Casual relationships between circulating metabolites and rheumatoid arthritis: A mendelian randomization study.

Background: Blood metabolites serve as pivotal indicators in identifying and predicting the course of rheumatoid arthritis (RA). However, empirical substantiation of a direct causal link between these serum biomarkers and the development of RA is still lacking comprehensive support. Method: In pursuit of a thorough exploration of the causal links between circulating blood metabolites and RA, we deployed a two-sample Mendelian randomization (MR) approach during our initial investigative phase. This method was utilized to examine the potential connections between 249 distinct circulating metabolites and the prevalence of RA. In the validation phase, we conducted replication analyses with a new metabolic dataset consisting of 123 metabolites. Furthermore, we employed the Mendelian randomization based on Bayesian model averaging (MR-BMA) technique to pinpoint key metabolic characteristics that have significant causal implications. Results: In our primary analysis, we found that acetate, acetoacetate and pyruvate exhibited a consistent protective causal association with rheumatoid arthritis, while lactate demonstrated a positive correlation with rheumatoid arthritis risk. It is also noteworthy that a substantial subset of traits related to both saturated and unsaturated fatty acids showed causal influences. Subsequent secondary analyses substantiated these observations, revealing that traits associated with the average number of methylene groups in a fatty acid chain exhibited protective effects. Ultimately, our MR-BMA analyses unveiled that the ratio of polyunsaturated fatty acids (PUFAs) to total fatty acids assumes a paramount role in increasing the susceptibility to rheumatoid arthritis. Conclusions: By employing systemic MR analyses, our study has successfully generated an all-encompassing atlas elucidating the intricate connections between circulating metabolites and the susceptibility to rheumatoid arthritis. Our results indicate the high unsaturation degree is a dominant risk factors correlated with rheumatoid arthritis.

Unraveling the causal association between leukocyte telomere length and infertility: A two-sample Mendelian randomization study.

Infertility is a significant challenge in modern society, and observed studies have reported the association between telomere length and infertility. Whether this relationship is causal remains controversial.We employed two-sample mendelian randomization (MR) to investigate the causal relationship between leukocyte telomere length (LTL) and major causes of infertility, including male and female infertility, sperm abnormalities, and endometriosis. MR analyses were mainly performed using the inverse variance weighted (IVW) method and complemented with other MR methods.Our findings demonstrate a causal association between LTL and endometriosis (OR1.304, 95% CI (1.122,1.517), p = 0.001), suggesting its potential as a biomarker for this condition. However, we did not observe a significant causal relationship between LTL and other infertility causes.Our study presents compelling evidence on the relationship between LTL and endometriosis. Meanwhile, our study demonstrates that there is no causal relationship between LTL and infertility. This research contributes to the field by shedding light on the importance of LTL in the early diagnosis and intervention of endometriosis.

HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

MOTIVATION: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. RESULTS: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. AVAILABILITY AND IMPLEMENTATION: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.

Association between perioperative self-reported sleep disturbances and delirium risk in elderly patients following total joint arthroplasty: a cohort study.

Perioperative sleep disturbance may increase delirium risk. However, the role of perioperative sleep disturbance in delirium following total joint arthroplasty remains unclear. This prospective cohort study aimed to observe the delirium risk in patients with sleep disturbances. After excluding pre-existing sleep disturbances, older patients scheduled for total joint arthroplasty from July 17, 2022, to January 12, 2023, were recruited. Preoperative sleep disturbance or postoperative sleep disturbance was defined as a Chinese version of the Richards-Campbell Sleep Questionnaire (RCSQ) score of <50 during hospitalisation. A cut-off score of 25 was used to classify the severity of sleep disturbance. The primary outcome was the incidence of postoperative delirium. In all, 11.6% of cohort patients (34/294) developed delirium. After multivariate analysis, a preoperative Day 1 RCSQ score of ≤25 (odds ratio [OR] 3.62, 95% confidence interval [CI] 1.19-10.92; p = 0.02), occurrence of sleep disturbances (OR 2.76, 95% CI 1.19-6.38; p = 0.02) and RCSQ score of ≤25(OR 2.91, 95% CI 1.33-6.37; p = 0.007) postoperatively were strong independent predictors of delirium. After sensitivity analysis for daily delirium, a postoperative Day 1 RCSQ score of ≤25 (OR 9.27, 95% CI 2.72-36.15; p < 0.001) was associated with a greater risk of delirium on postoperative Day 1, with a reasonable discriminative area under the curve of 0.730. We concluded that postoperative but not preoperative sleep disturbances may be an independent factor for delirium risk. Sleep disturbance on the first night after surgery was a good predictor of subsequent delirium, no matter the nature of self-reported sleep disturbance.

Structural variants involved in high-altitude adaptation detected using single-molecule long-read sequencing.

Structural variants (SVs), accounting for a larger fraction of the genome than SNPs/InDels, are an important pool of genetic variation, enabling environmental adaptations. Here, we perform long-read sequencing data of 320 Tibetan and Han samples and show that SVs are highly involved in high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict enhancers, target genes and biological functions. We reveal diverse Tibetan-specific SVs affecting the regulatory circuitry of biological functions, including the hypoxia response, energy metabolism and pulmonary function. We find a Tibetan-specific deletion disrupts a super-enhancer and downregulates EPAS1 using enhancer reporter, cellular knock-out and DNA pull-down assays. Our study expands the global SV landscape, reveals the role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles of SVs in human biology.

Ceramide-1-phosphate alleviates high-altitude pulmonary edema by stabilizing circadian ARNTL-mediated mitochondrial dynamics.

INTRODUCTION: High-altitude pulmonary edema (HAPE) is a severe and potentially fatal condition with limited treatment options. Although ceramide kinase (CERK)-derived ceramide-1-phosphate (C1P) has been demonstrated to offer protection against various pulmonary diseases, its effects on HAPE remain unclear. OBJECTIVES: Our study aimed to investigate the potential role of CERK-derived C1P in the development of HAPE and to reveal the molecular mechanisms underlying its protective effects. We hypothesized that CERK-derived C1P could protect against HAPE by stabilizing circadian rhythms and maintaining mitochondrial dynamics. METHODS: To test our hypothesis, we used CERK-knockout mice and established HAPE mouse models using a FLYDWC50-1C hypobaric hypoxic cabin. We utilized a range of methods, including lipidomics, transcriptomics, immunofluorescence, Western blotting, and transmission electron microscopy, to identify the mechanisms of regulation. RESULTS: Our findings demonstrated that CERK-derived C1P played a protective role against HAPE. Inhibition of CERK exacerbated HAPE induced by the hypobaric hypoxic environment. Specifically, we identified a novel mechanism in which CERK inhibition induced aryl hydrocarbon receptor nuclear translocator-like (ARNTL) autophagic degradation, inducing the circadian rhythm and triggering mitochondrial damage by controlling the expression of proteins required for mitochondrial fission and fusion. The decreased ARNTL caused by CERK inhibition impaired mitochondrial dynamics, induced oxidative stress damage, and resulted in defects in mitophagy, particularly under hypoxia. Exogenous C1P prevented ARNTL degradation, alleviated mitochondrial damage, neutralized oxidative stress induced by CERK inhibition, and ultimately relieved HAPE. CONCLUSIONS: This study provides evidence for the protective effect of C1P against HAPE, specifically, through stabilizing circadian rhythms and maintaining mitochondrial dynamics. Exogenous C1P therapy may be a promising strategy for treating HAPE. Our findings also highlight the importance of the circadian rhythm and mitochondrial dynamics in the pathogenesis of HAPE, suggesting that targeting these pathways may be a potential therapeutic approach for this condition.

Recurrent RNA edits in human preimplantation potentially enhance maternal mRNA clearance.

Posttranscriptional modification plays an important role in key embryonic processes. Adenosine-to-inosine RNA editing, a common example of such modifications, is widespread in human adult tissues and has various functional impacts and clinical consequences. However, whether it persists in a consistent pattern in most human embryos, and whether it supports embryonic development, are poorly understood. To address this problem, we compiled the largest human embryonic editome from 2,071 transcriptomes and identified thousands of recurrent embryonic edits (>=50% chances of occurring in a given stage) for each early developmental stage. We found that these recurrent edits prefer exons consistently across stages, tend to target genes related to DNA replication, and undergo organized loss in abnormal embryos and embryos from elder mothers. In particular, these recurrent edits are likely to enhance maternal mRNA clearance, a possible mechanism of which could be introducing more microRNA binding sites to the 3'-untranslated regions of clearance targets. This study suggests a potentially important, if not indispensable, role of RNA editing in key human embryonic processes such as maternal mRNA clearance; the identified editome can aid further investigations.

HFIP: an integrated multi-omics data and knowledge platform for the precision medicine of heart failure.

As the terminal clinical phenotype of almost all types of cardiovascular diseases, heart failure (HF) is a complex and heterogeneous syndrome leading to considerable morbidity and mortality. Existing HF-related omics studies mainly focus on case/control comparisons, small cohorts of special subtypes, etc., and a large amount of multi-omics data and knowledge have been generated. However, it is difficult for researchers to obtain biological and clinical insights from these scattered data and knowledge. In this paper, we built the Heart Failure Integrated Platform (HFIP) for data exploration, fusion analysis and visualization by collecting and curating existing multi-omics data and knowledge from various public sources and also provided an auto-updating mechanism for future integration. The developed HFIP contained 253 datasets (7842 samples), multiple analysis flow, and 14 independent tools. In addition, based on the integration of existing databases and literature, a knowledge base for HF was constructed with a scoring system for evaluating the relationship between molecular signals and HF. The knowledge base includes 1956 genes and annotation information. The literature mining module was developed to assist the researcher to overview the hotspots and contexts in basic and clinical research. HFIP can be used as a data-driven and knowledge-guided platform for the basic and clinical research of HF. Database URL: http://heartfailure.medical-bigdata.com.

Chinese Home-Based Cardiac Rehabilitation Model Delivered by Smartphone Interaction Improves Clinical Outcomes in Patients With Coronary Heart Disease.

Purpose: We evaluated the long-term effect of a smartphone-facilitated home-based cardiac rehabilitation (HBCR) model in revascularized patients with coronary heart disease (CHD) on major adverse cardiac events (MACE), and secondary outcomes, including safety, quality of life, and physical capacity. Methods: It was a prospective observational cohort study including a total of 335 CHD patients after successful percutaneous coronary intervention (PCI) referred to the CR clinic in China between July 23, 2015 and March 1, 2018. Patients were assigned to two groups: HBCR tailored by monitoring and telecommunication via smartphone app (WeChat) (HBCR group, n = 170) or usual care (control group, n = 165), with follow-up for up to 42 months. Propensity score matching was conducted to match patients in the HBCR group with those in the control group. The patients in the HBCR group received educational materials weekly and individualized exercise prescription monthly, and the control group only received 20-min education at baseline in the CR clinic. The primary outcome was MACE, analyzed by Cox regression models. The changes in the secondary outcomes were analyzed by paired t-test among the matched cohort. Results: One hundred thirty-five HBCR patients were matched with the same number of control patients. Compared to the control group, the HBCR group had a much lower incidence of MACE (1.5 vs. 8.9%, p = 0.002), with adjusted HR = 0.21, 95% CI 0.07-0.85, and also had reduced unscheduled readmission (9.7 vs. 23.0%, p = 0.002), improved exercise capacity [maximal METs (6.2 vs. 5.1, p = 0.002)], higher Seattle Angina Questionnaire score, and better control of risk factors. Conclusions: The Chinese HBCR model using smartphone interaction is a safe and effective approach to decrease cardiovascular risks of patients with CHD and improve patients' wellness. Clinical Trial Registration: http://www.chictr.org.cn, identifier: ChiCTR1800015042.

Development and Validation of a Risk Prediction Model for Ventricular Arrhythmia in Elderly Patients with Coronary Heart Disease.

BACKGROUND: Sudden cardiac death is a leading cause of death from coronary heart disease (CHD). The risk of sudden cardiac death (SCD) increases with age, and sudden arrhythmic death remains a major cause of mortality in elderly individuals, especially ventricular arrhythmias (VA). We developed a risk prediction model by combining ECG and other clinical noninvasive indexes including biomarkers and echocardiology for VA in elderly patients with CHD. METHOD: In the retrospective study, a total of 2231 consecutive elderly patients (≥60 years old) with CHD hospitalized were investigated, and finally 1983 patients were enrolled as the model group. The occurrence of VA within 12 months was mainly collected. Study parameters included clinical characteristics (age, gender, height, weight, BMI, and past medical history), ECG indexes (QTcd, Tp-e/QT, and HRV indexes), biomarker indexes (NT-proBNP, Myo, cTnT, CK-MB, CRP, K+, and Ca2+), and echocardiology indexes. In the respective study, 406 elderly patients (≥60 years old) with CHD were included as the verification group to verify the model in terms of differentiation and calibration. RESULTS: In the multiparameter model, seven independent predictors were selected: LVEF, LAV, HLP, QTcd, sex, Tp-e/QT, and age. Increased HLP, Tp-e/QT, QTcd, age, and LAV were risk factors (RR > 1), while female and increased LVEF were protective factors (RR < 1). This model can well predict the occurrence of VA in elderly patients with CHD (for model group, AUC: 0.721, 95% CI: 0.669∼0.772; for verification group, AUC: 0.73, 95% CI: 0.648∼0.818; Hosmer-Lemeshow χ 2 = 13.541, P=0.095). After adjusting the predictors, it was found that the combination of clinical indexes and ECG indexes could predict VA more efficiently than using clinical indexes alone. CONCLUSIONS: LVEF, LAV, QTcd, Tp-e/QT, gender, age, and HLP were independent predictors of VA risk in elderly patients with CHD. Among these factors, the echocardiology indexes LVEF and LAV had the greatest influence on the predictive efficiency of the model, followed by ECG indexes, QTcd and Tp-e/QT. After verification, the model had a good degree of differentiation and calibration, which can provide a certain reference for clinical prediction of the VA occurrence in elderly patients with CHD.

Altitude Cardiomyopathy Is Associated With Impaired Stress Electrocardiogram and Increased Circulating Inflammation Makers.

Many sea-level residents suffer from acute mountain sickness (AMS) when first visiting altitudes above 4,000 m. Exercise tolerance also decreases as altitude increases. We observed exercise capacity at sea level and under a simulated hypobaric hypoxia condition (SHHC) to explore whether the response to exercise intensity represented by physiological variables could predict AMS development in young men. Eighty young men from a military academy underwent a standard treadmill exercise test (TET) and biochemical blood test at sea level, SHHC, and 4,000-m altitude, sequentially, between December 2015 and March 2016. Exercise-related variables and 12-lead electrocardiogram parameters were obtained. Exercise intensity and AMS development were investigated. After exposure to high altitude, the count of white blood cells, alkaline phosphatase and serum albumin were increased (P < 0.05). There were no significant differences in exercise time and metabolic equivalents (METs) between SHHC and high-altitude exposures (7.05 ± 1.02 vs. 7.22 ± 0.96 min, P = 0.235; 9.62 ± 1.11 vs. 9.38 ± 1.12, P = 0.126, respectively). However, these variables were relatively higher at sea level (8.03 ± 0.24 min, P < 0.01; 10.05 ± 0.31, P < 0.01, respectively). Thus, subjects displayed an equivalent exercise tolerance upon acute exposure to high altitude and to SHHC. The trends of cardiovascular hemodynamics during exercise under the three different conditions were similar. However, both systolic blood pressure and the rate-pressure product at every TET stage were higher at high altitude and under the SHHC than at sea level. After acute exposure to high altitude, 19 (23.8%) subjects developed AMS. Multivariate logistic regression analysis showed that METs under the SHHC {odds ratio (OR) 0.355 per unit increment [95% confidence intervals (CI) 0.159-0.793], P = 0.011}, diastolic blood pressure (DBP) at rest under SHHC [OR 0.893 per mmHg (95%CI 0.805-0.991), P = 0.030], and recovery DBP 3 min after exercise at sea level [OR 1.179 per mmHg (95%CI 1.043-1.333), P = 0.008] were independently associated with AMS. The predictive model had an area under the receiver operating characteristic curve of 0.886 (95%CI 0.803-0.969, P < 0.001). Thus, young men have similar exercise tolerance in acute exposure to high altitude and to SHHC. Moreover, AMS can be predicted with superior accuracy using characteristics easily obtainable with TET.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.

Impacts of the Plateau Environment on the Gut Microbiota and Blood Clinical Indexes in Han and Tibetan Individuals.

The intestinal microbiota is significantly affected by the external environment, but our understanding of the effects of extreme environments such as plateaus is far from adequate. In this study, we systematically analyzed the variation in the intestinal microbiota and 76 blood clinical indexes among 393 healthy adults with different plateau living durations (Han individuals with no plateau living, with plateau living for 4 to 6 days, with plateau living for >3 months, and who returned to the plain for 3 months, as well as plateau-living Tibetans). The results showed that the high-altitude environment rapidly (4 days) and continually (more than 3 months) shaped both the intestinal microbiota and clinical indexes of the Han population. With prolongation of plateau living, the general characteristics of the intestinal microbiota and clinical indexes of the Han population were increasingly similar to those of the Tibetan population. The intestinal microbiota of the Han population that returned to the plain area for 3 months still resembled that of the plateau-living Han population rather than that of the Han population on the plain. Moreover, clinical indexes such as blood glucose were significantly lower in the plateau groups than in the nonplateau groups, while the opposite result was obtained for testosterone. Interestingly, there were Tibetan-specific correlations between glucose levels and Succinivibrio and Sarcina abundance in the intestine. The results of this study suggest that a hypoxic environment could rapidly and lastingly affect both the human intestinal microbiota and blood clinical indexes, providing new insights for the study of plateau adaptability.IMPORTANCE The data presented in the present study demonstrate that the hypoxic plateau environment has a profound impact on the gut microbiota and blood clinical indexes in Han and Tibetan individuals. The plateau-changed signatures of the gut microbiota and blood clinical indexes were not restored to the nonplateau status in the Han cohorts, even when the individuals returned to the plain from the plateau for several months. Our study will improve the understanding of the great impact of hypoxic environments on the gut microbiota and blood clinical indexes as well as the adaptation mechanism and intervention targets for plateau adaptation.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform.

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as 'hubs' in protein-protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

Predictors of adherence to home-based cardiac rehabilitation program among coronary artery disease outpatients in China.

BACKGROUND: Cardiac rehabilitation (CR) has proven beneficial for patients with coronary artery disease. However, adherence to CR programs is the key to the health improvement in those patients. Identifying predictors for adherence, which is very much unknown in China, would be valuable for effective rehabilitation. This study aims to determine the adherence to home-based CR programs in Chinese coronary artery disease patients and determine predictors of adherence. METHODS: The current study included 1033 outpatients with coronary heart disease in the First Medical Center of Chinese PLA General Hospital in Beijing from July 2015 to June 2017. Participants were given an exercise prescription and took part in home-based exercise training lasting for 3-24 months. A questionnaire was used to evaluate the completion of the CR program, understanding of the program, motivation of the patients, and family/peer support. RESULTS: Two thirds of the patients adhered well to the home-based CR program. Elder patients (≥ 65-year-old) adhere to the program better, while men adhered better than women. Patients who used to exercise (B = 6.756, P < 0.001), understood the program (B = 0.078, P = 0.002), with stronger motivation to participate (B = 0.376, P < 0.001), and received better family support (B = 0.487, P < 0.001) also adhere better to the program. CONCLUSIONS: Understanding the program, self-motivation of patients, and family support help to keep patients engaged in a home-based CR program. Improvement of family support by educating both patients and families may be helpful in improving adherence to home-based CR programs.

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types.

The study of cancer prognosis serves as an important part of cancer research. Large-scale cancer studies have identified numerous genes and microRNAs (miRNAs) associated with prognosis. These informative genes and miRNAs represent potential biomarkers to predict survival and to elucidate the molecular mechanism of tumour progression. MiRNAs and transcription factors (TFs) can work cooperatively as essential mediators of gene expression, and their dysregulation affects cancer prognosis. A panoramic view of cancer prognosis at the system level, considering the co-regulation roles of miRNA and TF, remains elusive. Here, we establish 12 prognosis-related miRNA-TF co-regulatory networks. The characteristics of prognostic target genes and their regulators in the network are depicted. Although the target genes and co-regulatory patterns exhibit cancer-specific properties, some miRNAs and TFs are highly conserved across cancers. We illustrate and interpret the roles of these conserved regulators by building a model associated with cancer hallmarks, functional enrichment analysis, network community detection, and exhaustive literature research. The elaborated system-level prognostic miRNA-TF co-regulation landscape, including the highlighted roles of conserved regulators, provides a novel and powerful insights into further biological and medical discoveries.

Stable H3K4me3 is associated with transcription initiation during early embryo development.

MOTIVATION: During development of the mammalian embryo, histone modification H3K4me3 plays an important role in regulating gene expression and exhibits extensive reprograming on the parental genomes. In addition to these dramatic epigenetic changes, certain unchanging regulatory elements are also essential for embryonic development. RESULTS: Using large-scale H3K4me3 chromatin immunoprecipitation sequencing data, we identified a form of H3K4me3 that was present during all eight stages of the mouse embryo before implantation. This 'stable H3K4me3' was highly accessible and much longer than normal H3K4me3. Moreover, most of the stable H3K4me3 was in the promoter region and was enriched in higher chromatin architecture. Using in-depth analysis, we demonstrated that stable H3K4me3 was related to higher gene expression levels and transcriptional initiation during embryonic development. Furthermore, stable H3K4me3 was much more active in blood tumor cells than in normal blood cells, suggesting a potential mechanism of cancer progression. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks.

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.

Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.