One of the fundamental questions in biology is how a microorganism functions across a broad temperature range while maintaining cellular homeostasis and sub-cellular organization. We take a single molecule approach to study how bacteria adapt to different temperatures. Such knowledge will contribute to the progress in research, applied sciences, and industrial microbiology applications.
Single molecule tracking in live bacteria

The development of super-resolution fluorescence microscopy has allowed the visualization of diffraction-limited subcellular structures at the nanometer scale. PALM and dSTORM techniques rely on sequential detection and localization of individual fluorescent molecules over time through repeated, random switching of fluorophores between fluorescent and non-fluorescent states. This can be achieved either by using genetically encoded photoactivatable fluorescent proteins or synthetic dyes (e.g., TMR, JF549) bound to self-labelling protein tags (e.g., HaloTag, SNAP-tag). These dyes are several times brighter and more stable than conventional fluorescent proteins, allowing for superior localization data of individual molecules. Single-molecule tracking follows individual molecules’ motion in real-time, and in live cells. Localization and mobility of a molecule inside a cell report on the molecular interactions and reactions providing information on diffusion coefficients, dissociation rates, and spatial localization of interaction sites of the reactants. This methodology can be used for any endogenous protein, and it captures not only stochasticity in molecular dynamics but also their heterogeneity inside and between cells.

Super-resolution techniques have become well established for many bacteria and yeast in recent years but have been mostly limited to moderate temperatures. For extremophiles, standard fluorescence microscopy is rarely used, and virtually no super-resolution studies have been reported. My team will investigate the dynamics of different macromolecules, e.g. RNA polymerase and ribosome, at the single-molecule level in bacteria growing at different temperatures, from optimal to restrictive, to describe how the cytoplasmic state and molecule activity changes with temperature.

Bacteria growing at different temperatures

Microorganisms have evolved to grow at temperatures ranging from near 0°C up to 100°C and beyond. For example, Geobacillus stearothermophilus, a thermophile, grows at 30°C to 75°C (optimum at 65°C) while Polaromonas vacuolate, a psychrophile, grows in temperatures ranging from -1.5°C to 12°C (optimum at 4°C). Biochemical reaction rates approximately double with every 10°C increase in temperature, resulting in faster cellular processes, e.g. growth and embryogenesis. Nevertheless, the thermophiles and psychrophiles must follow the same physical and chemical laws of life and use the same biochemical building blocks. Sub-optimal temperatures affect the growth of a microorganism in different ways: a higher than optimal temperature increases the rate of enzymatic reactions and diffusion of molecules inside a cell but at the same time decreases the stability of molecules leading to protein denaturation; at temperatures much lower than optimal, the cytoplasm and membranes become too stiff for efficient nutrient transport, cellular processes and bioenergetic functions to occur. Temperature also affects mechanical properties of DNA and RNA, impacting transcription, RNA degradation and translation. Still, relatively minor genetic adaptations allow extremophiles to grow in drastically different temperatures. Understanding the survival mechanisms of extremophiles will allow us to understand how organisms can thrive under extreme temperatures and what the boundary conditions of life are.

We will study bacteria evolved to optimally grow near 0°C (psychrophiles), moderate temperatures (mesophiles), and at extreme temperatures up to 100°C (thermophiles) to determine differences in cytoplasmic state and organization and to see if different strategies to overcome detrimental effects from temperature fluctuations exist. To identify the possible mechanisms, neural networks, machine learning, and stochastic modeling will be used to make sense of single-cell and molecule-level data. Understanding the temperature dependency of the intracellular environment and the fundamental consequences on cellular processes is crucial in interpreting cellular adaptation and proliferation under various environmental conditions. Such knowledge will contribute to the progress in research, applied sciences, and industrial microbiology applications.