Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

Reaction dynamics analysis of a reconstituted Escherichia coli protein translation system by computational modeling.

To elucidate the dynamic features of a biologically relevant large-scale reaction network, we constructed a computational model of minimal protein synthesis consisting of 241 components and 968 reactions that synthesize the Met-Gly-Gly (MGG) peptide based on an Escherichia coli-based reconstituted in vitro protein synthesis system. We performed a simulation using parameters collected primarily from the literature and found that the rate of MGG peptide synthesis becomes nearly constant in minutes, thus achieving a steady state similar to experimental observations. In addition, concentration changes to 70% of the components, including intermediates, reached a plateau in a few minutes. However, the concentration change of each component exhibits several temporal plateaus, or a quasi-stationary state (QSS), before reaching the final plateau. To understand these complex dynamics, we focused on whether the components reached a QSS, mapped the arrangement of components in a QSS in the entire reaction network structure, and investigated time-dependent changes. We found that components in a QSS form clusters that grow over time but not in a linear fashion, and that this process involves the collapse and regrowth of clusters before the formation of a final large single cluster. These observations might commonly occur in other large-scale biological reaction networks. This developed analysis might be useful for understanding large-scale biological reactions by visualizing complex dynamics, thereby extracting the characteristics of the reaction network, including phase transitions.

A single fast Hebbian-like process enabling one-shot class addition in deep neural networks without backbone modification.

One-shot learning, the ability to learn a new concept from a single instance, is a distinctive brain function that has garnered substantial interest in machine learning. While modeling physiological mechanisms poses challenges, advancements in artificial neural networks have led to performances in specific tasks that rival human capabilities. Proposing one-shot learning methods with these advancements, especially those involving simple mechanisms, not only enhance technological development but also contribute to neuroscience by proposing functionally valid hypotheses. Among the simplest methods for one-shot class addition with deep learning image classifiers is "weight imprinting," which uses neural activity from a new class image data as the corresponding new synaptic weights. Despite its simplicity, its relevance to neuroscience is ambiguous, and it often interferes with original image classification, which is a significant drawback in practical applications. This study introduces a novel interpretation where a part of the weight imprinting process aligns with the Hebbian rule. We show that a single Hebbian-like process enables pre-trained deep learning image classifiers to perform one-shot class addition without any modification to the original classifier's backbone. Using non-parametric normalization to mimic brain's fast Hebbian plasticity significantly reduces the interference observed in previous methods. Our method is one of the simplest and most practical for one-shot class addition tasks, and its reliance on a single fast Hebbian-like process contributes valuable insights to neuroscience hypotheses.

Synthetic model ecosystem of 12 cryopreservable microbial species allowing for a noninvasive approach.

Simultaneous understanding of both population and ecosystem dynamics is crucial in an era marked by the degradation of ecosystem services. Experimental ecosystems are a powerful tool for understanding these dynamics; however, they often face technical challenges, typically falling into two categories: "complex but with limited replicability microcosms" and "highly replicable but overly simplistic microcosms." Herein, we present a high-throughput synthetic microcosm system comprising 12 functionally and phylogenetically diverse microbial species. These species are axenically culturable, cryopreservable, and can be measured noninvasively via microscopy, aided by machine learning. This system includes prokaryotic and eukaryotic producers and decomposers, and eukaryotic consumers to ensure functional redundancy. Our model system exhibited key features of a complex ecosystem: (i) various positive and negative interspecific interactions, (ii) higher-order interactions beyond two-species dynamics, (iii) probabilistic dynamics leading to divergent outcomes, and (iv) stable nonlinear transitions. We identified several conditions under which at least one species from each of the three functional groups-producers, consumers, and decomposers-and one functionally redundant species, persisted for over six months. These conditions set the stage for detailed investigations in the future. Given its designability and experimental replicability, our model ecosystem offers a promising platform for deeper insights integrating both population and ecosystem dynamics.

Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states.

Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

Synthetic symbiosis between a cyanobacterium and a ciliate toward novel chloroplast-like endosymbiosis.

Chloroplasts are thought to have co-evolved through endosymbiosis, after a cyanobacterial-like prokaryote was engulfed by a eukaryotic cell; however, it is impossible to observe the process toward chloroplasts. In this study, we constructed an experimental symbiosis model to observe the initial stage in the process from independent organisms to a chloroplast-like organelle. Our system of synthetic symbiosis is capable of long-term coculture of two model organisms: a cyanobacterium (Synechocystis sp. PCC6803) as a symbiont and a ciliate (Tetrahymena thermophila) as a host with endocytic ability. The experimental system was clearly defined, because we used a synthetic medium and the cultures were shaken to avoid spatial complexity. We determined the experimental conditions for sustainable coculture, by analyzing population dynamics using a mathematical model. We experimentally demonstrated that the coculture was sustainable for at least 100 generations, through serial transfers. Moreover, we found that cells isolated after the serial transfer improved the probability of coexistence of both species without extinction in re-coculture. The constructed system will be useful for understanding the initial stage of primary endosymbiosis from cyanobacteria to chloroplasts, i.e., the origin of algae and plants.

Kinetic analysis of ß-galactosidase and ß-glucuronidase tetramerization coupled with protein translation.

Both ß-galactosidase (GAL) and ß-glucuronidase (GUS) are tetrameric enzymes used widely as reporter proteins. However, little is known about the folding and assembly of these enzymes. Although the refolding kinetics of GAL from a denatured enzyme have been reported, it is not known how the kinetics differ when coupled with a protein translation reaction. Elucidating the assembly kinetics of GAL and GUS when coupled with protein translation will illustrate the differences between these two reporter proteins and also the assembly process under conditions more relevant to those in vivo. In this study, we used an in vitro translation/transcription system to synthesize GAL and GUS, measured the time development of the activity and oligomerization state of these enzymes, and determined the rate constants of the monomer to tetramer assembly process. We found that at similar concentrations, GAL assembles into tetramers faster than GUS. The rate constant of monomer to dimer assembly of GAL was 50-fold faster when coupled with protein translation than that of refolding from the denatured state. Furthermore, GAL synthesis was found to lack the rate-limiting step in the assembly process, whereas GUS has two rate-limiting steps: monomer to dimer assembly and dimer to tetramer assembly. The consequence of these differences when used as reporter proteins is discussed.

Identification of two forms of Q{beta} replicase with different thermal stabilities but identical RNA replication activity.

The enzyme Qß replicase is an RNA-dependent RNA polymerase, which plays a central role in infection by the simple single-stranded RNA virus bacteriophage Qß. This enzyme has been used in a number of applications because of its unique activity in amplifying RNA from an RNA template. Determination of the thermal stability of Qß replicase is important to gain an understanding of its function and potential applications, but data reported to date have been contradictory. Here, we provide evidence that these previous inconsistencies were due to the heterogeneous forms of the replicase with different stabilities. We purified two forms of replicase expressed in Escherichia coli, which differed in their thermal stability but showed identical RNA replication activity. Furthermore, we found that the replicase undergoes conversion between these forms due to oxidation, and the Cys-533 residue in the catalytic ß subunit and Cys-82 residue in the EF-Tu subunit of the replicase are essential prerequisites for this conversion to occur. These results strongly suggest that the thermal stable replicase contains the intersubunit disulfide bond between these cysteines. The established strategies for isolating and purifying a thermally stable replicase should increase the usefulness of Qß replicase in various applications, and the data regarding thermal stability obtained in this study may yield insight into the precise mechanism of infection by bacteriophage Qß.

Compartmentalization in a water-in-oil emulsion repressed the spontaneous amplification of RNA by Q beta replicase.

During RNA replication mediated by Qbeta replicase, self-replicating RNAs (RQ RNAs) are amplified without the addition of template RNA. This undesired amplification makes the study of target RNA replication difficult, especially for long RNA such as genomic RNA of Qbeta phage. This perhaps is one of the reasons why the precise rate of genomic RNA replication in the presence of host factor Hfq has not been reported in vitro. Here, we report a method to repress RQ RNA amplification by compartmentalization of the reaction using a water-in-oil emulsion but maintaining the activity of Qbeta replicase. This method allowed us to amplify the phage Qbeta genome RNA exponentially without detectable amplification of RQ RNA. Furthermore, we found that the rate constant of genome RNA replication in the exponential phase at the optimum Hfq concentration was approximately 4.6 times larger than that of a previous report, close to in vivo data. This result indicates that the replication rate in vivo is largely explained by the presence of Hfq. This easy method paves the way for the study of genomic RNA replication without special care for the undesired RQ RNA amplification.

A novel sequence-specific RNA quantification method using nicking endonuclease, dual-labeled fluorescent DNA probe, and conformation-interchangeable oligo-DNA.

We have developed a novel, single-step, isothermal, signal-amplified, and sequence-specific RNA quantification method (L-assay). The L-assay consists of nicking endonuclease, a dual-labeled fluorescent DNA probe (DL-probe), and conformation-interchangeable oligo-DNA (L-DNA). This signal-amplified assay can quantify target RNA concentration in a sequence-specific manner with a coefficient of variation (Cv) of 5% and a lower limit of detection of 0.1 nM. Moreover, this assay allows quantification of target RNA even in the presence of a several thousandfold excess by weight of cellular RNA. In addition, this assay can be used to measure the changes in RNA concentration in real-time and to quantify short RNAs (<30 nucleotides). The L-assay requires only incubation under isothermal conditions, is inexpensive, and is expected to be useful for basic research requiring high-accuracy, easy-to-use RNA quantification, and real-time quantification.

Cellular compartment model for exploring the effect of the lipidic membrane on the kinetics of encapsulated biochemical reactions.

One of the important characteristics of the cellular system is that interactions between the plasma membrane and water-soluble molecules in the cytoplasm are enhanced by the confinement of the molecules to the small volume of the intracellular space. Studying this effect in a model cell system, we measured the time evolution of an enzymatic hydrolysis reaction and a cell-free protein synthesis reaction taking place in giant liposomes having various size and phospholipid compositions by a flow cytometry. This single vesicle-based assay of a large number of liposomes enabled us to examine the volume dependence of enclosed reactions in detail, revealing that the presence of specific lipid affected the specific kinetic parameters of encapsulated reactions.

Importance of translation-replication balance for efficient replication by the self-encoded replicase.

In all living systems, the genetic information is replicated by the self-encoded replicase (Rep); this can be said to be a self-encoding system. Recently, we constructed a self-encoding system in liposomes as an artificial cell model, consisting of a reconstituted translation system and an RNA encoding the catalytic subunit of Qbeta Rep and the RNA was replicated by the self-encoded Rep produced by the translation reaction. In this system, both the ribosome (Rib) and Rep bind to the same RNA for translation and replication, respectively. Thus, there could be a dilemma: effective RNA replication requires high levels of Rep translation, but excessive translation in turn inhibits replication. Herein, we actually observed the competition between the Rib and Rep, and evaluated the effect for RNA replication by constructing a kinetic model that quantitatively explained the behavior of the self-encoding system. Both the experimental and theoretical results consistently indicated that the balance between translation and replication is critical for an efficient self-encoded system, and we determined the optimum balance.

Replication of genetic information with self-encoded replicase in liposomes.

In all living systems, the genome is replicated by proteins that are encoded within the genome itself. This universal reaction is essential to allow the system to evolve. Here, we have constructed a simplified system involving encapsulated macromolecules termed a "self-encoding system", in which the genetic information is replicated by self-encoded replicase in liposomes. That is, the universal reaction was reconstituted within a microcompartment bound by a lipid bilayer. The system was assembled by using one template RNA sequence as the information molecule and an in vitro translation system reconstituted from purified translation factors as the machinery for decoding the information. In this system, the catalytic subunit of Qbeta replicase is synthesized from the template RNA that encodes the protein. The replicase then replicates the template RNA that was used for its production. This in-liposome self-encoding system is one of the simplest such systems available; it consists of only 144 gene products, while the information and the function for its replication are encoded on different molecules and are compartmentalized into the microenvironment for evolvability.

Robustness of a Reconstituted Escherichia coli Protein Translation System Analyzed by Computational Modeling.

Robustness against environmental changes is one of the major features of biological systems, but its origin is not well understood. We recently constructed a large-scale computational model of an Escherichia coli-based reconstituted in vitro translation system that enumerates all protein synthesis processes in detail. Our model synthesizes a formyl-Met-Gly-Gly tripeptide (MGG peptide) from 27 initial molecular components through 968 biochemical reactions. Among the 968 kinetic parameters, 483 are nonzero parameters, and the simulator was used to determine how perturbations of 483 individual reactions affect the complex reaction network. We found that even when the kinetic parameter was changed from 100- to 0.01-fold, 94% of the changes hardly affected the two indicators of reaction dynamics in MGG peptide synthesis, which represent the yield of the MGG peptide and the initial lag-time of the peptide synthesis. Moreover, none of the indicators increased proportionally to these changes: e.g., a 100-fold increase in the kinetic parameter increased the yield by only 2.2-fold at most, indicating the insensitivity of the reaction network to perturbation. Robustness and insensitivity are likely to be a common feature of large-scale biological reaction networks.

Attraction of posture and motion-trajectory elements of conspecific biological motion in medaka fish.

Visual recognition of conspecifics is necessary for a wide range of social behaviours in many animals. Medaka (Japanese rice fish), a commonly used model organism, are known to be attracted by the biological motion of conspecifics. However, biological motion is a composite of both body-shape motion and entire-field motion trajectory (i.e., posture or motion-trajectory elements, respectively), and it has not been revealed which element mediates the attractiveness. Here, we show that either posture or motion-trajectory elements alone can attract medaka. We decomposed biological motion of the medaka into the two elements and synthesized visual stimuli that contain both, either, or none of the two elements. We found that medaka were attracted by visual stimuli that contain at least one of the two elements. In the context of other known static visual information regarding the medaka, the potential multiplicity of information regarding conspecific recognition has further accumulated. Our strategy of decomposing biological motion into these partial elements is applicable to other animals, and further studies using this technique will enhance the basic understanding of visual recognition of conspecifics.

Population-reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems.

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population-reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population-reaction model. We also show that population-reaction models can be applied to various ecological concepts, such as predator-prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Transcriptome analysis of a microbial coculture in which the cell populations are separated by a membrane.

The microbial coculture of multiple cell populations is used to study community evolution and for bioengineering applications. The cells in coculture undergo dynamic changes because of cell-cell and cell-environment interactions. Transcriptome analysis allows us to study the molecular basis of these changes in cell physiology. For transcriptome analysis, it is essential that the cell populations in the coculture are harvested separately. Here, we describe a method for transcriptome analysis of a microbial coculture in which two different cell populations are separated by a porous membrane.

Cooperative target tracking by a mobile bionanosensor network.

This paper describes a mobile bionanosensor network designed for target tracking. The mobile bionanosensor network is composed of bacterium-based autonomous biosensors that coordinate their movement through the use of two types of signaling molecules, repellents and attractants. In search of a target, the bacterium-based autonomous biosensors release repellents to quickly spread over the environment, while, upon detecting a target, they release attractants to recruit other biosensors in the environment toward the location around the target. A mobility model of bacterium-based autonomous biosensors is first developed based on the rotational diffusion model of bacterial chemotaxis, and from this their collective movement to track a moving target is demonstrated. In simulation experiments, the mobile bionanosensor network is evaluated based on the mean tracking time. Simulation results show a set of parameter values that can optimize the mean tracking time, providing an insight into how bacterium-based autonomous biosensors may be designed and engineered for target tracking.

Adaptation of a cyanobacterium to a biochemically rich environment in experimental evolution as an initial step toward a chloroplast-like state.

Chloroplasts originated from cyanobacteria through endosymbiosis. The original cyanobacterial endosymbiont evolved to adapt to the biochemically rich intracellular environment of the host cell while maintaining its photosynthetic function; however, no such process has been experimentally demonstrated. Here, we show the adaptation of a model cyanobacterium, Synechocystis sp. PCC 6803, to a biochemically rich environment by experimental evolution. Synechocystis sp. PCC 6803 does not grow in a biochemically rich, chemically defined medium because several amino acids are toxic to the cells at approximately 1 mM. We cultured the cyanobacteria in media with the toxic amino acids at 0.1 mM, then serially transferred the culture, gradually increasing the concentration of the toxic amino acids. The cells evolved to show approximately the same specific growth rate in media with 0 and 1 mM of the toxic amino acid in approximately 84 generations and evolved to grow faster in the media with 1 mM than in the media with 0 mM in approximately 181 generations. We did not detect a statistically significant decrease in the autotrophic growth of the evolved strain in an inorganic medium, indicating the maintenance of the photosynthetic function. Whole-genome resequencing revealed changes in the genes related to the cell membrane and the carboxysome. Moreover, we quantitatively analyzed the evolutionary changes by using simple mathematical models, which evaluated the evolution as an increase in the half-maximal inhibitory concentration (IC50) and estimated quantitative characteristics of the evolutionary process. Our results clearly demonstrate not only the potential of a model cyanobacterium to adapt to a biochemically rich environment without a significant decrease in photosynthetic function but also the properties of its evolutionary process, which sheds light of the evolution of chloroplasts at the initial stage.

Construction of bacteria-eukaryote synthetic mutualism.

Mutualism is ubiquitous in nature but is known to be intrinsically vulnerable with regard to both population dynamics and evolution. Synthetic ecology has indicated that it is feasible for organisms to establish novel mutualism merely through encountering each other by showing that it is feasible to construct synthetic mutualism between organisms. However, bacteria-eukaryote mutualism, which is ecologically important, has not yet been constructed. In this study, we synthetically constructed mutualism between a bacterium and a eukaryote by using two model organisms. We mixed a bacterium, Escherichia coli (a genetically engineered glutamine auxotroph), and an amoeba, Dictyostelium discoideum, in 14 sets of conditions in which each species could not grow in monoculture but potentially could grow in coculture. Under a single condition in which the bacterium and amoeba mutually compensated for the lack of required nutrients (lipoic acid and glutamine, respectively), both species grew continuously through several subcultures, essentially establishing mutualism. Our results shed light on the establishment of bacteria-eukaryote mutualism and indicate that a bacterium and eukaryote pair in nature also has a non-negligible possibility of establishing novel mutualism if the organisms are potentially mutualistic.