Essential genome of <i>Pseudomonas aeruginosa</i>in cystic fibrosis sputum

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Abstract

Defining the essential genome of bacterial pathogens is central to developing an understanding of the biological processes controlling disease. This has proven elusive for Pseudomonas aeruginosaduring chronic infection of the cystic fibrosis (CF) lung. In this paper, using a Monte Carlo simulation-based method to analyze high-throughput transposon sequencing data, we establish the P. aeruginosaessential genome with statistical precision in laboratory media and CF sputum. Reconstruction of the global requirements for growth in CF sputum compared with defined growth conditions shows that the latter requires several cofactors including biotin, riboflavin, and pantothenate. Comparison of P. aeruginosastrains PAO1 and PA14 demonstrates that essential genes are primarily restricted to the core genome; however, some orthologous genes in these strains exhibit differential essentiality. These results indicate that genes with similar molecular functions may have distinct genetic roles in different P. aeruginosastrains during growth in CF sputum. We also show that growth in a defined growth medium developed to mimic CF sputum yielded virtually identical fitness requirements to CF sputum, providing support for this medium as a relevant in vitro model for CF microbiology studies.

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Differential expression analysis for sequence count data

Simon Anders, Wolfgang Huber (2010)

High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

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Tn-seq; high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms

Tim van Opijnen, Kip Bodi, Andrew Camilli (2009)

Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing of the flanking regions en masse. These changes are used to calculate each mutant’s fitness. Fitness was determined for each gene of the gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions identified both alleviating and aggravating interactions that could be further divided into seven distinct categories. Due to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

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Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective.

Anders Folkesson, Lars Jelsbak, Lei Yang … (2012)

The airways of patients with cystic fibrosis (CF) are nearly always infected with many different microorganisms. This environment offers warm, humid and nutrient-rich conditions, but is also stressful owing to frequent antibiotic therapy and the host immune response. Pseudomonas aeruginosa is commonly isolated from the airways of patients with CF, where it most often establishes chronic infections that usually persist for the rest of the lives of the patients. This bacterium is a major cause of mortality and morbidity and has therefore been studied intensely. Here, we discuss how P. aeruginosa evolves from a state of early, recurrent intermittent colonization of the airways of patients with CF to a chronic infection state, and how this process offers opportunities to study bacterial evolution in natural environments. We believe that such studies are valuable not only for our understanding of bacterial evolution but also for the future development of new therapeutic strategies to treat severe chronic infections.