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BackgroundMembers of the bacterial family Flavobacteriaceae are widely distributed in the marine environment and often found associated with algae, fish, detritus or marine invertebrates. Yet, little is known about the characteristics that drive their ubiquity in diverse ecological niches. Here, we provide an overview of functional traits common to taxonomically diverse members of the family Flavobacteriaceae from different environmental sources, with a focus on the Marine clade. We include seven newly sequenced marine sponge-derived strains that were also tested for gliding motility and antimicrobial activity. ResultsComparative genomics revealed that genome similarities appeared to be correlated to 16S rRNA gene phylogeny, while differences were mostly associated with nutrient acquisition, such as carbohydrate metabolism and gliding motility. The high frequency and diversity of genes encoding polymer-degrading enzymes support the capacity of marine Flavobacteriaceae to utilize diverse carbon sources. Homologs of gliding proteins were widespread among all studied Flavobacteriaceae in contrast to members of other phyla, highlighting the particular presence of this feature within the Bacteroidetes. Notably, not all gliding bacteria formed spreading colonies. Genome mining uncovered a diverse secondary metabolite biosynthesis arsenal of Flavobacteriaceae with high prevalence of gene clusters encoding pathways for the production of antimicrobial, antioxidant and cytotoxic compounds. Antimicrobial activity tests showed, however, that the phenotype differed from the genome-derived predictions for the seven tested strains.ConclusionsOur study elucidates the functional repertoire of marine Flavobacteriaceae and highlights the need to combine genomic and experimental data while using the appropriate stimuli to unlock their uncharted metabolic potential.

BackgroundThe use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors.ResultsSynchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned.ConclusionsThe genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.