well as the altered Schaedler flora, a defined commensal community [15].

In light of these previous discoveries, it is reasonable to assume that alterations in the relative abundance of specific symbiotic strains in T1D- and HT-associated microbiomes, such as Akkermansia and Bacteroides [76,96], may impact the plasticity of effector T cell differentiation and hence the course of disease. Both of these studies show the remarkable impact that colonization by a single bacterium can have with associated alterations in specific immune cell-types affecting autoimmune diabetes development.

The indigenous microbiota also indirectly regulate gut barrier integrity and the expression of innate immunity. In the extreme example, GF mice exhibit an altered mucosal layer, an impaired development of GALTs [117], and a lower expression of intestinal bacterial pattern recognition receptors (PRRs). The expression of functional PRRs, such as Toll- and NOD-like receptors, by intestinal epithelial cells (IECs) and GALT-resident myeloid cells is essential for the establishment of host-microbial symbiosis and for host control of microbiota composition. Upon bacterial recognition, PRRs drive inflammatory responses and initiate processes involved in mucus production, regeneration of IECs, and antimicrobial peptide production [59,118]. Dysbiosis has been reported in several mouse models of innate immune deficiency (e.g. MyD88−/− and NOD2−/−), and activation of these innate receptors has been shown to influence the incidence and severity of T1D in NOD mice.

Therefore, the microbiome can be viewed as a changeable component of both innate and adaptive immunity, influencing their function, while living symbiotically inside the gastrointestinal tract.

Intestinal permeability: the ‘leaky gut’

Increased intestinal permeability occurs in the setting of disruption of gut homeostasis, allowing food-derived antigens, intestinal toxins, and microbial factors to breach the endothelial barrier.

Aberrant functional integrity of the gut has been reported in both human and animal studies of T1D [68,78,119]. As such, down-regulated intestinal expression of tight junction encoding genes and up-regulation of serum levels of zonulin, a marker of gut permeability, were documented in human T1D subjects accompanied by changes in microbiome composition [65,120]. Moreover, increased intestinal permeability, as measured by the lactulose/mannitol test, was detectable prior to clinical onset [121], strengthening the hypothesis of a causative role of ‘leaky gut’ in T1D development. In support of this concept, Costa et al. [122] observed bacterial translocation to pancreatic lymph nodes in streptozotocin-injected wild-type mice and further demonstrated that this translocation mediates inflammation and hyperglycemia by activating the NOD2 innate receptor in pancreas. In a more recent study, using BDC2.5XNOD mice, which carry a beta cell-specific T cell receptor but do not develop spontaneous T1D, Sorini et al. showed that disruption of intestinal integrity activates the islet-specific T cells within the gut mucosa in a microbiota-dependent manner. These T cells, expressing a gut homing marker, can subsequently appear or be tracked to the pancreas. Hence, this study provides evidence that diabetogenic T cells are activated by intestinal microbiota under ‘leaky gut’ conditions and later can migrate to the pancreas to induce diabetes [123].

Similarly, altered intestinal permeability evaluated by the lactulose/mannitol test, was reported in HT patients together with morphological changes in duodenal enterocytes [124]. In HT patients, the decreased metabolism and longer gastrointestinal transit time, typical of hypothyroidism, have been found to impair microvilli function and affect the intestinal homeostasis. This intestinal motor dysfunction may lead to small intestinal bacterial overgrowth (SIBO), which has been reported in HT patients [59,66,125]. Eventually, bacterial overgrowth may prompt bacterial translocation, inducing systemic inflammatory complications such as autoimmune thyroiditis [59]. Since these changes may be a consequence rather than a cause of HT, further investigations are needed to address this thyroid-GI motility-microbiota nexus.

In both diseases, a ‘leaky gut’ may be triggered by an imbalance between microbiota taxa that enhance the mucous barrier and mucolytic strains when dysbiosis occurs, prior to manifestation of the disease (Figure 1). In a T1D cross-sectional study, metaproteomic analysis of microbial proteins in human stool samples (integrated with microbiota taxonomic profiling data) revealed alterations in mucin degradation in seroconverters and depletion of microbial taxa associated with particular host proteins involved in maintaining the mucous barrier and exocrine pancreas function in new-onset T1D subjects [78]. Likewise, alterations in the composition of mucin degrading bacteria was associated with early development of anti-islet cell autoimmunity in children [97]. Since T1D per se may alter the microbiota through several functional changes, the studies that show abnormalities prior to full T1D development have the greatest significance.

Gut microbial-derived metabolites and pathways

The gut microbiome participates in multiple crucial physiological processes that impact host immunity and energy metabolism, including synthesis of microbiota-specific metabolites and vitamins (B and K), production and secretion of (intestinal) hormones (leptin, ghrelin and glucagon-like peptide 1), synthesis of secondary bile acids, and absorption of mineral nutrients by competing with the host (e.g. iodide, iron, and zinc important for thyroid function) [46]. We next discuss the roles of a few microbial metabolites linked to inflammation and the pathophysiology of both T1D and HT [46].

Short-chain fatty acids

The saccharolytic fermentation of non-digestible complex carbohydrates leads to the production of short-chain fatty acids (SCFAs), predominantly butyrate, acetate and propionate (in a general ratio of 60 : 20 : 20) primarily by phylum Bacteroidetes organisms. After production, most (∼95%) SCFAs are absorbed by colonocytes through diffusion or co-transport [126]. SCFAs have been intensively researched due to their roles in metabolic responses and their anti-inflammatory properties. SCFAs are histone deacetylase (HDAC) inhibitors and also ligands for G-protein coupled receptors (GPCR) 43, 41, and 109a, which are expressed by multiple cell-types including immune cells and IECs. Through GPCR signaling and epigenetic modulation, SCFAs, particularly butyrate, restrain the NFкB-elicited production of proinflammatory cytokines in myeloid cells, and promote generation of regulatory T cells and increase their suppressive activity via inhibition of proinflammatory HDAC [15,127–131]. Furthermore, butyrate is a major energy source for enterocytes and plays an important role in maintaining gut integrity by inducing mucus production and promoting tight junction expression in IECs [129]. Thus, SCFAs are vital for immune homeostasis by both counteracting proinflammatory responses and fortifying the IEC barrier [129].

In longstanding T1D patients, there was no significant difference in fecal SCFA levels, which constitute ∼5% of net SCFA production; however, a lower abundance of SCFA-producing microbes was observed, accompanied by lower levels of plasma SCFA and decreased fecal levels of the butyryl-CoA:acetate-CoA transferase gene [100]. Another study in human children genetically susceptible to diabetes hinted to a protective effect of butyrate in T1D development, since lower abundances of microbial genes for the butyrate production were associated with early autoantibody development [97]. These results were consistent with a previous murine study which found that blood and fecal concentration of the SCFAs acetate and butyrate were decreased in NOD mice with higher T1D incidence. Moreover, a 5-week acetate- and butyrate-yielding diet provided long-term protection against T1D development with a decline in autoreactive T cells, concomitant expansion of Treg, and improved gut barrier integrity [18,130]. In NOD mice, SCFAs promoted a tolerogenic pancreatic immune environment by controlling the beta cell-mediated production of the antimicrobial peptide CRAMP, which elicited a protective effect against diabetes onset [132].

Nevertheless, supplementation of butyrate alone to longstanding T1D patients did not impact immune responses or metabolic markers of disease [133]. Whether oral SCFAs can slow down autoimmunity-induced beta cell destruction in new-onset human T1D has yet to be studied.

Importantly, a multicenter prospective study revealed that T1D-associated microbiome alterations are taxonomically diverse but functionally more coherent: pathways related to bacterial fermentation and SCFA synthesis were the most significantly differential functional profiles of control and T1D microbiomes, but these changes were not consistently specific for microbial taxa across geographically diverse centers [85]. This suggests that functional rather than phylogenetic characterization of the microbiome might better serve for disease monitoring and biomarker discovery.

Small-scale human studies comparing HT and healthy individuals have observed a decreased abundance of SCFA-producing bacteria in HT [66,70]. However, fecal and plasma levels of SCFA in HT have yet to be studied. We speculate that SCFAs elicit beneficial immune functions in AITD, similar to those shown in T1D.

Secondary bile acids

The gut microbiome has an essential role in bile acid metabolism. The production of cholesterol-derived primary bile acids (BAs) is regulated by the nuclear farnesoid X receptor (FXR) and the G protein-coupled receptor TGR5 [134]. The colonic microbiome, of which primarily the genus Clostridium, carry out the conversion to secondary BAs through the bacterial 7α-dehydroxylation reaction [72]. Murine models indicate that one or more feedback loops between the host and the microbiota may characterize BA metabolism: the gut microbiota regulates host primary BA synthesis by reducing levels of a FXR antagonist [135], and FXR-regulated pathways can in turn alter bile acid composition, regulating microbiota composition [136,137].

BAs are recognized as signaling molecules that are involved in both lipid metabolism and energy expenditure by activating TGR5, and taurine-conjugated secondary BAs are the most potent TGR5 ligands. Activation of TGR5 stimulates type 2 iodothyronine deiodinase (D2) in brown adipocyte tissue, increasing local production of the bioactive thyroid hormone T3 [138,139]. In another feedback loop, thyroid hormone regulates BA metabolism by increasing the expression of CYP7A1 in the liver [140]. Additionally, deoxycholic acid (DCA) is proposed to have a strong and selective antimicrobial effect by inducing membrane damage and therefore reducing bacterial overgrowth [141]. Metabolic profiling showed that DCA is the dominant bile acid in HT patients [142,143], which may reflect that small intestinal bacterial overgrowth (SIBO) is common in patients with HT [125]. Whether or not DCA contributes to HT pathogenesis or merely reflects its presence is not known.

BAs also act on hepatic glucose metabolism through the FXR and TGR5 receptors [144,145]. In both mice and humans, complete cessation of insulin production alters bile acid pool size, composition, and homeostasis, perhaps suggesting a feedback loop between glucose and BA metabolism [146–148]. In addition, plasma levels of DCA were significantly higher in children with T1D, even when well-controlled [147]. However, it is uncertain whether these changes are causal factors in the onset and progression of T1D.

In conclusion, accumulating evidence implicates the microbiome in the modulation of (extra)intestinal immunopathologies. However, the exact nature of the molecular and cellular signals interconnecting the two remains an active area of research.

Future perspectives

  • Highlight importance of the field: In the Western world there is rapidly rising incidence of autoimmune type 1 diabetes and Hashimoto's thyroiditis. Current treatment consists of providing hormone replacement treatment, rather than intervening in the pathophysiology. In recent years, numerous studies have suggested a potential causal role of the gut microbiome in the pathogenesis of disease.

  • Provide a summary of current thinking: Both common and discordant changes in the gut microbiome signature in T1D and HT have been found: the overall structure of the T1D/HT-associated dysbiosis is characterized by loss of diversity, reduction in protective bacteria, such as SCFA producing strains, and overgrowth of potentially pathogenic strains, which provide proinflammatory signals and/or encompass mimic peptides of disease relevant autoantigens. These changes precede the clinical manifestation of the disease and appear to play a causal important role in the onset of disease.

  • Comment on future directions: The gut microbiota constitutes a new putative target of intervention in early stage of autoimmune disease. Restoration of healthy microbiota has the potential to re-establish intestinal and immune homeostasis and can be accomplished by fecal microbiota transplantation. The latter represents a safe promising therapeutic approach to counteract immune disorders and also an opportunity to expand our understandings of microbial-immune interactions for specific human diseases. This might well lead to new therapeutic options for dysbiosis-driven diseases: novel probiotics with multiple specific beneficial strains identified from the fecal infuses leading to personalized patient care.

Competing Interests

A.C.F., E.R., and E.F. declare not conflict of interest. M.N. is on the Scientific Advisory Board of Caelus Pharmaceuticals, the Netherlands; M.N. is on the Scientific Advisory Board of Kaleido, USA; M.J.B. is on Scientific Advisory Boards for Dupont, Procter & Gamble, Elysium, and Seed, Inc. None of these are directly relevant to the current paper. There are no patents, products in development or marketed products to declare. The other authors have no conflicts of interest.

Funding

The writing of this review was supported by Le Ducq consortium grant 17CVD01 and a Novo Nordisk Foundation GUT-MMM 2016 grant. M.N. is supported by ZONMW-VIDI 2013 grant (016.146.327) and Dutch Heart Foundation CVON IN CONTROL -II. Supported in part by U01-AI22285 from the National Institute of Allergy and Infectious Diseases, and the Sergai Zlinkoff Fund.

Author Contributions

All authors contributed to the writing and reviewing of the manuscript and all authors read and agreed to the final version.

Abbreviations

     
  • (f)T4

    (free) thyroxine

  •  
  • AITD

    autoimmune thyroid disease

  •  
  • AP-1

    activator protein 1

  •  
  • BA

    bile acids

  •  
  • Breg

    B regulatory lymphocytes

  •  
  • CRAMP

    cathelin-related antimicrobial peptide

  •  
  • CTLA-4

    cytotoxic T-lymphocyte-associated protein 4

  •  
  • D2

    deiodinase 2

  •  
  • DCA

    deoxycholic acid

  •  
  • EAT

    Experimental autoimmune thyroiditis, a mice model for Hashimoto's thyroiditis

  •  
  • F/B ratio

    Firmicutes to Bacteroidetes ratio

  •  
  • FXR

    nuclear farnesoid X receptor

  •  
  • GAD 65

    glutamic acid decarboxylase 65

  •  
  • GALT

    gut-associated lymphoid tissue

  •  
  • GCPR

    G-protein coupled receptors

  •  
  • GF

    germ-free mice

  •  
  • HDAC

    histone deacetylase

  •  
  • HLA

    human leukocyte antigen

  •  
  • HMP

    Human Microbiome Project

  •  
  • HT

    Hashimoto's thyroiditis

  •  
  • IA-2A

    tyrosine phosphatase like protein, autoantigen in T1D

  •  
  • IAA

    antibodies to insulin

  •  
  • IECs

    intestinal epithelial cells

  •  
  • IFIH1 gene

    interferon-induced helicase

  •  
  • IGRP

    islet-specific glucose-6-phosphatase catalytic subunit–related protein

  •  
  • IRF3

    Interferon regulatory factor 3

  •  
  • LPS

    lipopolysaccharide

  •  
  • Mgt

    bacterial magnesium transporter

  •  
  • MHC-1

    major histocompatibility complex I

  •  
  • MyD88

    Myeloid Differentiation Primary Response 88

  •  
  • NF-κB

    nuclear factor kappa-light-chain-enhancer of activated B cells

  •  
  • NIH

    national institute of health

  •  
  • NO

    nitric oxide

  •  
  • NOD mice

    non-obese diabetic mice, a polygenic model of spontaneous autoimmune diabetes

  •  
  • NOD2 receptors

    nucleotide-binding oligomerization domain containing 2 innate receptor

  •  
  • PBMCs

    peripheral blood mononuclear cells

  •  
  • PRR

    pattern recognition receptors

  •  
  • PSA

    polysaccharide A

  •  
  • PTPN22

    protein tyrosine phosphatase, non-receptor type 22

  •  
  • SAA

    serum amyloid A

  •  
  • SCFA

    short chain fatty acids

  •  
  • SFB

    segmented filamentous bacterium, a commensal microbe

  •  
  • SIBO

    small intestinal bacterial overgrowth

  •  
  • SPF mice

    specific pathogen-free mice

  •  
  • T1D

    type 1 diabetes

  •  
  • T3

    tri-iodothyronine

  •  
  • Tg

    thyroglobuline

  •  
  • TGF-β

    Transforming growth factor beta 1

  •  
  • TGR

    G-protein-coupled bile acid receptor or Gpbar1

  •  
  • Th1/2/17

    T helper lymphocytes

  •  
  • TLR

    toll-like receptor

  •  
  • TPO

    thyroid peroxidase

  •  
  • Treg

    T regulatory lymphocytes

  •  
  • TRIF

    TIR-domain-containing adapter-inducing interferon-β

  •  
  • TSH

    thyroid stimulating hormone

  •  
  • Znt8

    against Zinc transporter 8

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