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Nefrologia 2024Diabetes, dyslipidemia, older age, gender, urinary tract infections, and recent antibiotic intake have been associated with a decrease in the urobiome richness and other...
INTRODUCTION AND OBJECTIVES
Diabetes, dyslipidemia, older age, gender, urinary tract infections, and recent antibiotic intake have been associated with a decrease in the urobiome richness and other fluctuations in this microbiome. Gut and blood microbiome have been reported to be altered in patients with chronic kidney disease (CKD), and specifically in peritoneal dialysis (PD) patients. Still, there are currently no studies describing the urogenital microbiome in CKD-PD patients. In this study we characterized the urobiome profile in 46 PD patients and analyzed its clinical and inflammatory parameters.
MATERIALS AND METHODS
Mid-stream urine, fecal and blood samples were collected from 46 patients undergoing PD at Centro Hospitalar Universitário de São João (CHUSJ) in Porto, Portugal. Exclusion criteria were age under 18 years old, inability to give informed consent, history of infection in the last three months, and antibiotic intake in the last three months. The microbiome communities were analyzed by amplification and sequencing of the V3-V4 region of the bacterial 16S rRNA gene. Correlations with the patients' clinical data and inflammatory profile were performed.
RESULTS
CKD-PD patients presented a unique urobiome profile dominated by Bacillota, Actinomycetota and Pseudomonadota and characterized by a lower Shannon diversity than fecal and blood microbiome. The taxonomic profiles of urogenital samples were organized in multiple subtypes dominated by populations of Lactobacillus, Staphylococcus, Streptococcus, Gardnerella, Prevotella, Escherichia-Shigella, being similar to other non-PD-CKD patients. Gender, sCD14, residual diuresis and history of peritonitis were significantly associated to variations in the urobiome. Although not reaching statistical significance, diabetes and the time on PD also showed association with particular taxonomic groups. Depletion of Gardnerella, Staphylococcus, Corynebacterium, Lactobacillus or Dermabacter populations correlated with CKD-PD patients with history of diabetes, history of peritonitis and altered levels of sCD14.
CONCLUSIONS
Our results highlight urogenital microbiome as a potential partner and/or marker in the overall health state of CKD-PD patients.
Topics: Humans; Female; Male; Peritoneal Dialysis; Middle Aged; Microbiota; Renal Insufficiency, Chronic; Aged; Urogenital System; Adult; Feces
PubMed: 38697697
DOI: 10.1016/j.nefroe.2024.04.004 -
BMC Microbiology Jan 2024Reliable species identification of cultured isolates is essential in clinical bacteriology. We established a new study algorithm named NOVA - Novel Organism Verification...
Novel Organism Verification and Analysis (NOVA) study: identification of 35 clinical isolates representing potentially novel bacterial taxa using a pipeline based on whole genome sequencing.
BACKGROUND
Reliable species identification of cultured isolates is essential in clinical bacteriology. We established a new study algorithm named NOVA - Novel Organism Verification and Analysis to systematically analyze bacterial isolates that cannot be characterized by conventional identification procedures MALDI-TOF MS and partial 16 S rRNA gene sequencing using Whole Genome Sequencing (WGS).
RESULTS
We identified a total of 35 bacterial strains that represent potentially novel species. Corynebacterium sp. (n = 6) and Schaalia sp. (n = 5) were the predominant genera. Two strains each were identified within the genera Anaerococcus, Clostridium, Desulfovibrio, and Peptoniphilus, and one new species was detected within Citrobacter, Dermabacter, Helcococcus, Lancefieldella, Neisseria, Ochrobactrum (Brucella), Paenibacillus, Pantoea, Porphyromonas, Pseudoclavibacter, Pseudomonas, Psychrobacter, Pusillimonas, Rothia, Sneathia, and Tessaracoccus. Twenty-seven of 35 strains were isolated from deep tissue specimens or blood cultures. Seven out of 35 isolated strains identified were clinically relevant. In addition, 26 bacterial strains that could only be identified at the species level using WGS analysis, were mainly organisms that have been identified/classified very recently.
CONCLUSION
Our new algorithm proved to be a powerful tool for detection and identification of novel bacterial organisms. Publicly available clinical and genomic data may help to better understand their clinical and ecological role. Our identification of 35 novel strains, 7 of which appear to be clinically relevant, shows the wide range of undescribed pathogens yet to define.
Topics: Bacteria; Whole Genome Sequencing; Corynebacterium; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; RNA, Ribosomal, 16S; Bacterial Typing Techniques
PubMed: 38178003
DOI: 10.1186/s12866-023-03163-7