Quantification of Motility in Bacillus subtilis at Temperatures Up to 84°C Using a Submersible Volumetric Microscope and Automated Tracking

Extant life detection using label-free video microscopy in analog aquatic environments

The ability of microbial active motion, morphology, and optical properties to serve as biosignatures was investigated by in situ video microscopy in a wide range of extreme field sites where such imaging had not been performed previously. These sites allowed for sampling seawater, sea ice brines, cryopeg brines, hypersaline pools and seeps, hyperalkaline springs, and glaciovolcanic cave ice. In all samples except the cryopeg brine, active motion was observed without any sample treatment. Active motion was observed in the cryopeg brines when samples were subjected to a temperature gradient above in situ. In general, levels of motility were low in the field samples collected at temperatures <  4ºC. Non-motile cells could be distinguished from microminerals by differences in passive motion (e.g., density measured by sinking/floating), refractive index and/or absorbance, or morphology in the case of larger eukaryotes. Dramatic increases in the fraction of motile cells were seen with simple stimuli such as warming or the addition of L-serine. Chemotaxis and thermotaxis were also observed in select samples. An open-source, autonomous software package with computational requirements that can be scaled to spaceflight computers was used to classify the data. These results demonstrate the utility of volumetric light microscopy for life detection, but also suggest the importance of developing methods to stimulate cells in situ and process data using the restrictions imposed by mission bandwidth, as well as instruments to capture cell-like objects for detailed chemical analysis.

Synergistic phenotypic adaptations of motile purple sulphur bacteria Chromatium okenii during lake-to-laboratory domestication

Isolating microorganisms from natural environments for cultivation under optimized laboratory settings has markedly improved our understanding of microbial ecology. Artificial growth conditions often diverge from those in natural ecosystems, forcing wild isolates into distinct selective pressures, resulting in diverse eco-physiological adaptations mediated by modification of key phenotypic traits. For motile microorganisms we still lack a biophysical understanding of the relevant traits emerging during domestication and their mechanistic interplay driving short-to-long-term microbial adaptation under laboratory conditions. Using microfluidics, atomic force microscopy, quantitative imaging, and mathematical modeling, we study phenotypic adaptation of Chromatium okenii, a motile phototrophic purple sulfur bacterium from meromictic Lake Cadagno, grown under laboratory conditions over multiple generations. Our results indicate that naturally planktonic C. okenii leverage shifts in cell-surface adhesive interactions, synergistically with changes in cell morphology, mass density, and distribution of intracellular sulfur globules, to suppress their swimming traits, ultimately switching to a sessile lifeform. A computational model of cell mechanics confirms the role of such phenotypic shifts in suppressing the planktonic lifeform. By investigating key phenotypic traits across different physiological stages of lab-grown C. okenii, we uncover a progressive loss of motility during the early stages of domestication, followed by concomitant deflagellation and enhanced surface attachment, ultimately driving the transition of motile sulfur bacteria to a sessile state. Our results establish a mechanistic link between suppression of motility and surface attachment via phenotypic changes, underscoring the emergence of adaptive fitness under laboratory conditions at the expense of traits tailored for natural environments.

Degradable living plastics programmed by engineered spores

Plastics are widely used materials that pose an ecological challenge because their wastes are difficult to degrade. Embedding enzymes and biomachinery within polymers could enable the biodegradation and disposal of plastics. However, enzymes rarely function under conditions suitable for polymer processing. Here, we report degradable living plastics by harnessing synthetic biology and polymer engineering. We engineered Bacillus subtilis spores harboring the gene circuit for the xylose-inducible secretory expression of Burkholderia cepacia lipase (BC-lipase). The spores that were resilient to stresses during material processing were mixed with poly(caprolactone) to produce living plastics in various formats. Spore incorporation did not compromise the physical properties of the materials. Spore recovery was triggered by eroding the plastic surface, after which the BC-lipase released by the germinated cells caused near-complete depolymerization of the polymer matrix. This study showcases a method for fabricating green plastics that can function when the spores are latent and decay when the spores are activated and sheds light on the development of materials for sustainability.

Advancements in bacterial chemotaxis: Utilizing the navigational intelligence of bacteria and its practical applications

The fascinating world of microscopic life unveils a captivating spectacle as bacteria effortlessly maneuver through their surroundings with astonishing accuracy, guided by the intricate mechanism of chemotaxis. This review explores the complex mechanisms behind this behavior, analyzing the flagellum as the driving force and unraveling the intricate signaling pathways that govern its movement. We delve into the hidden costs and benefits of this intricate skill, analyzing its potential to propagate antibiotic resistance gene while shedding light on its vital role in plant colonization and beneficial symbiosis. We explore the realm of human intervention, considering strategies to manipulate bacterial chemotaxis for various applications, including nutrient cycling, algal bloom and biofilm formation. This review explores the wide range of applications for bacterial capabilities, from targeted drug delivery in medicine to bioremediation and disease control in the environment. Ultimately, through unraveling the intricacies of bacterial movement, we can enhance our comprehension of the intricate web of life on our planet. This knowledge opens up avenues for progress in fields such as medicine, agriculture, and environmental conservation.

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks

MOTIVATION

Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics.

RESULTS

We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications.

AVAILABILITY AND IMPLEMENTATION

MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.