Nasal Microbiome in Granulomatosis with Polyangiitis Compared to Chronic Rhinosinusitis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Metagenomic Analysis
- Automatic sample demultiplexing;
- Generating FASTQ files containing raw reads.
- Checking the quality of the readings:
- Analysing the error profile of individual samples and the dynamic generation of parameters for quality control (FIGARO tool);
- Carrying out a quality control based on the coefficient of the maximum expected errors of the sample.
- Preprocessing data using the Cutadapt tool:
- Removing adapter sequences;
- Rejecting too short readings (a minimum length of 30 nt).
- Selecting unique ASV sequences (using the DADA2 package) by the following process:
- Filtering out the sequences containing errors created during the sequencing process (denoising);
- Combining paired readings—to increase the accuracy of sequencing, they are performed in paired-end mode so that at later stages of the analysis, the corresponding forward and reverse readings must be combined;
- Merging of identical, unique sequences while maintaining the number of springs and the quality profile (dereplicating);
- Removing constructs resulting from incorrect sequence assembly during PCR (filtering chimeras).
- Assigning a taxonomy to the ASV sequence based on the Silva reference base and using a hybrid approach:
- Comparing ASV sequences against the base in search of identical reference sequences (VSEARCH);
- Classifying the atypical sequences remaining after the previous step based on methods using machine learning (sklearn).
2.2. Statistical Analysis
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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GPA | CRS | NSP | |
Staphylococcus | 29.65% | 17.47% | 17.42% |
Corynebacterium | 11.65% | 7.93% | 3.58% |
Unidentified bacteria | 11.16% | 29.60% | 18.47% |
Haemophilus | 7.70% | 0.50% | 20.03% |
Streptococcus | 5.97% | 2.93% | 15.16% |
Prevotella | 3.68% | 0.12% | 0.21% |
Moraxella | 2.77% | 7.05% | 0.32% |
Cutibacterium | 2.49% | 8.80% | 3.20% |
Rheinheimera | 1.95% | 0.03% | 0.01% |
Peptoniphilus | 1.84% | 0.37% | 1.01% |
Lawsonella | 1.71% | 4.10% | 0.48% |
Prauserella | 1.70% | 0.50% | 1.04% |
Alteribacillus | 1.56% | 0.46% | 0.23% |
Rubrobacter | 1.39% | 1.26% | 0.94% |
Anaerococcus | 1.06% | 0.83% | 0.75% |
Neisseriaceae unidentified | 0.69% | 3.80% | 0.01% |
Acinetobacter | 0.98% | 1.27% | 0.24% |
Brevundimonas | 0.59% | 1.19% | 1.35% |
Chryseobacterium | 0.00% | 1.18% | 0.22% |
(A) | |||||
Family | U Mann–Whitney Test | ||||
Median GPA | Median NPS | p-Value | |||
Actinomycetaceae | 0.00% | 0.05% | 0.016 | ||
Flavobacteriaceae | 0.00% | 0.04% | 0.038 | ||
Weeksellaceae | 0.00% | 0.13% | 0.016 | ||
Streptococcaceae | 0.42% | 11.22% | 0.025 | ||
Lachnospiraceae | 0.00% | 0.39% | 0.025 | ||
Beijerinckiaceae | 0.00% | 0.23% | 0.020 | ||
Rhodobacteraceae | 0.01% | 0.32% | 0.031 | ||
(B) | |||||
Family | U Mann–Whitney Test | ||||
Median GPA | Median CRS | p-Value | |||
Propionibacteriaceae | 1.15% | 4.78% | 0.026 | ||
Weeksellaceae | 0.00% | 0.18% | <0.001 | ||
Caulobacteraceae | 0.25% | 0.89% | 0.041 | ||
Beijerinckiaceae | 0.00% | 0.14% | 0.041 | ||
Xanthobacteraceae | 0.00% | 0.12% | 0.041 | ||
Sphingomonadaceae | 0.02% | 0.28% | 0.015 | ||
Moraxellaceae | 0.02% | 2.57% | 0.033 | ||
(C) | |||||
Family | U Mann–Whitney Test | ||||
Median NPS | Median CRS | p-Value | |||
Lachnospiraceae | 0.39% | 0.00% | 0.035 | ||
Neisseriaceae | 0.13% | 0.78% | 0.005 |
(A) | |||
Genus | U Mann–Whitney Test | ||
Median GPA | Median NSP | p-Value | |
Actinomyces | 0.00% | 0.03% | 0.008 |
Streptococcus | 0.17% | 11.22% | 0.008 |
Methylobacterium-Methylorubrum | 0.00% | 0.11% | 0.014 |
Paracoccus | 0.00% | 0.32% | 0.046 |
(B) | |||
Genus | U Mann–Whitney test | ||
Median GPA | Median CRS | p-Value | |
Kocuria | 0.00% | 0.17% | 0.022 |
Rothia | 0.00% | 0.36% | 0.017 |
Cutibacterium | 1.15% | 4.72% | 0.036 |
Streptococcus | 0.17% | 1.64% | 0.046 |
Methylobacterium-Methylorubrum | 0.00% | 0.14% | 0.022 |
Tepidimonas | 0.00% | 0.25% | 0.046 |
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Brożek-Mądry, E.; Burska, Z.; Życińska, K.; Sierdziński, J. Nasal Microbiome in Granulomatosis with Polyangiitis Compared to Chronic Rhinosinusitis. Diagnostics 2024, 14, 1673. https://doi.org/10.3390/diagnostics14151673
Brożek-Mądry E, Burska Z, Życińska K, Sierdziński J. Nasal Microbiome in Granulomatosis with Polyangiitis Compared to Chronic Rhinosinusitis. Diagnostics. 2024; 14(15):1673. https://doi.org/10.3390/diagnostics14151673
Chicago/Turabian StyleBrożek-Mądry, Eliza, Zofia Burska, Katarzyna Życińska, and Janusz Sierdziński. 2024. "Nasal Microbiome in Granulomatosis with Polyangiitis Compared to Chronic Rhinosinusitis" Diagnostics 14, no. 15: 1673. https://doi.org/10.3390/diagnostics14151673