Dear Dayan Lozano!

Here is the method for collecting of nasal samples for Metagenomics analyses different to UVTM. I hope it is uesfull!

Sincerely yours!

Dr. Bratko Filipič

Email: [email protected]

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Methods

Ethics approval and consent to participate

All participants in this study were part of the AsthMaP2 (Asthma Severity Modifying Polymorphisms) Study [20]. AsthMaP2 is an ongoing study of urban children and adolescents designed to find associations among airway microbes, environmental exposures, allergic sensitivities, genetics, and asthma. AsthMaP2 and the study presented here were approved by the Children’s National Medical Center Institutional Review Board (Children’s National IRB), which requires that consent is obtained and documented prior to conducting study procedures and collection of samples for research. Written consent was obtained from all independent participants or their legal guardians using the Children’s National IRB approved informed consent documents (IRB No PRO00002517).

Samples and molecular analyses

A total of 30 children and adolescents (ages 6 to 17 years) were recruited from the metropolitan Washington, DC, area. All had been physician-diagnosed with asthma for at least one year prior to recruitment. Individuals who reported a medical history of chronic or complex cardiorespiratory disease were ineligible. Their nasopharynges were simultaneously sampled using both nasal washes (NW) and nasal brushes (NB). NW samples were procured by instilling 5 ml of isotonic sterile saline buffer into each nare, holding it for 10 s, and then blowing into a specimen collection container. NB samples were procured by brushing the mucosa of the inner section of the inferior turbinate of each nostril with a sterile nylon bristle cytology brush (CytoSoft Cytology Brush No. CYB-1, Medical Packaging Corporation Camarillo, CA). That mucosa is covered with a mucus blanket that traps smaller particulate matter and bacteria [21]. Moreover, cytology brushes are designed to harvest epithelial cells, meaning NB sampling is generally more abrasive than NW sampling, and so, more likely to collect the microorganisms closely attached to the nasal mucosa. Nonetheless, since the cytology brushes are not protected, we cannot ensure that these samples represent the inferior turbinate microbial community alone, and it is possible that upon removal of the brush from the sinuses, unintended mucus, hair, and epithelial cells from the nares were also collected. Nasal washes are non-specific and may target a different microbial population than nasal brushes, which will pick up microbes attached to the mucosal surface instead of non-attached microbes along the sinonasal cavity (including the inferior turbinate).

Total DNA was extracted using the QIAGEN QIAamp DNA Kit (Catalog # 51304). Before adding the ATL buffer, samples were pre-incubated in 100 uL of lysozyme-TE buffer pH = 8.0 for 30 min at 37 °C. All extractions yielding >50 ng of total DNA (as indicated by NanoDrop 2000 UV-vis Spectrophotometer measuring) were further processed. DNA extractions were prepared for sequencing using the Schloss’ MiSeq_WetLab_SOP protocol (09.2015) in Kozich et al. [22]. Each DNA sample was amplified for the V4 region (~250 bp) of the 16S rRNA gene, and libraries were sequenced using the Illumina MiSeq sequencing platform at University of Michigan Medical School.

Sequence analyses

Raw FASTQ files were processed in mothur v1.35.1 [23]. Default settings were used to minimize sequencing errors as described in Schloss et al. [24]. Clean sequences were aligned to the SILVA_v123 bacterial reference alignment at www.mothur.org. Chimeras were removed using uchime [25], and non-chimeric sequences were classified using the naïve Bayesian classifier of Wang et al. [26]. Sequences were clustered into Operational Taxonomic Units (OTUs) at the 0.03 threshold (species level). OTU sequence representatives and taxonomy were imported (BIOM format) into QIIME [27] for subsequent analyses. The mothur OTU table was filtered to a minimum of two observations (sequences) per OTU. Samples were subsampled (rarefaction analysis) to the smallest sample size (1,100 sequences) to remove the effect of sample size bias on community composition. Nonetheless, OTU differential abundance tests that take advantage of full sample sizes (metagenomeSeq zero-inflated Gaussian [28]) were also applied for comparison.

Trees for phylogenetic diversity calculations were constructed using FastTree [29]. Taxonomic alpha-diversity was estimated as the number of observed OTUs and by the Chao1, Simpson, and Shannon indexes. Phylogenetic alpha-diversity (PD) was calculated by the Faith’s phylogenetic diversity index [30]. Similarly, both taxonomic (Bray-Curtis and Euclidean) and phylogenetic (unweighted and weighted UniFrac) beta-diversity metrics were calculated. Community dissimilarity was estimated using principal coordinates analysis (PCoA). Alpha- and beta-diversity metrics were compared between samples grouped by sampling strategy (NW versus NB) using both parametric and non-parametric versions of the t test. Taxonomic and phylogenetic distances were also compared among those groups using the non-parametric PERMANOVA test from the vegan R’s library [31]. Significance was determined through 10,000 permutations. Finally, OTU abundance differences between NW and NB groups and between paired samples (e.g., NW patient 1 versus NB patient 1) were assessed using rarefied (White’s non-parametric ttest [32] and Fisher’s exact test, respectively) and non-rarefied (metagenomeSeq zero-inflated Gaussian) data. Core microbiome analyses were also performed to identify resident bacteria. Bonferroni or Benjamini-Hochberg FDR multiple test correction methods were applied. All analyses were performed in mothur, QIIME, STAMP [33], and RStudio [34].

Availability of the data and materials

Sequence data have been uploaded to the GenBank under SRA accession number SRP069020. All materials used for nasal brushing and washing were processed as regular samples using the same QIAGEN DNA purification kit and PCR protocols. No PCR band was visible on an agarose gel.

Results

Sequences and OTUs

Sixty nasal microbiomes corresponding to 30 asthmatic children were analyzed via MiSeq sequencing of 16S rRNA V4 amplicons. A total of 1,474,497 sequences ranging from 1100 to 62,148 sequences per sample (mean = 24,575; median = 20,599) were obtained after quality control analyses and OTU filtering. From these data, we identified 33–272 OTUs (mean = 157) per sample in the NW group and 120–408 OTUs (mean = 186) per sample in the NB group.

Microbial composition of the nasopharynx

Nasopharyngeal microbiomes were dominated by the following seven genera, which showed different proportions in NW and NB groups: Moraxella (NW = 38.1 %, NB = 27.8 %), Staphylococcus (NW = 15.4 %, NB = 10.2 %), Corynebacterium (NW = 8.6 %, NB = 20.4 %), Haemophilus (NW = 8.4 %, NB = 4.2 %), Fusobacterium (NW = 5.8 %, NB = 1.1 %), Prevotella (NW = 3.8 %, NB = 1.1 %), and Dolosigranulum (NW = 3.7 %, NB = 8.7 %) (see Fig. 1). The core microbiome analysis (OTUs present in 95 % of the samples) identified five OTUs of the genera Moraxella, Staphylococcus, Haemophilus, Streptococcus, and Enterococcus in NB and four OTUs of the genera Moraxella, Pseudomonas, Enterococcus, and Bacteroides in NW. This suggests that NW and NB may contain different resident bacteria. All dominant genera reported here for asthmatic children and adolescents have been detected in other microbiome studies of the nasopharynx in infants with and without respiratory infections [1, 3, 16, 35], although in different proportions. Similarly, microbial profiles observed in our children also looked different from those described in the nasopharynx of adults [19, 36, 37].

📷Fig. 1Taxonomic profiles of most abundant bacterial genera (OTU proportion >0.1 %) in 60 nasopharyngeal microbiomes from 30 asthmatic children. NW nasal wash, NB nasal brush

Nasopharyngeal washes and brushes sampled different microbiotas

Microbial profiles of the most abundant bacterial genera (Fig. 1) and alpha-diversity indices (observed OTUs, Chao1, Shannon, Simpson, and PD) varied greatly between NW and NB groups (Fig. 2), with NB showing greater estimates for all indices. These differences were significant in all cases (non-parametric ttest; P 

Thaks very much! Filipic