RESUMEN
Understanding how cells dynamically adapt to their environment is a primary focus of biology research. Temporal information about cellular behavior is often limited by both small numbers of data time-points and the methods used to analyze this data. Here, we apply unsupervised machine learning to a data set containing the activity of 1805 native promoters in E. coli measured every 10 minutes in a high-throughput microfluidic device via fluorescence time-lapse microscopy. Specifically, this data set reveals E. coli transcriptome dynamics when exposed to different heavy metal ions. We use a bioinformatics pipeline based on Independent Component Analysis (ICA) to generate insights and hypotheses from this data. We discovered three primary, time-dependent stages of promoter activation to heavy metal stress (fast, intermediate, and steady). Furthermore, we uncovered a global strategy E. coli uses to reallocate resources from stress-related promoters to growth-related promoters following exposure to heavy metal stress.
Asunto(s)
Escherichia coli , Metales Pesados , Escherichia coli/genética , Transcriptoma , Regiones Promotoras Genéticas/genética , Biología Computacional , Perfilación de la Expresión GénicaRESUMEN
Hierarchical organization in ecology, whereby interactions are nested in a manner that leads to a dominant species, naturally result in the exclusion of all but the dominant competitor. Alternatively, non-hierarchical competitive dynamics, such as cyclical interactions, can sustain biodiversity. Here, we designed a simple microbial community with three strains of E. coli that cyclically interact through (i) the inhibition of protein production, (ii) the digestion of genomic DNA, and (iii) the disruption of the cell membrane. We find that intrinsic differences in these three major mechanisms of bacterial warfare lead to an unbalanced community that is dominated by the weakest strain. We also use a computational model to describe how the relative toxin strengths, initial fractional occupancies, and spatial patterns affect the maintenance of biodiversity. The engineering of active warfare between microbial species establishes a framework for exploration of the underlying principles that drive complex ecological interactions.
Asunto(s)
Escherichia coli/citología , Viabilidad Microbiana , Biodiversidad , Simulación por Computador , Escherichia coli/genética , Escherichia coli/inmunología , Inmunidad/genética , Modelos BiológicosRESUMEN
The last decade has seen bacteria at the forefront of biotechnological innovation, with applications including biomolecular computing, living therapeutics, microbiome engineering and microbial factories. These emerging applications are all united by the need to precisely control complex microbial dynamics in spatially extended environments, requiring tools that can bridge the gap between intracellular and population-level coordination. To address this need, we engineer an inducible quorum sensing system which enables precise tunability of bacterial dynamics both at the population and community level. As a proof-of-principle, we demonstrate the advantages of this system when genetically equipped for cargo delivery. In addition, we exploit the absence of cross-talk with respect to the majority of well-characterized quorum sensing systems to demonstrate inducibility of multi-strain communities. More broadly, this work highlights the unexplored potential of remotely inducible quorum sensing systems which, coupled to any gene of interest, may facilitate the translation of circuit designs into applications.