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Alterations and Mechanism of Gut Microbiota in Graves’ Disease and Hashimoto’s Thyroiditis Cover

Alterations and Mechanism of Gut Microbiota in Graves’ Disease and Hashimoto’s Thyroiditis

Polish Journal of Microbiology
Volume 71 (2022): Issue 2 (June 2022)
By: Hong Zhao,  Lijie Yuan,  Dongli Zhu,  Banghao Sun,  Juan Du and  Jingyuan Wang  
Open Access
|Jun 2022

Figures & Tables

Fig. 1

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 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.

Fig. 1

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. C) histogram of horizontal flora composition of “family”, D) histogram of horizontal flora composition of “genus”, E) PlS-DA analysis with group supervision.

Fig. 1

The gut microbiota of GD and HT patients were different from that of the healthy control group. F) ANOSIM analysis.
The gut microbiota of GD and HT patients were different from that of the healthy control group. F) ANOSIM analysis.

Fig. 2

Bacterial flora classification map obtained by LEfSe analysis. A) LEfSe shows the greatest difference in abundance (taxa) between the three groups (LDA threshold > 3).
Bacterial flora classification map obtained by LEfSe analysis. A) LEfSe shows the greatest difference in abundance (taxa) between the three groups (LDA threshold > 3).

Fig. 2

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). *p < 0.05; ** p < 0.01; ***p < 0.001.
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). *p < 0.05; ** p < 0.01; ***p < 0.001.

Fig. 2

E, F, G
E, F, G

Fig. 3

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. A) Random forest analysis between the GD and healthy control groups, and B) between the HT group and control groups.

Fig. 3

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.
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.

Fig. 4

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. *p < 0.05; **p < 0.01, ***p < 0.001. 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. 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. *p < 0.05; **p < 0.01, ***p < 0.001. 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.

Fig. 4

Prediction Results using the COG and KEGG databases. C, D) the difference in the KEGG function prediction between the disease and control groups. *p < 0.05; **p < 0.01, ***p < 0.001.
Prediction Results using the COG and KEGG databases. C, D) the difference in the KEGG function prediction between the disease and control groups. *p < 0.05; **p < 0.01, ***p < 0.001.

Fig. 4

Prediction Results using the COG and KEGG databases. E, F) the difference in the COG abundance prediction between the disease and control groups. *p < 0.05; **p < 0.01, ***p < 0.001. 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. E, F) the difference in the COG abundance prediction between the disease and control groups. *p < 0.05; **p < 0.01, ***p < 0.001. 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.

Fig. 4

Prediction Results using the COG and KEGG databases. G, H) the difference in the KEGG enzyme prediction between the disease and the control groups. *p < 0.05; **p < 0.01, ***p < 0.001.
Prediction Results using the COG and KEGG databases. G, H) the difference in the KEGG enzyme prediction between the disease and the control groups. *p < 0.05; **p < 0.01, ***p < 0.001.

Fig. 5

Diagram of random forest differential strains.
Diagram of random forest differential strains.

Clinical characteristics of patients and healthy controls (average ± standard deviation)_

GD (n = 27)HT (n = 27)Controls (n = 16)
Age (years)49.20 ± 8.6856.77 ± 12.4449.31 ± 13.36
Sex (M/F)8/1911/167/9
FT3 (pmol/l)14.74 ± 8.65**3.93 ± 1.225.13 ± 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 ± 0.660.68 ± 0.2

The first ten types of function prediction based on KEGG of the RANDOM forest differential strains_

GDCommonHT
ko00240ko00350ko03010
ko00330ko00642ko00550
ko00860ko00626ko00300
ko00680ko02010ko00010
ko00520 ko00400
ko00620 ko03030
ko02020 ko02020
ko00720 ko00720
ko00190 ko00190
ko02010 ko02010
DOI: https://doi.org/10.33073/pjm-2022-016 | Journal eISSN: 2544-4646 | Journal ISSN: 1733-1331
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Language: English
Page range: 173 - 189
Submitted on: Jan 12, 2022
Accepted on: Mar 7, 2022
Published on: Jun 9, 2022
Published by: Polish Society of Microbiologists
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
gut microbiota,
autoimmune thyroid disease,
Graves’ disease,
Hashimoto’s thyroiditis
Related subjects:
Life sciences,
Microbiology and virology

© 2022 Hong Zhao, Lijie Yuan, Dongli Zhu, Banghao Sun, Juan Du, Jingyuan Wang, published by Polish Society of Microbiologists
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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