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Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

The cell is the most basic building block of an organism, containing all materials and information to maintain life in various strategies and cell behavior is thus an ensemble of subsystem dynamics. Mathematical modeling provides a framework to describe and integrate the complexity of relationships between components in the cell system. Computer simulations based on mathematical models correctly replicating or feasibly describing cell systems allow us to (1) predict the cellular response to a given perturbation, (2) provide a mechanistic understanding of the cell behavior that cannot be investigated by the analysis of individual components alone, and thereby (3) reduce the number of trial-and-error experiments in the laboratory, as well as (4) evaluate control strategies to engineer the cell behavior. To this end, a variety of simulation paradigms, methodologies, and tools have emerged depending on the target cell systems, the trade-offs between the level of abstraction and the cost of modeling and simulation, or the availability of the required information. This article sheds light on these paradigms and perspectives around cell modeling and simulation.

Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The mechanisms that maintain pluripotency, or that cause its destabilization to initiate development, are complex and incompletely understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to input signals. Our strategy used random asynchronous Boolean simulations (R‐ABS) to simulate single‐cell fate transitions and strongly connected components (SCCs) strategy to represent population heterogeneity. This framework was applied to a reverse‐engineered and curated core GRN for mouse embryonic stem cells (mESCs) and used to simulate cellular responses to combinations of five signaling pathways. Our simulations predicted experimentally verified cell population compositions and input signal combinations controlling specific cell fate transitions. Extending the model to PSC differentiation, we predicted a combination of signaling activators and inhibitors that efficiently and robustly generated a Cdx2+Oct4− cells from naïve mESCs. Overall, this platform provides new strategies to simulate cell fate transitions and the heterogeneity that typically occurs during development and differentiation.

Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently and robustly generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.One Sentence SummaryPredictive control of pluripotent stem cell fate transitions

Lenvatinib is a multiple receptor tyrosine kinase inhibitor targeting mainly vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) receptors. We investigated the immunomodulatory activities of lenvatinib in the tumor microenvironment and its mechanisms of enhanced antitumor activity when combined with a programmed cell death-1 (PD-1) blockade. Antitumor activity was examined in immunodeficient and immunocompetent mouse tumor models. Single-cell analysis, flow cytometric analysis, and immunohistochemistry were used to analyze immune cell populations and their activation. Gene co-expression network analysis and pathway analysis using RNA sequencing data were used to identify lenvatinib-driven combined activity with anti-PD-1 antibody (anti-PD-1). Lenvatinib showed potent antitumor activity in the immunocompetent tumor microenvironment compared with the immunodeficient tumor microenvironment. Antitumor activity of lenvatinib plus anti-PD-1 was greater than that of either single treatment. Flow cytometric analysis revealed that lenvatinib reduced tumor-associated macrophages (TAMs) and increased the percentage of activated CD8+ T cells secreting interferon (IFN)-γ+ and granzyme B (GzmB). Combination treatment further increased the percentage of T cells, especially CD8+ T cells, among CD45+ cells and increased IFN-γ+ and GzmB+ CD8+ T cells. Transcriptome analyses of tumors resected from treated mice showed that genes specifically regulated by the combination were significantly enriched for type-I IFN signaling. Pretreatment with lenvatinib followed by anti-PD-1 treatment induced significant antitumor activity compared with anti-PD-1 treatment alone. Our findings show that lenvatinib modulates cancer immunity in the tumor microenvironment by reducing TAMs and, when combined with PD-1 blockade, shows enhanced antitumor activity via the IFN signaling pathway. These findings provide a scientific rationale for combination therapy of lenvatinib with PD-1 blockade to improve cancer immunotherapy.

Metabolome analyses assisted by capillary electrophoresis-mass spectrometry (CE-MS) have allowed us to systematically grasp changes in small molecular metabolites under disease conditions. We applied CE-MS to mine out biomarkers in hepatic ischemia-reperfusion. Rat livers were exposed to ischemia by clamping of the portal inlet followed by reperfusion. Metabolomic profiling revealed that l contents of taurine in liver and plasma were significantly increased. Of interest is an elevation of hypotaurine, collectively suggesting significance of hypotaurine/taurine in post-ischemic responses. Considering the anti-oxidative capacity of hypotaurine, we examined if supplementation of the compound or its precursor amino acids could affect hepatocellular viability and contents of taurine in liver and plasma. Administration of hypotaurine, N-acetylcysteine or methionine upon reperfusion comparablly attenuated the post-ischemic hepatocellular injury but with different metabolomic profiling among groups: rats treated with methionine or N-acetylcysteine but not those treated with hypotaurine, exhibited significant elevation of hepatic lactate generation without notable recovery of the energy charge. Furthermore, the group treated with hypotaurine exhibited elevation of the plasma taurine, suggesting that the exogenously administered compound was utilized as an antioxidant. These results suggest that taurine serves as a surrogate marker for ischemia-reperfusion indicating effectiveness of hypotaurine as an energy-saving hepatoprotective amino acid.

There is currently no effective method for preventing ATP and 2,3-bisphosphoglycerate (2,3-BPG) depletion during long-term erythrocyte storage in the cold, although these metabolites are strongly associated with cell viability and oxygen delivery after transfusion. Metabolite reduction is caused by whole metabolic networks in the cell, which are regulated by various physical or chemical factors. Mathematical modeling is a powerful tool for integrating such complex and dynamic systems. Here, we developed a mathematical model to predict metabolism in erythrocytes preserved with a mannitol-adenine-phosphate solution (MAP) at 4 °C, by modifying a published model of large-scale erythrocyte metabolism. Our model successfully reproduced the reported decreases in ATP and 2,3-BPG during storage. Analysis of our model identified several enzymatic reactions and factors related to ATP and 2,3-BPG depletions, which may serve as possible targets for improving blood storage methods. We also performed metabolome analysis of laboratory-made MAP-stored erythrocytes using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), which provided a comprehensive view of the metabolism dynamics. Alterations in the metabolic intermediate concentrations after long storage were qualitatively predicted by the model. Finally, through further systematic analysis, we also discuss the usability of our model.

Although intraerythrocytic ATP and 2,3-bisphophoglycerate (2,3-BPG) are known as direct indicators of the viability of preserved red blood cells and the efficiency of post-transfusion oxygen delivery, no current blood storage method in practical use has succeeded in maintaining both these metabolites at high levels for long periods. In this study, we constructed a mathematical kinetic model of comprehensive metabolism in red blood cells stored in a recently developed blood storage solution containing adenine and guanosine, which can maintain both ATP and 2,3-BPG. The predicted dynamics of metabolic intermediates in glycolysis, the pentose phosphate pathway, and purine salvage pathway were consistent with time-series metabolome data measured with capillary electrophoresis time-of-flight mass spectrometry over 5 weeks of storage. From the analysis of the simulation model, the metabolic roles and fates of the 2 major additives were illustrated: (1) adenine could enlarge the adenylate pool, which maintains constant ATP levels throughout the storage period and leads to production of metabolic waste, including hypoxanthine; (2) adenine also induces the consumption of ribose phosphates, which results in 2,3-BPG reduction, while (3) guanosine is converted to ribose phosphates, which can boost the activity of upper glycolysis and result in the efficient production of ATP and 2,3-BPG. This is the first attempt to clarify the underlying metabolic mechanism for maintaining levels of both ATP and 2,3-BPG in stored red blood cells with in silico analysis, as well as to analyze the trade-off and the interlock phenomena between the benefits and possible side effects of the storage-solution additives.

Local responses of energy metabolism during brain ischemia are too heterogeneous to decipher redox distribution between anoxic core and adjacent salvageable regions such as penumbra. Imaging mass spectrometry combined by capillary electrophoresis/mass spectrometry providing quantitative metabolomics revealed spatio-temporal changes in adenylates and NADH in a mouse middle-cerebral artery occlusion model. Unlike the core where ATP decreased, the penumbra displayed paradoxical elevation of ATP despite the constrained blood supply. It is noteworthy that the NADH elevation in the ischemic region is clearly demarcated by the ATP-depleting core. Results suggest that metabolism in ischemic penumbra does not respond passively to compromised circulation, but actively compensates energy charges. Antioxid. Redox Signal. 13, 1157–1167.

Upon hypoxia, erythrocytes utilize hemoglobin (Hb) to trigger activation of glycolysis through its interaction with band 3. This process contributes to maintenance of ATP, a portion of which is released extracellularly to trigger endothelium-dependent vasorelaxation. However, whether the ATP release results either from metabolic activation of the cells secondarily or from direct regulation of the gating through Hb allostery remains unknown. This study aimed to examine if stabilization of T-state Hb could induce steady-state and hypoxia-induced alterations in glycolysis and the ATP release from erythrocytes. Treatment of deoxygenated erythrocytes with a nitric oxide (NO) donor generated alpha-NO Hb that is stabilized T-state allostery. Under these circumstances, the release of ATP was significantly elevated even under normoxia and not further enhanced upon hypoxia. These events did not coincide with activation of glycolysis of the cells, so far as judged by the fact that intracellular ATP was significantly decreased by the NO treatment. Collectively, the present study suggests that hypoxia-induced ATP release is triggered through mechanisms involving R-T transition of Hb, and the gating process might occur irrespective of hypoxia-responsive regulation of glycolysis.

The human red blood cell (RBC) has long been used for modeling of complex biological networks, for elucidation of a wide variety of dynamic phenomena, and for understanding the fundamental topology of metabolic pathways. Here, we introduce our recent work on an RBC metabolic model using the E-Cell Simulation Environment. The model is sufficiently detailed to predict the temporal hypoxic response of each metabolite and, at the same time, successfully integrates modulation of metabolism and of the oxygen transporting capacity of hemoglobin. The model includes the mechanisms of RBC maintenance as a single cell system and the functioning of RBCs as components of a higher order system. Modeling of RBC metabolism is now approaching a fully mature stage of realistic predictions at the molecular level and will be useful for predicting conditions in biotechnological applications such as long-term cold storage of RBCs.

Upon hypoxia, erythrocytes utilize hemoglobin (Hb) to trigger acti-vation of glycolysis through its interaction with band 3. This process contributes to maintenance of ATP, a portion of which is released extracellularly to trigger endothelium-dependent vasorelaxation. However, whether the ATP release results either from metabolic activation of the cells secondarily or from direct regulation of the gating through Hb allostery remains unknown. This study aimed to examine if stabilization of T-state Hb could induce steady-state and hypoxia-induced alterations in glycolysis and the ATP release from erythrocytes. Treatment of deoxygenated erythrocytes with a nitric oxide (NO) donor generated α-NO Hb that is stabilized T-state allostery. Under these circumstances, the release of ATP was significantly elevated even under normoxia and not further enhanced upon hypoxia. These events did not coincide with activation of glycolysis of the cells, so far as judged by the fact that intracellular ATP was significantly decreased by the NO treatment. Collectively, the present study suggests that hypoxia-induced ATP release is triggered through mechanisms involving R-T transition of Hb, and the gating process might occur irrespective of hypoxia-responsive regulation of glycolysis.