Modular Design Platform for Activatable Fluorescence Probes Targeting Carboxypeptidases Based on ProTide Chemistry.

Carboxypeptidases (CPs) are a family of hydrolases that cleave one or more amino acids from the C-terminal of peptides or proteins and play indispensable roles in various physiological and pathological processes. However, only a few highly activatable fluorescence probes for CPs have been reported, and there is a need for a flexibly tunable molecular design platform to afford a range of fluorescence probes for CPs for biological and medical research. Here, we focused on the unique activation mechanism of ProTide-based prodrugs and established a modular design platform for CP-targeting florescence probes based on ProTide chemistry. In this design, probe properties such as fluorescence emission wavelength, reactivity/stability, and target CP can be readily tuned and optimized by changing the four probe modules: the fluorophore, the substituent on the phosphorus atom, the linker amino acid at the P1 position, and the substrate amino acid at the P1' position. In particular, switching the linker amino acid at position P1 enabled us to precisely optimize the reactivity for target CPs. As a proof-of-concept, we constructed probes for carboxypeptidase M (CPM) and prostate-specific membrane antigen (also known as glutamate carboxypeptidase II). The developed probes were applicable for the imaging of CP activities in live cells and in clinical specimens from patients. This design strategy should be useful in studying CP-related biological and pathological phenomena.

Cyano-Hydrol green derivatives: Expanding the 9-cyanopyronin-based resonance Raman vibrational palette.

9-cyanopyronin is a promising scaffold that exploits resonance Raman enhancement to enable sensitive, highly multiplexed biological imaging. Here, we developed cyano-Hydrol Green (CN-HG) derivatives as resonance Raman scaffolds to expand the color palette of 9-cyanopyronins. CN-HG derivatives exhibit sufficiently long wavelength absorption to produce strong resonance Raman enhancement for near-infrared (NIR) excitation, and their nitrile peaks are shifted to a lower frequency than those of 9-cyanopyronins. The fluorescence of CN-HG derivatives is strongly quenched due to the lack of the 10th atom, unlike pyronin derivatives, and this enabled us to detect spontaneous Raman spectra with high signal-to-noise ratios. CN-HG derivatives are powerful candidates for high performance vibrational imaging.

Selective recognition between aromatics and aliphatics by cage-shaped borates supported by a machine learning approach.

Selective recognition between hydrocarbon moieties is a longstanding issue. Although we developed a π-pocket Lewis acid catalyst with high selectivity for aromatic aldehydes over aliphatic ones, a general strategy for catalyst design remains elusive. As an approach that transfers the molecular recognition based on multiple cooperative non-covalent interactions within the π-pocket to a rational catalyst design, herein, we demonstrate Lewis acid catalysts showing improved selectivity through the support of an ensemble algorithm with random forest, Ada Boost, and XG Boost as a machine learning (ML) approach. Using 7963 explanatory variables extracted from model hetero-Diels-Alder reactions, the ensemble algorithm predicted the chemoselectivity of unlearned catalysts. Experiments confirmed the prediction. The proposed catalyst shows the highest selective recognition, reminiscing enzymatic catalytic activity. Additionally, a SHapley Additive exPlanations (SHAP) method suggested that the selectivity originates from the polarizability and three-dimensional size of the catalyst. This insight leads to rational design guidelines for Lewis acid catalysts with dispersion forces.

Pericentromeric noncoding RNA changes DNA binding of CTCF and inflammatory gene expression in senescence and cancer.

Cellular senescence causes a dramatic alteration of chromatin organization and changes the gene expression profile of proinflammatory factors, thereby contributing to various age-related pathologies through the senescence-associated secretory phenotype (SASP). Chromatin organization and global gene expression are maintained by the CCCTC-binding factor (CTCF); however, the molecular mechanism underlying CTCF regulation and its association with SASP gene expression remains unclear. We discovered that noncoding RNA (ncRNA) derived from normally silenced pericentromeric repetitive sequences directly impairs the DNA binding of CTCF. This CTCF disturbance increases the accessibility of chromatin and activates the transcription of SASP-like inflammatory genes, promoting malignant transformation. Notably, pericentromeric ncRNA was transferred into surrounding cells via small extracellular vesicles acting as a tumorigenic SASP factor. Because CTCF blocks the expression of pericentromeric ncRNA in young cells, the down-regulation of CTCF during cellular senescence triggers the up-regulation of this ncRNA and SASP-related inflammatory gene expression. In this study, we show that pericentromeric ncRNA provokes chromosomal alteration by inhibiting CTCF, leading to a SASP-like inflammatory response in a cell-autonomous and non-cell-autonomous manner and thus may contribute to the risk of tumorigenesis during aging.

Network-based prediction approach for cancer-specific driver missense mutations using a graph neural network.

BACKGROUND: In cancer genomic medicine, finding driver mutations involved in cancer development and tumor growth is crucial. Machine-learning methods to predict driver missense mutations have been developed because variants are frequently detected by genomic sequencing. However, even though the abnormalities in molecular networks are associated with cancer, many of these methods focus on individual variants and do not consider molecular networks. Here we propose a new network-based method, Net-DMPred, to predict driver missense mutations considering molecular networks. Net-DMPred consists of the graph part and the prediction part. In the graph part, molecular networks are learned by a graph neural network (GNN). The prediction part learns whether variants are driver variants using features of individual variants combined with the graph features learned in the graph part. RESULTS: Net-DMPred, which considers molecular networks, performed better than conventional methods. Furthermore, the prediction performance differed by the molecular network structure used in learning, suggesting that it is important to consider not only the local network related to cancer but also the large-scale network in living organisms. CONCLUSIONS: We propose a network-based machine learning method, Net-DMPred, for predicting cancer driver missense mutations. Our method enables us to consider the entire graph architecture representing the molecular network because it uses GNN. Net-DMPred is expected to detect driver mutations from a lot of missense mutations that are not known to be associated with cancer.

Activatable Raman Probes Utilizing Enzyme-Induced Aggregate Formation for Selective Ex Vivo Imaging.

Detecting multiple enzyme activities simultaneously with high spatial specificity is a promising strategy to investigate complex biological phenomena, and Raman imaging would be an excellent tool for this purpose due to its high multiplexing capabilities. We previously developed activatable Raman probes based on 9CN-pyronins, but specific visualization of cells with target enzyme activities proved difficult due to leakage of the hydrolysis products from the target cells after activation. Here, focusing on rhodol bearing a nitrile group at the position of 9 (9CN-rhodol), we established a novel mechanism for Raman signal activation based on a combination of aggregate formation (to increase local dye concentration) and the resonant Raman effect along with the bathochromic shift of the absorption, and utilized it to develop Raman probes. We selected the 9CN-rhodol derivative 9CN-JCR as offering a suitable combination of increased stimulated Raman scattering (SRS) signal intensity and high aggregate-forming ability, resulting in good retention in target cells after probe activation. By using isotope-edited 9CN-JCR-based probes, we could simultaneously detect ß-galactosidase, γ-glutamyl transpeptidase, and dipeptidyl peptidase-4 activities in live cultured cells and distinguish cell regions expressing target enzyme activity in Drosophila wing disc and fat body ex vivo.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery.

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.

Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.

Individual health-disease phase diagrams for disease prevention based on machine learning.

Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data. However, it is challenging to identify what aspects should be improved for disease prevention based on future disease-onset prediction because of the complex relationships among multiple biomarkers. Here, we present a health-disease phase diagram (HDPD) that represents an individual's health state by visualizing the future-onset boundary values of multiple biomarkers that fluctuate early in the disease progression process. In HDPDs, future-onset predictions are represented by perturbing multiple biomarker values while accounting for dependencies among variables. We constructed HDPDs for 11 diseases using longitudinal health checkup cohort data of 3,238 individuals, comprising 3,215 measurement items and genetic data. The improvement of biomarker values to the non-onset region in HDPD remarkably prevented future disease onset in 7 out of 11 diseases. HDPDs can represent individual physiological states in the onset process and be used as intervention goals for disease prevention.

Droplet Array-Based Platform for Parallel Optical Analysis of Dynamic Extracellular Vesicle Secretion from Single Cells.

Extracellular vesicles (EVs) are essential intercellular communication tools, but the regulatory mechanisms governing heterogeneous EV secretion are still unclear due to the lack of methods for precise analysis. Monitoring the dynamics of secretion from individually isolated cells is crucial because in bulk analysis, secretion activity can be perturbed by cell-cell interactions, and a cell population rarely performs secretion in a magnitude- or duration-synchronized manner. Although various microfluidic techniques have been adopted to evaluate the abundance of single-cell-derived EVs, none can track their secretion dynamics continually for extended periods. Here, we have developed a droplet array-based method that allowed us to optically quantify the EV secretion dynamics of >300 single cells every 2 h for 36 h, which covers the cell doubling time of many cell types. The experimental results clearly show the highly heterogeneous nature of single-cell EV secretion and suggest that cell division facilitates EV secretion, showing the usefulness of this platform for discovering EV regulation machinery.

Measuring the pH of Acidic Vesicles in Live Cells with an Optimized Fluorescence Lifetime Imaging Probe.

Acidification of intracellular vesicles, such as endosomes and lysosomes, is a key pathway for regulating the function of internal proteins. Most conventional methods of measuring pH are not satisfactory for quantifying the pH inside these vesicles. Here, we investigated the molecular requirements for a fluorescence probe to measure the intravesicular acidic pH in living cells by means of fluorescence lifetime imaging microscopy (FLIM). The developed probe, m-DiMeNAF488, exhibits a pH-dependent equilibrium between highly fluorescent and moderately fluorescent forms, which has distinct and detectable fluorescence lifetimes of 4.36 and 0.58 ns, respectively. The pKa(τ) value of m-DiMeNAF488 was determined to be 4.58, which would be favorable for evaluating the pH in the acidic vesicles. We were able to monitor the pH changes in phagosomes during phagocytosis by means of FLIM using m-DiMeNAF488. This probe is expected to be a useful tool for investigating acidic pH-regulated biological phenomena.

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development.

Fluorescence Probes for Imaging Basic Carboxypeptidase Activity in Living Cells with High Intracellular Retention.

Basic carboxypeptidases (basic CPs) cleave the C-terminal basic amino acid of peptides, and their activity is upregulated in some types of cancers. Therefore, detecting the activity of basic CPs in living cells would be important not only for studying the physiological functions of these enzymes but also for visualization of cancerous tissues. Here, we report two fluorescein diacetate (FDA)-based activatable fluorescence probes, named 5ArgAF-FDA and 5LysAF-FDA, in which the substrate amino acid arginine or lysine is conjugated to the benzene moiety via an azoformyl linker. In live-cell fluorescence imaging of CPM, one of the seven basic CPs, 5ArgAF-FDA showed a larger intracellular fluorescence increase than did 5LysAF-FDA within a few minutes. This increase was inhibited by coincubation with 2-mercaptomethyl-3-guanidinoethylthiopropanoic acid (MGTA), an inhibitor of basic CPs. When 5ArgAF-FDA was applied to a coculture of two breast cancer cell lines with different CPM activities, the fluorescence increase in individual cells was correlated with the expression level of CPM, suggesting that 5ArgAF-FDA has the ability to distinguish cell lines having different levels of CPM activity, owing to its high intracellular retention. We believe these probes will be useful for imaging cancers with upregulated basic CP activity.

Building sophisticated sensors of extracellular cues that enable mammalian cells to work as "doctors" in the body.

Mammalian cells are inherently capable of sensing extracellular environmental signals and activating complex biological functions on demand. Advances in synthetic biology have made it possible to install additional capabilities, which can allow cells to sense the presence of custom biological molecules and provide defined outputs on demand. When implanted/infused in patients, such engineered cells can work as intrabody "doctors" that diagnose disease states and produce and deliver therapeutic molecules when and where necessary. The key to construction of such theranostic cells is the development of a range of sensor systems for detecting various extracellular environmental cues that can be rewired to custom outputs. In this review, we introduce the state-of-art engineering principles utilized in the design of sensor systems to detect soluble factors and also to detect specific cell contact, and we discuss their potential role in treating intractable diseases by delivering appropriate therapeutic functions on demand. We also discuss the challenges facing these emerging technologies.

γ-Glutamyltranspeptidase (GGT)-Activatable Fluorescence Probe for Durable Tumor Imaging.

γ-Glutamyltranspeptidase (GGT) is overexpressed in several types of cancer. Existing GGT-targeting fluorescence probes can image these cancers, but the fluorescent hydrolysis product leaks from the target cancer cells during prolonged incubation or fixation. Here, we present a functionalized fluorescence probe for GGT, 4-CH2 F-HMDiEtR-gGlu, which is designed to generate an azaquinone methide intermediate during activation by GGT; this intermediate reacts with intracellular nucleophiles to generate a fluorescent adduct that is trapped inside the cells, without loss of the target enzyme activity. Application of the probe to patient-derived xenograft (PDX) mice enabled in vivo cancer imaging for a prolonged period and was also compatible with fixation and immunostaining of the cancer tissue.

Multicolor Activatable Raman Probes for Simultaneous Detection of Plural Enzyme Activities.

Raman probes based on alkyne or nitrile tags hold promise for highly multiplexed imaging. However, sensing of enzyme activities with Raman probes is difficult because few mechanisms are available to modulate the vibrational response. Here we present a general strategy to prepare activatable Raman probes that show enhanced Raman signals due to electronic preresonance (EPR) upon reaction with enzymes under physiological conditions. We identified a xanthene derivative bearing a nitrile group at position 9 (9CN-JCP) as a suitable scaffold dye, and synthesized four types of activatable Raman probes, which are targeted to different enzymes (three aminopeptidases and a glycosidase) and tuned to different vibrational frequencies by isotope editing of the nitrile group. We validated the activation of the Raman signals of these probes by the target enzymes and succeeded in simultaneous imaging of the four enzyme activities in live cells. Different cell lines showed different patterns of these enzyme activities.

Spontaneously Blinking Fluorophores Based on Nucleophilic Addition/Dissociation of Intracellular Glutathione for Live-Cell Super-resolution Imaging.

Single-molecule localization microscopy (SMLM) allows the reconstruction of super-resolution images but generally requires prior intense laser irradiation and in some cases additives to induce blinking of conventional fluorophores. We previously introduced a spontaneously blinking rhodamine fluorophore based on an intramolecular spirocyclization reaction for live-cell SMLM under physiological conditions. Here, we report a novel principle of spontaneous blinking in living cells, which utilizes reversible ground-state nucleophilic attack of intracellular glutathione (GSH) upon a xanthene fluorophore. Structural optimization afforded two pyronine fluorophores with different colors, both of which exhibit equilibrium (between the fluorescent dissociated form and the nonfluorescent GSH adduct form) and blinking kinetics that enable SMLM of microtubules or mitochondria in living cells. Furthermore, by using spontaneously blinking fluorophores working in the near-infrared (NIR) and green ranges, we succeeded in dual-color live-cell SMLM without the need for optimization of the imaging medium.

Nonimmune cells equipped with T-cell-receptor-like signaling for cancer cell ablation.

The ability to engineer custom cell-contact-sensing output devices into human nonimmune cells would be useful for extending the applicability of cell-based cancer therapies and for avoiding risks associated with engineered immune cells. Here we have developed a new class of synthetic T-cell receptor-like signal-transduction device that functions efficiently in human nonimmune cells and triggers release of output molecules specifically upon sensing contact with a target cell. This device employs an interleukin signaling cascade, whose OFF/ON switching is controlled by biophysical segregation of a transmembrane signal-inhibitory protein from the sensor cell-target cell interface. We further show that designer nonimmune cells equipped with this device driving expression of a membrane-penetrator/prodrug-activating enzyme construct could specifically kill target cells in the presence of the prodrug, indicating its potential usefulness for target-cell-specific, cell-based enzyme-prodrug cancer therapy. Our study also contributes to the advancement of synthetic biology by extending available design principles to transmit extracellular information to cells.

Fluorescence Detection of Prostate Cancer by an Activatable Fluorescence Probe for PSMA Carboxypeptidase Activity.

Prostate cancer (PCa) is a common malignant tumor among adult males, and convenient intraoperative detection of PCa would reduce the risk of leaving positive surgical margins, especially during nerve-sparing procedures. To achieve rapid, fluorescence-based visualization of PCa, we focused on the glutamate carboxypeptidase (CP) activity of prostate-specific membrane antigen (PSMA), a type II transmembrane glycoprotein that is attracting attention as a PCa biomarker. Based on our finding that aryl glutamate conjugates with an azoformyl linker are recognized by PSMA and have a sufficiently low LUMO (lowest unoccupied molecular orbital) energy level to quench the fluorophore through photoinduced electron transfer, we designed and synthesized a first-in-class activatable fluorescence probe for CP activity of PSMA. The developed probe allowed us to visualize the CP activity of PSMA in living cells and in clinical specimens from PCa patients and is expected to be useful for rapid intraoperative detection and diagnosis of PCa.

Synthetic Biology: Engineering Mammalian Cells To Control Cell-to-Cell Communication at Will.

Cell-to-cell communication plays a key role in the regulation of many natural biological processes. Recent advances in mammalian synthetic biology are making it possible to rationally engineer cell-to-cell communication for therapeutic and other purposes. Here, we review state-of-the-art engineering principles to control cell-to-cell communication, focusing on communication between mammalian cells with diffusible factors (e.g., small molecules or exosomes) or direct cell contact, and on interkingdom communication between mammalian cells and bacteria. Potential applications include construction of artificial tissues able to perform complex computations, sophisticated cell-based cancer therapies, use of mammalian cells as a new class of cargo delivery modality, development of design principles to control pattern formation of cell populations, and treatment of infectious diseases. We also discuss the challenges facing practical applications, and possible enabling technologies to overcome them.