Phylogenomics of two ST1 antibiotic-susceptible non-clinical <i>Acinetobacter baumannii</i> strains reveals multiple lineages and complex evolutionary history in global clone 1.

Acinetobacter baumannii is an opportunistic pathogen that is difficult to treat due to its resistance to extreme conditions, including desiccation and antibiotics. Most strains causing outbreaks around the world belong to two main global lineages, namely global clones 1 and 2 (GC1 and GC2). Here, we used a combination of Illumina short read and MinION (Oxford Nanopore) long-read sequence data with a hybrid assembly approach to complete the genome sequence of two antibiotic-sensitive GC1 strains, Ex003 and Ax270, recovered in Lebanon from water and a rectal swab of a cat, respectively. Phylogenetic analysis of Ax270 and Ex003 with 186 publicly available GC1 genomes revealed two major clades, including five main lineages (L1-L5), and four single-isolate lineages outside of the two clades. Ax270 and Ex003, along with AB307-0294 and MRSN7213 (both predicted antibiotic-susceptible isolates) represent these individual lineages. Antibiotic resistance islands and transposons interrupting the comM gene remain important features in L1-L5, with L1 associated with the AbaR-type resistance islands, L2 with AbaR4, L3 strains containing either AbaR4 or its variants as well as Tn6022::ISAba42, and L4 and L5 associated with Tn6022 or its variants. Analysis of the capsule (KL) and outer core (OCL) polysaccharide loci further revealed a complex evolutionary history probably involving many recombination events. As more genomes become available, more GC1 lineages continue to emerge. However, genome sequence data from more diverse geographical regions are needed to draw a more accurate population structure of this globally distributed clone

Positive selection inhibits gene mobilization and transfer in soil bacterial communities

Horizontal gene transfer (HGT) between bacterial lineages is a fundamental evolutionary process that accelerates adaptation. Sequence analyses show that conjugative plasmids are principal agents of HGT in natural communities. However, we lack understanding of how the ecology of bacterial communities and their environments affect the dynamics of plasmid-mediated gene mobilization and transfer. Here we show, in simple experimental soil bacterial communities containing a conjugative mercury resistance plasmid, the repeated, independent mobilization of transposon-borne genes from chromosome to plasmid, plasmid to chromosome and, in the absence of mercury selection, interspecific gene transfers from the chromosome of one species to the other via the plasmid. By reducing conjugation, positive selection for plasmid-encoded traits, like mercury resistance, can consequently inhibit HGT. Our results suggest that interspecific plasmid-mediated gene mobilization is most likely to occur in environments where plasmids are infectious, parasitic elements rather than those where plasmids are positively selected, beneficial elements

Predictive biology: modelling, understanding and harnessing microbial complexity

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.Defence Threat Reduction Agency (Grant HDTRA1-15-1-0051