Graves’ disease (GD) and Hashimoto’s thyroiditis (HT) are the most common autoimmune thyroid disease (AITD) types (Fallahi et al. 2019; Rayman 2019; Knezevic et al. 2020). AITD is a set of organ-specific autoimmune diseases with similar genetic and immunological features. The pathogenesis of AITD depends on multiple factors, but the exact mechanism is still unclear. It is generally believed that genetic susceptibility, environmental and survival factors (gender difference), stress, and other factors have important roles (Ajjan and Weetman 2015; Yoo and Chung 2016; Banga and Schott 2018). Moreover, recent evidence has suggested that the gut microbiota is closely associated with some immune-related diseases, including type 1 diabetes mellitus (T1DM) (Kugelberg 2017), rheumatoid arthritis (RA) (Lynch and Pedersen 2016; Horta-Baas et al. 2017), multiple sclerosis, Graves’ ophthalmopathy (Covelli and Ludgate 2017; Shi et al. 2019), HT, and inflammatory bowel disease (Masetti et al. 2018; Zhao et al. 2018; Kozhieva et al. 2019). The gut microbiota has a crucial role in the metabolism, absorption, immune function, and defense mechanism against pathogens (Pickard et al. 2017; Azad et al. 2018; Reddel et al. 2019). Nevertheless, the exact effect of gut microbiota on AITD, particularly HT and GD, is still not well defined.
It has been suggested that gut microbiota targets the TSH receptor (Knezevic 2020; Yao et al. 2020). The combination of microbe and thyroid autoantibody suggests that it may have a role in AITD (Kristensen 2016). Therefore, a deep understanding of the exact mechanism behind these changes and their relationship with AITD may help develop new prevention and treatment strategies. This study explored the alterations and putative activity of gut microbiota in GD and HT. Fecal samples from the GD, HT patients, and healthy people were collected and analyzed using 16s rRNA sequencing.
The inclusion criteria for patients with AITD (GD and HT group) were: (1) age 18–70 years; (2) GD group had the clinical hypermetabolic symptoms and signs, the FT3 of the thyroid function test was > 6.8 pmol/l, FT4 was > 22 pmol/l, TSH was < 0.27 mIU/l, TRAb was > 1.22 IU/l, and thyroid ultrasound indicated a diffuse thyromegaly; in the HT group, FT4 was < 12 pmol/l, TSH was > 4.2 MIU/l, TPOAb was > 34 IU/ml, thyroid ultrasound indicated that it was consistent with Hashimoto’s disease; (3) the patients did not receive anti-thyroid or replacement therapy. In the control group, all thyroid function, TGAb, TPOAb and TRAb, and thyroid ultrasound were within the normal range. The reference range is defined as follows: FT3: 3.1–6.8 pmol/l, FT4: 12–22 pmol/l, TSH: 0.27–4.2 mIU/l, TPOAb: 0–34 IU/ml, TGAb: 0–115 IU/ml, TRAb: 0–1.22 IU/l.
The exclusion criteria were: (1) hypertension, diabetes, lipid disorders, pregnancy, lactation, smoking, alcohol addiction, use of antibiotics in recent three months; use of probiotics, prebiotics, symbiosis, hormone drugs, laxatives, proton pump inhibitors, insulin sensitization agent, and Chinese herbal medicine; (2) other autoimmune diseases such as multiple sclerosis, rheumatoid arthritis, irritable bowel syndrome, and malignant tumor; (3) previous onset of gastrointestinal surgery (e.g., gastrectomy, bariatric surgery, colon resection, resection of the ileum, cholecystectomy, or appendectomy).
All subjects were examined in the morning after overnight fasting (≥ 8 hours). Peripheral blood (6 ml) was collected from all subjects and stored at the temperature of 4°C in EDTA tubes; then, thyroid function and thyroid antibody levels were analyzed. In addition, all subjects were provided with a toilet specimen collection kit to collect feces. Each fecal sample was divided into equal samples, frozen with dry ice, and stored at –80°C.
Clinical characteristics of patients and healthy controls (average ± standard deviation).
GD |
HT |
Controls |
|
---|---|---|---|
Age (years) | 49.20 ± 8.68 | 56.77 ± 12.44 | 49.31 ± 13.36 |
Sex (M/F) | 8/19 | 11/16 | 7/9 |
FT3 (pmol/l) | 14.74 ± 8.65** | 3.93 ± 1.22 | 5.13 ± 0.76 |
FT4 (pmol/l) | 52.19 ± 24.83** | 7.73 ± 2.99* | 17.91 ± 1.88 |
TSH (mIU/l) | 0.005 ± 0.000** | 38.798 ± 32.452** | 3.030 ± 0.806 |
ATG (IU/ml) | 371.84 ± 320.30** | 1248.39 ± 2623.73** | 56.72 ± 26.04 |
ATPO (IU/ml) | 352.04 ± 148.07** | 519.40 ± 833.86** | 12.27 ± 8.43 |
TRAb (IU/ml) | 8.69 ± 2.90** | 1.21 ± 0.66 | 0.68 ± 0.2 |
Compared with the control group *
The gut microbiota of GD and HT patients were different from that of the healthy control group.
A) The rank-abundance curve of the GD group, B) the rank-abundance curve of the HT group.
The dilution curve analysis showed that the gut microbiota of the GD and HT patients had a similar species richness compared to the healthy group. A total of 686 OTUs were detected in all the samples, among which 389 were commonly shared among groups. Sixty-three, 61, and 21 unique OTUs were identified in the GD, HT, and healthy control samples.
Next, taxon-dependent analysis was performed using the Ribosome Database Project (RDP) classifier to describe gut microbiota composition in different groups. The HT group had the highest content of Proteobacteria and Actinomycetes, followed by the GD group and the healthy control group. Notably, the HT group contained a small number of
The gut microbiota of GD and HT patients were different from that of the healthy control group.
C) histogram of horizontal flora composition of “family”, D) histogram of horizontal flora composition of “genus”, E) PlS-DA analysis with group supervision.
The gut microbiota of GD and HT patients were different from that of the healthy control group.
F) ANOSIM analysis.
These results indicated that the levels of bacterial abundance and diversity in the gut microbiota of the GD and HT patients were similar to those of the healthy controls. In contrast, the overall structure of the gut microbiota of both patients and healthy controls were significantly different.
Bacterial flora classification map obtained by LEfSe analysis.
A) LEfSe shows the greatest difference in abundance (taxa) between the three groups (LDA threshold > 3).
The abundance of Negativicutes in healthy control samples and Proteobacteria and Erysipelotrichia in GD patient samples increased.
At the “phylum” level, the proportions of Cyanobacteria in the GD samples were higher than those in the healthy control samples, while the proportions of abnormal cocci and Cyanobacteria were lower (Fig. 2B). Moreover, the proportions of Cyanobacteria in the samples of the HT patients were higher than that of the healthy control group, while the proportions of abnormal Coccinobacteria and Cyanobacteria were lower (Fig. 2C).
Bacterial flora classification map obtained by LEfSe analysis.
B–G) the difference in microbiota between the GD group or HT groups and the healthy control group at the phylum level (B, C), at the family level (D, E), and at the genus level (F, G). *
At the level of “family”,
E, F, G
At the level of “genera”,
These data suggest a difference in the microbiome of GD and HT patients compared to the healthy control group. Although there was no significant change in bacterial diversity, the abnormal composition of fecal microflora indicated gut microbiota imbalance in the GD and HT patients.
Random forest analysis and validation information.
A) Random forest analysis between the GD and healthy control groups, and B) between the HT group and control groups.
Random forest analysis and validation information.
C) verification information of the first three genera of random forest results from the GD group and healthy control group, and D) between the HT group and healthy control group.
Prediction Results using the COG and KEGG databases.
A, B) The difference in the COG functional prediction between the disease and control groups; C, D) the difference in the KEGG function prediction between the disease and control groups; E, F) the difference in the COG abundance prediction between the disease and control groups; G, H) the difference in the KEGG enzyme prediction between the disease and the control groups. *
ko02010 – ABC transporters, ko00230 – purine metabolism, ko00520 – amino sugar and nucleotide sugar metabolism, ko02020 – two-component system, ko00330 – arginine and proline metabolism, ko00970 – aminoacyl-tRNA biosynthesis, ko00500 – starch and sucrose metabolism, ko00680 – methane metabolism, ko00250 – alanine, aspartate and glutamate metabolism, ko00010 – glycolysis/gluconeogenesis, ko00190 – oxidative phosphorylation, ko00860 – porphyrin and chlorophyll metabolism, ko00270 – cysteine and methionine metabolism, ko00720 – carbon fixation pathways in prokaryotes, ko00620 – pyruvate metabolism, ko03010 – ribosome, ko00240 – pyrimidine metabolism, ko03440 – homologous recombination.
According to the KEGG distribution in Fig. 4C, there were significant differences in purine metabolism, aminoacyl tRNA biosynthesis, cysteine, and methionine metabolism between the GD group and the healthy control group (all
Prediction Results using the COG and KEGG databases.
C, D) the difference in the KEGG function prediction between the disease and control groups. *
According to the COG database, the enzyme “glycosyltransferase” was a specific enzyme in the GD group (Fig. 4E). Also, the resolving enzyme has been suggested as a specific enzyme for the HT group (Fig. 4F).
According to the KEGG database, the “ATP-dependent RNA helicase DHX58” (EC 3.6.3.14) was the highest in the GD group, followed by the healthy control group, while it was the lowest in the HT group (Fig. 4G and 4H). “Glutamine synthase” (EC 6.3.1.2) (Fig. 4G) and “DNA-directed RNA polymerase B subunit” (EC 2.7.7.6) (Fig. 4H) were the specific enzymes for the GD group and HT group, respectively.
Prediction Results using the COG and KEGG databases.
E, F) the difference in the COG abundance prediction between the disease and control groups.
*
ko02010 – ABC transporters, ko00230 – purine metabolism, ko00520 – amino sugar and nucleotide sugar metabolism, ko02020 – two-component system, ko00330 – arginine and proline metabolism, ko00970 – aminoacyl-tRNA biosynthesis, ko00500 – starch and sucrose metabolism, ko00680 – methane metabolism, ko00250 – alanine, aspartate and glutamate metabolism, ko00010 – glycolysis/gluconeogenesis, ko00190 – oxidative phosphorylation, ko00860– porphyrin and chlorophyll metabolism, ko00270 – cysteine and methionine metabolism, ko00720 – carbon fixation pathways in prokaryotes, ko00620 – pyruvate metabolism, ko03010 – ribosome, ko00240 – pyrimidine metabolism, ko03440 – homologous recombination.
Prediction Results using the COG and KEGG databases.
G, H) the difference in the KEGG enzyme prediction between the disease and the control groups.
*
Ten different strains of the two groups were divided into three categories, as shown in Fig. 5. Table II shows the top ten predictions using the KEGG database for the abundance of these three categories. The metabolic pathway of the “ABC transporter” (responsible for ATP transport) existed in the prediction results of the three different strains, indicating that this metabolic pathway is highly correlated with the occurrence of GD and HT.
Diagram of random forest differential strains.
The first ten types of function prediction based on KEGG of the RANDOM forest differential strains.
GD | Common | HT |
---|---|---|
ko00240 | ko00350 | ko03010 |
ko00330 | ko00642 | ko00550 |
ko00860 | ko00626 | ko00300 |
ko00680 | ko02010 | ko00010 |
ko00520 | ko00400 | |
ko00620 | ko03030 | |
ko02020 | ko02020 | |
ko00720 | ko00720 | |
ko00190 | ko00190 | |
ko02010 | ko02010 |
Next, we conducted a Venn diagram analysis based on the differential strains in Fig. 3A and 3B. Ten differential strains were further divided into two groups and three categories, as shown in Fig. 5. Table II shows the prediction results according to the KEGG database of the top ten strains different in abundance. The “ABC transporter” pathway (responsible for ATP transport) was found in the predicted results of three different strains, suggesting that this pathway was strongly associated with the development of the GD and HT.
GD and HT are two major representative diseases of AITD. A previous study suggested an association between gut microbiota imbalance and HT or GD (Virili et al. 2018; Yao 2020). However, thus far, no studies have reported a common imbalance of gut microbiota in GD and HT patients. Our study found multiple bacteria with similar change direction and shared metabolic pathways involved in the GD and HT patients.
In this study, genomic DNA was extracted from the GD, HT, and healthy subjects feces and analyzed using the 16S rRNA gene sequencing. We found that the abundance and diversity of gut microbiota in the GD and HT patients were similar to the healthy control group. However, we also discovered that Actino-bacteria and Proteobacteria contents were the highest in the HT group, followed by the GD group, while they were the lowest in the control group. A previous retrospective study showed the highest alteration in the abundance of Bacteroidetes, Proteobacteria, and Firmicutes between the systemic inflammatory disease group and the healthy group (Nam et al. 2013; Clemente et al. 2018; Faucher et al. 2020). Zhao et al. (2018) found a higher gut microbiota richness and diversity in HT patients with normal thyroid function. Firmicutes were the most abundant, while Bacteroides were less common in HT patients, consistent with our findings. Nevertheless, in this study, HT patients were all hypothyroidism patients, that are different from the study reported by Zhao et al. (2018). Furthermore, Zhou et al. (2014) showed the gut microbiota diversity in the GD patients;
The LEfSe analysis showed similar trends in bacteria in the GD and HT groups, where the most apparent changes included an increased abundance of Erysipelotrichia, Cyanobacteria,
The results of random forest analysis indicated that the areas under the verification information curve of
In this study, the COG database was used to predict the function of the HT, GD, and healthy control groups. We found that HT and GD patients were enriched in carbohydrate transport and metabolism compared to the control group but had lower amino acid transport and metabolism activity. According to the COG prediction of the function of different strains, four unique strains of the HT group and six common strains of the HT and GD groups were identified as “S” (Function unknown). It may indicate that there is still an unknown metabolic pathway in the development of such diseases, and it might be the focus of subsequent research. In this study, KEGG was used to predict the functions of differential bacteria unique to the GD and HT groups and the bacteria common to these two groups. Venn diagram analysis was simultaneously performed. We found that the “ABC transporter” metabolic pathway existed in the predicted results of three different strains, indicating that this metabolic pathway was highly correlated with the occurrence of GD and HT. Studies have shown that the levels of L-arginine, L-ornithine, lysine, and guanbutamine in the GD and HT patients are higher than those in the healthy group, while the levels of putrescine, 1,3-diaminopropylene, spermine, and N-acetylputrescine are lower than those in the healthy group (Song et al. 2019). Some polyamine metabolites were only different in the GD or HT patients compared with the healthy group. Spermidine proportions were significantly reduced in all the patients. This study confirmed that most metabolites of the GD and HT had similar patterns compared with the healthy group, suggesting a common pathophysiological basis or metabolic pathway.
This study has a few limitations. First, it was a single-center study with a relatively small sample size, which may lead to bias. Second, the specific mechanism of abnormal gut microbiota involved in GD and HT was not examined. Third, all the people were selected from inland North China in this study. Considering the differences in dietary structure and ethnicity, the higher iodine intake in coastal areas, thyroid function, and gut microbiota may differ. Thus, further in-depth multicenter studies with a large sample size should be carried out to confirm these findings.
Our study demonstrated that