Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota

Culture-independent studies of cystic fibrosis lung microbiota have provided few mechanistic insights into the polymicrobial basis of disease. Deciphering the specific contributions of individual taxa to CF pathogenesis requires comprehensive understanding of their ecophysiology at the site of infection. We hypothesize that only a subset of CF microbiota are translationally active and that these activities vary between subjects. Here, we apply bioorthogonal non-canonical amino acid tagging (BONCAT) to visualize and quantify bacterial translational activity in expectorated sputum. We report that the percentage of BONCAT-labeled (i.e. active) bacterial cells varies substantially between subjects (6-56%). We use fluorescence-activated cell sorting (FACS) and genomic sequencing to assign taxonomy to BONCAT-labeled cells. While many abundant taxa are indeed active, most bacterial species detected by conventional molecular profiling show a mixed population of both BONCAT-labeled and unlabeled cells, suggesting heterogeneous growth rates in sputum. Differentiating translationally active subpopulations adds to our evolving understanding of CF lung disease and may help guide antibiotic therapies targeting bacteria most likely to be susceptible.

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RC Hunter
Apr 9, 2020 Flow cytometry data was collected using a BD FACS Aria ii cytometer with BD-FACSDiva software (v8.0.1). Sequencing data were collected on an Illumina MiSeq instrument with Illumina software. Imaging data were collected on an Olympus IX83 inverted microscope with a Hamamatsu ORCA-Flash4.0 v2 camera. Image acquisition and post-acquisition image analysis was performed using CellSens software (v1.14, Olympus) Image analysis was also performed using FIJI (v.2.0). Flow cytometry data was analyzed using FlowJo software (v.10.5). All analyses of Illumina MiSeq generated 16S rRNA gene sequences was done using the R language within R Studio software (v.1.2.1335). Sequences were trimmed and filtered for quality using cutadapt (v.2.8). The DADA2 (v.1.14) package was used to model sequencing errors and determine amplicon sequence variants (ASVs) from raw sequencing reads. Decontam (v.1.2)was used to filter out contaminant sequences. phangorn (v.2.5.5) was used to approximate a phylogenetic tree. 'Phyloseq' (v.1.30.0) was used for data quality filtering. Taxonomy was assigned using RDP classifier and SILVA SSU database (release 132). A custom R function was written to calculate and plot fold changes in relative abundance (Fig.5, Supp. Fig. 12, 13) All R code used in sequence analysis in this publication is available as a Github repository (https://github.com/hunterlabumn/Valentini_et_al_2020).
Raw 16S rRNA gene sequence data ( Fig. 5 and Supplementary Figures 8, 11-13) that support the findings of this study were deposited and are available as fastq files nature research | reporting summary

October 2018
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No sample size calculations were performed for this proof-of-concept study. The small sample size (n=3 each for imaging and flow cytometry) was sufficient to demonstrate the applicability and reproducibility of the method across patients.
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All human samples and in vitro experiments were performed and analyzed in triplicate. All attempts at replication were successful.
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