Population Pharmacokinetic/Pharmacodynamic Models for P2Y12 Inhibitors: A Systematic Review and Clinical Appraisal Using Exposure Simulation.

BACKGROUND AND OBJECTIVE: Recent research indicates a correlation between plasma concentration of P2Y12 inhibitors and clinical events, particularly bleeding, which significantly impeded their clinical therapeutic performance. It is therefore vital to delve into the factors that might affect the plasma concentration. The study aims to summarize population pharmacokinetics/pharmacodynamics (PopPKPD) models for commonly prescribed P2Y12 inhibitors (clopidogrel, prasugrel, and ticagrelor) and assess bleeding risk in specific individual groups. METHODS: The PopPKPD models of P2Y12 inhibitors were collected and summarized based on predetermined inclusion and exclusion criteria. The collected models were replicated in simulations, which were used to assess factors affecting plasma concentrations of P2Y12 inhibitors. Simulation results for special populations were compared to therapeutic window based on reported exposure-effect relationships (PK/PD-related bleeding and thrombotic clinical outcomes) to predict bleeding risk in special populations with different dosing regimens and cumulative covariates. RESULT: Finally, 12 studies were included for PK simulation, 7 of which that also included PD data were subjected to further analysis, with the majority being based on Phase I or II trials. Simulations showed that several covariates such as female gender, weight, elderly can significantly impact on exposure, with special populations reaching up to 179% of the general population. However, after dose adjustment, blood concentrations for special populations can reach approximately ±20% of general population exposure. Therefore, lowering the maintenance dose of ticagrelor from 90 to 60 mg bid was first recommended to reduce bleeding risk without significantly increasing ischemic risk, particularly in elderly, small-weight Asian females. CONCLUSION: Lowering the maintenance dose of ticagrelor from 90 to 60 mg bid effectively reduces bleeding risk without increasing thrombotic infarction risk in elderly, small-weight Asian females.

Safety and immunogenicity of the COVID-19 mRNA vaccine CS-2034: A randomized, double-blind, dose-exploration, placebo-controlled multicenter Phase I clinical trial in healthy Chinese adults.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) is an infectious disease caused by the infection of a novel coronavirus known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has resulted in millions of deaths. We aimed to evaluate the safety and immunogenicity of the COVID-19 mRNA vaccine (CS-2034, CanSino, Shanghai, China) in adults without COVID-19 infection from China. METHOD: This is a multicenter Phase I clinical trial with a randomized, double-blinded, dose-exploration, placebo-controlled design. The trial recruited 40 seronegative participants aged 18-59 years who had neither received any COVID-19 vaccine nor been infected before. They were divided into a low-dose group (administered with either the CS-2034 vaccine containing 30 µg of mRNA or a placebo of 0.3 ml type 5 adenovirus vector) and a high-dose group (administered with either the CS-2034 vaccine containing 50 µg of mRNA or a placebo of 0.5 ml type 5 adenovirus vector). Participants were randomly assigned in a 3:1 ratio to receive either the mRNA vaccine or a placebo on days 0 and 21 according to a two-dose immunization schedule. The first six participants in each dosage group were assigned as sentinel subjects. Participants were sequentially enrolled in a dose-escalation manner from low to high dose and from sentinel to non-sentinel subjects. Blood samples were collected from all participants on the day before the first dose (Day 0), the day before the second dose (day 21), 14 days after the second dose (day 35), and 28 days after the second dose (day 49) to evaluate the immunogenicity of the CS-2034 vaccine. Participants were monitored for safety throughout the 28-day follow-up period, including solicited adverse events, unsolicited adverse events, adverse events of special interest (AESI), and medically attended adverse events (MAE). This report focuses solely on the safety and immunogenicity analysis of adult participants aged 18-59 years, while the long-term phase of the study is still ongoing. This study is registered at ClinicalTrials.gov, NCT05373485. FINDINGS: During the period from May 17, 2022, to August 8, 2022, a total of 155 participants aged 18-59 years were screened for this study. Among them, 115 participants failed the screening process, and 40 participants were randomly enrolled (15 in the low-dose group, 15 in the high-dose group, and 10 in the placebo group). Throughout the 28-day follow-up period, the overall incidence of adverse reactions (related to vaccine administration) in the low-dose group, high-dose group, and placebo group was 93.33% (14/15), 100.00% (15/15), and 80.00% (8/10), respectively. There was a statistically significant difference in the incidence of local adverse reactions (soreness, pruritus, swelling at the injection site) among the low-dose group, high-dose group, and placebo group (P = 0.002). All adverse reactions were mainly of severity grade 1 (mild) or 2 (moderate), and no adverse events of severity grade 4 or higher occurred. Based on the analysis of Spike protein Receptor Binding Domain (S-RBD) IgG antibodies against the BA.1 strain, the seroconversion rates of antibodies at day 21 after the first dose were 86.67%, 93.33%, and 0.00% in the low-dose group, high-dose group, and placebo group, respectively. The geometric mean titer (GMT) of antibodies was 61.2(95%CI 35.3-106.2), 55.4(95%CI 36.3-84.4), and 15.0(95%CI 15.0-15.0), and the geometric mean fold increase (GMI) was 4.08(95%CI 2.35-7.08), 3.69(95%CI 2.42-5.63), and 1.00(95%CI 1.00-1.00) for each group. At day 28 after the full vaccination, the seroconversion rates of antibodies were 100.00%, 93.33%, and 0.00%, and the GMT of antibodies was 810.0(95%CI 511.4-1283.0), 832.2(95%CI 368.1-1881.6), and 15.0(95%CI 15.0-15.0), and the GMI was 54.00(95%CI 34.09-85.53), 55.48(95%CI 24.54-125.44), and 1.00(95%CI 1.00-1.00) for each group, respectively. Based on the analysis of CD3+/CD4+ cell cytokine response, the percentages of IL-2+, IL-4+, IFN-γ+, and TNF-α+ cells increased after 14 days and 28 days of full vaccination in both the low-dose group and high-dose group. The increase was most pronounced in the high-dose group. INTERPRETATION: At day 28 after the full vaccination, both the low-dose and the high-dose CS-2034 vaccine were able to induce the production of high titers of S-RBD IgG antibodies against the BA.1 strain. Adverse reactions in the low-dose and high-dose groups were mainly of severity grade 1 or 2, and no trial-limiting safety concerns were identified. These findings support further development of this vaccine.

External validation of population pharmacokinetic models of vancomycin in postoperative neurosurgical patients.

OBJECTIVE: Vancomycin is commonly used in the prevention and treatment of intracranial infections in postoperative neurosurgical patients with narrow therapeutic window and large pharmacokinetic variations. Several population pharmacokinetic (PPK) models of vancomycin have been established for neurosurgical patients. But comprehensive external evaluation has not been performed for almost all models. The objective of this study was to evaluate the predictive ability of published vancomycin PPK models in adult postoperative neurosurgical patients using an independent dataset. METHOD: PubMed, Embase and China National Knowledge Internet databases were searched to identify published vancomycin PPK models in adult postoperative neurosurgical patients. Prediction-based and simulation-based diagnostics were used to evaluate model predictability. Bayesian forecasting was used to assess the influence of prior concentration on model prediction performance. RESULT: A total of 763 vancomycin plasma concentrations from 493 postoperative neurosurgical patients were included in the external dataset. Eight population pharmacokinetic models of vancomycin in postoperative neurosurgical patients were included and evaluated. The model by Zhang et al. exhibited the best predictive performance in prediction-based diagnostics and prediction-corrected visual predictive checks, followed by the model by Shen et al. The predictive performance of other models was not satisfactory. The normalized predictive distribution error test shows that none of the models is suitable to describe our data. The predictive performance of vancomycin models was obviously improved by maximum a posteriori Bayesian forecasting. CONCLUSION: The published PPK models for adult postoperative neurosurgical patients show extensive variation in predictive performance in our patients. Although it is challenging to recommend initial doses of vancomycin from these predictive models, the combination of model-based prediction and therapeutic drug monitoring can be used for dose optimization.

Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation. However, it is difficult for the former to reconcile the contradiction between completely filtering and avoiding signal distortion, while the latter performs poorly in the case of a time-varying PLI. To solve these, a novel synchrosqueezed-wavelet-transform (SWT)-based PLI filter is proposed. The local SWT was developed to reduce the computation cost while maintaining the frequency resolution. A ridge location method based on an adaptive threshold is presented. In addition, two ridge extraction methods (REMs) are proposed to fit different application requirements. Parameters were optimized before further study. Notch filtering, spectral interpolation, and the proposed filter were evaluated on the simulated signals and real signals. The output signal-to-noise ratio (SNR) ranges of the proposed filter with two different REMs are 18.53-24.57 and 18.57-26.92. Both the quantitative index and the time-frequency spectrum diagram show that the performance of the proposed filter is significantly better than that of the other filters.

Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study.

PURPOSE: This study aimed to develop an interpretable machine learning model to predict the onset of myopia based on individual daily information. METHOD: This study was a prospective cohort study. At baseline, non-myopia children aged 6-13 years old were recruited, and individual data were collected through interviewing students and parents. One year after baseline, the incidence of myopia was evaluated based on visual acuity test and cycloplegic refraction measurement. Five algorithms, Random Forest, Support Vector Machines, Gradient Boosting Decision Tree, CatBoost and Logistic Regression were utilized to develop different models and their performance was validated by area under curve (AUC). Shapley Additive exPlanations was applied to interpret the model output on the individual and global level. RESULT: Of 2221 children, 260 (11.7%) developed myopia in 1 year. In univariable analysis, 26 features were associated with the myopia incidence. Catboost algorithm had the highest AUC of 0.951 in the model validation. The top 3 features for predicting myopia were parental myopia, grade and frequency of eye fatigue. A compact model using only 10 features was validated with an AUC of 0.891. CONCLUSION: The daily information contributed reliable predictors for childhood's myopia onset. The interpretable Catboost model presented the best prediction performance. Oversampling technology greatly improved model performance. This model could be a tool in myopia preventing and intervention that can help identify children who are at risk of myopia, and provide personalized prevention strategies based on contributions of risk factors to the individual prediction result.

LncRNA Osilr9 coordinates promoter DNA demethylation and the intrachromosomal loop structure required for maintaining stem cell pluripotency.

Nuclear reprogramming of somatic cells into a pluripotent status has the potential to create patient-specific induced pluripotent stem cells for regenerative medicine. Currently, however, the epigenetic mechanisms underlying this pluripotent reprogramming are poorly understood. To delineate this epigenetic regulatory network, we utilized a chromatin RNA in situ reverse transcription sequencing (CRIST-seq) approach to identify long noncoding RNAs (lncRNAs) embedded in the 3-dimensional intrachromosomal architecture of stem cell core factor genes. By combining CRIST-seq and RNA sequencing, we identified Oct4-Sox2 interacting lncRNA 9 (Osilr9) as a pluripotency-associated lncRNA. Osilr9 expression was associated with the status of stem cell pluripotency in reprogramming. Using short hairpin RNA (shRNA) knockdown, we showed that this lncRNA was required for the optimal maintenance of stem cell pluripotency. Overexpression of Osilr9 induced robust activation of endogenous stem cell core factor genes in fibroblasts. Osilr9 participated in the formation of the intrachromosomal looping required for the maintenance of pluripotency. After binding to the Oct4 promoter, Osilr9 recruited the DNA demethylase ten-eleven translocation 1, leading to promoter demethylation. These data demonstrate that Osilr9 is a critical chromatin epigenetic modulator that coordinates the promoter activity of core stem cell factor genes, highlighting the critical role of pluripotency-associated lncRNAs in stem cell pluripotency and reprogramming.

[Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory].

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Chromatin lncRNA Platr10 controls stem cell pluripotency by coordinating an intrachromosomal regulatory network.

BACKGROUND: A specific 3-dimensional intrachromosomal architecture of core stem cell factor genes is required to reprogram a somatic cell into pluripotency. As little is known about the epigenetic readers that orchestrate this architectural remodeling, we used a novel chromatin RNA in situ reverse transcription sequencing (CRIST-seq) approach to profile long noncoding RNAs (lncRNAs) in the Oct4 promoter. RESULTS: We identify Platr10 as an Oct4 - Sox2 binding lncRNA that is activated in somatic cell reprogramming. Platr10 is essential for the maintenance of pluripotency, and lack of this lncRNA causes stem cells to exit from pluripotency. In fibroblasts, ectopically expressed Platr10 functions in trans to activate core stem cell factor genes and enhance pluripotent reprogramming. Using RNA reverse transcription-associated trap sequencing (RAT-seq), we show that Platr10 interacts with multiple pluripotency-associated genes, including Oct4, Sox2, Klf4, and c-Myc, which have been extensively used to reprogram somatic cells. Mechanistically, we demonstrate that Platr10 helps orchestrate intrachromosomal promoter-enhancer looping and recruits TET1, the enzyme that actively induces DNA demethylation for the initiation of pluripotency. We further show that Platr10 contains an Oct4 binding element that interacts with the Oct4 promoter and a TET1-binding element that recruits TET1. Mutation of either of these two elements abolishes Platr10 activity. CONCLUSION: These data suggest that Platr10 functions as a novel chromatin RNA molecule to control pluripotency in trans by modulating chromatin architecture and regulating DNA methylation in the core stem cell factor network.

Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology.

Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects' health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.

Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

Meta-analysis of clinical trials to assess denosumab over zoledronic acid in bone metastasis.

Background Bone metastases-induced skeletal complications result in reduced patient survival, lower quality of life, and an increase in healthcare costs. Previously, zoledronic acid (ZA) was the standard choice of treatment for bone metastases, but another drug, denosumab, has also shown promise. However, the clinical utility of these two drugs requires further exploration. Aim of the review Due to the lack of direct comparisons regarding the efficacy of these drugs in both solid tumors and multiple myeloma (MM), we herein tried to conduct a meta-analysis to compare their efficacy in parallel for bone metastases treatment in both solid tumor and MM patients. Methods Multiple databases including Cochrane Library, MEDLINE, EMBASE, and Web of Science were searched to identify randomized controlled trials (RCTs) reported up to March 2019 directly comparing denosumab with ZA in solid tumors and MM. Information about the following events was primarily searched: time to first on-study skeletal-related event (SRE), time to first and subsequent SREs, and overall survival. Information about secondary outcomes including disease progression, pain, health-related quality of life, and adverse events was also recorded. Results Overall, we analyzed data from four distinct RCTs including 7441 patients, and our analysis revealed that patients in the denosumab group had a significantly delayed incidence to the first and subsequent SREs. In addition, denosumab resulted in a higher incidence of hypocalcemia and osteonecrosis of the jaw (ONJ), and a lower incidence of renal toxicity and acute phase reactions, in comparison to ZA. Conclusion Overall, denosumab showed superiority in delaying the first and subsequent SREs, and hence seems to be a promising choice for managing bone metastases in both solid tumors and MM. However, it can induce a higher incidence of ONJ and hypocalcaemia, but these are preventable and manageable effects.

Role of hypoxia inducible factor-1 in cancer stem cells (Review).

Cancer stem cells (CSCs) have been found to play a decisive role in cancer recurrence, metastasis, and chemo­, radio­ and immuno­resistance. Understanding the mechanism of CSC self­renewal and proliferation may help overcome the limitations of clinical treatment. The microenvironment of tumor growth consists of a lack of oxygen, and hypoxia has been confirmed to induce cancer cell invasion, metastasis and epithelial­mesenchymal transition, and is usually associated with poor prognosis and low survival rates. Hypoxia inducible factor­1 (HIF­1) can be stably expressed under hypoxia and act as an important molecule to regulate the development of CSCs, but the specific mechanism remains unclear. The present review attempted to explain the role of HIF­1 in the generation and maintenance of CSCs from the perspective of epigenetics, metabolic reprogramming, tumor immunity, CSC markers, non­coding RNA and signaling pathways associated with HIF­1, in order to provide novel targets with HIF­1 as the core for clinical treatment, and extend the life of patients.

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder.

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

The Role and Specific Mechanism of OCT4 in Cancer Stem Cells: A Review.

Recently, evidences show that cancer stem cells (CSCs) are a type of cancer cell group with self-renewal and play a huge role in tumor recurrence, metastasis, and drug resistance. Finding new treatment directions and targets for cancer prognosis and reducing mortality has become a top priority. OCT4, as a transcription factor, participates in maintaining the stem characteristics of CSCs, but the mechanism of OCT4 is often overlooked. In this review, we try to illustrate the mechanism by which OCT4 plays a role in CSCs from the perspective of genetic modification of OCT4, non-coding RNA, complexes and signaling pathways associated with OCT4. Our ultimate goal is to provide new targets for cancer treatment to prolong the survival of cancer patients.

Ribosomes: An Exciting Avenue in Stem Cell Research.

Stem cell research has focused on genomic studies. However, recent evidence has indicated the involvement of epigenetic regulation in determining the fate of stem cells. Ribosomes play a crucial role in epigenetic regulation, and thus, we focused on the role of ribosomes in stem cells. Majority of living organisms possess ribosomes that are involved in the translation of mRNA into proteins and promote cellular proliferation and differentiation. Ribosomes are stable molecular machines that play a role with changes in the levels of RNA during translation. Recent research suggests that specific ribosomes actively regulate gene expression in multiple cell types, such as stem cells. Stem cells have the potential for self-renewal and differentiation into multiple lineages and, thus, require high efficiency of translation. Ribosomes induce cellular transdifferentiation and reprogramming, and disrupted ribosome synthesis affects translation efficiency, thereby hindering stem cell function leading to cell death and differentiation. Stem cell function is regulated by ribosome-mediated control of stem cell-specific gene expression. In this review, we have presented a detailed discourse on the characteristics of ribosomes in stem cells. Understanding ribosome biology in stem cells will provide insights into the regulation of stem cell function and cellular reprogramming.

LncRNA Functions as a New Emerging Epigenetic Factor in Determining the Fate of Stem Cells.

Pluripotent stem cells have broad applications in regenerative medicine and offer ideal models for understanding the biological process of embryonic development and specific diseases. Studies suggest that the self-renewal and multi-lineage differentiation of stem cells are regulated by a complex network consisting of transcription factors, chromatin regulators, signaling factors, and non-coding RNAs. It is of great interest to identify RNA regulatory factors that determine the fate of stem cells. Long non-coding RNA (lncRNA), a class of non-coding RNAs with more than 200 bp in length, has been shown to act as essential epigenetic regulators of stem cell pluripotency and specific lineage commitment. In this review, we focus on recent research progress related to the function and epigenetic mechanisms of lncRNA in determining the fate of stem cells, particularly pluripotency maintenance and lineage-specific differentiation. We discuss the role of the Oct4 and Sox2 promoter-interacting lncRNA as identified by Chromatin RNA In Situ reverse Transcription sequencing (CRIST-seq). Further understanding of their potential actions will provide a basis for the development of regenerative medicine for clinical application. This work offers comprehensive details and better understanding of the role of lncRNA in determining the fate of stem cells and paves the way for clinical stem cell applications.

Long non-coding RNA DANCR alleviates hypoxia-caused H9c2 cells damage through up regulation of HIF-1α.

Hypoxia is an important cause of myocardial cell loss, further inducing various heart illnesses, including acute myocardial infraction (AMI). Long non-coding RNA (LncRNA) discrimination antagonising non-protein coding RNA (DANCR) was firstly identified as epidermal cell differentiation suppressor. Here, we aimed to explore the effects and mechanism of DNACR in hypoxia-induced H9c2 cells. Hypoxic cells were made through 94% N2, 5% CO2, and 1% O2 environment for 24 h. Cell viability and apoptosis were detected via methyl thiazolyl tetrazolium (MTT) method and flow cytometry analysis, respectively. The expression of DANCR and HIF-1α was examined via qRT-PCR. The expression of proteins related to cell apoptosis and PI3K/AKT/mTOR and ERK1/2 signal pathways was examined through western blot analysis. We found that hypoxia induced obvious cell activity inhibition and apoptosis increasing in H9c2 cells. DANCR was negatively regulated under hypoxia condition. Overexpression of DANCR rescued activity and attenuated apoptosis. Moreover, the overexpression of DANCR elevated the activation of PI3K/AKT/mTOR and ERK1/2 pathways. Further study indicated that DANCR could up-regulate the expression of HIF-1α. Si-HIF-1α transfection could remove the beneficial effects of DANCR overexpression in hypoxia-caused H9c2 cells damage. In conclusion, DANCR alleviated hypoxia-caused H9c2 cells damage through positive regulation of HIF-1α.

Genome-wide interaction target profiling reveals a novel Peblr20-eRNA activation pathway to control stem cell pluripotency.

Background: Long non-coding RNAs (lncRNAs) constitute an important component of the regulatory apparatus that controls stem cell pluripotency. However, the specific mechanisms utilized by these lncRNAs in the control of pluripotency are not fully characterized. Methods: We utilized a RNA reverse transcription-associated trap sequencing (RAT-seq) approach to profile the mouse genome-wide interaction targets for lncRNAs that are screened by RNA-seq. Results: We identified Peblr20 (Pou5F1 enhancer binding lncRNA 20) as a novel lncRNA that is associated with stem cell reprogramming. Peblr20 was differentially transcribed in fibroblasts compared to induced pluripotent stem cells (iPSCs). Notably, we found that Peblr20 utilized a trans mechanism to interact with the regulatory elements of multiple stemness genes. Using gain- and loss-of-function experiments, we showed that knockdown of Peblr20 caused iPSCs to exit from pluripotency, while overexpression of Peblr20 activated endogenous Pou5F1 expression. We further showed that Peblr20 promoted pluripotent reprogramming. Mechanistically, we demonstrated that Peblr20 activated endogenous Pou5F1 by binding to the Pou5F1 enhancer in trans, recruiting TET2 demethylase and activating the enhancer-transcribed RNAs. Conclusions: Our data reveal a novel epigenetic mechanism by which a lncRNA controls the fate of stem cells by trans-regulating the Pou5F1 enhancer RNA pathway. We demonstrate the potential for leveraging lncRNA biology to enhance the generation of stem cells for regenerative medicine.

Profiling the epigenetic interplay of lncRNA RUNXOR and oncogenic RUNX1 in breast cancer cells by gene in situ cis-activation.

RUNX1 is frequently mutated as chromosomal translocations in a variety of hematological malignancies. Recent studies show that RUNX1 is also mutated somatically in many solid tumors. We have recently identified a 260 kb un-spliced intragenic overlapping long noncoding RNA RUNXOR in the RUNX1 locus, yet its role as an epigenetic regulator in tumors remains to be characterized. To delineate this RUNXOR-RUNX1 regulatory interplay in breast cancer cells, we devised a novel "gene in situ cis-activation" approach to activate the endogenous RUNXOR gene. We found that the in situ activation of RUNXOR lncRNA upregulated RUNX1 in cis from the P1 promoter. The preferred activation of the P1 promoter caused a shift to the RUNX1c isoform expression. Using a chromatin conformation capture (3C) approach, we showed that RUNXOR lncRNA epigenetically activated the RUNX1 P1 promoter in cis by altering the local chromatin structure. The binding of RUNXOR lncRNA triggered DNA demethylation and induced active histone modification markers in the P1 CpG island. Changes in RUNX1 isoform composition correlated with a trend to cell cycle arrest at G0/G1, although cell proliferation rate, apoptosis, and migration ability were not significantly changed. Our results reveal an underlying epigenetic mechanism by which the lncRNA regulates in cis the RUNX1 promoter usage in breast cancer cells, thereby shedding light on potential genetic therapies in malignancies in which RUNX1 loss-of-function mutations frequently occur.