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.

Virulence of Fungi Isolated from Ambrosia Beetles to Acer amoenum Branches.

In Japan, no association between the ambrosia beetle and their fungal symbionts causing branch dieback or tree mortality on maple, Acer amoenum, has been reported. However, we identified dieback of several branches and numerous holes created by three species of ambrosia beetles, Euwallacea fornicatus, Euwallacea interjectus, and Platypus calamus, on Acer amoenum trees at the University of Tokyo Tanashi Forest, Tokyo Metropolis, Japan, in 2016. The high attack density of the beetles was observed on the weakened trees; however, the contribution of the associated fungi to the branch dieback was still unknown. We isolated fungi carried by these three beetles and inoculated them to Acer amoenum cut main trunks and sapling branches to determine whether the associated fungi caused the branch dieback. Fusarium euwallaceae was isolated from all Euwallacea fornicatus and Euwallacea interjectus, whereas Arthrinium phaeospermum, Raffaelea cyclorhipidia, and Epicoccum nigrum were isolated from P. calamus, with 35, 15, and 5% isolation frequencies, respectively. Inoculation with F. euwallaceae and R. cyclorhipidia induced statistically significantly wider sapwood discoloration (six and four times wider for F. euwallaceae and R. cyclorhipidia, respectively) than the controls, and larger water-conductance loss (2 and 1.7 times larger for F. euwallaceae and R. cyclorhipidia, respectively) than the controls. However, the observed lesions were not large enough to cause discoloration, and symptoms of dieback were not observed, even 13 months after the inoculation. Therefore, we concluded that the virulence of the four investigated fungi to Acer amoenum was very low and that these fungi were likely not the primary cause of the branch dieback.

Stem Cell Aging in Skeletal Muscle Regeneration and Disease.

Skeletal muscle comprises 30-40% of the weight of a healthy human body and is required for voluntary movements in humans. Mature skeletal muscle is formed by multinuclear cells, which are called myofibers. Formation of myofibers depends on the proliferation, differentiation, and fusion of muscle progenitor cells during development and after injury. Muscle progenitor cells are derived from muscle satellite (stem) cells (MuSCs), which reside on the surface of the myofiber but beneath the basement membrane. MuSCs play a central role in postnatal maintenance, growth, repair, and regeneration of skeletal muscle. In sedentary adult muscle, MuSCs are mitotically quiescent, but are promptly activated in response to muscle injury. Physiological and chronological aging induces MuSC aging, leading to an impaired regenerative capability. Importantly, in pathological situations, repetitive muscle injury induces early impairment of MuSCs due to stem cell aging and leads to early impairment of regeneration ability. In this review, we discuss (1) the role of MuSCs in muscle regeneration, (2) stem cell aging under physiological and pathological conditions, and (3) prospects related to clinical applications of controlling MuSCs.

Analysis of cardiomyocyte movement in the developing murine heart.

The precise assemblage of several types of cardiac precursors controls heart organogenesis. The cardiac precursors show dynamic movement during early development and then form the complicated heart structure. However, cardiomyocyte movements inside the newly organized mammalian heart remain unclear. We previously established the method of ex vivo time-lapse imaging of the murine heart to study cardiomyocyte behavior by using the Fucci (fluorescent ubiquitination-based cell cycle indicator) system, which can effectively label individual G1, S/G2/M, and G1/S-transition phase nuclei in living cardiomyocytes as red, green, and yellow, respectively. Global analysis of gene expression in Fucci green positive ventricular cardiomyocytes confirmed that cell cycle regulatory genes expressed in G1/S, S, G2/M, and M phase transitions were upregulated. Interestingly, pathway analysis revealed that many genes related to the cell cycle were significantly upregulated in the Fucci green positive ventricular cardiomyocytes, while only a small number of genes related to cell motility were upregulated. Time-lapse imaging showed that murine proliferating cardiomyocytes did not exhibit dynamic movement inside the heart, but stayed on site after entering the cell cycle.

Comparison of Sapwood Discoloration in Fagaceae Trees After Inoculation with Isolates of Raffaelea quercivora, Cause of Mass Mortality of Japanese Oak Trees.

The mass mortality of oak trees has been prevalent in Japan since the late 1980s. The fungus Raffaelea quercivora is transmitted by an ambrosia beetle, Platypus quercivorus, which causes mortality. The beetle is able to bore galleries into the sapwood of most Fagaceae trees in Japan; however, the level of mortality caused by R. quercivora and P. quercivorus differs greatly among tree species. Previous studies by our research group have demonstrated that the virulence of R. quercivora differs among isolates when inoculated into Quercus serrata logs. However, interactions between the virulence of R. quercivora isolates and the susceptibility of other fagaceous species have yet to be elucidated. In this study, we inoculated the fresh logs of 11 fagaceous species with isolates of low and high virulence, and measured the tangential widths of discolored sapwoods 3 weeks after inoculation. Although the discoloration widths of Q. crispula sapwood were similar among all isolates, those of Q. serrata and Q. acutissima tended to increase with the more virulent isolates. Sapwood discoloration in Q. glauca, Q. acuta, Q. salicina, Lethocarpus edulis, and Castanopsis sieboldii was greatly increased by highly virulent isolates. Discoloration in Fagus japonica was not influenced by any of the isolates. The logs of Q. crispula and Q. serrata but not Q. glauca were significantly more discolored by a low-virulence isolate compared with standing trees. The various virulent isolates induced unique sapwood discoloration characteristics in each species, which may explain species-specific differences in mortality rates.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

[The role of angiogenetic factors in the pathogenesis and the progression of cardiac valve disease].

Recently, the aging of population and the changing dietary habit to western-style have led to increase in atherosclerotic diseases, such as myocardial infarction and cerebral infarction, in our country. Aortic valve diseases, whose mechanism is similar to atherosclerosis, are also increasing. The medication proved to be effective does not exist, and surgical management is major in treatment of these diseases. Although development of safer medication is expected, little is known about their molecular mechanism of the pathogenesis and the progression. The balance of angiogenetic and angioinhibitory factors is important for normal developing and homeostasis in many organs. The tissue of cardiac valves is mostly avascular like cartilages and tendons, although the heart is the organ having abundance of vessels. We focused on angiogenetitic and angioinhibitory factors expressing in the cartilages and tendons, and found Chondromodulin- I, Tenomodulin and Periostin have crucial roles in degeneration of the cardiac valve by controlling angiogenesis and production of Matrix metalloproteinase protein (MMP) . Now, we review the mechanism of cardiac valve disease focusing on the role of angiogenetic factors.

Machine learning in cardiology: Clinical application and basic research.

Machine learning is a subfield of artificial intelligence. The quality and versatility of machine learning have been rapidly improving and playing a critical role in many aspects of social life. This trend is also observed in the medical field. Generally, there are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Each type of learning is adequately selected for the purpose and type of data. In the field of medicine, various types of information are collected and used, and research using machine learning is becoming increasingly relevant. Many clinical studies are conducted using electronic health and medical records, including in the cardiovascular area. Machine learning has also been applied in basic research. Machine learning has been widely used for several types of data analysis, such as clustering of microarray analysis and RNA sequence analysis. Machine learning is essential for genome and multi-omics analyses. This review summarizes the recent advancements in the use of machine learning in clinical applications and basic cardiovascular research.

Spatial heterogeneity of bone marrow endothelial cells unveils a distinct subtype in the epiphysis.

Bone marrow endothelial cells (BMECs) play a key role in bone formation and haematopoiesis. Although recent studies uncovered the cellular taxonomy of stromal compartments in the bone marrow (BM), the complexity of BMECs is not fully characterized. In the present study, using single-cell RNA sequencing, we defined a spatial heterogeneity of BMECs and identified a capillary subtype, termed type S (secondary ossification) endothelial cells (ECs), exclusively existing in the epiphysis. Type S ECs possessed unique phenotypic characteristics in terms of structure, plasticity and gene expression profiles. Genetic experiments showed that type S ECs atypically contributed to the acquisition of bone strength by secreting type I collagen, the most abundant bone matrix component. Moreover, these cells formed a distinct reservoir for haematopoietic stem cells. These findings provide the landscape for the cellular architecture in the BM vasculature and underscore the importance of epiphyseal ECs during bone and haematopoietic development.

Wnt2 accelerates cardiac myocyte differentiation from ES-cell derived mesodermal cells via non-canonical pathway.

The efficient induction of cardiomyocyte differentiation from embryonic stem (ES) cells is crucial for cardiac regenerative medicine. Although Wnts play important roles in cardiac development, complex questions remain as to when, how and what types of Wnts are involved in cardiogenesis. We found that Wnt2 was strongly up-regulated during cardiomyocyte differentiation from ES cells. Therefore, we investigated when and how Wnt2 acts in cardiogenesis during ES cell differentiation. Wnt2 was strongly expressed in the early developing murine heart. We applied this embryonic Wnt2 expression pattern to ES cell differentiation, to elucidate Wnt2 function in cardiomyocyte differentiation. Wnt2 knockdown revealed that intrinsic Wnt2 was essential for efficient cardiomyocyte differentiation from ES cells. Moreover, exogenous Wnt2 increased cardiomyocyte differentiation from ES cells. Interestingly, the effects on cardiogenesis of intrinsic Wnt2 knockdown and exogenous Wnt2 addition were temporally restricted. During cardiomyocyte differentiation from ES cells, Wnt2 didn't activate canonical Wnt pathway but utilizes JNK/AP-1 pathway which is required for cardiomyocyte differentiation from ES cells. Therefore we conclude that Wnt2 plays strong positive stage-specific role in cardiogenesis through non-canonical Wnt pathway in murine ES cells.

Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.

Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that differentiate into various cell types. iPSCs are expected to be used for disease modeling and for developing novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein-protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.

Regeneration of tree species after 11 years of canopy gap creation and deer exclusion in a warm temperate broad-leaved forest over-browsed by sika deer.

Deer overpopulation is a major threat to forest ecosystems worldwide resulting in loss of natural vegetation cover and increased sapling mortality. To resolve this problem of deer overpopulation, different strategies such as deer exclusion and gap creation have been explored to determine more efficient methods to restore deer-damaged forest ecosystems. In the current study, we applied a 2 × 2 factorial design of four different treatment groups in warm temperate secondary forests: closed canopy with deer as control, closed canopy without deer, clearcut with deer and clearcut without deer. We compared the decadal change in tree foliar cover and tree species richness among treatment groups to assess tree regeneration success. We also selected six tree species (Abies firma, Quercus acuta, Eurya japonica, Cinnamomum tenuifolium, Castanopsis sieboldii and Neolitsea sericea) that are common in the studied region and compared their regeneration success among the treatment groups. In the absence of deer, clearcutting increased the diversity of tree species and accelerated sapling growth, while under closed canopy conditions sapling heights did not exceed two meters. Tree saplings tended to be less abundant in treatments with deer compared to their counterpart, suggesting limited successful recruitment of saplings at the current deer density (10-13.5 deer km-2). In clearcut-with-deer treatment, non-tree species became abundant, and negatively affected recruitment of tree species as was suggested by regression analysis. However, these general trends were not equal for all tree species. Although clearcut-without-deer treatment facilitated sapling recruitment of all six tree species, Q. acuta, C. tenuifolium and C. sieboldii required deer exclusion for sapling recruitment while A. firma, N. sericea and E. japonica required increased light availability. Consequently, informed decisions can be made by identifying whether certain tree species are capable of naturally recruiting without human intervention and how best to ensure successful recruitment if necessary. By implementing effective strategies, time and resources will be saved, and management goals such as reestablishing tree cover rapidly and increasing tree species diversity can be achieved.

Tumor-specific interendothelial adhesion mediated by FLRT2 facilitates cancer aggressiveness.

Blood vessel abnormalization alters cancer cell metabolism and promotes cancer dissemination and metastasis. However, the biological features of the abnormalized blood vessels that facilitate cancer progression and whether they can be targeted therapeutically have not been fully investigated. Here, we found that an axon guidance molecule, fibronectin leucine-rich transmembrane protein 2 (FLRT2), is expressed preferentially in abnormalized vessels of advanced colorectal cancers in humans and that its expression correlates negatively with long-term survival. Endothelial cell-specific deletion of Flrt2 in mice selectively pruned abnormalized vessels, resulting in a unique metabolic state termed "oxygen-glucose uncoupling," which suppressed tumor metastasis. Moreover, Flrt2 deletion caused an increase in the number of mature vessels, resulting in a significant increase in the antitumor effects of immune checkpoint blockers. Mechanistically, we found that FLRT2 forms noncanonical interendothelial adhesions that safeguard against oxidative stress through homophilic binding. Together, our results demonstrated the existence of tumor-specific interendothelial adhesions that enable abnormalized vessels to facilitate cancer aggressiveness. Targeting this type of adhesion complex could be a safe and effective therapeutic option to suppress cancer progression.

Coupling of angiogenesis and odontogenesis orchestrates tooth mineralization in mice.

The skeletal system consists of bones and teeth, both of which are hardened via mineralization to support daily physical activity and mastication. The precise mechanism for this process, especially how blood vessels contribute to tissue mineralization, remains incompletely understood. Here, we established an imaging technique to visualize the 3D structure of the tooth vasculature at a single-cell level. Using this technique combined with single-cell RNA sequencing, we identified a unique endothelial subtype specialized to dentinogenesis, a process of tooth mineralization, termed periodontal tip-like endothelial cells. These capillaries exhibit high angiogenic activity and plasticity under the control of odontoblasts; in turn, the capillaries trigger odontoblast maturation. Metabolomic analysis demonstrated that the capillaries perform the phosphate delivery required for dentinogenesis. Taken together, our data identified the fundamental cell-to-cell communications that orchestrate tooth formation, angiogenic-odontogenic coupling, a distinct mechanism compared to the angiogenic-osteogenic coupling in bones. This mechanism contributes to our understanding concerning the functional diversity of organotypic vasculature.

The complement C3-complement factor D-C3a receptor signalling axis regulates cardiac remodelling in right ventricular failure.

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure.

Anti-senescent drug screening by deep learning-based morphology senescence scoring.

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.

Blood and lymphatic systems are segregated by the FLCN tumor suppressor.

Blood and lymphatic vessels structurally bear a strong resemblance but never share a lumen, thus maintaining their distinct functions. Although lymphatic vessels initially arise from embryonic veins, the molecular mechanism that maintains separation of these two systems has not been elucidated. Here, we show that genetic deficiency of Folliculin, a tumor suppressor, leads to misconnection of blood and lymphatic vessels in mice and humans. Absence of Folliculin results in the appearance of lymphatic-biased venous endothelial cells caused by ectopic expression of Prox1, a master transcription factor for lymphatic specification. Mechanistically, this phenotype is ascribed to nuclear translocation of the basic helix-loop-helix transcription factor Transcription Factor E3 (TFE3), binding to a regulatory element of Prox1, thereby enhancing its venous expression. Overall, these data demonstrate that Folliculin acts as a gatekeeper that maintains separation of blood and lymphatic vessels by limiting the plasticity of committed endothelial cells.

The application of convolutional neural network to stem cell biology.

Induced pluripotent stem cells (iPSC) are one the most prominent innovations of medical research in the last few decades. iPSCs can be easily generated from human somatic cells and have several potential uses in regenerative medicine, disease modeling, drug screening, and precision medicine. However, further innovation is still required to realize their full potential. Machine learning is an algorithm that learns from large datasets for pattern formation and classification. Deep learning, a form of machine learning, uses a multilayered neural network that mimics human neural circuit structure. Deep neural networks can automatically extract features from an image, although classical machine learning methods still require feature extraction by a human expert. Deep learning technology has developed recently; in particular, the accuracy of an image classification task by using a convolutional neural network (CNN) has exceeded that of humans since 2015. CNN is now used to address several tasks including medical issues. We believe that CNN would also have a great impact on the research of stem cell biology. iPSCs are utilized after their differentiation to specific cells, which are characterized by molecular techniques such as immunostaining or lineage tracing. Each cell shows a characteristic morphology; thus, a morphology-based identification system of cell type by CNN would be an alternative technique. The development of CNN enables the automation of identifying cell types from phase contrast microscope images without molecular labeling, which will be applied to several researches and medical science. Image classification is a strong field among deep learning tasks, and several medical tasks will be solved by deep learning-based programs in the future.

Isolation and identification of alectrol as (+)-orobanchyl acetate, a germination stimulant for root parasitic plants.

Alectrol, a germination stimulant for root parasitic plants, was purified from root exudates of red clover (Trifolium pratense L.) and identified as a strigolactone, (+)-orobanchyl acetate [(3aS,4S,8bS,E)-8,8-dimethyl-3-(((R)-4-methyl-5-oxo-2,5-dihydrofuran-2-yloxy)methylene)-2-oxo-3,3a,4,5,6,7,8,8b-octahydro-2H-indeno[1,2-b]furan-4-yl acetate], by 1D and 2D NMR spectroscopy and ESI- and EI-MS spectrometry. Orobanchyl acetate afforded an [M-42](+) ion in EI-MS and thus had been recognized as an isomer of strigol. Orobanchyl acetate was detected in root exudates of soybean (Glycine max L.) and cowpea (Vigina unguiculata L.) along with orobanchol.