The marine bacterium Vibrio alginolyticus has a single flagellum as a locomotory organ at the cell pole, which is rotated by the Na⁺-motive force to swim in a liquid. The base of the flagella has a motor composed of a stator and rotor, which serves as a power engine to generate torque through the rotor–stator interaction coupled to Na⁺ influx through the stator channel. The MS-ring, which is embedded in the membrane at the base of the flagella as part of the rotor, is the initial structure required for flagellum assembly. It comprises 34 molecules of the two-transmembrane protein FliF. FliG, FliM, and FliN form a C-ring just below the MS-ring. FliG is an important rotor protein that interacts with the stator PomA and directly contributes to force generation. We previously found that FliG promotes MS-ring formation in E. coli. In the present study, we constructed a fliF–fliG fusion gene, which encodes an approximately 100 kDa protein, and the successful production of this protein effectively formed the MS-ring in E. coli cells. We observed fuzzy structures around the ring using either electron microscopy or high-speed atomic force microscopy (HS-AFM), suggesting that FliM and FliN are necessary for the formation of a stable ring structure. The HS-AFM movies revealed flexible movements at the FliG region.

Herein, I review our recent work toward developing methods for generating three-dimensional (3D) droplet arrays driven by capillarity. Microdroplet array-based systems are useful for bioassays and bioengineering because they require only small amounts of samples and reagents and provide the high throughput. Various methods have been developed for preparing droplet arrays, among which methods based on capillarity have attracted considerable attention owing to their simplicity. I and collaborators have developed such methods based on capillary flow, including a method for preparing droplet arrays via oil–water replacement. We recently proposed our own concept of “fluid–fluid interfacial energy driven 3D structure emergence in a micropillar scaffold (FLUID3EAMS)” and its application. FLUID3EAMS allows a 3D droplet (or hydrogel bead) array to be generated in a micropillar scaffold by passing a fluid–fluid interface through the scaffold. This approach is useful for applications requiring ordered or arrayed microdroplets in biosensors, biophysics, biology, and tissue engineering. This review is an extended version of the article “FLUID3EAMS: Fluid–Fluid Interfacial Energy Driven 3D Structure Emergence in a Micropillar Scaffold and Development in Bioengineering” published in Seibutsu Butsuri (vol. 62, p. 110–113, 2022).

Optogenetic techniques offer a high spatiotemporal resolution to manipulate cellular activity. For instance, Channelrhodopsin-2 with global light illumination is the most widely used to control neuronal activity at the cellular level. However, the cellular scale is much larger than the diffraction limit of light (<1 μm) and does not fully exploit the features of the “high spatial resolution” of optogenetics. For instance, until recently, there were no optogenetic methods to induce synaptic plasticity at the level of single synapses. To address this, we developed an optogenetic tool named photoactivatable CaMKII (paCaMKII) by fusing a light-sensitive domain (LOV2) to CaMKIIα, which is a protein abundantly expressed in neurons of the cerebrum and hippocampus and essential for synaptic plasticity. Combining photoactivatable CaMKII with two-photon excitation, we successfully activated it in single spines, inducing synaptic plasticity (long-term potentiation) in hippocampal neurons. We refer to this method as “Local Optogenetics”, which involves the local activation of molecules and measurement of cellular responses. In this review, we will discuss the characteristics of LOV2, the recent development of its derivatives, and the development and application of paCaMKII.

Is it a miracle that life exists on the Earth, or is it a common phenomenon in the universe? If extraterrestrial organisms exist, what are they like? To answer these questions, we must understand what kinds of molecules could evolve into life, or in other words, what properties are generally required to perform biological functions and store genetic information. This review summarizes recent findings on simple ancestral proteins, outlines the basic knowledge in textbooks, and discusses the generally required properties for biological molecules from structural biology viewpoints (e.g., restriction of shapes, and types of intra- and intermolecular interactions), leading to the conclusion that proteins and nucleic acids are at least one of the simplest (and perhaps very common) forms of catalytic and genetic biopolymers in the universe. This review article is an extended version of the Japanese article, On the Origin of Life: Coevolution between RNA and Peptide, published in SEIBUTSU BUTSURI Vol. 61, p. 232-235 (2021).

In Paramecium, a mechanical stimulus applied to the posterior portion of the cell causes a transient increase in membrane permeability to potassium ions, transiently rendering the membrane in a hyperpolarized state. Hyperpolarization causes a transient increase in Cyclic adenosine monophosphate (cAMP) concentration in the cilia, resulting in a transient fast-forward swimming of the cell. Schultz and coworkers (1992) reported that a unique adenylate cyclase (AC)-coupled potassium channel is involved in the reaction underlying this response, which is known as the “escape response.” However, the AC responsible for this reaction remains to be identified. Moreover, the molecular linkage between mechanoreception and AC activation has not been elucidated adequately. Currently, we can perform an efficient and simple gene-knockdown technique in Paramecium using RNA interference (RNAi). Paramecium is one of the several model organisms for which whole-genome sequences have been elucidated. The RNAi technique can be applied to whole genome sequences derived from the Paramecium database (ParameciumDB) to investigate the types of proteins that elicit specific biological responses and compare them with those of other model organisms. In this review, we describe the applications of the RNAi technique in elucidating the molecular mechanism underlying the escape response and identifying the AC involved in this reaction. The findings of this study highlight the advantages of the RNAi technique and ParameciumDB.

Most motile bacteria use supramolecular motility machinery called bacterial flagellum, which converts the chemical energy gained from ion flux into mechanical rotation. Bacterial cells sense their external environment through a two-component regulatory system consisting of a histidine kinase and response regulator. Combining these systems allows the cells to move toward favorable environments and away from their repellents. A representative example of flagellar motility is run-and-tumble swimming in Escherichia coli, where the counter-clockwise (CCW) rotation of a flagellar bundle propels the cell forward, and the clockwise (CW) rotation undergoes cell re-orientation (tumbling) upon switching the direction of flagellar motor rotation from CCW to CW. In this mini review, we focus on several types of chemotactic behaviors that respond to changes in flagellar shape and direction of rotation. Moreover, our single-cell analysis demonstrated back-and-forth swimming motility of an original E. coli strain. We propose that polymorphic flagellar changes are required to enhance bacterial movement in a structured environment as a colony spread on an agar plate.

To computationally investigate the recent experimental finding such that extracellular ATP release caused by exogeneous mechanical forces promote wound closure, we introduce a mathematical model, the Cellular Potts Model (CPM), which is a popular discretized model on a lattice, where the movement of a “cell” is determined by a Monte Carlo procedure. In the experiment, it was observed that there is mechanosensitive ATP release from the leading cells facing the wound gap and the subsequent extracellular Ca²⁺ influx. To model these phenomena, the Reaction-Diffusion equations for extracellular ATP and intracellular Ca²⁺ concentrations are adopted and combined with CPM, where we also add a polarity term because the cell migration is enhanced in the case of ATP release. From the numerical simulations using this hybrid model, we discuss effects of the collective cell migration due to the ATP release and the Ca²⁺ influx caused by the mechanical forces and the consequent promotion of wound closure.

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.