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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 for the first time thousands of Recurrent Edits (REs; >=50% chances of occurring in a given stage) for each early developmental stages. We found that these REs 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, REs are likely to enhance maternal mRNA clearance by 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.

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.

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.

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

Introductory paragraphThe pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that 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 application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the 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 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.

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., 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.

Lnc2Catlas (http://lnc2catlas.bioinfotech.org/) is an atlas of long noncoding RNAs (lncRNAs) associated with cancer risk. LncRNAs are a class of functional noncoding RNAs with lengths over 200 nt and play a vital role in diverse biological processes. Increasing evidence shows that lncRNA dysfunction is associated with many human cancers/diseases. It is therefore important to understand the underlying relationship between lncRNAs and cancers. To this end, we developed Lnc2Catlas to compile quantitative associations between lncRNAs and cancers using three computational methods, assessing secondary structure disruption, lncRNA-protein interactions, and co-expression networks. Lnc2Catlas was constructed based on 27,670 well-annotated lncRNAs, 31,749,216 SNPs, 1,473 cancer-associated proteins, and 10,539 expression profiles of 33 cancers from The Cancer Genome Atlas (TCGA). Lnc2Catlas contains 247,124 lncRNA-SNP pairs, over two millions lncRNA-protein interactions, and 6,902 co-expression clusters. We deposited Lnc2Catlas on Alibaba Cloud and developed interactive, mobile device-compatible, user-friendly interfaces to help users search and browse Lnc2Catlas with ultra-low latency. Lnc2Catlas can aid in the investigation of associations between lncRNAs and cancers and can provide candidate lncRNAs for further experimental validation. Lnc2Catlas will facilitate an understanding of the associations between lncRNAs and cancer and will help reveal the critical role of lncRNAs in cancer.

A novel porous aerogel of cellulose nanofiber (CNF)/cationic water-soluble poly[9,9-bis[3′-(N,N-dimethy)-N-ethylammonium)propyl)-2,7-fluorene-alt-1,4-phenylene]dibromide (CPFD) is prepared by freeze drying. CNF can effectively prevent aggregation of the conjugated polymer CPFD backbones. The CNF/CPFD hybrid aerogel is use for the detection of nitroaromatic (NAC) vapors. Due to the porous structure, the CNF/CPFD hybrid aerogel possesses a large number of accessible cavities, which could be sufficiently large to allow the diffusion of NAC vapors into the aerogel. As a result, the CNF/CPFD aerogel sensor shows high sensitivity toward NAC vapors. For 120-s exposure, the fluorescence quenching efficiency of the CNF/CPFD aerogel sensor toward 2,4-dinitrotoluene vapor is up to 85.9%, which is much larger than that of the spin-cast CPFD film. Furthermore, the sensing performance of the CNF/CPFD hybrid aerogel is not heavily dependent on the thickness, and the sensing process of the CNF/CPFD aerogel sensor is reversible.

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 with higher accuracy. 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.Author summaryWe developed a novel method, DeepHiC, for enhancing Hi-C data resolution from low-coverage sequencing data using generative adversarial network. DeepHiC is capable of reproducing highresolution (10-kb) Hi-C data with high quality even using 1/100 downsampled reads. Our method outperforms the previous methods in Hi-C data resolution enhancement, boosting accuracy in chromatin loops identification and TADs detection. Application of DeepHiC on mouse embryonic development data shows that enhancements afforded by DeepHiC facilitates the chromatin loops identification of these data achieving higher accuracy. We also developed a user-friendly web server (http://sysomics.com/deephic) that allows researchers to enhance their own low-resolution Hi-C data (40kb-1 Mb) with just few clicks.

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.

During early mammalian embryonic development, different epigenetic mechanisms undergo dramatic changes; yet how these interconnected epigenetic layers function together to coordinate expression of the genetic code in a spatiotemporal manner remains unknown. Here, we describe a time-resolved study of the hierarchy of epigenetic marks and events, which we used to model transcriptional programs that cannot be understood by investigating steady state. We found that, following fertilization, the re-establishment of accessible chromatin together with transcription factors inherited from oocytes initiates transcription at the 2-cell stage, and then the introduction of active histone modification, H3K4me3, facilitates gene up-regulation at the 4-cell stage, then stabilization of higher-order chromatin structures at the 8-cell stage further enhances transcriptional activity. During the first lineage specification at ICM, transcription activity multifaceted regulation of epigenetic marks. Finally, we quantitatively model the stage succession of different epigenetic marks on transcriptional programs during early embryonic 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.

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.

Essential genes are those whose functions govern critical processes that sustain life in the organism. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis for comprehensively and systematically elucidating the genetic and regulatory characteristics of human essential genes. First, essential genes act as “hubs” in protein-protein interactions networks, in chromatin structure, and in epigenetic modifications, thus are essential for cell growth. Second, essential genes represent the conserved biological processes across species although gene essentiality changes itself. Third, essential genes are import for cell development due to its discriminate transcription activity in both embryo development and oncogenesis. In addition, we develop an interactive web server, the Human Essential Genes Interactive Analysis Platform (HEGIAP) (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to give a global, multidimensional interpretation of gene essentiality. Our study provides a new view for understanding human essential genes.

TiB2/Ta-W composites and Ta-10W alloy are prepared by hot-press sintering. The composites and alloy are oxidized in air at 700°C, 800°C and 900°C, respectively. The phase components of the as-prepared composites, alloy and oxidized layer were analyzed through XRD and SEM/EDS. The results showed that TiB2 reacted with both tantalum and tantalum oxide on the surface of Ta particles, which leads to the formation of intermetallic compounds Ta2B and Ta3B2. An oxidation layer of Ta2O5 solid solution was identified. Oxidation kinetics curves of all samples in air obeyed liner law, because a stable protective oxide scale could not be formed on the surface of samples.

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.