Disruption and recovery of the nasal microbiome after mupirocin treatment in carriers and non-carriers of Staphylococcus aureus

Study population and study design

This is a prospective interventional cohort study. S. aureus carriers and non-carriers in the Netherlands. All experiments were performed in accordance with the Dutch law on medical research involving human subjects (WMO). The study protocol was approved by the local medical ethics committee of the Erasmus University Medical Center in Rotterdam, the Netherlands (MEC-2018-091). Written informed consent was obtained for all participants. The participants were recruited via advertisements in Dutch universities and the research teams’ social networks. Exclusion criteria were age

After completing an eligibility questionnaire, all volunteers were selected to S. aureus carriage as previously described23. S. aureus carriage was determined by quantitative culture of 2 weekly nasal swabs. Persistent S. aureus carriers were defined as 2 positive cultures with >8 CFU/mL for each culture. Non-carriers were defined as 2 S. aureus– negative cultures. Intermittent S. aureus carriers were excluded from further participation in the study. Eligible volunteers were registered on a first-come, first-served basis.

Eligible participants were asked to complete a questionnaire regarding risk factors for S. aureus acquisition. All participants received decolonization treatment. Decolonization consisted of mupirocin nasal ointment (2%, GlaxoSmithKline BV, Zeist, The Netherlands) twice daily and chlorhexidine gluconate dermal solution (4% w/v, Regent Medical Overseas Limited, Oldham, United Kingdom). -Uni) once a day, both for 5 days.

Nasal swabs were taken 1 day before decolonization (D0) and 2 days (D7), 1 month (M1), 3 months (M3) and 6 months (M6) after decolonization. All participants received a personal nasal sampling demonstration by the executive researcher. Subsequently, all samples were collected by participants by inserting a swab (ESwab, 490CE.A, Copan Italia, Brescia, Italy) into one nostril and twisting 5 times, repeating this in the second nostril using the same swab. Swabs were collected in a container filled with 1 ml of modified Liquid Amies, a collection and transport solution, and sent by regular mail (not temperature controlled) or dropped off personally at the laboratory.

Staphylococcus aureus quantitative culture

Quantitative S. aureus cultures were conducted to examine the dynamics of S. aureus carriage during the 6-month follow-up period after decolonization. Swab containers were vortexed for 20 s before plating. Serial dilutions of Amies medium were plated on phenol mannitol-salt agar (PHMA) and incubated for 2 days at 37°C. Swabs were placed in Phenol Mannitol Salt Broth (PHMB) and incubated for 7 days at 37°C for enrichment. S. aureus growth was confirmed by latex agglutination test (Staph Plus Latex Kit, Diamondial, Vienna, Austria). Morphologically different S. aureus colonies were selected for spa typing and screening for methicillin resistance using BBL CHROMagar MRSA II agar (BD, Breda, The Netherlands).

Spa typing

Molecular typing of S. aureus isolates was performed to infer whether recolonization with S. aureus in decolonized carriers involved the same spa-type. The strike was limited to the last S. aureus positive cultural moment and the last S. aureus positive culture time after decolonization in recolonized carriers. S. aureus DNA lysates were prepared by boiling in 10 mM Tris-HCl, 1 mM disodium EDTA, pH 8.0 or extraction with the QIAamp DNA Mini Kit (QIAGEN, Venlo, The Netherlands) according to the manufacturer’s instructions. Amplification of S. aureus protein A (spa) repeated region was performed by PCR with 2 sets of primers. One set consisted of a front primer spa-1113, 5′-TAAAGACGATCCTTCGGTGAGC-3′ and reverse primer spa-1514, 5′-CAGCAGTAGTGCCGTTTGCTT-3′24. The other set consisted of front primers spa-F1, 5′-AACAACGTAACGGCTTCATCC-3′ and spa-F2 5′-AGACGATCCTTCAGTGAGC-3′ and reverse primer spa-R1 5′-GCTTTTGCAATGTCATTTACTG-3′. Amplicons were purified with ExoSAP-IT (Applied Biosystems) according to the manufacturer’s instructions and sent for sequence analysis (Baseclear, Leiden, The Netherlands). The resulting sequences were analyzed using BioNumerics v7.6 (Applied Maths NV, Sint-Martens-Latem, Belgium) and the spa types were assigned using the RidomStaphType database (Ridom GmbH, Würzburg, Germany).

16S ribosomal RNA sequencing of the nasal microbiota

The impact of decolonization on the nasal microbiome and the recovery of microbiome structure after decolonization were examined using 16S rRNA metabarcoding. The Amies medium from each nasal swab container was stored at -80°C on the day of receipt at the study laboratory in Rotterdam, NL, then sent at -80°C to the microbiome analysis laboratory in Lyon, FR. To properly capture the impact of decolonization on living microbiota, metabarcoding used RNA-based 16S ribosomal RNA (rRNA, which is retained in living cells but rapidly cleared after cell death or lysis) rather than the DNA coding sequence, because DNA can persist for extended periods of time after cell death25,26,27,28. RNA was extracted using Mag Bind® Tissue protocol of the Total RNA 96 kit (Omega Bio-tek) from 150 µL of sample material. Cell lysis was performed using beads (Disruptor plate C plus – Omega Bio-tek) and proteinase K for 15 min at 2600 rpm, followed by 10 min at room temperature without shaking, and completed by DNase digestion. I 20 min at room temperature. RNA was quantified using the QuantiFluor RNA kit on Tecan Safire (TECAN). 10 ng of total RNA was used for reverse transcription using FIREScript RT cDNA Synthesis Kit (Solis Biodyne) with random primers, then cDNA was purified with SPRIselect reagent (Beckman coulter) and quantified.

The V1–V3 rRNA region was amplified by PCR using the 5× HOT BIOAmp® BlendMaster Mix 12.5 mM MgCl 2 (Biofidal), 10× GC rich Enhancer (Biofidal) and BSA 20 mg/mL. The PCR reaction consisted of 30 cycles at 56 °C using forward primer 27F, 5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG AGAGTTTGATCCTGGCTCAG-3′ and reverse primer 534R, 5′-GTCTCGTGGGCTCGGAGATGTGTATAAGACAGATTACCGCGGCTTGCTGG-3′ in 25 μL of solution. The PCR products were purified using SPRIselect beads (Beckman Coulter) in 20 μL of nuclease-free water and quantified using QuantiFluor dsDNA (Promega). Samples were indexed with lllumina barcodes with the same PCR reagents during a 12-cycle PCR, then purified and quantified as mentioned previously. Samples were normalized and pooled, then sequenced using the Illumina MiSeq V3 flow cell following the manufacturer’s recommendations for a 2 × 300 bp paired application. An average of 130,000 readbacks per sample was obtained.

Experiment buffers were used as negative controls to detect non-sample bacterial RNA contamination. RNA extraction was controlled using a proprietary mixture of Staphylococcus aureus ATCC29213 and Escherichia coli ATCC25922 in equal proportions, allowing evaluation of extraction bias in Gram-positive and negative bacteria. PCR amplification bias was checked using a commercial DNA mix of 8 bacterial species (ZymoBIOMICS™ Microbial Community DNA Standard).

Bioinformatics and statistical analyzes

Sequencing reads were checked and clipped. Matched read pairs were merged using BBMap version 38.49 (available at https://sourceforge.net/projects/bbmap/), with default options in addition to a minimum single size of 150 bp with an average Phred quality score greater than 10, and a total pair size of at least 400 bp. PCR adapters were removed with cutadapt v.2.1 (Martin 2011) then de-replicated with vsearch v.2.12.029 with size option. For species assignment, reads were aligned to sequences from the NCBI blast 16S_ribosomal_RNA database (version date 03.12.2020) using Blastn v.2.11.0+30.31, keeping a maximum of 20 reference targets. The number of reads per bacterial species was normalized to account for taxon-specific variations in 16S rRNA gene copy number using the NCBI rrnDB-5.5 database based on mean gene copy number in the taxon.

To optimize the resolution of sequencing read taxonomic assignment, we used publicly available in-house bioinformatics software at https://github.com/rasigadelab/taxonresolve. In short, when a read matches sequences from multiple species with identical alignment scores, taxonomic assignment pipelines typically output the top taxonomic level such as genus (e.g., Staphylococcus spp. when a reading matches S. aureus and S. epidermidis). This loss of information can be problematic when discrimination at the species level is important. To avoid losing information at the species level, the taxonresolve the software assigns reads with uncertain species to species groups rather than genera.

Bacterial species found to be present from contaminating sources such as kit reagents and found in negative controls, primarily Bacillus genera, have been deleted. In total, 1376 species or groups of species were selected. The rarefaction curves corresponding to the sequencing effort to assess species richness in the samples are shown in Fig. 3 extra. Most samples plateaued after 40,000 sequences.

Given the small sample size relative to the number of variables and species considered in this study, no hypothesis testing was performed and we provide a descriptive assessment of the results. In figures, the 95% confidence intervals of the means were calculated on the basis of the normal approximation, after logarithmic transformation for the CFU/mL and logarithmic transformation of the odds for the restricted amounts. [0, 1] interval, like proportions.

In the microbial diversity analyses, we selected the 9 most common bacterial species and grouped the other species into an “Other” category. To assess disruption and eventual recovery of the microbiota, the divergence of the sampled microbiota from the initial microbiota before treatment (D0) was assessed using the Bray-Curtis dissimilarity at each sampling time point by compared to the first sample from the same patient. .

The analysis software code is available at https://github.com/rasigadelab/macotra-metabarcoding. Data is available at https://zenodo.org/record/6382657. Analyzes and figures used R software v3.6.032 with dplyr plans33ggplot234vegan35and MicrobiomAnalyst available on https://www.microbiomeanalyst.ca36.37.

Previous Are you still on Elon Musk's Twitter? How to Avoid or Stop Toxic Conversations
Next AWS Resource Explorer offers unified search capabilities