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Background Venous thromboembolism (VTE) is one of the leading complications in glioma patients. Neutrophil extracellular traps (NETs) have been reported to play a critical role in the physiopathology of cancer. We aimed to investigate the presence and potential role of NETs in the hypercoagulable state in glioma patients. Moreover, we evaluated the interaction between NETs and endothelial cells (ECs) in glioma patients. Methods The plasma levels of NETs were detected by enzyme‐linked immunosorbent assay. The NET procoagulant activity was performed based on fibrin formation assays. The NET generation and NET‐treated ECs in vitro were observed by confocal microscopy. Activated platelets (PLTs) and PLT‐neutrophil aggregates were detected by flow cytometry. Results Plasma NET markers were significantly higher in stage III/IV glioma patients than in stage I/II glioma patients and healthy subjects. PLTs from glioma patients tended to induce NET formation than those from healthy subjects. NETs contributed to the hypercoagulable state in glioma patients. After ECs were incubated with NETs isolated from stage III/IV glioma patients, they lost their intercellular connections and were converted into procoagulant phenotypes. Combining DNase I and activated protein C markedly decreased endothelial dysfunction. Conclusions Our results showed the interaction between NETs and hypercoagulability in glioma patients. Targeting NETs may be a potential therapeutic and prevention direction for thrombotic complications in glioma patients., The plasma levels of neutrophil extracellular traps (NETs) are increased in samples from high‐grade glioma. PLT induces the generation of NETs in glioma patients. NETs contribute to procoagulant in glioma patients and platelet activation and convert endothelial cells (ECs) to thrombogenicity., Highlights •The plasma levels of NETs are increased in samples from high‐grade glioma.•PLT induce the generation of NETs in glioma patients.•NETs contribute to procoagulant in glioma patients and platelet activation and convert endothelial cells (ECs) to thrombogenicity.

Background Severe carotid stenosis is a common cause of stroke. In addition, previous clinical studies revealed that patients symptomatic of carotid stenosis suffer from increased episodes of stroke compared with their asymptomatic counterparts. However, the mechanism underlying these differences in the recurrence of stroke remains unclear. Objective The present study aimed to evaluate the levels of neutrophil extracellular traps (NETs) in the plasma of patients with severe carotid stenosis and investigate whether NETs induced procoagulant activity (PCA) in severe carotid stenosis. The study also sought to investigate the interactions between platelets or endothelial cells (ECs) and NETs. Methods The levels of NETs in plasma were quantified using enzyme‐linked immunosorbent assay (ELISA). In addition, NETting neutrophils and neutrophil‐platelet aggregates were detected through flow cytometry. On the other hand, the morphology of NETs formation and endothelial cells were analyzed through confocal microscopy. Finally, the procoagulant activity (PCA) of NETs and endothelial cells were assessed through ELISA and fibrin formation. Results Patients with symptomatic carotid stenosis patients had significantly higher levels of NETs markers compared with their asymptomatic counterparts and healthy subjects. In addition, increased levels of neutrophil‐platelet aggregates induced the generation of NETs in patients with symptomatic carotid stenosis. Moreover, NETs contributed to PCA through tissue factor (TF), in patients with carotid stenosis. Furthermore, NETs disrupted the endothelial barrier and converted endothelial cells (ECs) into PCA to enhance the PCA in patients with carotid stenosis. Conclusions The current study revealed differences in the levels of NETs in the plasma of symptomatic and asymptomatic patients suffering from carotid stenosis. The study also uncovered the interaction between NETs and thrombogenicity in carotid stenosis. Therefore, inhibiting NETs may be a potential biomarker and therapeutic target for recurring stroke in severe carotid stenosis., The plasma levels of neutrophil extracellular traps (NETs) are increased in samples from severe carotid stenosis patients. • Platelets (PLT) initiate the formation of NETs in severe carotid stenosis patients. • NETs induce hypercoagulation state in severe carotid stenosis patients and convert endothelial cells (ECs) to procoagulant phenotype.

Theanine (thea) is one of the most important plant-derived characteristic secondary metabolites and a major healthcare product because of its beneficial biological activities, such as being an antianxiety agent, promoting memory, and lowering blood pressure. Thea mostly accumulates in Camellia plants and is especially rich in Camellia sinensis (tea plant). Although some functional genes (e.g., TS, GOGAT, and GS) attributed to thea accumulation have been separately well explored in tea plants, the evolution of a regulatory module (highly interacting gene group) related to thea metabolism remains to be elaborated. Herein, a thea-associated regulatory module (TARM) was mined by using a comprehensive analysis of a weighted gene coexpression network in Camellia and non-Camellia species. Comparative genomic analysis of 84 green plant species revealed that TARM originated from the ancestor of green plants (algae) and that TARM genes were recruited from different evolutionary nodes with the most gene duplication events at the early stage. Among the TARM genes, two core transcription factors of NAC080 and LBD38 were deduced, which may play a crucial role in regulating the biosynthesis of thea. Our findings provide the first insights into the origin and evolution of TARM and indicate a promising paradigm for identifying vital regulatory genes involved in thea metabolism.

Tea plant (Camellia sinensis) is one of the world’s most important beverage crops due to its numerous secondary metabolites conferring tea quality and health effects. However, only a small fraction of tea genes (especially for those metabolite-related genes) have been functionally characterized to date. A cohesive bioinformatics platform is thus urgently needed to aid in the functional determination of the remaining genes. TeaCoN, a database of gene co-expression network for tea plant, was established to provide genome-wide associations in gene co-expression to survey gene modules (i.e., co-expressed gene sets) for a function of interest. TeaCoN featured a comprehensive collection of 261 high-quality RNA-Seq experiments that covered a wide range of tea tissues as well as various treatments for tea plant. In the current version of TeaCoN, 31,968 (94% coverage of the genome) tea gene models were documented. Users can retrieve detailed co-expression information for gene(s) of interest in four aspects: 1) co-expressed genes with the corresponding Pearson correlation coefficients (PCC-values) and statistical P-values, 2) gene information (gene ID, description, symbol, alias, chromosomal location, GO and KEGG annotation), 3) expression profile heatmap of co-expressed genes across seven main tea tissues (e.g., leaf, bud, stem, root), and 4) network visualization of co-expressed genes. We also implemented a gene co-expression analysis, BLAST search function, GO and KEGG enrichment analysis, and genome browser to facilitate use of the database. The TeaCoN project can serve as a beneficial platform for candidate gene screening and functional exploration of important agronomical traits in tea plant. TeaCoN is freely available at http://teacon.wchoda.com .

Background Recent evidence showed cancerous inhibitor of protein phosphatase 2A (CIP2A) plays carcinogenesis roles in several types of human cancer. However, the expression and function of CIP2A in gliomas are unknown. Methods qRT-PCR, IHC and Western blot were used to evaluate CIP2A expression in glioma tissues and cell lines. The influence of CIP2A on prognosis was analyzed by KM curve and Cox regression. CCK8, clonal formation, transwell and tumor xenograft assays were used to analyze cell proliferation and invasion. The upstream microRNA of CIP2A was verified by luciferase and RIP assays. Results CIP2A was overexpressed in gliomas and associated with tumor size, WHO grade and postoperative overall survival rate. Depletion of CIP2A inhibited glioma cellular proliferation, invasion and xenograft tumorigenicity. miR-383 could bind to the 3'-UTR of CIP2A and inhibit CIP2A expression by forming an RNA-induced silencing complex with Ago2. Conclusion CIP2A plays a carcinogenesis role in glioma progression and is one of the potential targets of miR-383.

Theanine (thea) is the most abundant free amino acid in tea plant (Camellia sinensis) and one of the most important secondary metabolites conferring tea quality and health benefits. Great effort has recently been made to functionally dissect enzyme genes (e.g., GS, GDH, GOGAT) responsible for in vivo thea accumulation. However, the transcriptional regulation of its biosynthesis remains to be explored. Starting from publicly available (condition-independent) tea transcriptome data, we performed an exhaustive coexpression analysis between transcription factor (TF) genes and thea enzyme genes in tea plant. Our results showed that two typical plant-specialized (secondary) metabolites related TF families, such as MYB, bHLH, together with WD40 domain proteins, were prominently involved, suggesting a potential MYB-bHLH-WD40 (MBW) complex-mediated regulatory pattern in thea pathway. Aiming at the most involved MYB family, we screened seven MYB genes as thea candidate regulators through a stringent multistep selection (e.g., filtering with condition-specific nitrogen-treated transcriptome data). The control of MYB regulators in thea biosynthesis was further demonstrated using an integrated analysis of thea accumulation and MYB expression in several major tea tissues, including leave, bud, root, and stem. Our investigation aided tea researchers in having a comprehensive view of transcriptional regulatory landscape in thea biosynthesis, serving as the first platform for studying molecular regulation in thea pathway and a paradigm for understanding the characteristic components biosynthesis in nonmodel plants.

In plants, the bZIP family plays vital roles in various biological processes, including seed maturation, flower development, light signal transduction, pathogen defense, and various stress responses. Tea, as a popular beverage, is widely cultivated and has withstood a degree of environmental adversity. Currently, knowledge of the bZIP gene family in tea plants remains very limited. In this study, a total of 76 CsbZIP genes in tea plant were identified for the whole genome. Phylogenetic analysis with Arabidopsis counterparts revealed that CsbZIP proteins clustered into 13 subgroups, among which 13 ABFs related to the ABA signaling transduction pathway were further identified by conserved motif alignment and named CsABF1-13, these belonged to the A and S subgroups of CsbZIP and had close evolutionary relationships, possessing uniform or similar motif compositions. Transcriptome analysis revealed the expression profiles of CsABF genes in different tissues (bud, young leaf, mature leaf, old leaf, stem, root, flower, and fruit) and under diverse environmental stresses (drought, salt, chilling, and MeJA). Several CsABF genes with relatively low tissue expression, including CsABF1, CsABF5, CsABF9, and CsABF10, showed strong expression induction in stress response. Thirteen CsABF genes, were examined by qRT-PCR in two tea plant cultivars, drought-tolerant “Taicha 12” and drought-sensitive “Fuyun 6”, under exogenous ABA and drought stress. Furthermore, CsABF2, CsABF8, and CsABF11, were screened out as key transcription factors regulating drought tolerance of tea cultivars. Subsequently, some potential target genes regulated by CsABFs were screened by co-expression network and enrichment analysis. This study update CsbZIP gene family and provides a global survey of the ABF gene family in tea plant. The resolution of the molecular mechanism of drought resistance in different varieties could be helpful for improving stress resistance in tea plant via genetic engineering.

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

Understanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework—CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.

Exercise is an effective way to prevent osteoporosis, but its mechanism remains unclear. MicroRNAs (miRNAs) play an essential role in bone metabolism. Recently, mechanical loading was reported to induce changes in miRNA expression in osteoblasts. However, the role of miRNAs in bone under exercise and its underlining mechanisms of action still remain unknown. MiR-214 was reported to regulate the process of osteogenesis and is considered a biomarker of osteoporosis. In this study, we aimed to investigate whether exercise could induce changes in miRNA expression in bone and to study the effects of miR-214 on mechanical loading-induced osteogenesis in osteoblasts. The results showed that miR-214 was down-regulated in both tibia from C57BL/6 mice after exercise in vivo and in osteoblasts after mechanical strain in vitro. Mechanical strain could enhance the ALP activity, promote matrix mineralization, up-regulate the expression of osteogenic factors such as ATF4, Osterix, ALP and β-catenin, and down-regulate RANKL and RANK expression. Over-expression of miR-214 not only inhibited the expression of these osteogenic factors but also attenuated mechanical strain-enhanced osteogenesis in osteoblasts. Collectively, our results indicated that miR-214 could attenuate the osteogenic effects of mechanical loading on osteoblasts, suggesting that inhibition of miR-214 may be one of the ways in which exercise prevents osteoporosis.

High-throughput biological technologies (e.g. ChIP-seq, RNA-seq and single-cell RNA-seq) rapidly accelerate the accumulation of genome-wide omics data in diverse interrelated biological scenarios (e.g. cells, tissues and conditions). Integration and differential analysis are two common paradigms for exploring and analyzing such data. However, current integrative methods usually ignore the differential part, and typical differential analysis methods either fail to identify combinatorial patterns of difference or require matched dimensions of the data. Here, we propose a flexible framework CSMF to combine them into one paradigm to simultaneously reveal Common and Specific patterns via Matrix Factorization from data generated under interrelated biological scenarios. We demonstrate the effectiveness of CSMF with four representative applications including pairwise ChIP-seq data describing the chromatin modification map between K562 and Huvec cell lines; pairwise RNA-seq data representing the expression profiles of two different cancers; RNA-seq data of three breast cancer subtypes; and single-cell RNA-seq data of human embryonic stem cell differentiation at six time points. Extensive analysis yields novel insights into hidden combinatorial patterns in these multi-modal data. Results demonstrate that CSMF is a powerful tool to uncover common and specific patterns with significant biological implications from data of interrelated biological scenarios.

Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

Site-directed mutagenesis for large plasmids is a difficult task that cannot easily be solved by the conventional methods used in many laboratories. In this study, we developed an effective method for Site-directed Mutagenesis for Large Plasmids (SMLP) based on a PCR technique. The SMLP method combines several effective approaches, including a high-efficiency DNA polymerase for the large DNA amplification, two independent PCR reactions and a fast recombinational ligation. Using this method, we have achieved a variety of mutants for the filamin A gene (7.9 kb) cloned in the pcDNA (5.4 kb) or the pLV-U6-CMV-EGFP (9.4 kb) plasmids, indicating that this method can be applied to site-directed mutagenesis for the plasmids up to 17.3 kb. We show that the SMLP method has a greater advantage than the conventional methods tested in this study, and this method can be applied to substitution, deletion, and insertion mutations for both large and small plasmids as well as the assembly of three fragments from PCR reactions. Altogether, the SMLP method is simple, effective, and beneficial to the laboratories that require completing the mutagenesis of large plasmids.

Accurate prediction of lymph-node metastasis in cancers is pivotal for the next targeted clinical interventions that allow favorable prognosis for patients. Different molecular profiles (mRNA and non-coding RNAs) have been widely used to establish classifiers for cancer prediction (e.g., tumor origin, cancerous or non-cancerous state, cancer subtype). However, few studies focus on lymphatic metastasis evaluation using these profiles, and the performance of classifiers based on different profiles has also not been compared. Here, differentially expressed mRNAs, miRNAs, and lncRNAs between lymph-node metastatic and non-metastatic groups were identified as molecular signatures to construct classifiers for lymphatic metastasis prediction in different cancers. With this similar feature selection strategy, support vector machine (SVM) classifiers based on different profiles were systematically compared in their prediction performance. For representative cancers (a total of nine types), these classifiers achieved comparative overall accuracies of 81.00% (67.96–92.19%), 81.97% (70.83–95.24%), and 80.78% (69.61–90.00%) on independent mRNA, miRNA, and lncRNA datasets, with a small set of biomarkers (6, 12, and 4 on average). Therefore, our proposed feature selection strategies are economical and efficient to identify biomarkers that aid in developing competitive classifiers for predicting lymph-node metastasis in cancers. A user-friendly webserver was also deployed to help researchers in metastasis risk determination by submitting their expression profiles of different origins.

SUMMARYThe newly emerged SARS-CoV-2 caused a global pandemic with astonishing mortality and morbidity. The mechanisms underpinning its highly infectious nature remain poorly understood. We report here that SARS-CoV-2 exploits cellular CTP synthetase 1 (CTPS1) to promote CTP synthesis and suppress interferon (IFN) induction. Screening a SARS-CoV-2 expression library identified ORF7b and ORF8 that suppressed IFN induction via inducing the deamidation of interferon regulatory factor 3 (IRF3). Deamidated IRF3 fails to bind the promoters of classic IRF3-responsible genes, thus muting IFN induction. Conversely, a shRNA-mediated screen focused on cellular glutamine amidotransferases corroborated that CTPS1 deamidates IRF3 to inhibit IFN induction. Functionally, ORF7b and ORF8 activate CTPS1 to promote de novo CTP synthesis while shutting down IFN induction. De novo synthesis of small-molecule inhibitors of CTPS1 enabled CTP depletion and IFN induction in SARS-CoV-2 infection, thus impeding SARS-CoV-2 replication. Our work uncovers a strategy that a viral pathogen couples immune evasion to metabolic activation to fuel viral replication. Inhibition of the cellular CTPS1 offers an attractive means for developing antiviral therapy that would be resistant to SARS-CoV-2 mutation.

Modeling a protein functional network in concerned species is an efficient approach for identifying novel genes in certain biological pathways. Tea plant (Camellia sinensis) is an important commercial crop abundant in numerous characteristic secondary metabolites (e.g., polyphenols, alkaloids, alkaloids) that confer tea quality and health benefits. Decoding novel genes responsible for tea characteristic components is an important basis for applied genetic improvement and metabolic engineering. Herein, a high-quality protein functional network for tea plant (TeaPoN) was predicted using cross-species protein functional associations transferring and integration combined with a stringent biological network criterion control. TeaPoN contained 31,273 nonredundant functional interactions among 6,634 tea proteins (or genes), with general network topological properties such as scale-free and small-world. We revealed the modular organization of genes related to the major three tea characteristic components (theanine, caffeine, catechin) in TeaPoN, which served as strong evidence for the utility of TeaPoN in novel gene mining. Importantly, several case studies regarding gene identification for tea characteristic components were presented. To aid in the use of TeaPoN, a concise web interface for data deposit and novel gene screening was developed (http://teapon.wchoda.com). We believe that TeaPoN will serve as a useful platform for functional genomics studies associated with characteristic secondary metabolites in tea plant.

Mounting evidence has shown the involvement of long non-coding RNAs (lncRNAs) during various cancer metastatic events (abbreviated as CMEs, e.g. cancer cell invasion, intravasation, extravasation, proliferation, etc.) that may cooperatively facilitate malignant tumor spread and cause massive patient deaths. The study of lncRNA-CME associations might help understand lncRNA functions in metastasis and present reliable biomarkers for early dissemination detection and optimized treatment. Therefore, we developed a database named ‘lncR2metasta’ by manually compiling experimentally supported lncRNAs during various CMEs from existing studies. LncR2metasta documents 1238 associations between 304 lncRNAs and 39 CMEs across 54 human cancer subtypes. Each entry of lncR2metasta contains detailed information on a lncRNA-CME association, including lncRNA symbol, a specific CME, brief description of the association, lncRNA category, lncRNA Entrez or Ensembl ID, lncRNA genomic location and strand, lncRNA experiment, lncRNA expression pattern, detection method, target gene (or pathway) of lncRNA, lncRNA regulatory role on a CME, cancer name and the literature reference. An easy-to-use web interface was deployed in lncR2metasta for its users to easily browse, search and download as well as to submit novel lncRNA-CME associations. LncR2metasta will be a useful resource in cancer research community. It is freely available at http://lncR2metasta.wchoda.com.

Fungal mycelia can eliminate almost all cocultured cyanobacterial cells within a short time. However, molecular mechanisms of algicidal fungi are poorly understood. In this study, a time‐course transcriptomic analysis of algicidal fungus Bjerkandera adusta T1 was applied to investigate gene expression and regulation. A total of 132, 300, 422, and 823 differentially expressed genes (DEGs) were identified at 6, 12, 24, and 48 hr, respectively. Most DEGs exhibited high endopeptidase activity, cellulose catabolic process, and transmembrane transporter activity by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Many decomposition genes encoding endopeptidases were induced a little later in B. adusta T1 when compared with previously investigated algicidal fungus Trametes versicolor F21a. Besides, the accumulated expression of Polysaccharide lyases8 (PL8) gene with peptidoglycan and alginate decomposition abilities was greatly delayed in B. adusta T1 relative to T. versicolor F21a. It was implied that endopeptidases and enzymes of PL8 might be responsible for the strong algicidal ability of B. adusta T1 as well as T. versicolor F21a., Algicidal process of fungus Bjerkandera adusta T1 was investigated by a time‐course transcriptomic analysis. The results showed that most differentially expressed genes exhibited high endopeptidase activity, cellulose catabolic process, and transmembrane transporter activity. Comparison of gene expression between B. adusta T1 and Trametes versicolor F21a during the algicidal process suggested that endopeptidases together with enzymes of the polysaccharide lyases family 8 might be responsible for the strong algicidal activity of B. adusta T1 as well as T. versicolor F21a.

Land plants evolve drought acclimation. Existing knowledge of gene regulation mainly comes from short-term drought treatment. However, common regulatory mechanism shared by multiple varieties under long-term drought is little explored. Here we investigated changes in physiology, hormones and transcriptomes in leaves of Nicotiana varieties K326 and Basma Xanthi with/without drought treatment at time courses spanning 1 month. Analyses of deep RNA-Seq data and further full-length Iso-Seq data revealed an atlas of dynamic changes of transcripts, spliced isoforms, gene expression, associated Gene Ontology, and metabolism pathways. Fewer differentially expressed genes (DEGs) were induced by drought in high tolerance variety than susceptible variety. Comparison among seven hormone signal pathways identified that genes in both abscisic acid and auxin signaling pathways were highly induced although specific genes were depended on the variety. Common hormone regulatory network analysis revealed that genes encoding clade A protein phosphatase 2C gene (PP2C) in abscisic acid pathway was the pivotal hub. Expressional regulation in photosynthesis was also common and variety specific. We conclude that long-term drought inducing gene regulatory networks of hormones and photosynthesis are variety dependent, and PP2C is the center of the common hormone regulatory network. Thus, this study improves our understanding of gene regulatory network in drought response.

With the rapid development of biotechnology, multi-dimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA–gene module network and a two-level gene–drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. This module discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.

With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies., Author summary The number and size of biological datasets (e.g., single-cell RNA-seq datasets) are booming recently. How to mine the relationships of genes across datasets is becoming an important issue. Computational tools of identifying differentially expressed genes have been comprehensively studied, but the interrelated genes across datasets are always neglected. Detecting of highly interrelated genes across datasets is hindered because the samples of them are always different and they could have different numbers of samples. To solve this problem, we present a new algorithm that can identify interrelated genes across datasets based on distance correlation. Our proposed algorithm is very efficient and works well in different technologies, i.e., RNA-seq, single-cell RNA-seq and single-cell ATAC-seq. Also, we found that the number of such highly interrelated genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.

Some black teas demand high market prices. Black tea samples (306) collected from 10 geographic origins, including China (Guxi, Likou, Jinzipai, Guichi, Dongzhi, Changning, Wuyishan, Shaowu), India (Darjeeling), and Sri Lanka (Kandy), were analyzed using headspace volatilization followed by GC/MS (HS-GC/MS). Forty-eight volatile compounds were identified. The aroma compounds were mainly identified as alcohols, aldehydes, ketones, and esters. Analysis of either full-spectrum data or 22 tea compounds shared among the samples with k-Nearest Neighbor (k-NN) and Random Forest (RF) models discriminated all origins at 100% using KNN and 95% with RF using either data set. The discrimination rates using 2 key aroma compounds (linalool and geraniol) by k-NN were 100% for nine origins, with the rate for Guxi area at 89%, because 3 samples were classified to Jinzipai. The findings support the use of HS-GC/MS combined with chemometrics as a tool to identify the origin of black tea.

The rapid accumulation of single-cell chromatin accessibility data offers a unique opportunity to investigate common and specific regulatory mechanisms across different cell types. However, existing methods for cis-regulatory network reconstruction using single-cell chromatin accessibility data were only designed for cells belonging to one cell type, and resulting networks may be incomparable directly due to diverse cell numbers of different cell types. Here, we adopt a computational method to jointly reconstruct cis-regulatory interaction maps (JRIM) of multiple cell populations based on patterns of co-accessibility in single-cell data. We applied JRIM to explore common and specific regulatory interactions across multiple tissues from single-cell ATAC-seq dataset containing ~80,000 cells across 13 mouse tissues. Reconstructed common interactions among 13 tissues indeed relate to basic biological functions, and individual cis-regulatory network shows strong tissue specificity and functional relevance. More importantly, tissue-specific regulatory interactions are mediated by coordination of histone modifications and tissue related TFs, and many of them reveal novel regulatory mechanisms (e.g., a kidney-specific promoter-enhancer loop of clock-controlled gene Gys2).

Background With the maturity of next generation sequencing technology, a huge amount of epigenomic data have been generated by several large consortia in the last decade. These plenty resources leave us the opportunity about sufficiently utilizing those data to explore biological problems. Results Here we developed an integrative and comparative method, CsreHMM, which is based on a hidden Markov model, to systematically reveal cell type-specific regulatory elements (CSREs) along the whole genome, and simultaneously recognize the histone codes (mark combinations) charactering them. This method also reveals the subclasses of CSREs and explicitly label those shared by a few cell types. We applied this method to a data set of 9 cell types and 9 chromatin marks to demonstrate its effectiveness and found that the revealed CSREs relates to different kinds of functional regulatory regions significantly. Their proximal genes have consistent expression and are likely to participate in cell type-specific biological functions. Conclusions These results suggest CsreHMM has the potential to help understand cell identity and the diverse mechanisms of gene regulation. Electronic supplementary material The online version of this article (10.1186/s12864-018-5274-9) contains supplementary material, which is available to authorized users.

Elucidating the regulatory mechanisms of human brain evolution is essential to understanding human cognition and mental disorders. We generated multi-omics profiles and constructed a high-resolution map of 3D genome architecture of rhesus macaque during corticogenesis. By comparing the 3D genomes of human, macaque, and mouse brains, we identified many human-specific chromatin structure changes, including 499 topologically associating domains (TADs) and 1,266 chromatin loops. The human-specific loops are significantly enriched in enhancer-enhancer interactions, and the regulated genes show human-specific expression changes in the subplate, a transient zone of the developing brain critical for neural circuit formation and plasticity. Notably, many human-specific sequence changes are located in the human-specific TAD boundaries and loop anchors, which may generate new transcription factor binding sites and chromatin structures in human. Collectively, the presented data highlight the value of comparative 3D genome analyses in dissecting the regulatory mechanisms of brain development and evolution.

MicroRNAs (miRNAs) are a class of small non-coding RNAs, which play key roles in gene regulation. Previous studies have revealed that they are likely to work together to regulate their common target genes. Discovery of the underlying combinatorial regulation between miRNAs and genes can provide useful information for understanding their functions as well as their close relevance to cancer. In this paper, we propose a two-stage method for identifying miRNA-gene regulatory modules by integrating miRNA and mRNA expression profiles and miRNA genomic cluster data. We first develop a multiple-output sparse group lasso (MSGL) regression model to predict a miRNA-gene association matrix (i.e., a miRNA-gene regulatory network). Further, we propose a ${L_{0}} $ -regularized singular value decomposition ( ${L_{0}} $ -SVD) to identify miRNA-gene joint modules from the predicted regulatory network. We test our method on the matched miRNA and mRNA expression profiles in breast cancer from TCGA project and identify ten miRNA-gene regulatory modules. We find that 1) the modules are significantly associated in the predicted miRNA-gene regulatory network; 2) the modules are significantly enriched in GO biological processes and KEGG pathways, respectively; 3) many miRNAs and genes in the modules are related with breast cancer. On average, 51% of the miRNAs and 30% of the genes are related with breast cancer. The results demonstrate that miRNA-gene regulatory modules provide insights into the mechanisms of the combinatorial regulation between miRNAs and genes.

A great number of microRNAs (miRNAs) have been identified in responding and acting in gene regulatory networks associated with plant tolerance to abiotic stress conditions, such as drought, salinity, and high temperature. The topological exploration of target genes regulated by abiotic-stress-responsible miRNAs (ASRmiRs) in a network facilitates to discover the molecular basis of plant abiotic stress response. This study was based on the staple food rice (Oryza sativa) in which ASRmiRs were manually curated. After having compared the topological properties of target genes (stress-miR-targets) with those (non-stress-miR-targets) not regulated by ASRmiRs in a rice interactome network, we found that stress-miR-targets exhibited distinguishable topological properties. The interaction probability analysis and k-core decomposition showed that stress-miR-targets preferentially interacted with non-stress-miR-targets and located at the peripheral positions in the network. Our results indicated an obvious topological distinction between the two types of genes, reflecting the specific mechanisms of action of stress-miR-targets in rice abiotic stress response. Also, the results may provide valuable clues to elucidate molecular mechanisms of crop response to abiotic stress.

Zearalenone (ZEN) is a mycotoxin that causes serious health problems in humans and animals. However, few studies have focused on the destruction of the intestinal barrier caused by ZEN. In this study, rats were exposed to different dosages of ZEN (0, 0.2, 1.0 and 5.0 mg/kg bw) by gavage for 4 weeks. The results showed that 1.0 and 5.0 mg/kg ZEN impaired gut morphology, induced the inflammatory response, reduced mucin expression, increased intestinal permeability, decreased the expression of TJ proteins and activated the RhoA/ROCK pathway. However, 0.2 mg/kg ZEN had no significant effect on intestinal barrier except for reducing the expression of some TJ proteins and mucins. Moreover, exposure to ZEN led to slight imbalance in microbiota. In conclusion, ZEN exposure resulted in intestinal barrier dysfunction by inducing intestinal microbiota dysbiosis, decreasing the expression of TJ proteins, activating the RhoA/ROCK pathway, and inducing the inflammatory response.

Theanine (thea) is a unique non-protein amino acid in tea plant (Camellia sinensis) and one of the most important small molecular compounds for tea quality and health effects. The molecular mechanism that maintains thea biosynthesis is not clear but may be reflected in complicated biological networks as other secondary metabolites in plants. We performed an integrative transcriptomic analysis of tea seedlings bud and leave over the time-course of ethylamine (EA) treatment that activated thea pathway. We identified 54 consistent differentially expressed genes (cDEGs, 25 upregulated and 29 downregulated) during thea activation. Gene Ontology (GO) functional enrichment analysis of upregulated genes and downregulated genes showed that they may function as a cascade of biological events during their cooperative contribution to thea biosynthesis. Among the total cDEGs, a diversity of functional genes (e.g., enzymes, transcription factors, transport and binding proteins) were identified, indicating a hierarchy of gene control network underlying thea biosynthesis. A gene network associated with thea biosynthesis was modeled and three interconnected gene functional modules were identified. Among the gene modules, several topologically important genes (e.g., CsBCS-1, CsRP, CsABC2) were experimentally validated using a combined thea content and gene expression analysis. Collectively, we presented here for the first time a comprehensive landscape of the biosynthetic mechanism of thea controlled by a underling gene network, which might provide a theoretical basis for the identification of key genes that contribute to thea biosynthesis.

With the rapid development of deep sequencing technologies, many programs are generating multi-platform genomic profiles (e.g., somatic mutation, DNA methylation, and gene expression) for a large number of tumors. This activity has provided unique opportunities and challenges to stratify tumors and decipher tumor heterogeneity. In this chapter, we summarize several computational methods to address the challenge of tumor stratification with different types of genomic data. We further introduce their applications in emerging large-scale genomic data to show their effectiveness in deciphering tumor heterogeneity and clinical relevance.

The chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. However, existing methods for domain detection were only designed based on symmetric Hi-C maps, ignoring long-range interaction structures between domains. To this end, we proposed a generic and efficient method to identify multi-scale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets. We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multi-scale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the-state-of-art methods in terms of accuracy, flexibility and efficiency.

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

Significance A high-quality genome assembly of Camellia sinensis var. sinensis facilitates genomic, transcriptomic, and metabolomic analyses of the quality traits that make tea one of the world’s most-consumed beverages. The specific gene family members critical for biosynthesis of key tea metabolites, monomeric galloylated catechins and theanine, are indicated and found to have evolved specifically for these functions in the tea plant lineage. Two whole-genome duplications, critical to gene family evolution for these two metabolites, are identified and dated, but are shown to account for less amplification than subsequent paralogous duplications. These studies lay the foundation for future research to understand and utilize the genes that determine tea quality and its diversity within tea germplasm., Tea, one of the world’s most important beverage crops, provides numerous secondary metabolites that account for its rich taste and health benefits. Here we present a high-quality sequence of the genome of tea, Camellia sinensis var. sinensis (CSS), using both Illumina and PacBio sequencing technologies. At least 64% of the 3.1-Gb genome assembly consists of repetitive sequences, and the rest yields 33,932 high-confidence predictions of encoded proteins. Divergence between two major lineages, CSS and Camellia sinensis var. assamica (CSA), is calculated to ∼0.38 to 1.54 million years ago (Mya). Analysis of genic collinearity reveals that the tea genome is the product of two rounds of whole-genome duplications (WGDs) that occurred ∼30 to 40 and ∼90 to 100 Mya. We provide evidence that these WGD events, and subsequent paralogous duplications, had major impacts on the copy numbers of secondary metabolite genes, particularly genes critical to producing three key quality compounds: catechins, theanine, and caffeine. Analyses of transcriptome and phytochemistry data show that amplification and transcriptional divergence of genes encoding a large acyltransferase family and leucoanthocyanidin reductases are associated with the characteristic young leaf accumulation of monomeric galloylated catechins in tea, while functional divergence of a single member of the glutamine synthetase gene family yielded theanine synthetase. This genome sequence will facilitate understanding of tea genome evolution and tea metabolite pathways, and will promote germplasm utilization for breeding improved tea varieties.

Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary DNA sequence. In addition to DNA sequences, histone modifications and chromatin accessibility are also important factors influencing their activity. They have been explored to predict TFBSs recently. However, current methods rarely take into account histone modifications and chromatin accessibility using CNN in an integrative framework. To this end, we developed a general CNN model to integrate these data for predicting TFBSs. We systematically benchmarked a series of architecture variants by changing network structure in terms of width and depth, and explored the effects of sample length at flanking regions. We evaluated the performance of the three types of data and their combinations using 256 ChIP-seq experiments and also compared it with competing machine learning methods. We find that contributions from these three types of data are complementary to each other. Moreover, the integrative CNN framework is superior to traditional machine learning methods with significant improvements.

Pilot scale experiments with forced aeration were conducted to estimate effects of aeration rates on the performance of composting penicillin mycelial dreg using sewage sludge as inoculation. Three aeration rates of 0.15, 0.50 and 0.90L/(min·kg) organic matter (OM) were examined. The principal physicochemical parameters were monitored during the 32day composting period. Results showed that the higher aeration rate of 0.90L/(min·kg) did not corresponded to a longer thermophilic duration and higher rates of OM degradation; but the lower aeration rate of 0.15L/(min·kg) did induce an accumulation of NH4(+)-N contents due to the inhibition of nitrification. On the other hand, aeration rate has little effect on degradation of penicillin. The results show that the longest phase of thermophilic temperatures≥55°C, the maximum NO3(-)-N content and seed germination, and the minimum C/N ratio were obtained with 0.50L/(min·kg) OM. Therefore, aeration rates of 0.50L/(min·kg) OM can be recommended for composting penicillin mycelial dreg.

Metagenomic research uses sequencing technologies to investigate the genetic biodiversity of microbiomes presented in various ecosystems or animal tissues. The composition of a microbial community is highly associated with the environment in which the organisms exist. As large amount of sequencing short reads of microorganism genomes obtained, accurately estimating the abundance of microorganisms within a metagenomic sample is becoming an increasing challenge in bioinformatics. In this paper, we describe a hierarchical taxonomy tree-based mixture model (HTTMM) for estimating the abundance of taxon within a microbial community by incorporating the structure of the taxonomy tree. In this model, genome-specific short reads and homologous short reads among genomes can be distinguished and represented by leaf and intermediate nodes in the taxonomy tree, respectively. We adopt an expectation-maximization algorithm to solve this model. Using simulated and real-world data, we demonstrate that the proposed method is superior to both flat mixture model and lowest common ancestry-based methods. Moreover, this model can reveal previously unaddressed homologous genomes.

DNA methylation is a key functional regulatory mechanism in human genome, which plays critical roles in development, differentiation and many diseases. With rapid progress of large-scale projects (e.g. ENCODE), many DNA methylation data across diverse cell lines have been produced. However, common methylation patterns, cell lineage- and cell line-specific DNA methylation patterns across multiple cell lines have not yet been explored completely. Using the DNA methylation data across 54 human cell lines, we identified 35 276 local DNA methylation regions called local clusters of CpG sites (LCCSs). We constructed an LCCS co-methylation network and investigated the common DNA methylation patterns across all cell lines, which reveal two distinct groups in terms of their methylation level and genomic characteristics. We further detected diverse sets of cell lineage-specific high- and low-methylation patterns, which were depleted in promoter, CpG island (CGI) and repeat regions but enriched in gene body and non-CGI regions, especially the CGI shore regions. We discovered that the cell lineage-specific low-methylated LCCSs were enriched with functional transcriptional factor binding motif regions. Moreover, the detected cell line-specific high- and low-methylated patterns show distinct enrichments in cell line-specific chromatin states and functional relevance with the corresponding cell lines.

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions.

Histone modifications have been widely elucidated to play vital roles in gene regulation and cell identity. The Roadmap Epigenomics Consortium generated a reference catalogue of several key histone modifications across >100s of human cell types and tissues. Decoding these epigenomes into functional regulatory elements is a challenging task in computational biology. To this end, we adopted a differential chromatin modification analysis framework to comprehensively determine and characterize cell type-specific regulatory elements (CSREs) and their histone modification codes in the human epigenomes of five histone modifications across 127 tissues or cell types. The CSREs show significant relevance with cell type-specific biological functions and diseases and cell identity. Clustering of CSREs with their specificity signals reveals diverse histone codes, demonstrating the diversity of functional roles of CSREs within the same cell or tissue. Last but not least, dynamics of CSREs from close cell types or tissues can give a detailed view of developmental processes such as normal tissue development and cancer occurrence.

A modified anaerobic/anoxic/oxic (mA2/O) process based on utilizing the internal carbon source and adding polypropylene carriers was operated for 90d to investigate the nutrients removal performance and bacterial community. This system exhibited a stable and efficient performance, particularly, in removing the NH4+-N and total phosphorus. The results of high-throughput sequencing showed that the 13 dominant genera containing Pseudomonas, Comamonas, Arcobacter, Nitrobacteria, Nitrosospira, Nitrosomonas, Bacteroides, Flavobacterium, Rhizobium, Acinetobacter, Zoogloea, Rhodocyclus and Moraxella were shared by five zones, inferring that they were the essential players in treating low C/N (below 5.0) municipal wastewater around 10°C. The average abundance of Nitrosospira (4.21%) was higher than that of Nitrosomonas (2.93%), suggested that Nitrosospira performed well under low temperature for nitrification. Additionally, both known Rhodocyclus-related PAOs and GAOs Competibacter were not detected possibly due to low temperature. Redundancy analysis (RDA) indicated that DO played more important roles in regulating bacterial community composition than HRT.

Deoxynivalenol (DON), zearalenone (ZEN) and fumonisin B1 (FB1) are among the most toxicologically important Fusarium toxins commonly found in nature that lead to nephrotoxicity in animals. The present study investigated that the individual and combined effects of subcytotoxic DON (0.25 μM), ZEN (20 μM) and FB1 (10 μM) on oxidative stress and apoptosis in porcine kidney cells (PK15). In addition, the protective effect of N-acetylcysteine (NAC) against the toxicity of Fusarium toxins was also evaluated. Our results showed that the activities of glutathione reductase (GR) and total superoxide dismutase (SOD) were affected by DON, ZEN and FB1, and this change in activity induced reactive oxygen species (ROS) and malondialdehyde (MDA) production, increased apoptosis and regulated the mRNA expression of Bax, Bcl-2, caspase-3, caspase-9, cytochrome c (cyto c) and P53. This study demonstrated the complexity of combined mycotoxin infection since the combination of toxins exhibited more profound defects in the oxidative stress responses and apoptosis. Moreover, NAC reduced the oxidative damage and inhibited the apoptosis induced by Fusarium toxins. It was concluded that oxidative damage and apoptosis through the mitochondria-dependent channel were the mechanisms of Fusarium toxin mediated toxicity, and NAC reversed these damages to some extent.

Neph et al. (2012) (Circuitry and dynamics of human transcription factor regulatory networks. Cell, 150: 1274–1286) reported the transcription factor (TF) regulatory networks of 41 human cell types using the DNaseI footprinting technique. This provides a valuable resource for uncovering regulation principles in different human cells. In this paper, the architectures of the 41 regulatory networks and the distributions of housekeeping and specific regulatory interactions are investigated. The TF regulatory networks of different human cell types demonstrate similar global three-layer (top, core and bottom) hierarchical architectures, which are greatly different from the yeast TF regulatory network. However, they have distinguishable local organizations, as suggested by the fact that wiring patterns of only a few TFs are enough to distinguish cell identities. The TF regulatory network of human embryonic stem cells (hESCs) is dense and enriched with interactions that are unseen in the networks of other cell types. The examination of specific regulatory interactions suggests that specific interactions play important roles in hESCs. Published version

Liuwei-dihuang (LWDH) is widely used in traditional Chinese medicine (TCM), but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs) were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM.

Considerable studies demonstrate that plant transcription factors (TFs) play key regulatory roles in abiotic/biotic stress conditions, such as drought and pathogen attack. However, there is no effort dedicated to curate experimentally validated stress-TF regulatory relationships from these individual reports into a central database, which put an obstacle in the exploration of stress-TF regulations in plants. To address this issue, we presented a literature-curated database 'Stress2TF' that currently documented 1533 regulatory relationships between 71 abiotic/biotic stresses and 558 TFs in 47 plant species. Each entry in Stress2TF contains detailed information about a stress-TF relationship such as plant name, stress name, TF and brief description of stress-TF relationship. Stress2TF provided a user-friendly interface for entry browse, search and download. In addition, a submission page and several useful tools (e.g., BLAST, network visualization) were integrated. Stress2TF may be a valuable resource for the research of stress-TF regulatory mechanisms in plants. Stress2TF is available at http://csgenomics.ahau.edu.cn/Stress2TF.

In this work, an ultra-sensitive and simple chemiluminescence (CL) immunosensor has been constructed to detect Hg2+ in real water samples. It is based on the excellent properties of oxidized graphene and chitosan modified a novel flow cell to achieve satisfactory results. Oxidized graphene has good biocompatibility, can effectively immobilize antigens and construct an efficient biosensor interface. Chitosan has excellent biodegradability, excellent film-forming and biocompatibility. Hereby, oxidized graphene-chitosan composites provide a favorable microenvironment for immobilizing coating-antigen and introducing HRP-labeled secondary antibodies into the CL reaction of luminol-PIP-H2O2. With a competitive-type assay mode, the Hg2+ in solution competed with the immobilized coating antigen for the limited binding sites of monoclonal antibody, which resulted in the CL of this system decreases because of less HRP. At the optimal conditions, the CL signal displayed a good linear relation toward Hg2+ in the range of 0.001–300 ng mL−1 with a detection limit (3σ) of 0.33 pg mL−1. The immunosensor possessed acceptable accuracy, high specificity and reproducibility, and was applied to examine Hg2+ in real water samples and cream with favorable results. The successful development of this method also provides reliable prospects and technical support for the follow-up detection of similar environmental pollutants.

Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements, especially those are specific to cell types or tissues. In this current study, we developed a computational method using differential chromatin modification analysis (dCMA) to identify cell-type-specific genomic regions with distinctive chromatin modifications. We then apply this method to a public data set with modification profiles of nine marks for nine cell types to evaluate its effectiveness. We found cell-type-specific elements unique to each cell type investigated. These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements. These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

Plant growth models able to simulate phenotypic plasticity are increasingly required because (i) they should enable better predictions of the observed variations in crop morphology and production, and (ii) their parameters are expected to have a more robust genetic basis compared to conventional crop models, with possible implications for selection of quantitative traits such as interplant competition tolerance. In this paper, plant density effect on maize canopy morphological development was studied in China during 2009, with a particular focus on characterizing canopy morphological response to increase plant density. Field experiment was composed of four plant densities i.e. 2, 6, 12 and 20 plants m-2 with three replicates. Detailed records on extension of laminae, sheaths and internodes were made throughout the vegetative stages from the field experiment. The relationships of organ size changes to increased plant density were quantified using either polynomial or linear equations. As such, the findings in this study will be used in predicting canopy morphological characteristics as affected as interplant competition.

To investigate the commonalities and specificities across tumor lineages, we perform a systematic pan-cancer transcriptomic study across 6744 specimens. We find six pan-cancer subnetwork signatures which relate to cell cycle, immune response, Sp1 regulation, collagen, muscle system and angiogenesis. Moreover, four pan-cancer subnetwork signatures demonstrate strong prognostic potential. We also characterize 16 cancer type-specific subnetwork signatures which show diverse implications to somatic mutations, somatic copy number aberrations, DNA methylation alterations and clinical outcomes. Furthermore, some of them are strongly correlated with histological or molecular subtypes, indicating their implications with tumor heterogeneity. In summary, we systematically explore the pan-cancer common and cancer type-specific gene subnetwork signatures across multiple cancers, and reveal distinct commonalities and specificities among cancers at transcriptomic level.

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called 'multi-dimensional genomic data'. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional 'omic' data.