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, 9 (1), 3459

High-resolution in Situ Transcriptomics of Pseudomonas Aeruginosa Unveils Genotype Independent Patho-Phenotypes in Cystic Fibrosis Lungs

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High-resolution in Situ Transcriptomics of Pseudomonas Aeruginosa Unveils Genotype Independent Patho-Phenotypes in Cystic Fibrosis Lungs

Elio Rossi et al. Nat Commun.

Abstract

Life-long bacterial infections in cystic fibrosis (CF) airways constitute an excellent model both for persistent infections and for microbial adaptive evolution in complex dynamic environments. Using high-resolution transcriptomics applied on CF sputum, we profile transcriptional phenotypes of Pseudomonas aeruginosa populations in patho-physiological conditions. Here we show that the soft-core genome of genetically distinct populations, while maintaining transcriptional flexibility, shares a common expression program tied to the lungs environment. We identify genetically independent traits defining P. aeruginosa physiology in vivo, documenting the connection between several previously identified mutations in CF isolates and some of the convergent phenotypes known to develop in later stages of the infection. In addition, our data highlight to what extent this organism can exploit its extensive repertoire of physiological pathways to acclimate to a new niche and suggest how alternative nutrients produced in the lungs may be utilized in unexpected metabolic contexts.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Experimental design and patients included in the study. a Schematic representation of the experimental design. In the thick mucus layer, bacterial communities thrive in a harsh environment. To capture the best representation of in vivo transcription, sputum samples were collected directly from adult CF patients followed at the Copenhagen Cystic Fibrosis Clinic at Rigshospitalet and nucleic acid content was stabilized in less than 1 min using sputum pre-lysis and preservation buffer (SLP buffer), followed by total RNA isolation, RNA-seq library preparation, and sequencing (for a complete description see Methods). From the same or a second sputum sample, we isolated, for some patients, more than ten single P. aeruginosa clones and studied their gene expression in laboratory condition (in vitro). b Simplified schematic representation of the in silico analysis workflow. Total reads were quality-filtered and any rRNA contaminant removed. Human reads were separated by mapping high-quality reads directly on human GRCh38 genome. Reads not assignable to human genome were used to evaluate microbial community composition and to assess in vivo P. aeruginosa gene expression. Transcription deriving from in vitro cultures of P. aeruginosa were used as a reference for identifying differentially gene expressed in vivo. c Overview of longitudinally collected samples. Clone types colonizing each patient are reported. Assignment of clone type was obtained by whole genome sequences from isolates obtained during this study or from previous isolates. A generic indication of antibiotic treatment and intravenous administration is provided (for a complete overview see Supplementary Table 1)
Fig. 2
Fig. 2
Core genome analysis reduces genetic variability uncovering a shared transcriptional program. a Gene expression correlation expressed as Pearson’s correlation coefficient (r) and visualized as heat map of expression profiles deriving from the transcriptional activity of the whole genome (left panel, n = 5976 CDS) or soft-core genome (right panel, CDS conserved in 95% of the strains considered; n = 5102 CDS). Row and column clustering is based on results from pvclust analysis. Major significant clusters are highlighted by dashed rectangles, and solid lines in the side dendrograms. Dashed lines in dendrograms represent branches with AU values <95% and thus not considered significantly supported by data. b Cluster refinement by principal component analysis (PCA) and group identification based on k-means clustering on PCA data considering gene expression from the whole genome (left panel) or only coding sequences conserved in the soft-core genome of P. aeruginosa (right panel). Clusters identified by k-means analysis are labeled depending on major features of samples included in the group. For both analysis, rLog-normalized counts were used as representation of gene expression (see Methods)
Fig. 3
Fig. 3
Differentially expressed genes regulated by in vivo conditions and their functional classification. a Venn diagram of total differentially regulated genes in the CF expectorates (Cluster I) compared to strains grown in laboratory conditions in exponential (Cluster I vs. Cluster II) and stationary phase (Cluster I vs. Cluster III). Shared genes in the center are highlighted by a white outline. b Volcano plot showing the magnitude of the differential gene expression shared between Cluster I vs. Cluster II and Cluster I vs. Cluster III comparisons. Each dot represents one coding sequence with detectable expression in both conditions. Thresholds for defining a gene significantly differentially expressed (log2(FoldChange) ≥ |1.3|, adj. p value ≤0.05) are shown as dashed and solid lines, respectively. Red dots: genes consistently induced by in vivo conditions. Blue dots: genes consistently repressed by in vivo conditions. c Distribution of differentially expressed genes both common and unique to the comparisons Cluster I vs. II and Cluster I vs. III is based on COGs and KEGG pathway classification systems. Gene association for each category was obtained from Pseudomonas.com database. In each plot, the percentage of genes significantly upregulated (red bars on the right) or downregulated (blue bars on the left) associated to each functional category is reported. Asterisks denote functional categories significantly enriched (adjusted p value ≤0.05, hypergeometric test after Bonferroni correction). Open black bars represent the proportion of the entire genome in the specific category
Fig. 4
Fig. 4
Effects of the CF lung environment on Pseudomonas aeruginosa central carbon metabolism. Significant changes of the transcriptomic pattern between the in vitro- and in vivo-grown bacteria are reported. Transcripts, encoded enzymes, and catalyzed reaction (arrows) enriched during growth in CF lung are given in red, and decreased transcripts are indicated in blue. Genes and reactions not statistically significant are reported in black. Dashed lines connecting metabolic intermediates indicate that the reaction is carried out by multiple consecutive enzymes not reported
Fig. 5
Fig. 5
Transcriptional patterns of differentially expressed genes involved in iron acquisition and metabolism. Heat maps report the intensity of expression of each gene (rows) expressed as log-regularized read counts and scaled for each row. Each column represents a sample analyzed and is clustered based on the result of pvclust analysis. Growth conditions, clone type of each sample, presence of genetic mutations in fur and pchR genes (black bars), and a schematic representation of the regulation of pyochelin operon are reported

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