Lower respiratory tract microbiome composition and community interactions in smokers.

The lung microbiome impacts on lung function, making any smoking-induced changes in the lung microbiome potentially significant. The complex co-occurrence and co-avoidance patterns between the bacterial taxa in the lower respiratory tract (LRT) microbiome were explored for a cohort of active (AS), former (FS) and never (NS) smokers. Bronchoalveolar lavages (BALs) were collected from 55 volunteer subjects (9 NS, 24 FS and 22 AS). The LRT microbiome composition was assessed using 16S rRNA amplicon sequencing. Identification of differentially abundant taxa and co-occurrence patterns, discriminant analysis and biomarker inferences were performed. The data show that smoking results in a loss in the diversity of the LRT microbiome, change in the co-occurrence patterns and a weakening of the tight community structure present in healthy microbiomes. The increased abundance of the genus Ralstonia in the lung microbiomes of both former and active smokers is significant. Partial least square discriminant and DESeq2 analyses suggested a compositional difference between the cohorts in the LRT microbiome. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS. The linear discriminant analysis effect size (LEfSe) analyses identified several bacterial taxa as potential biomarkers of smoking status. Network-based clustering analysis highlighted different co-occurring and co-avoiding microbial taxa in the three groups. The analysis found a cluster of bacterial taxa that co-occur in smokers and non-smokers alike. The clusters exhibited tighter and more significant associations in NS compared to FS and AS. Higher degree of rivalry between clusters was observed in the AS. The groups were sufficiently distinct from each other to suggest that cessation of smoking may not be sufficient for the lung microbiota to return to a similar composition to that of NS.

Deep sequencing analyses expands the Pseudomonas aeruginosa AmpR regulon to include small RNA-mediated regulation of iron acquisition, heat shock and oxidative stress response.

Pathogenicity of Pseudomonas aeruginosa, a major cause of many acute and chronic human infections, is determined by tightly regulated expression of multiple virulence factors. Quorum sensing (QS) controls expression of many of these pathogenic determinants. Previous microarray studies have shown that the AmpC ß-lactamase regulator AmpR, a member of the LysR family of transcription factors, also controls non-ß-lactam resistance and multiple virulence mechanisms. Using RNA-Seq and complementary assays, this study further expands the AmpR regulon to include diverse processes such as oxidative stress, heat shock and iron uptake. Importantly, AmpR affects many of these phenotypes, in part, by regulating expression of non-coding RNAs such as rgP32, asRgsA, asPrrF1 and rgRsmZ. AmpR positively regulates expression of the major QS regulators LasR, RhlR and MvfR, and genes of the Pseudomonas quinolone system. Chromatin immunoprecipitation (ChIP)-Seq and ChIP-quantitative real-time polymerase chain reaction studies show that AmpR binds to the ampC promoter both in the absence and presence of ß-lactams. In addition, AmpR directly binds the lasR promoter, encoding the QS master regulator. Comparison of the AmpR-binding sequences from the transcriptome and ChIP-Seq analyses identified an AT-rich consensus-binding motif. This study further attests to the role of AmpR in regulating virulence and physiological processes in P. aeruginosa.

Better primer design for metagenomics applications by increasing taxonomic distinguishability.

Current methods of understanding microbiome composition and structure rely on accurately estimating the number of distinct species and their relative abundance. Most of these methods require an efficient PCR whose forward and reverse primers bind well to the same, large number of identifiable species, and produce amplicons that are unique. It is therefore not surprising that currently used universal primers designed many years ago are not as efficient and fail to bind to recently cataloged species. We propose an automated general method of designing PCR primer pairs that abide by primer design rules and uses current sequence database as input. Since the method is automated, primers can be designed for targeted microbial species or updated as species are added or deleted from the database. In silico experiments and laboratory experiments confirm the efficacy of the newly designed primers for metagenomics applications.