Metabolic Tug-of-War between Glycolysis and Translation Revealed by Biochemical Reconstitution.

Inside cells, various biological systems work cooperatively for homeostasis and self-replication. These systems do not work independently as they compete for shared elements like ATP and NADH. However, it has been believed that such competition is not a problem in codependent biological systems such as the energy-supplying glycolysis and the energy-consuming translation system. In this study, we biochemically reconstituted the coupling system of glycolysis and translation using purified elements and found that the competition for ATP between glycolysis and protein synthesis interferes with their coupling. Both experiments and simulations revealed that this interference is derived from a metabolic tug-of-war between glycolysis and translation based on their reaction rates, which changes the threshold of the initial substrate concentration for the success coupling. By the metabolic tug-of-war, translation energized by strong glycolysis is facilitated by an exogenous ATPase, which normally inhibits translation. These findings provide chemical insights into the mechanism of competition among biological systems in living cells and provide a framework for the construction of synthetic metabolism in vitro.

Switching from weak to strong cortical attachment of microtubules accounts for the transition from nuclear centration to spindle elongation in metazoans.

The centrosome is a major microtubule organizing center in animal cells. The position of the centrosomes inside the cell is important for cell functions such as cell cycle, and thus should be tightly regulated. Theoretical models based on the forces generated along the microtubules have been proposed to account for the dynamic movements of the centrosomes during the cell cycle. These models, however, often adopted inconsistent assumptions to explain distinct but successive movements, thus preventing a unified model for centrosome positioning. For the centration of the centrosomes, weak attachment of the astral microtubules to the cell cortex was assumed. In contrast, for the separation of the centrosomes during spindle elongation, strong attachment was assumed. Here, we mathematically analyzed these processes at steady state and found that the different assumptions are proper for each process. We experimentally validated our conclusion using nematode and sea urchin embryos by manipulating their shapes. Our results suggest the existence of a molecular mechanism that converts the cortical attachment from weak to strong during the transition from centrosome centration to spindle elongation.

Titanium Culture Vessel Presenting Temperature Gradation for the Thermotolerance Estimation of Cells.

Hyperthermia can be induced to exploit the thermal intolerance of cancer cells, which is worse than that of normal cells, as a potential noninvasive cancer treatment. To develop an effective hyperthermia treatment, thermal cytotoxicity of cells should be comprehensively investigated. However, to conduct such investigations, the culture temperature must be accurately regulated. We previously reported a culture system in which the culture temperature could be accurately regulated by employing metallic culture vessels. However, appropriate temperature conditions for hyperthermia depend on the cell species. Consequently, several experiments need to be conducted, which is a bottleneck of inducing hyperthermia. Hence, we developed a cell culture system with temperature gradation on a metallic culture surface. Michigan Cancer Foundation-7 cells and normal human dermal fibroblasts were used as cancer and normal cell models, respectively. Normal cells showed stronger thermal tolerance; this was because the novel system immediately exhibited a temperature gradation. Thus, the developed culture system can be used to investigate the optimum thermal conditions for effective hyperthermia treatment. Furthermore, as the reactions of cultured cells can be effectively assessed with the present results, further research involving the thermal stimulation of cells is possible.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

An explainable deep learning-based algorithm with an attention mechanism for predicting the live birth potential of mouse embryos.

In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, rates of live birth following clinical ART remain low worldwide. Grading is based on the embryo shape at a limited number of stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Here, we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from the nuclear structure in live-cell fluorescence images of mouse embryos from zygote to across a wide range of stages. The input is morphological features of cell nuclei, which were extracted as multivariate time-series data by using the segmentation algorithm for mouse embryos. The classification accuracy of our method (83.87%) greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. Our method also has a new attention mechanism that allows us to determine which values of multivariate time-series data, used to describe nuclear morphology, were the basis for the prediction. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.

The number of patients with heart failure and related deaths is rapidly increasing worldwide, making it a major problem. Cardiac hypertrophy is a crucial preliminary step in heart failure, but its treatment has not yet been fully successful. In this study, we established a system to evaluate cardiomyocyte hypertrophy using a deep learning-based high-throughput screening system and identified drugs that inhibit it. First, primary cultured cardiomyocytes from neonatal rats were stimulated by both angiotensin II and endothelin-1, and cellular images were captured using a phase-contrast microscope. Subsequently, we used a deep learning model for instance segmentation and established a system to automatically and unbiasedly evaluate the cardiomyocyte size and perimeter. Using this system, we screened 100 FDA-approved drugs library and identified 12 drugs that inhibited cardiomyocyte hypertrophy. We focused on ezetimibe, a cholesterol absorption inhibitor, that inhibited cardiomyocyte hypertrophy in a dose-dependent manner in vitro. Additionally, ezetimibe improved the cardiac dysfunction induced by pressure overload in mice. These results suggest that the deep learning-based system is useful for the evaluation of cardiomyocyte hypertrophy and drug screening, leading to the development of new treatments for heart failure.

Intercellular interaction mechanisms promote diversity in intracellular ATP concentration in Escherichia coli populations.

In fluctuating environments, many microorganisms acquire phenotypic heterogeneity as a survival tactic to increase the likelihood of survival of the overall population. One example of this interindividual heterogeneity is the diversity of ATP concentration among members of Escherichia coli populations under glucose deprivation. Despite the importance of such environmentally driven phenotypic heterogeneity, how the differences in intracellular ATP concentration emerge among individual E. coli organisms is unknown. In this study, we focused on the mechanism through which individual E. coli achieve high intracellular ATP concentrations. First, we measured the ATP retained by E. coli over time when cultured at low (0.1 mM) and control (22.2 mM) concentrations of glucose and obtained the chronological change in ATP concentrations. Then, by comparing these chronological change of ATP concentrations and analyzing whether stochastic state transitions, periodic oscillations, cellular age, and intercellular communication-which have been reported as molecular biological mechanisms for generating interindividual heterogeneity-are involved, we showed that the appearance of high ATP-holding individuals observed among E. coli can be explained only by intercellular transmission. By performing metabolomic analysis of post-culture medium, we revealed a significant increase in the ATP, especially at low glucose, and that the number of E. coli that retain significantly higher ATP can be controlled by adding large amounts of ATP to the medium, even in populations cultured under control glucose concentrations. These results reveal for the first time that ATP-mediated intercellular transmission enables some individuals in E. coli populations grown at low glucose to retain large amounts of ATP.

BioSimulators: a central registry of simulation engines and services for recommending specific tools.

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

High quality genome assembly of the anhydrobiotic midge provides insights on a single chromosome-based emergence of extreme desiccation tolerance.

Non-biting midges (Chironomidae) are known to inhabit a wide range of environments, and certain species can tolerate extreme conditions, where the rest of insects cannot survive. In particular, the sleeping chironomid Polypedilum vanderplanki is known for the remarkable ability of its larvae to withstand almost complete desiccation by entering a state called anhydrobiosis. Chromosome numbers in chironomids are higher than in other dipterans and this extra genomic resource might facilitate rapid adaptation to novel environments. We used improved sequencing strategies to assemble a chromosome-level genome sequence for P. vanderplanki for deep comparative analysis of genomic location of genes associated with desiccation tolerance. Using whole genome-based cross-species and intra-species analysis, we provide evidence for the unique functional specialization of Chromosome 4 through extensive acquisition of novel genes. In contrast to other insect genomes, in the sleeping chironomid a uniquely high degree of subfunctionalization in paralogous anhydrobiosis genes occurs in this chromosome, as well as pseudogenization in a highly duplicated gene family. Our findings suggest that the Chromosome 4 in Polypedilum is a site of high genetic turnover, allowing it to act as a 'sandbox' for evolutionary experiments, thus facilitating the rapid adaptation of midges to harsh environments.

Cas9-mediated genome editing reveals a significant contribution of calcium signaling pathways to anhydrobiosis in Pv11 cells.

Pv11 is an insect cell line established from the midge Polypedilum vanderplanki, whose larval form exhibits an extreme desiccation tolerance known as anhydrobiosis. Pv11 itself is also capable of anhydrobiosis, which is induced by trehalose treatment. Here we report the successful construction of a genome editing system for Pv11 cells and its application to the identification of signaling pathways involved in anhydrobiosis. Using the Cas9-mediated gene knock-in system, we established Pv11 cells that stably expressed GCaMP3 to monitor intracellular Ca2+ mobilization. Intriguingly, trehalose treatment evoked a transient increase in cytosolic Ca2+ concentration, and further experiments revealed that the calmodulin-calcineurin-NFAT pathway contributes to tolerance of trehalose treatment as well as desiccation tolerance, while the calmodulin-calmodulin kinase-CREB pathway conferred only desiccation tolerance on Pv11 cells. Thus, our results show a critical contribution of the trehalose-induced Ca2+ surge to anhydrobiosis and demonstrate temporally different roles for each signaling pathway.

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Genome-Wide Role of HSF1 in Transcriptional Regulation of Desiccation Tolerance in the Anhydrobiotic Cell Line, Pv11.

The Pv11, an insect cell line established from the midge Polypedilum vanderplanki, is capable of extreme hypometabolic desiccation tolerance, so-called anhydrobiosis. We previously discovered that heat shock factor 1 (HSF1) contributes to the acquisition of desiccation tolerance by Pv11 cells, but the mechanistic details have yet to be elucidated. Here, by analyzing the gene expression profiles of newly established HSF1-knockout and -rescue cell lines, we show that HSF1 has a genome-wide effect on gene regulation in Pv11. The HSF1-knockout cells exhibit a reduced desiccation survival rate, but this is completely restored in HSF1-rescue cells. By comparing mRNA profiles of the two cell lines, we reveal that HSF1 induces anhydrobiosis-related genes, especially genes encoding late embryogenesis abundant proteins and thioredoxins, but represses a group of genes involved in basal cellular processes, thus promoting an extreme hypometabolism state in the cell. In addition, HSF1 binding motifs are enriched in the promoters of anhydrobiosis-related genes and we demonstrate binding of HSF1 to these promoters by ChIP-qPCR. Thus, HSF1 directly regulates the transcription of anhydrobiosis-related genes and consequently plays a pivotal role in the induction of anhydrobiotic ability in Pv11 cells.

3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis.

During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

Author Correction: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11.

Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

XitoSBML: A Modeling Tool for Creating Spatial Systems Biology Markup Language Models From Microscopic Images.

XitoSBML is a software tool designed to create an SBML (Systems Biology Markup Language) Level 3 Version 1 document from microscopic cellular images. It is implemented as an ImageJ plug-in and is designed to create spatial models that reflect the three-dimensional cellular geometry. With XitoSBML, users can perform spatial model simulations based on realistic cellular geometry by using SBML-supported software tools, including simulators such as Virtual Cell and Spatial Simulator. XitoSBML is open-source and is available at https://github.com/spatialsimulator/XitoSBML/. XitoSBML is confirmed to run on most 32/64-bit operating systems: Windows, MacOS, and Linux.

Predicting the future direction of cell movement with convolutional neural networks.

Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatically identify those morphological features that influence future cell movement.