Alterations of the gut microbiota in type 2 diabetics with or without subclinical hypothyroidism

Alternations in the human skin, gut and vaginal microbiomes in perimenopausal or postmenopausal Vulvar lichen sclerosus

Vulvar lichen sclerosus (VLS) is a chronic and progressive dermatologic condition that can cause physical dysfunction, disfigurement, and impaired quality of life. However, the etiology of VLS remains unknown. The vulvar skin, intestinal and vaginal microbiomes have been postulated to play important roles in the pathogenesis of this disease. The aim of this study was to compare the compositional characteristics of the vulvar skin, vagina, and gut microbiota between perimenopausal or postmenopausal VLS patients and healthy controls. The study involved six perimenopausal or postmenopausal VLS patients which were based on characteristic clinical manifestations and histologic confirmation and five healthy controls. The pruritus severity of each patient was evaluated using the NRS scale, and the dermatology-specific health-related quality of life was assessed using the Skindex-16. Metagenomic sequencing was performed, and the results were analyzed for alpha and beta diversity. LEfSe analysis were used to investigate the microbial alterations in vulvar skin, gut and vagina. KEGG databases were used to analyze differences in functional abundance. The study found significant differences in alpha diversity between the two groups in stool and vaginal samples (P < 0.05). Patients with VLS had a higher abundance of Enterobacter cloacae, Flavobacterium_branchiophilum, Mediterranea_sp._An20, Parabacteroides_johnsoniiand Streptococcus_bovimastitidis on the vulvar skin, while Corynebacterium_sp._zg-913 was less abundant compared to the control group. The relative abundance of Sphingomonas_sp._SCN_67_18, Sphingobium_sp._Ant17, and Pontibacter_sp_BT213 was significantly higher in the gut samples of patients with VLS.Paenibacillus_popilliae,Gemella_asaccharolytica, and Coriobacteriales_bacterium_DNF00809 compared to the control group. Additionally, the vaginal samples of patients with VLS exhibited a significantly lower relative abundance of Bacteroidales_bacterium_43_8, Bacteroides_sp._CAG:20, Blautia_sp._AM28-10, Fibrobacter_sp._UWB16, Lachnospiraceae_bacterium_AM25-39, Holdemania_filiformis, Lachnospiraceae_bacterium_GAM79, and Tolumonas_sp. Additionally, the butyrate-producing bacterium SS3/4 showed a significant difference compared to the controls. The study found a negative relationship between Sphingobium_sp._Ant17 in stool and Skindex-16 (P < 0.05), while Mediterranea_sp._An20 had a positive correlation with Skindex-16 (P < 0.05) in the skin. Additionally, our functional analysis revealed alterations in Aminoacyl_tRNA_biosynthesis, Glutathione_metabolism, the pentose phosphate pathway, and Alanine__aspartate_and_glutamate_metabolism in the VLS patient group. The study suggests that perimenopausal or postmenopausal patients with VLS have a modified microbiome in the vulvar skin, gut, and vagina. This modification is linked to abnormal energy metabolism, increased oxidative stress, and abnormal amino acid metabolism.

Bioinformatics analysis of the diversity of gut microbiota and different microbiota on insulin resistance in diabetes mellitus patients

It aimed to explore the diversity of gut microbiota (GM) and the effect of different microbiota on insulin resistance in diabetes mellitus (DM) patients through bioinformatics analysis.

MATERIAL AND METHOD

Microarray data were obtained from GEO database. GM samples from DM patients and healthy controls were collected, and 16S rRNA gene sequencing was carried out adopting high-throughput sequencing technology. The differential expression genes were screened using the Qlucore Omics Explorer 3.0 software. Subsequently, online tools such as STRING and DAVID were utilized for bioinformatics analysis of the differential expression genes. The differences in bacterial diversity between DM patients and healthy controls were evaluated by analyzing the diversity indicators of the microbiota, such as Shannon and Chao1 indexes. Differential abundance and functional prediction analysis were adopted to explore the different microbiota and its possible metabolic pathways between DM patients and controls. And differences in insulin resistance in specific bacterial taxa were analyzed.

RESULT

GM diversity between DM patients and controls had significant differences. GM diversity was lower in DM patients compared with controls, as indicated by a decrease in Shannon and Chao1 indexes. The differential abundance analysis showed that there were multiple different bacterial communities between DM patients and controls, including some bacterial communities at the genus-level. Functional prediction analysis also revealed potential metabolic pathways related to GM and insulin resistance in DM patients. HEXB, ZC3H12A, CCR, CXCR3, GBR10, CDK9, TXN, IGFBP3, PDHA1, and NDUFB3 genes may be potential targets for treatment.

CONCLUSION

There are differences in GM diversity between DM patients and healthy controls, and the different microbiota may be related to the occurrence and development of insulin resistance.