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Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing

  • Authors:
    • Baramett Somtha
    • Suthida Visedthorn
    • Thunvarat Saejew
    • Preeyarat Pavatung
    • Wannasit Wathanavasin
    • Talerngsak Kanjanabuch
    • Sunchai Payungporn
  • View Affiliations / Copyright

    Affiliations: Medical Science Program, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand, Department of Biochemistry, Center of Excellence in Systems Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand, Department of Internal Medicine, Center of Excellence in Kidney Metabolic Disorders, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand, Department of Medicine, Nephrology Unit, Charoenkrung Pracharak Hospital, Bangkok 10120, Thailand
    Copyright: © Somtha et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 105
    |
    Published online on: July 22, 2026
       https://doi.org/10.3892/br.2026.2178
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Abstract

Chronic kidney disease (CKD) is a medical condition affecting >800 million patients globally, with end‑stage kidney disease representing the most severe stage, usually requiring dialysis as a form of renal replacement therapy. As these patients have an increased risk of sepsis‑associated mortality, and due to the limitations that arise from the use of traditional methods, prompt and accurate approaches in pathogen identification are required to ensure appropriate clinical management. The present study aimed to identify and analyze the bacterial profile of hemodialysis (HD) catheters obtained from patients with CKD who were undergoing hemodialysis using 16S ribosomal DNA (rDNA) amplicon sequencing. The present study proposed the use of the metagenomic approach in clinical laboratory settings. The results obtained in the present study revealed that the bacterial profile between site A (from the patient to the dialysis machine) and site V (from the machine back into the patient) had notable differences, with α‑ and β‑diversity indices suggesting an increased diversity at site V. In addition, analyses of the relative abundance and linear discriminant analysis effect size revealed the presence of known pathogens, including Klebsiella pneumoniae, Gardnerella vaginalis, Escherichia coli, Staphylococcus epidermidis, Acinetobacter baumannii, Corynebacterium striatum and Stenotrophomonas maltophilia. In summary, the findings of the present study highlighted the potential use of 16S rDNA amplicon sequencing as a culture‑independent alternative for determining pathogens in patients undergoing HD.
View Figures

Figure 1

Comparison of bacterial α-diversity
between HD catheter sites A and V. The α-diversity of bacterial
communities in HD catheter samples was assessed using (A) Chao1
richness and (B) Shannon diversity indices. Box plots illustrate
the distribution of diversity values, including median,
interquartile range, and overall range, comparing samples from site
A (patient-to-dialysis machine) and site V (dialysis
machine-to-patient).

Figure 2

β-diversity analysis of bacterial
communities between HD catheter sites A and V using principal
coordinate analysis (PCoA) based on Bray-Curtis dissimilarity.
β-diversity was assessed using ordination plots generated by PCoA
based on the Bray-Curtis dissimilarity distance to visualize
differences in bacterial community composition between samples from
site A (patient-to-dialysis machine) and site V (dialysis
machine-to-patient).

Figure 3

Bacterial relative abundance at the
phylum, genus, and species levels shows stacked distributions
across samples. Stacked bar charts show (A) all bacterial phyla,
(B) the 20 most abundant genera and (C) the 20 most abundant
species. Site A, from the patient to the dialysis machine; site V,
from the dialysis machine to the patient.

Figure 4

An LDA effect size comparison between
site A and V. The analysis revealed a significant separation in
bacterial composition between the two catheter sites. Site A, from
the patient to the dialysis machine; site V, from the dialysis
machine to the patient; LDA, linear discriminant analysis.

Figure 5

Correlations between clinical
variables and the top 20 bacterial species. A heatmap analysis of
the Spearman's rank correlation coefficients between clinical
variables (catheter site, age, sex and CRBSI status) and the
relative abundance of bacterial species. Catheter site, sex and
CRBSI status were coded as follows: The patient to the dialysis
machine site, being biologically female and having a CRBSI were
ranked as 0; the dialysis machine to the patient site, being
biologically male and having a non-CRBSI were ranked as 1. The red
and blue colors represent positive and negative correlations,
respectively. Correlation coefficients are shown within each cell.
*P<0.05, **P<0.01 and
***P<0.001. Hierarchical clustering was carried out
on bacterial species according to their correlation profiles.
CRBSI, catheter-related bloodstream infection.
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Spandidos Publications style
Somtha B, Visedthorn S, Saejew T, Pavatung P, Wathanavasin W, Kanjanabuch T and Payungporn S: Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing. Biomed Rep 25: 105, 2026.
APA
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., & Payungporn, S. (2026). Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing. Biomedical Reports, 25, 105. https://doi.org/10.3892/br.2026.2178
MLA
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., Payungporn, S."Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing". Biomedical Reports 25.3 (2026): 105.
Chicago
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., Payungporn, S."Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing". Biomedical Reports 25, no. 3 (2026): 105. https://doi.org/10.3892/br.2026.2178
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Spandidos Publications style
Somtha B, Visedthorn S, Saejew T, Pavatung P, Wathanavasin W, Kanjanabuch T and Payungporn S: Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing. Biomed Rep 25: 105, 2026.
APA
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., & Payungporn, S. (2026). Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing. Biomedical Reports, 25, 105. https://doi.org/10.3892/br.2026.2178
MLA
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., Payungporn, S."Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing". Biomedical Reports 25.3 (2026): 105.
Chicago
Somtha, B., Visedthorn, S., Saejew, T., Pavatung, P., Wathanavasin, W., Kanjanabuch, T., Payungporn, S."Bacterial metagenomic analysis of patients with chronic kidney disease undergoing hemodialysis based on 16S rDNA amplicon sequencing". Biomedical Reports 25, no. 3 (2026): 105. https://doi.org/10.3892/br.2026.2178
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