SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data

Transcriptional Signatures of a Dynamic Epilepsy Process Reveal Potential Immune Regulation

Epilepsy is a progression of development and advancement over time. However, the molecular features of epilepsy were poorly studied from a dynamic developmental perspective. We intend to investigate the key mechanisms in the process of epilepsy by exploring the roles of stage-specifically expressed genes. By using time-course transcriptomic data of epileptic samples, we first analyzed the molecular features of epilepsy in different stages and divided it into progression and remission stages based on their transcriptomic features. 34 stage-specifically expressed genes were then identified by the Tau index and verified in other epileptic datasets. These genes were then enriched for immune-related biological functions. Furthermore, we found that the level of immune infiltration and mechanisms at different stages were different, which may result from different types of immune cells playing leading roles in distinct stages. Our findings indicated an essential role of immune regulation as the potential mechanism of epilepsy development.

A comprehensive landscape of transcription profiles and data resources for human leukemia

As a heterogeneous group of hematologic malignancies, leukemia has been widely studied at the transcriptome level. However, a comprehensive transcriptomic landscape and resources for different leukemia subtypes are lacking. Thus, in this study, we integrated the RNA sequencing data sets of >3000 samples from 14 leukemia subtypes and 53 related cell lines via a unified analysis pipeline. We depicted the corresponding transcriptomic landscape and developed a user-friendly data portal LeukemiaDB. LeukemiaDB was designed with 5 main modules: protein-coding gene, long noncoding RNA (lncRNA), circular RNA, alternative splicing, and fusion gene modules. In LeukemiaDB, users can search and browse the expression level, regulatory modules, and molecular information across leukemia subtypes or cell lines. In addition, a comprehensive analysis of data in LeukemiaDB demonstrates that (1) different leukemia subtypes or cell lines have similar expression distribution of the protein-coding gene and lncRNA; (2) some alternative splicing events are shared among nearly all leukemia subtypes, for example, MYL6 in A3SS, MYB in A5SS, HMBS in retained intron, GTPBP10 in mutually exclusive exons, and POLL in skipped exon; (3) some leukemia-specific protein-coding genes, for example, ABCA6, ARHGAP44, WNT3, and BLACE, and fusion genes, for example, BCR-ABL1 and KMT2A-AFF1 are involved in leukemogenesis; (4) some highly correlated regulatory modules were also identified in different leukemia subtypes, for example, the HOXA9 module in acute myeloid leukemia and the NOTCH1 module in T-cell acute lymphoblastic leukemia. In summary, the developed LeukemiaDB provides valuable insights into oncogenesis and progression of leukemia and, to the best of our knowledge, is the most comprehensive transcriptome resource of human leukemia available to the research community.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

Epigenetic regulation of seed-specific gene expression by DNA methylation valleys in castor bean

BACKGROUND

Understanding the processes governing angiosperm seed growth and development is essential both for fundamental plant biology and for agronomic purposes. Master regulators of angiosperm seed development are expressed in a seed-specific manner. However, it is unclear how this seed specificity of transcription is established. In some vertebrates, DNA methylation valleys (DMVs) are highly conserved and strongly associated with key developmental genes, but comparable studies in plants are limited to Arabidopsis and soybean. Castor bean (Ricinus communis) is a valuable model system for the study of seed biology in dicots and source of economically important castor oil. Unlike other dicots such as Arabidopsis and soybean, castor bean seeds have a relatively large and persistent endosperm throughout seed development, representing substantial structural differences in mature seeds. Here, we performed an integrated analysis of RNA-seq, whole-genome bisulfite sequencing, and ChIP-seq for various histone marks in the castor bean.

RESULTS

We present a gene expression atlas covering 16 representative tissues and identified 1162 seed-specific genes in castor bean (Ricinus communis), a valuable model for the study of seed biology in dicots. Upon whole-genome DNA methylation analyses, we detected 32,567 DMVs across five tissues, covering ~33% of the castor bean genome. These DMVs are highly hypomethylated during development and conserved across plant species. We found that DMVs have the potential to activate transcription, especially that of tissue-specific genes. Focusing on seed development, we found that many key developmental regulators of seed/endosperm development, including AGL61, AGL62, LEC1, LEC2, ABI3, and WRI1, were located within DMVs. ChIP-seq for five histone modifications in leaves and seeds clearly showed that the vast majority of histone modification peaks were enriched within DMVs, and their remodeling within DMVs has a critical role in the regulation of seed-specific gene expression. Importantly, further experiment analysis revealed that distal DMVs may act as cis-regulatory elements, like enhancers, to activate downstream gene expression.

CONCLUSIONS

Our results point to the importance of DMVs and special distal DMVs behaving like enhancers, in the regulation of seed-specific genes, via the reprogramming of histone modifications within DMVs. Furthermore, these results provide a comprehensive understanding of the epigenetic regulator roles in seed development in castor bean and other important crops.

Hypothalamic long noncoding RNA AK044061 is involved in the development of dietary obesity in mice

BACKGROUND

Long noncoding RNAs (lncRNAs) have been implicated in various important biological processes, however, its role in energy balance and obesity remains largely unknown.

METHODS

Differentially expressed lncRNAs in the hypothalamus of diet-induced obesity (DIO) mice versus chow-fed mice were identified by RNA sequencing. Lentivirus-mediated overexpression and knockdown of a novel lncRNA, AK044061, were used to assess its role in energy balance and the development of DIO. RNA immunoprecipitation (RIP) and pull down assays were carried out to analyze the interaction between lncRNA AK044061 and RelA, an NF-κB subunit.

RESULTS

LncRNA AK044061 was upregulated in the hypothalamus of DIO mice. Acute intracerebroventricular (i.c.v.) infusion of glucose reduced the expression of lncRNA AK044061, whereas an overnight of fasting enhanced its expression. RNA in situ hybridization data showed that AK044061 was expressed in the neurons of the arcuate nucleus (ARC). Lentivirus-mediated overexpression of AK044061 in ARC cells, or in the neurons of the ARC nucleus led to an obesity-like phenotype and related metabolic disorders. Furthermore, knockdown of lncRNA AK044061 in Agouti-related peptide (AgRP)-expressing neurons mitigated DIO and its related metabolic dysregulations. In mechanism, we showed that lncRNA AK044061 was associated with RelA and could enhance the NF-κB reporter activity. The effect of lncRNA AK044061 on energy balance is mediated by NF-κB.

CONCLUSIONS

Our findings suggest that excessive lncRNA AK044061 in the ARC nucleus leads to energy imbalance and obesity. LncRNA AK044061 expressed in the AgRP neurons is important in the development of dietary obesity in mice.

ImmReg: the regulon atlas of immune-related pathways across cancer types

Immune system gene regulation perturbation has been found to be a major cause of the development of various types of cancer. Numbers of mechanisms contribute to gene expression regulation, thus, systematically identification of potential regulons of immune-related pathways is critical to cancer immunotherapy. Here, we comprehensively chart the landscape of transcription factors, microRNAs, RNA binding proteins and long noncoding RNAs regulation in 17 immune-related pathways across 33 cancers. The potential immunology regulons are likely to exhibit higher expressions in immune cells, show expression perturbations in cancer, and are significantly correlated with immune cell infiltrations. We also identify a panel of clinically relevant immunology regulons across cancers. Moreover, the regulon atlas of immune-related pathways helps prioritizing cancer-related genes (i.e. ETV7, miR-146a-5p, ZFP36 and HCP5). We further identified two molecular subtypes of glioma (cold and hot tumour phenotypes), which were characterized by differences in immune cell infiltrations, expression of checkpoints, and prognosis. Finally, we developed a user-friendly resource, ImmReg (http://bio-bigdata.hrbmu.edu.cn/ImmReg/), with multiple modules to visualize, browse, and download immunology regulation. Our study provides a comprehensive landscape of immunology regulons, which will shed light on future development of RNA-based cancer immunotherapies.

New Genes Interacted with Recent Whole Genome Duplicates in the Fast Stem Growth of Bamboos

As drivers of evolutionary innovations, new genes allow organisms to explore new niches. However, clear examples of this process remain scarce. Bamboos, the unique grass lineage diversifying into the forest, have evolved with a key innovation of fast growth of woody stem, reaching up to 1 m/day. Here, we identify 1,622 bamboo-specific orphan genes that appeared in recent 46 million years, and 19 of them evolved from noncoding ancestral sequences with entire de novo origination process reconstructed. The new genes evolved gradually in exon−intron structure, protein length, expression specificity, and evolutionary constraint. These new genes, whether or not from de novo origination, are dominantly expressed in the rapidly developing shoots, and make transcriptomes of shoots the youngest among various bamboo tissues, rather than reproductive tissue in other plants. Additionally, the particularity of bamboo shoots has also been shaped by recent whole-genome duplicates (WGDs), which evolved divergent expression patterns from ancestral states. New genes and WGDs have been evolutionarily recruited into coexpression networks to underline fast-growing trait of bamboo shoot. Our study highlights the importance of interactions between new genes and genome duplicates in generating morphological innovation.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

A comprehensive survey for human transcription factors on expression, regulation, interaction, phenotype and cancer survival

Transcription factors (TFs) act as key regulators in biological processes through controlling gene expression. Here, we conducted a systematic study for all human TFs on the expression, regulation, interaction, mutation, phenotype and cancer survival. We revealed that the average expression levels of TFs in normal tissues were lower than 50% expression of non-TFs, whereas TF expression was increased in cancers. TFs that are specifically expressed in an individual tissue or cancer may be potential marker genes. For instance, TGIF2LX/Y were preferentially expressed in testis and NEUROG1, PRDM14, SRY, ZNF705A and ZNF716 were specifically highly expressed in germ cell tumors. We found different distributions of target genes and TF co-regulations in different TF families. Some small TF families have huge protein interaction pairs, suggesting their central roles in transcriptional regulation. The bZIP family is a small family involving many signaling pathways. Survival analysis indicated that most TFs significantly affect survival of one or more cancers. Some survival-related TFs were also specifically highly expressed in the corresponding cancer types, which may be potential targets for cancer therapy. Finally, we identified 43 TFs whose mutations were closely correlated to survival, suggesting their cancer-driven roles. The systematic analysis of TFs provides useful clues for further investigation of TF regulatory mechanisms and the role of TFs in diseases.

The genomic architecture of the sex-determining region and sex-related metabolic variation in Ginkgobiloba

Sex differences and evolutionary differences are critical biological issues. Ginkgo is an ancient lineage of dioecious gymnosperms with special value for studying the mechanism of sex determination in plants. However, the major genetic basic underlying sex chromosomes remains to be uncovered. In this study, we identify the sex-determining region of Ginkgo and locate it to the area from megabases 48 to 75 on chromosome 2. We find that the male sex-determining region of Ginkgo contains more than 200 genes, including four MADS-box genes, demonstrating that the Ginkgo sex determination system is of the XY type. We also find that genetic sex differences result in specialized flavonoid metabolism and regulation in each sex. These findings establish a foundation for revealing the molecular mechanism of sexual dimorphism and promoting the development of the Ginkgo industry.

hTFtarget: A Comprehensive Database for Regulations of Human Transcription Factors and Their Targets

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

EVmiRNA: a database of miRNA profiling in extracellular vesicles

Extracellular vesicles (EVs), such as exosomes and microvesicles, acted as cell-to-cell communication vectors and potential biomarkers for diseases. microRNAs (miRNAs) are the most well studied molecules in EVs, thus a comprehensive investigation of miRNA expression profiles in EVs will be helpful to explore their functions and biomarkers. We curated 462 small RNA sequencing samples of EVs from 17 sources/diseases and constructed the EVmiRNA database (http://bioinfo.life.hust.edu.cn/EVmiRNA) to show the miRNA expression profiles. We found >1000 miRNAs expressed in these EVs and detected specific miRNAs for EVs of each source/disease. EVmiRNA provides three functional modules: (i) the miRNA expression profiles and the sample information of EVs from different sources (such as blood, breast milk etc.); (ii) the specifically expressed miRNAs in different EVs that would be helpful for biomarker identification; (iii) the miRNA annotations including the miRNA expression in EVs and TCGA cancer types, miRNA pathway regulations as well as miRNA function and publications. EVmiRNA has a user-friendly web interface with powerful browse and search functions, as well as data downloading. It is the first database focusing on miRNA expression profiles in EVs and will be useful for the research and application community of EV biomarker, miRNA function and liquid biopsy.

Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

FFLtool: a web server for transcription factor and miRNA feed forward loop analysis in human

Summary

Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The ‘FFL Analysis’ module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The ‘Browse FFLs’ module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases.

Availability and implementation

FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Landscape of cancer diagnostic biomarkers from specifically expressed genes

Although there has been great progress in cancer treatment, cancer remains a serious health threat to humans because of the lack of biomarkers for diagnosis, especially for early-stage diagnosis. In this study, we comprehensively surveyed the specifically expressed genes (SEGs) using the SEGtool based on the big data of gene expression from the The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. In 15 solid tumors, we identified 233 cancer-specific SEGs (cSEGs), which were specifically expressed in only one cancer and showed great potential to be diagnostic biomarkers. Among them, three cSEGs (OGDH, MUDENG and ACO2) had a sample frequency >80% in kidney cancer, suggesting their high sensitivity. Furthermore, we identified 254 cSEGs as early-stage diagnostic biomarkers across 17 cancers. A two-gene combination strategy was applied to improve the sensitivity of diagnostic biomarkers, and hundreds of two-gene combinations were identified with high frequency. We also observed that 13 SEGs were targets of various drugs and nearly half of these drugs may be repurposed to treat cancers with SEGs as their targets. Several SEGs were regulated by specific transcription factors in the corresponding cancer, and 39 cSEGs were prognosis-related genes in 7 cancers. This work provides a survey of cancer biomarkers for diagnosis and early diagnosis and new insights to drug repurposing. These biomarkers may have great potential in cancer research and application.