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 aeruginosa during 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. aeruginosa essential 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. aeruginosa strains 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. aeruginosa strains 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.

Communities of microbes can live almost anywhere and contain many different species. Interactions between members of these communities often determine the state of the habitat in which they live. When these habitats include sites on the human body, these interactions can affect health and disease. Polymicrobial synergy can occur during infection, in which the combined effect of two or more microbes on disease is worse than seen with any of the individuals alone. Powerful genomic methods are increasingly used to study microbial communities, including metagenomics to reveal the members and genetic content of a community and metatranscriptomics to describe the activities of community members. Recent efforts focused toward a mechanistic understanding of these interactions have led to a better appreciation of the precise bases of polymicrobial synergy in communities containing bacteria, eukaryotic microbes, and/or viruses. These studies have benefited from advances in the development of in vivo models of polymicrobial infection and modern techniques to profile the spatial and chemical bases of intermicrobial communication. This review describes the breadth of mechanisms microbes use to interact in ways that impact pathogenesis and techniques to study polymicrobial communities.

Antimicrobial-resistant bacteria pose a serious threat in the clinic. This is particularly true for opportunistic pathogens that possess high intrinsic resistance. Though many studies have focused on understanding the acquisition of bacterial resistance upon exposure to antimicrobials, the mechanisms controlling intrinsic resistance are not well understood. In this study, we subjected the model opportunistic superbug Pseudomonas aeruginosa to 14 antimicrobials under highly controlled conditions and assessed its response using expression- and fitness-based genomic approaches. Our results reveal that gene expression changes and mutant fitness in response to sub-MIC antimicrobials do not correlate on a genomewide scale, indicating that gene expression is not a good predictor of fitness determinants. In general, fewer fitness determinants were identified for antiseptics and disinfectants than for antibiotics. Analysis of gene expression and fitness data together allowed the prediction of antagonistic interactions between antimicrobials and insight into the molecular mechanisms controlling these interactions.

Triangulation is an approach to research that is becoming increasingly popular among nurse researchers. Five types of triangulation are used in nursing research: data, methodological, theoretical, researcher, and analytical triangulation. Methodological triangulation is an attempt to improve validity by combining various techniques in one study. In this article, an example of quantitative and qualitative triangulation is discussed to illustrate the procedures used and the results achieved. The secondary data used as an example are from a previous study that was conducted by the researcher and investigated nursing interventions used by pediatric oncology nurses to provide social support to siblings of children with cancer. Results show that methodological triangulation was beneficial in this study for three reasons. First, the careful comparison of quantitative and qualitative data added support for the social support variables under investigation. Second, the comparison showed more in-depth dimensions about pediatric oncology nurses providing social support to siblings of children with cancer. Finally, the use of methodological triangulation provided insight into revisions for the quantitative instrument.