Characteristics of the microbiota in the urine of women with type 2 diabetes

Draft genome assemblies of one Lactobacillus gasseri strain and two Lactobacillus jensenii strains isolated from voided urine samples

We present the draft genome for three Lactobacillus strains isolated from female urine specimens: Lactobacillus gasseri UMB1673, Lactobacillus jensenii UMB1855, and Lactobacillus jensenii UMB5069. Focusing on strains within the female urinary microbiome can provide a more well-rounded understanding of the microbial community and its influence on health and disease.

Draft genome sequences of three Lactobacillus crispatus strains, isolated from the female urinary tract

Lactobacillus crispatus is a frequent member of the female urogenital microbiota. Here, we present the draft genome assemblies of three L. crispatus strains: UMB4356, UMB5661, and UMB6244. All strains were isolated from voided urine samples from females with type 2 diabetes.

Draft genome assembly of Lactobacillus johnsonii UMB3423 from a voided urine sample

While the urogenital microbiota of asymptomatic females is often dominated by species of Lactobacillus, Lactobacillus johnsonii is not a common member. It is more frequently found in the gastrointestinal tract. Here, we present the draft genome sequence of L. johnsonii UMB3423, which was isolated from a voided urine sample.

Effect of mastectomy on gut microbiota and its metabolites in patients with breast cancer

Background

The relationship between gut microbiota and breast cancer has been extensively studied; however, changes in gut microbiota after breast cancer surgery are still largely unknown.

Materials and methods

A total of 20 patients with breast cancer underwent routine open surgery at the First Affiliated Hospital of Hainan Medical College from 1 June 2022 to 1 December 2022. Stool samples were collected from the patients undergoing mastectomy for breast cancer preoperatively, 3 days later, and 7 days later postoperatively. The stool samples were subjected to 16s rRNA sequencing.

Results

Surgery did not affect the α-diversity of gut microbiota. The β-diversity and composition of gut microorganisms were significantly affected by surgery in breast cancer patients. Both linear discriminant analysis effect size (LEfSe) analysis and between-group differences analysis showed that surgery led to a decrease in the abundance of Firmicutes and Lachnospiraceae and an increase in the abundance of Proteobacteria and Enterobacteriaceae. Moreover, 127 differential metabolites were screened and classified into 5 categories based on their changing trends. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed significant changes in the phenylalanine metabolic pathway and exogenous substance metabolic pathway. Eight characterized metabolites were screened using ROC analysis.

Conclusion

Our study found that breast cancer surgery significantly altered gut microbiota composition and metabolites, with a decrease in beneficial bacteria and an increase in potentially harmful bacteria. This underscores the importance of enhanced postoperative management to optimize gut microbiota.

Impact of coexisting type 2 diabetes mellitus on the urinary microbiota of kidney stone patients

Objectives

Type 2 diabetes mellitus (T2DM) commonly complicates kidney stone disease (KSD). Our objective is to investigate the variations in the urinary microbiota between individuals with KSD alone and those with KSD plus T2DM. This exploration could have implications for disease diagnosis and treatment strategies.

Methods

During lithotripsy, a ureterscope was employed, and 1 mL of urine was collected from the renal pelvis after bladder disinfection. Sequencing targeting the V3-V4 hypervariable region was performed using the 16S rRNA and Illumina Novaseq platform.

Results

The Shannon index showed a significant decrease in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.041). Principal Coordinate Analysis (PCoA) demonstrated a distinct bacterial community in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.027). The abundance of Sphingomonas, Corynebacterium, and Lactobacillus was significantly higher in the KSD plus T2DM group than in the KSD-only group (false discovery rate < 0.05). Furthermore, Enhydrobacter, Chryseobacterium, and Allobaculum were positively correlated with fasting blood glucose and HbA1c values (P < 0.05).

Conclusions

The urinary microbiota in the renal pelvis exhibits differences between patients with KSD plus T2DM and those with KSD alone. Further studies employing animal models are necessary to validate these distinctions, potentially paving the way for therapeutic developments based on the urinary microbiota.

Draft genome sequences of Lactobacillus paragasseri strains isolated from the female urinary tract

Lactobacillus species are often associated with a healthy environment in the female urogenital tract. Here, we present the draft genome assemblies for three L. paragasseri strains isolated from voided urine samples from females with type II diabetes; two of the strains were collected from the same individual 3 months apart.

Draft genome sequences of five Lactobacillus gasseri strains isolated from voided urine samples

Lactobacillus gasseri is a member of the gut, oral, and female urogenital microbiota. Here, we present the draft genome assemblies of L. gasseri UMB1549, UMB1579, UMB1644, UMB3348, and UMB5890, which were isolated from voided urine samples from females with Type 2 diabetes.

Draft genomes of Lactobacillus jensenii UMB0908, UMB1545, and UMB5777 from the urinary tract

Lactobacillus jensenii is a member of the female urogenital microbiome. Previous research has identified strains of L. jensenii that are capable of inhibiting or killing uropathogens, including E. coli. Here, we present the draft genomes of three L. jensenii strains collected from urine samples.

Draft genome sequences of Lactobacillus mulieris strains isolated from the female urinary tract

Lactobacillus mulieris is a frequent member of the healthy female urogenital tract. Here, we present the draft genomes of two L. mulieris strains isolated from urine samples: UMB0446 and UMB3420. The draft genomes presented here further expand our understanding of the female urinary microbiome.

Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile

The gut microbiota plays an important role in the regulation of the immune system and the metabolism of the host. The aim of the present study was to characterize the gut microbiota of patients with type 2 diabetes mellitus (T2DM). A total of 118 participants with newly diagnosed T2DM and 89 control subjects were recruited in the present study; six clinical parameters were collected and the quantity of 10 different types of bacteria was assessed in the fecal samples using quantitative PCR. Taking into consideration the six clinical variables and the quantity of the 10 different bacteria, 3 predictive models were established in the training set and test set, and evaluated using a confusion matrix, area under the receiver operating characteristic curve (AUC) values, sensitivity (recall), specificity, accuracy, positive predictive value and negative predictive value (npv). The abundance of Bacteroides, Eubacterium rectale and Roseburia inulinivorans was significantly lower in the T2DM group compared with the control group. However, the abundance of Enterococcus was significantly higher in the T2DM group compared with the control group. In addition, Faecalibacterium prausnitzii, Enterococcus and Roseburia inulinivorans were significantly associated with sex status while Bacteroides, Bifidobacterium, Enterococcus and Roseburia inulinivorans were significantly associated with older age. In the training set, among the three models, support vector machine (SVM) and XGboost models obtained AUC values of 0.72 and 0.70, respectively. In the test set, only SVM obtained an AUC value of 0.77, and the precision and specificity were both above 0.77, whereas the accuracy, recall and npv were above 0.60. Furthermore, Bifidobacterium, age and Roseburia inulinivorans played pivotal roles in the model. In conclusion, the SVM model exhibited the highest overall predictive power, thus the combined use of machine learning tools with gut microbiome profiling may be a promising approach for improving early prediction of T2DM in the near feature.

Multiple bacteria associated with the more dysbiotic genitourinary microbiomes in patients with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) influences the human health and can cause significant illnesses. The genitourinary microbiome profiles in the T2DM patients remain poorly understood. In the current study, a series of bioinformatic and statistical analyses were carried out to determine the multiple bacteria associated with the more dysbiotic genitourinary microbiomes (i.e., those with lower dysbiosis ratio) in T2DM patients, which were sequenced by Illumina-based 16S rRNA gene amplicon sequencing. All the genitourinary microbiomes from 70 patients with T2DM were clustered into three clusters of microbiome profiles, i.e., Cluster_1_T2DM, Cluster_2_T2DM and Cluster_3_T2DM, with Cluster_3_T2DM at the most dysbiotic genitourinary microbial status. The three clustered T2DM microbiomes were determined with different levels of alpha diversity indices, and driven by distinct urinalysis variables. OTU12_Clostridiales and OTU28_Oscillospira were likely to drive the T2DM microbiomes to more dysbiotic status, while OTU34_Finegoldia could play a vital role in maintaining the least dysbiotic T2DM microbiome (i.e., Cluster_1_T2DM). The functional metabolites K08300_ribonuclease E, K01223_6-phospho-beta-glucosidase and K00029_malate dehydrogenase (oxaloacetate-decarboxylating) (NADP+) were most associated with Cluster_1_T2DM, Cluster_2_T2DM and Cluster_3_T2DM, respectively. The characteristics and multiple bacteria associated with the more dysbiotic genitourinary microbiomes in T2DM patients may help with the better diagnosis and management of genitourinary dysbiosis in T2DM patients.

UPΦ phages, a new group of filamentous phages found in several members of Enterobacteriales

Filamentous phages establish chronic infections in their bacterial hosts, and new phages are secreted by infected bacteria for multiple generations, typically without causing host death. Often, these viruses integrate in their host's genome by co-opting the host's XerCD recombinase system. In several cases, these viruses also encode genes that increase bacterial virulence in plants and animals. Here, we describe a new filamentous phage, UPϕ901, which we originally found integrated in a clinical isolate of Escherichia coli from urine. UPϕ901 and closely related phages can be found in published genomes of over 200 other bacteria, including strains of Citrobacter koseri, Salmonella enterica, Yersinia enterocolitica, and Klebsiella pneumoniae. Its closest relatives are consistently found in urine or in the blood and feces of patients with urinary tract infections. More distant relatives can be found in isolates from other environments, including sewage, water, soil, and contaminated food. Each of these phages, which we collectively call 'UPϕ viruses', also harbors two or more novel genes of unknown function.