Nathan J. Kuwada

Quantitative single-cell characterization of bacterial interactions reveals type VI secretion is a double-edged sword

Interbacterial interaction pathways play an important role in defining the structure and complexity of bacterial associations. A quantitative description of such pathways offers promise for understanding the forces that contribute to community composition. We developed time-lapse fluorescence microscopy methods for quantitation of interbacterial interactions and applied these to the characterization of type VI secretion (T6S) in Pseudomonas aeruginosa. Our analyses allowed a direct determination of the efficiency of recipient cell lysis catalyzed by this intercellular toxin delivery pathway and provided evidence that its arsenal extends beyond known effector proteins. Measurement of T6S apparatus localization revealed correlated activation among neighboring cells, which, taken together with genetic data, implicate the elaboration of a functional T6S apparatus with a marked increase in susceptibility to intoxication. This possibility was supported by the identification of T6S-inactivating mutations in a genome-wide screen for resistance to T6S-mediated intoxication and by time-lapse fluorescence microscopy analyses showing a decreased lysis rate of recipient cells lacking T6S function. Our discoveries highlight the utility of single-cell approaches for measuring interbacterial phenomena and provide a foundation for studying the contribution of a widespread bacterial interaction pathway to community structure.

Surface sensing and lateral subcellular localization of WspA, the receptor in a chemosensory-like system leading to c-di-GMP production

Pseudomonas aeruginosa responds to growth on agar surfaces to produce cyclic‐di‐GMP, which stimulates biofilm formation. This is mediated by an alternative cellular function chemotaxis‐like system called Wsp. The receptor protein WspA, is bioinformatically indistinguishable from methyl‐accepting chemotaxis proteins. However, unlike standard chemoreceptors, WspA does not form stable clusters at cell poles. Rather, it forms dynamic clusters at both polar and lateral subcellular locations. To begin to study the mechanism of Wsp signal transduction in response to surfaces, we carried out a structure–function study of WspA and found that its C‐terminus is important for its lateral subcellular localization and function. When this region was replaced with that of a chemoreceptor for amino acids, WspA became polarly localized. In addition, introduction of mutations in the C‐terminal region of WspA that rendered this protein able to form more stable receptor–receptor interactions, also resulted in a WspA protein that was less capable of activating signal transduction. Receptor chimeras with a WspA C‐terminus and N‐terminal periplasmic domains from chemoreceptors that sense amino acids or malate responded to surfaces to produce c‐di‐GMP. Thus, the amino acid sequence of the WspA periplasmic region did not need to be conserved for the Wsp system to respond to surfaces.

Realization of a Feedback Controlled Flashing Ratchet

A flashing ratchet transports diffusive particles using a time-dependent, asymmetric potential. The particle speed is predicted to increase when a feedback algorithm based on the particle position is used. We have experimentally realized such a feedback ratchet using an optical line trap, and observed that use of feedback increases velocity by up to an order of magnitude. We compare two different feedback algorithms for small particle numbers, and find good agreement with simulations. We also find that existing algorithms can be improved to be more tolerant to feedback delay times.

Genome-scale quantitative characterization of bacterial protein localization dynamics throughout the cell cycle

Bacterial cells display both spatial and temporal organization, and this complex structure is known to play a central role in cellular function. Although nearly one‐fifth of all proteins in Escherichia coli localize to specific subcellular locations, fundamental questions remain about how cellular‐scale structure is encoded at the level of molecular‐scale interactions. One significant limitation to our understanding is that the localization behavior of only a small subset of proteins has been characterized in detail. As an essential step toward a global model of protein localization in bacteria, we capture and quantitatively analyze spatial and temporal protein localization patterns throughout the cell cycle for nearly every protein in E. coli that exhibits nondiffuse localization. This genome‐scale analysis reveals significant complexity in patterning, notably in the behavior of DNA‐binding proteins. Complete cell‐cycle imaging also facilitates analysis of protein partitioning to daughter cells at division, revealing a broad and robust assortment of asymmetric partitioning behaviors.

The Tumbleweed: Towards a synthetic protein motor

Biomolecular motors have inspired the design and construction of artificial nanoscale motors and machines based on nucleic acids, small molecules, and inorganic nanostructures. However, the high degree of sophistication and efficiency of biomolecular motors, as well as their specific biological function, derives from the complexity afforded by protein building blocks. Here, we discuss a novel bottom‐up approach to understanding biological motors by considering the construction of synthetic protein motors. Specifically, we present a design for a synthetic protein motor that moves along a linear track, dubbed the “Tumbleweed.” This concept uses three discrete ligand‐dependent DNA‐binding domains to perform cyclically ligand‐gated, rectified diffusion along a synthesized DNA molecule. Here we describe how de novo peptide design and molecular biology could be used to produce the Tumbleweed, and we explore the fundamental motor operation of such a design using numerical simulations. The construction of this and more sophisticated protein motors is an exciting challenge that is likely to enhance our understanding of the structure‐function relationship in biological motors.

Cytoplasmic Dynamics Reveals Two Modes of Nucleoid-Dependent Mobility

It has been proposed that forces resulting from the physical exclusion of macromolecules from the bacterial nucleoid play a central role in organizing the bacterial cell, yet this proposal has not been quantitatively tested. To investigate this hypothesis, we mapped the generic motion of large protein complexes in the bacterial cytoplasm through quantitative analysis of thousands of complete cell-cycle trajectories of fluorescently tagged ectopic MS2-mRNA complexes. We find the motion of these complexes in the cytoplasm is strongly dependent on their spatial position along the long axis of the cell, and that their dynamics are consistent with a quantitative model that requires only nucleoid exclusion and membrane confinement. This analysis also reveals that the nucleoid increases the mobility of MS2-mRNA complexes, resulting in a fourfold increase in diffusion coefficients between regions of the lowest and highest nucleoid density. These data provide strong quantitative support for two modes of nucleoid action: the widely accepted mechanism of nucleoid exclusion in organizing the cell and a newly proposed mode, in which the nucleoid facilitates rapid motion throughout the cytoplasm.

High-throughput cell-cycle imaging opens new doors for discovery.

During the life of a cell, numerous essential cellular processes must be coordinated both spatially and temporally, from DNA replication and chromosome segregation to gene expression and cytokinesis. In order to analyze these inherently dynamic and cell-cycle-dependent processes, it is essential to observe the dynamic localization of the cellular machinery throughout the entire cell cycle. Although some coarse features of cell-cycle dynamics can be captured in snapshot imaging, where cellular size or morphology can be used as a proxy for cell-cycle phase, the inherently stochastic nature of ultrastructures in the cell makes the direct visualization of subcellular dynamics an essential tool to differentiate between structural differences that are the result of biologically relevant dynamics versus cell-to-cell variation. With these goals in mind, we have developed a unique high-throughput imaging approach, and have recently applied this to characterize the cell-cycle localization of nearly every protein in the bacterial cell (Kuwada in Mol Microbiol, 95(1), 64–79, 2015). This approach combines large-format sample preparation with automated image capture, processing, and analysis to quantitatively characterize proteome localization of tens of thousands of complete cell cycles.

Unidirectional P-Body Transport during the Yeast Cell Cycle

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained “hovering” near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions.

A classical Master equation approach to modeling an artificial protein motor

Inspired by biomolecular motors, as well as by theoretical concepts for chemically driven nanomotors, there is significant interest in constructing artificial molecular motors. One driving force is the opportunity to create well-controlled model systems that are simple enough to be modeled in detail. A remaining challenge is the fact that such models need to take into account processes on many different time scales. Here we describe use of a classical Master equation approach, integrated with input from Langevin and molecular dynamics modeling, to stochastically model an existing artificial molecular motor concept, the Tumbleweed, across many time scales. This enables us to study how interdependencies between motor processes, such as center-of-mass diffusion and track binding/unbinding, affect motor performance. Results from our model help guide the experimental realization of the proposed motor, and potentially lead to insights that apply to a wider class of molecular motors.

Probing bacterial cell biology using image cytometry: Probing bacterial cell biology with image cytometry

Advances in automated fluorescence microscopy have made snapshot and time‐lapse imaging of bacterial cells commonplace, yet fundamental challenges remain in analysis. The vast quantity of data collected in high‐throughput experiments requires a fast and reliable automated method to analyze fluorescence intensity and localization, cell morphology and proliferation as well as other descriptors. Inspired by effective yet tractable methods of population‐level analysis using flow cytometry, we have developed a framework and tools for facilitating analogous analyses in image cytometry. These tools can both visualize and gate (generate subpopulations) more than 70 cell descriptors, including cell size, age and fluorescence. The method is well suited to multi‐well imaging, analysis of bacterial cultures with high cell density (thousands of cells per frame) and complete cell cycle imaging. We give a brief description of the analysis of four distinct applications to emphasize the broad applicability of the tool.