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

Hong Zhao
Hong Zhao
, 
Lijie Yuan
Lijie Yuan
, 
Dongli Zhu
Dongli Zhu
, 
Banghao Sun
Banghao Sun
, 
Juan Du
Juan Du
 e 
Jingyuan Wang
Jingyuan Wang
   | 09 giu 2022
Polish Journal of Microbiology's Cover Image
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Article Category: Original Paper
Pubblicato online: 09 giu 2022
Pagine: 173 - 189
Ricevuto: 12 gen 2022
Accettato: 07 mar 2022
DOI: https://doi.org/10.33073/pjm-2022-016
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© 2022 Hong Zhao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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 ± 8.68 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

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
eISSN:
2544-4646
Lingua:
Inglese
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Life Sciences, Microbiology and Virology
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