IncP-type plasmids carrying genes for antibiotic resistance or for aromatic compound degradation are prevalent in sequenced Aromatoleum and Thauera strains.

Self-transferable plasmids of the incompatibility group P-1 (IncP-1) are considered important carriers of genes for antibiotic resistance and other adaptive functions. In the laboratory, these plasmids have a broad host range; however, little is known about their in situ host profile. In this study, we discovered that Thauera aromatica K172T , a facultative denitrifying microorganism capable of degrading various aromatic compounds, contains a plasmid highly similar to the IncP-1 ε archetype pKJK5. The plasmid harbours multiple antibiotic resistance genes and is maintained in strain K172T for at least 1000 generations without selection pressure from antibiotics. In a subsequent search, we found additional nine IncP-type plasmids in a total of 40 sequenced genomes of the closely related genera Aromatoleum and Thauera. Six of these plasmids form a novel IncP-1 subgroup designated θ, four of which carry genes for anaerobic or aerobic degradation of aromatic compounds. Pentanucleotide sequence analyses (k-mer profiling) indicated that Aromatoleum spp. and Thauera spp. are among the most suitable hosts for the θ plasmids. Our results highlight the importance of IncP-1 plasmids for the genetic adaptation of these common facultative denitrifying bacteria and provide novel insights into the in situ host profile of these plasmids.

scMAR-Seq: a novel workflow for targeted single-cell genomics of microorganisms using radioactive labeling.

IMPORTANCE: A central question in microbial ecology is which member of a community performs a particular metabolism. Several sophisticated isotope labeling techniques are available for analyzing the metabolic function of populations and individual cells in a community. However, these methods are generally either insufficiently sensitive or throughput-limited and thus have limited applicability for the study of complex environmental samples. Here, we present a novel approach that combines highly sensitive radioisotope tracking, microfluidics, high-throughput sorting, and single-cell genomics to simultaneously detect and identify individual microbial cells based solely on their in situ metabolic activity, without prior information on community structure.