[The Potential and Governance of the Metaverse for Healthcare].

As the Metaverse continues to gain traction, applications the Metaverse in healthcare and wellbeing have also been growing. The rights of each user should be protected after clarifying who the service user is and the legal status of the service or action on the metaverse. Moreover, perspectives such as public equity and fairness are also critical. Appropriate agencies in Japan and abroad are required to develop guidelines and standards based on specific use cases which consider these points.

Identifying neurocognitive disorder using vector representation of free conversation.

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants' conversation was transcribed and the text data was decomposed from natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.

Cardiac fibroblasts regulate the development of heart failure via Htra3-TGF-ß-IGFBP7 axis.

Tissue fibrosis and organ dysfunction are hallmarks of age-related diseases including heart failure, but it remains elusive whether there is a common pathway to induce both events. Through single-cell RNA-seq, spatial transcriptomics, and genetic perturbation, we elucidate that high-temperature requirement A serine peptidase 3 (Htra3) is a critical regulator of cardiac fibrosis and heart failure by maintaining the identity of quiescent cardiac fibroblasts through degrading transforming growth factor-ß (TGF-ß). Pressure overload downregulates expression of Htra3 in cardiac fibroblasts and activated TGF-ß signaling, which induces not only cardiac fibrosis but also heart failure through DNA damage accumulation and secretory phenotype induction in failing cardiomyocytes. Overexpression of Htra3 in the heart inhibits TGF-ß signaling and ameliorates cardiac dysfunction after pressure overload. Htra3-regulated induction of spatio-temporal cardiac fibrosis and cardiomyocyte secretory phenotype are observed specifically in infarct regions after myocardial infarction. Integrative analyses of single-cardiomyocyte transcriptome and plasma proteome in human reveal that IGFBP7, which is a cytokine downstream of TGF-ß and secreted from failing cardiomyocytes, is the most predictable marker of advanced heart failure. These findings highlight the roles of cardiac fibroblasts in regulating cardiomyocyte homeostasis and cardiac fibrosis through the Htra3-TGF-ß-IGFBP7 pathway, which would be a therapeutic target for heart failure.

Public Attitudes Regarding Trade-offs Between the Functional Aspects of a Contact-Confirming App for COVID-19 Infection Control and the Benefits to Individuals and Public Health: Cross-sectional Survey.

BACKGROUND: It is expected that personal health information collected through mobile information terminals will be used to develop health strategies that benefit the public. Against this background, several countries have actively attempted to use mobile phones to control infectious diseases. These collected data, such as activity logs and contact history, are countermeasures against diseases such as COVID-19. In Japan, the Ministry of Health, Labor, and Welfare has developed and disseminated a contact-confirming app (COVID-19 Contact-Confirming Application [COCOA]) to the public, which detects and notifies individuals whether they have been near someone who had subsequently tested positive for COVID-19. However, there are concerns about leakage and misuse of the personal information collected by such information terminals. OBJECTIVE: This study aimed to investigate the possible trade-off between effectiveness in preventing infectious diseases and infringement of personal privacy in COCOA. In addition, we analyzed whether resistance to COCOA would reduce if the app contributed to public health or if a discount was provided on mobile phone charges. METHODS: A cross-sectional, quantitative survey of Japanese citizens was conducted using Survey Monkey, a general-purpose web-based survey platform. When developing the questions for the questionnaire, we included the installation status of COCOA and recorded the anxiety stemming from the potential leakage or misuse of personal information collected for COVID-19 infection control. The respondents were asked to rate various factors to determine their perceptions on a 5-point scale. RESULTS: In total, 1058 participants were included in the final analysis. In response to the question of whether the spread of the disease was being controlled by the infection control measures taken by the government, 25.71% (272/1058) of the respondents answered that they strongly agreed or agreed. One-quarter of the respondents indicated that they had already installed COCOA. This study found that the sense of resistance to government intervention was not alleviated by the benefits provided to individuals when using the app. The only factors that were positively associated with the response absolutely opposed to use of the app, even with a discount on mobile phone use charges, were those regarding leaks and misuse of personal information, which was true for all functions (function A: odds ratio [OR] 1.8, 95% CI 1.3-2.4; function B: OR 1.9, 95% CI 1.5-2.6; function C: OR 1.8, 95% CI 1.4-2.4). CONCLUSIONS: Public organizations need to emphasize the general benefits of allowing them to manage personal information and assure users that this information is being managed safely rather than offering incentives to individuals to provide such personal information. When collecting and using citizens' health information, it is essential that governments and other entities focus on contributing to the public good and ensuring safety rather than returning benefits to individual citizens.

Comprehensive and long-term surveys of COVID-19 sequelae in Japan, an ambidirectional multicentre cohort study: study protocol.

INTRODUCTION: The rapid spread of COVID-19 posed a global burden. Substantial number of people died of the disease in the acute phase of infection. In addition, a significant proportion of patients have been reported to suffer from post-acute phase symptoms, sequelae of COVID-19, which may negatively influence the quality of daily living and/or socioeconomic circumstances of the patients. However, no previous study has comprehensively and objectively assessed the quality of life of patients by using existing international scales. Further, evidence of socioeconomic consequences among patients with COVID-19 is scarce. To address the multidimensional issues from sequelae of COVID-19, evidence from comprehensive surveys beyond clinical perspectives is critical that investigates health, and social determinants of disease progression as well as socioeconomic consequences at a large scale. METHODS AND ANALYSIS: In this study, we plan to conduct a nationwide and comprehensive survey for the sequelae of COVID-19 in a total of 1000 patients diagnosed at 27 hospitals throughout Japan. This study will evaluate not only the health-related status of patients from clinical perspectives but also the Health-related Quality of Life (HRQoL) scores, socioeconomic status and consequences to discuss the sequelae of the disease and the related risk factors. The primary endpoint is the frequency of long-term complications of COVID-19 infection. The secondary endpoints are risk factors for progression to sequelae of COVID-19 infection. The study will provide robust and important evidence as a resource to tackle the issues from the sequelae of COVID-19 from the multi-dimensional perspectives. ETHICS AND DISSEMINATION: This trial was approved by the Keio University School of Medicine Ethics Committee (20200243, UMIN000042299). The results of this study will be reported at a society meeting or published in a peer-reviewed journal.

Genetic and epigenetic basis of hepatoblastoma diversity.

Hepatoblastoma (HB) is the most common pediatric liver malignancy; however, hereditary predisposition and acquired molecular aberrations related to HB clinicopathological diversity are not well understood. Here, we perform an integrative genomic profiling of 163 pediatric liver tumors (154 HBs and nine hepatocellular carcinomas) based on the data acquired from a cohort study (JPLT-2). The total number of somatic mutations is precious low (0.52/Mb on exonic regions) but correlated with age at diagnosis. Telomerase reverse transcriptase (TERT) promoter mutations are prevalent in the tween HBs, selective in the transitional liver cell tumor (TLCT, > 8 years old). DNA methylation profiling reveals that classical HBs are characterized by the specific hypomethylated enhancers, which are enriched with binding sites for ASCL2, a regulatory transcription factor for definitive endoderm in Wnt-pathway. Prolonged upregulation of ASCL2, as well as fetal-liver-like methylation patterns of IGF2 promoters, suggests their "cell of origin" derived from the premature hepatoblast, similar to intestinal epithelial cells, which are highly proliferative. Systematic molecular profiling of HB is a promising approach for understanding the epigenetic drivers of hepatoblast carcinogenesis and deriving clues for risk stratification.

TET1 upregulation drives cancer cell growth through aberrant enhancer hydroxymethylation of HMGA2 in hepatocellular carcinoma.

Ten-eleven translocation 1 (TET1) is an essential methylcytosine dioxygenase of the DNA demethylation pathway. Despite its dysregulation being known to occur in human cancer, the role of TET1 remains poorly understood. In this study, we report that TET1 promotes cell growth in human liver cancer. The transcriptome analysis of 68 clinical liver samples revealed a subgroup of TET1-upregulated hepatocellular carcinoma (HCC), demonstrating hepatoblast-like gene expression signatures. We performed comprehensive cytosine methylation and hydroxymethylation (5-hmC) profiling and found that 5-hmC was aberrantly deposited preferentially in active enhancers. TET1 knockdown in hepatoma cell lines decreased hmC deposition with cell growth suppression. HMGA2 was highly expressed in a TET1high subgroup of HCC, associated with the hyperhydroxymethylation of its intronic region, marked as histone H3K4-monomethylated, where the H3K27-acetylated active enhancer chromatin state induced interactions with its promoter. Collectively, our findings point to a novel type of epigenetic dysregulation, methylcytosine dioxygenase TET1, which promotes cell proliferation via the ectopic enhancer of its oncogenic targets, HMGA2, in hepatoblast-like HCC.

Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design.

Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between "well-being" and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace. Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data. Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being. Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants. Registration: UMIN000036814.

Mammalian cold-inducible RNA-binding protein facilitates wound healing through activation of AMP-activated protein kinase.

The skin is usually maintained within a temperature range that induces cold-inducible RNA-binding protein (Cirp). To determine whether Cirp plays a role in barrier function of the skin, we analyzed the skin wound healing in cirp-knockout (KO) mice. They exhibited delayed wound healing compared with wild-type littermates in the absence as well as presence of skin contraction. Dermal fibroblasts and keratinocytes from cirp-KO mice migrated slower than those from wild-type mice. When expression of Cirp was downregulated in cultured cells, migration rate was decreased. Cirp bound liver-kinase-B1 (LKB1) in the nucleus and was suggested to enhance its translocation to the cytoplasm, resulting in enhanced phosphorylation of AMP-activated protein kinase (AMPK) and cell motility. Stimulation of AMPK ameliorated the delayed wound healing in cirp-KO mice. These findings suggest that Cirp facilitates skin wound healing by enhancing cell migration via AMPK, indicating roles for Cirp in linking skin temperature with metabolism and defense mechanism.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Using speech recognition technology to investigate the association between timing-related speech features and depression severity.

BACKGROUND: There are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology. METHOD: Patients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity? RESULTS: We collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores. CONCLUSIONS: Depressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

Accumulation of Molecular Aberrations Distinctive to Hepatocellular Carcinoma Progression.

Cancer develops through the accumulation of genetic and epigenetic aberrations. To identify sequential molecular alterations that occur during the development of hepatocellular carcinoma (HCC), we compared 52 early and 108 overt HCC samples by genome sequencing. Gene mutations in the p53/RB1 pathway, WNT pathway, MLL protein family, SWI/SNF complexes, and AKT/PI3K pathway were common in HCC. In the early phase of all entities, TERT was the most frequently upregulated gene owing to diverse mechanisms. Despite frequent somatic mutations in driver genes, including CTNNB1 and TP53, early HCC was a separate molecular entity from overt HCC, as each had a distinct expression profile. Notably, WNT target genes were not activated in early HCC regardless of CTNNB1 mutation status because ß-catenin did not translocate into the nucleus due to the E-cadherin/ß-catenin complex at the membrane. Conversely, WNT targets were definitively upregulated in overt HCC, with CTNNB1 mutation associated with downregulation of CDH1 and hypomethylation of CpG islands in target genes. Similarly, cell-cycle genes downstream of the p53/RB pathway were upregulated only in overt HCC, with TP53 or RB1 gene mutations associated with chromosomal deletion of 4q or 16q. HCC was epigenetically distinguished into four subclasses: normal-like methylation, global-hypomethylation (favorable prognosis), stem-like methylation (poor prognosis), and CpG island methylation. These methylation statuses were globally maintained through HCC progression. Collectively, these data show that as HCC progresses, additional molecular events exclusive of driver gene mutations cooperatively contribute to transcriptional activation of downstream targets according to methylation status. SIGNIFICANCE: In addition to driver gene mutations in the WNT and p53 pathways, further molecular events are required for aberrant transcriptional activation of these pathways as HCC progresses.

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study.

BACKGROUND: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive. METHODS: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning. RESULTS: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43. LIMITATIONS: Limitations include the small number of subjects, especially the number of severe cases and young people. CONCLUSIONS: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

[Artificial Intelligence and Intellectual Property of the Medical Field].

In Japan, the revision of the Copyright Act and the Unfair Competition Prevention Act has been made toward the use of artificial intelligence (AI) and data. The Ministry of Economy, Trade, and Industry have issued "Contract guidelines on the use of AI data" and other rules are being developed. In the medical field, in addition to these general rules, it is important to protect patient rights and to protect intellectual property for appropriate evaluation of medical personnel for data creation.

Cooperation of PU.1 With IRF8 and NFATc1 Defines Chromatin Landscapes During RANKL-Induced Osteoclastogenesis.

Receptor activator of nuclear factor κB ligand (RANKL) induces osteoclast (OC) differentiation from bone marrow-derived macrophages (BMMs). The transcription factors nuclear factor of activated T cells 1 (NFATc1) and interferon regulatory factor (IRF) 8 play positive and negative roles, respectively, in this process. However, genomewide mapping of the active cis-regulatory elements regulating OC differentiation has not been performed, and little is known about the global landscape of OC-specific gene regulation. We used chromatin immunoprecipitation and formaldehyde-assisted isolation of regulatory elements followed by sequencing to show that PU.1 transcription factor binding motifs were overrepresented at active cis-regulatory regions in both murine BMMs and OCs, while IRF and NFAT binding motifs were selectively enriched at these regions in BMMs and OCs, respectively. We also found that RANKL induced the downregulation of Irf8 and upregulation of Nfatc1 expression, which was associated with dramatic alterations in histone modification. BMM-specific PU.1 binding sites were observed to overlap with IRF8 binding sites in BMMs, and this also occurred for OC-specific PU.1 binding sites and NFATc1 binding sites in OCs. The expression of genes with IRF8 peaks within BMM-specific PU.1 binding sites was significantly higher in BMMs than in OCs, while that of genes with NFATc1 peaks within OC-specific PU.1 binding sites was significantly higher in OCs than in BMMs. Our results suggest that PU.1 switches its transcription partner from IRF8 to NFATc1 and alters the binding regions during RANKL-induced osteoclastogenesis, which is associated with changes in epigenetic profiles and the control of cell type-specific gene expression. © 2019 American Society for Bone and Mineral Research.

High-throughput single-molecule RNA imaging analysis reveals heterogeneous responses of cardiomyocytes to hemodynamic overload.

BACKGROUND: The heart responds to hemodynamic overload through cardiac hypertrophy and activation of the fetal gene program. However, these changes have not been thoroughly examined in individual cardiomyocytes, and the relation between cardiomyocyte size and fetal gene expression remains elusive. We established a method of high-throughput single-molecule RNA imaging analysis of in vivo cardiomyocytes and determined spatial and temporal changes during the development of heart failure. METHODS AND RESULTS: We applied three novel single-cell analysis methods, namely, single-cell quantitative PCR (sc-qPCR), single-cell RNA sequencing (scRNA-seq), and single-molecule fluorescence in situ hybridization (smFISH). Isolated cardiomyocytes and cross sections from pressure overloaded murine hearts after transverse aortic constriction (TAC) were analyzed at an early hypertrophy stage (2 weeks, TAC2W) and at a late heart failure stage (8 weeks, TAC8W). Expression of myosin heavy chain ß (Myh7), a representative fetal gene, was induced in some cardiomyocytes in TAC2W hearts and in more cardiomyocytes in TAC8W hearts. Expression levels of Myh7 varied considerably among cardiomyocytes. Myh7-expressing cardiomyocytes were significantly more abundant in the middle layer, compared with the inner or outer layers of TAC2W hearts, while such spatial differences were not observed in TAC8W hearts. Expression levels of Myh7 were inversely correlated with cardiomyocyte size and expression levels of mitochondria-related genes. CONCLUSIONS: We developed a new image-analysis pipeline to allow automated and unbiased quantification of gene expression at the single-cell level and determined the spatial and temporal regulation of heterogenous Myh7 expression in cardiomyocytes after pressure overload.

5-Hydroxy-7-methoxyflavone derivatives from Kaempferia parviflora induce skeletal muscle hypertrophy.

Skeletal muscle plays a critical role in locomotion and energy metabolism. Maintenance or enhancement of skeletal muscle mass contributes to the improvement of mobility and prevents the development of metabolic diseases. The extracts from Kaempferia parviflora rhizomes contain at least ten methoxyflavone derivatives that exhibit enhancing effects on ATP production and glucose uptake in skeletal muscle cells. In the present study, we investigated the effects of ten K. parviflora-derived methoxyflavone derivatives (six 5,7-dimethoxyflavone (DMF) derivatives and four 5-hydroxy-7-methoxyflavone (HMF) derivatives) on skeletal muscle hypertrophy. Murine C2C12 myotubes and senescence-accelerated mouse-prone 1 (SAMP1) mice treated with methoxyflavones were used as experimental models to determine the effects of HMF derivatives on myotube diameter and size and muscle mass. The four HMF derivatives, but not the six DMF derivatives, increased myotube diameter. The 5-hydroxyflavone, 7-methoxyflavone, and 5,7-dihydroxyflavone had no influence on myotube size, a result that differed from HMF. Dietary administration of the mixture composed of the four HMF derivatives resulted in increase in the soleus muscle size and mass in SAMP1 mice. HMF derivatives also promoted protein synthesis in myotubes, and treatment with the intracellular Ca2+ chelator BAPTA-AM, which depletes intracellular Ca2+ levels, inhibited this promotion. Furthermore, BAPTA-AM inhibited HMF-promoted protein synthesis even when myotubes were incubated in Ca2+-free medium. These results indicate that HMF derivatives induce myotube hypertrophy and that both the 5-hydroxyl group and the 7-methoxy group in the flavones are necessary for myotube hypertrophy. Furthermore, these results suggest that HMF-induced protein synthesis requires intracellular Ca2+, but not extracellular Ca2+.

Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure.

Pressure overload induces a transition from cardiac hypertrophy to heart failure, but its underlying mechanisms remain elusive. Here we reconstruct a trajectory of cardiomyocyte remodeling and clarify distinct cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure, by integrating single-cardiomyocyte transcriptome with cell morphology, epigenomic state and heart function. During early hypertrophy, cardiomyocytes activate mitochondrial translation/metabolism genes, whose expression is correlated with cell size and linked to ERK1/2 and NRF1/2 transcriptional networks. Persistent overload leads to a bifurcation into adaptive and failing cardiomyocytes, and p53 signaling is specifically activated in late hypertrophy. Cardiomyocyte-specific p53 deletion shows that cardiomyocyte remodeling is initiated by p53-independent mitochondrial activation and morphological hypertrophy, followed by p53-dependent mitochondrial inhibition, morphological elongation, and heart failure gene program activation. Human single-cardiomyocyte analysis validates the conservation of the pathogenic transcriptional signatures. Collectively, cardiomyocyte identity is encoded in transcriptional programs that orchestrate morphological and functional phenotypes.