Guild-level signature of gut microbiome for diabetic kidney disease.

Current microbiome signatures for chronic diseases such as diabetic kidney disease (DKD) are mainly based on low-resolution taxa such as genus or phyla and are often inconsistent among studies. In microbial ecosystems, bacterial functions are strain specific, and taxonomically different bacteria tend to form co-abundance functional groups called guilds. Here, we identified guild-level signatures for DKD by performing in-depth metagenomic sequencing and conducting genome-centric and guild-based analysis on fecal samples from 116 DKD patients and 91 healthy subjects. Redundancy analysis on 1,543 high-quality metagenome-assembled genomes (HQMAGs) identified 54 HQMAGs that were differentially distributed among the young healthy control group, elderly healthy control group, early-stage DKD patients (EDG), and late-stage DKD patients (LDG). Co-abundance network analysis classified the 54 HQMAGs into two guilds. Compared to guild 2, guild 1 contained more short-chain fatty acid biosynthesis genes and fewer genes encoding uremic toxin indole biosynthesis, antibiotic resistance, and virulence factors. Guild indices, derived from the total abundance of guild members and their diversity, delineated DKD patients from healthy subjects and between different severities of DKD. Age-adjusted partial Spearman correlation analysis showed that the guild indices were correlated with DKD disease progression and with risk indicators of poor prognosis. We further validated that the random forest classification model established with the 54 HQMAGs was also applicable for classifying patients with end-stage renal disease and healthy subjects in an independent data set. Therefore, this genome-level, guild-based microbial analysis strategy may identify DKD patients with different severity at an earlier stage to guide clinical interventions. IMPORTANCE: Traditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.

Altered ocular surface microbiota in obesity: a case-control study.

Purpose: This study aimed to investigate the composition of ocular surface microbiota in patients with obesity. Methods: This case-control study, spanning from November 2020 to March 2021 at Henan Provincial People's Hospital, involved 35 patients with obesity and an equivalent number of age and gender-matched healthy controls. By employing 16S rRNA sequencing, this study analyzed the differences in ocular surface microbiota between the two groups. The functional prediction analysis of the ocular surface microbiota was conducted using PICRUSt2. Results: The alpha diversity showed no notable differences in the richness or evenness of the ocular surface microbiota when comparing patients with obesity to healthy controls (Shannon index, P=0.1003). However, beta diversity highlighted significant variances in the microbiota composition of these two groups (ANOSIM, P=0.005). LEfSe analysis revealed that the relative abundances of Delftia, Cutibacterium, Aquabacterium, Acidovorax, Caulobacteraceae unclassified, Comamonas and Porphyromonas in patients with obesity were significantly increased (P<0.05). Predictive analysis using PICRUSt2 highlighted a significant enhancement in certain metabolic pathways in patients with obesity, notably xenobiotics metabolism via cytochrome P450 (CYP450), lipid metabolism, and the oligomerization domain (NOD)-like receptor signaling pathway (P<0.05). Conclusions: Patients with obesity exhibit a distinct ocular surface core microbiome. The observed variations in this microbiome may correlate with increased activity in CYP450, changes in lipid metabolism, and alterations in NOD-like receptor signaling pathways.

Canagliflozin alters the gut, oral, and ocular surface microbiota of patients with type 2 diabetes mellitus.

Background: Modifications in the gut microbiota may be a crucial factor in the efficacy of canagliflozin (Cana) in managing patients with type 2 diabetes mellitus (T2DM). However, the interplay between oral and ocular surface microbiota and this treatment remains poorly explored. Aim: This study aimed to assess alterations in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment in patients with T2DM. Methods: In this 30-day, controlled before-and-after study, 21 treatment-naïve patients with T2DM received sole treatment with Cana (100 mg/day), and were matched with 10 healthy controls based on gender and age. Using 16S rRNA sequencing, changes in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment were assessed and compared with those of healthy controls. Concurrently, diabetes-related clinical parameters were recorded over the study period. The trial was registered in the Chinese Clinical Trial Registry (ChiCTR200034878). Results: A noticeable shift was observed in the gut, oral, and ocular surface microbiota pre- and post-Cana treatment. The post-Cana treatment gut microbiota was more similar to that of the healthy controls. Network correlation analysis revealed that modifications in the gut, oral, and ocular surface microbiota were related to changes in clinical parameters, especially for the ocular surface microbiota. Clinical parameters: A significant decrease in fasting plasma glucose (8.22 ± 2.19 vs 6.87 ± 1.09 mmol/L), glycated serum protein [291.00 (264.00, 353.00) vs 275.00 (251.00, 342.50) µmol/L], hemoglobin A1c (7.39 ± 1.18 vs 7.12 ± 1.33%), body mass index (25.32 ± 2.99 vs 24.83 ± 2.95 kg/m2), systolic blood pressure (129.05 ± 17.51 vs 123.43 ± 14.82 mmHg), and urinary creatinine [158.40 (74.75, 219.15) vs 79.70 (56.25, 138.10) µmmol/kg] levels was noted after 30-day Cana monotherapy (P < 0.05). Gut microbiome: Treatment with Cana resulted in an increase in the relative abundance of short-chain fatty acid (SCFA)-producing bacteria, particularly Lachnospiraceae UCG 004, Bacteroides, and Lachnospiraceae NK4A136 group. Oral microbiota: After Cana treatment, a significant increase of Prevotella and Veillonella, both of which are known to be closely associated with SCFAs, was observed. Ocular surface microbiota: Post-Cana administration, the ocular surface microbiota exhibited the most distinct changes in structure and composition. Remarkably, the majority of the increased ocular surface microbiota could produce SCFAs within the gut microbiota. Conclusion: Cana effectively improved the dysregulated glucose metabolism in patients with T2DM. This improvement can potentially be attributed to the restoration of balance among the gut, oral, and ocular surface microbial communities. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=56487, identifier ChiCTR2000034878.

Intermittent hypoxia is involved in gut microbial dysbiosis in type 2 diabetes mellitus and obstructive sleep apnea-hypopnea syndrome.

BACKGROUND: Obstructive sleep apnea (OSA)-hypopnea syndrome (OSAHS) has been recognized as a comorbidity of type 2 diabetes mellitus (T2DM); more than half of T2DM patients suffer from OSAHS. Intermittent hypoxia (IH) plays an important role in metabolic diseases, such as obesity and OSAHS, through various mechanisms, including altering the gut microecological composition and function. Therefore, it is important to study the role of gut microbiota in T2DM patients with OSAHS, which has a high incidence and is prone to several complications. AIM: To assess whether IH is involved in altering the fecal microbiome in T2DM patients with OSAHS. METHODS: Seventy-eight participants were enrolled from Henan Province People's Hospital and divided into healthy control (HC, n = 26), T2DM (n = 25), and T2DM + OSA (n = 27) groups based on their conditions. The fecal bacterial DNA of the research participants was extracted and subjected to 16S ribosomal RNA sequencing. The clinical indices, such as insulin resistance index, homocysteine (HCY) concentration, and the concentrations of inflammatory factors in the peripheral blood, were assessed and recorded. RESULTS: Group T2DM + OSA had the highest apnea-hypopnea index (AHI) (2.3 vs 3.7 vs 13.7), oxygen desaturation index (0.65 vs 2.2 vs 9.1), HCY concentration (9.6 µmol/L vs 10.3 µmol/L vs 13.81 µmol/L) and C-reactive protein (CRP) concentrations (0.3 mg/L vs 1.43 mg/L vs 2.11 mg/L), and lowest mean oxygen saturation (97.05% vs 96.6% vs 94.7%) among the three groups. Twelve and fifteen key differences in amplicon sequence variants were identified when comparing group T2DM + OSA with groups T2DM and HC, respectively. We found progressively decreased levels of Faecalibacterium, Eubacterium, and Lachnospiraceae, and an increase in the level of Actinomyces, which strongly correlated with the HCY, CRP, fasting plasma glucose, and hemoglobin A1c concentrations, AHI, mean oxygen saturation, and insulin resistance index in group T2DM + OSA (P < 0.05). CONCLUSION: For T2DM patients with OSAHS, IH may be involved in selective alterations of the gut microbiota, which may affect the pathophysiological development of T2DM and DM-related complications.