MVIP: multi-omics portal of viral infection.

Virus infections are huge threats to living organisms and cause many diseases, such as COVID-19 caused by SARS-CoV-2, which has led to millions of deaths. To develop effective strategies to control viral infection, we need to understand its molecular events in host cells. Virus related functional genomic datasets are growing rapidly, however, an integrative platform for systematically investigating host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MVIP (https://mvip.whu.edu.cn/). We manually collected available high-throughput sequencing data under viral infection, and unified their detailed metadata including virus, host species, infection time, assay, and target, etc. We processed multi-layered omics data of more than 4900 viral infected samples from 77 viruses and 33 host species with standard pipelines, including RNA-seq, ChIP-seq, and CLIP-seq, etc. In addition, we integrated these genome-wide signals into customized genome browsers, and developed multiple dynamic charts to exhibit the information, such as time-course dynamic and differential gene expression profiles, alternative splicing changes and enriched GO/KEGG terms. Furthermore, we implemented several tools for efficiently mining the virus-host interactions by virus, host and genes. MVIP would help users to retrieve large-scale functional information and promote the understanding of virus-host interactions.

Hydration and properties of hydrogen-based mineral phase transformation iron ore tailings as supplementary cementitious material.

Almost all iron ore tailings (IOTs) required activation prior to use as SCMs, which limited their application in building materials. This study investigated HMPT-IOTs and discovered that they possess latent hydraulic and pozzolanic properties. In order to better utilize as SCM, mechanical properties, hydration reactions, hydration products, microstructure, and pores were comprehensively studied through mechanical tests, hydration heat tests, XRD, SEM, TG, and MIP. The results show that when HMPT-IOTs replace cement at 10 wt%, 20 wt% and 30 wt%, the compressive strength at 28 days is 41.9 MPa, 47.9 MPa and 37.5 MPa, respectively. When the substitution amount reaches 30 wt%, it will reduce the cumulative heat of hydration and promote early hydration reactions. The main hydration products are ettringite and Ca(OH)2. As the nucleation site of C-S-H, hydration products are interconnected, making the microstructure denser. At this substitution level, Ca(OH)2 consumption was about 2% at 28 days of age. Simultaneously, the total pore volume was only 0.01 mL/g greater than that of the control group, and the number of micropores and transition pores decreased by approximately 3%.

TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs.

Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures.

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

TRCirc: a resource for transcriptional regulation information of circRNAs.

In recent years, high-throughput genomic technologies like chromatin immunoprecipitation sequencing (ChIp-seq) and transcriptome sequencing (RNA-seq) have been becoming both more refined and less expensive, making them more accessible. Many circular RNAs (circRNAs) that originate from back-spliced exons have been identified in various cell lines across different species. However, the regulatory mechanism for transcription of circRNAs remains unclear. Therefore, there is an urgent need to construct a database detailing the transcriptional regulation of circRNAs. TRCirc (http://www.licpathway.net/TRCirc) provides a resource for efficient retrieval, browsing and visualization of transcriptional regulation information of circRNAs. The current version of TRCirc documents 92 375 circRNAs and 161 transcription factors (TFs) from more than 100 cell types and together represent more than 765 000 TF-circRNA regulatory relationships. Furthermore, TRCirc provides other regulatory information about transcription of circRNAs, including their expression, methylation levels, H3K27ac signals in regulation regions and super-enhancers associated with circRNAs. TRCirc provides a convenient, user-friendly interface to search, browse and visualize detailed information about these circRNAs.

SEdb: a comprehensive human super-enhancer database.

Super-enhancers are important for controlling and defining the expression of cell-specific genes. With research on human disease and biological processes, human H3K27ac ChIP-seq datasets are accumulating rapidly, creating the urgent need to collect and process these data comprehensively and efficiently. More importantly, many studies showed that super-enhancer-associated single nucleotide polymorphisms (SNPs) and transcription factors (TFs) strongly influence human disease and biological processes. Here, we developed a comprehensive human super-enhancer database (SEdb, http://www.licpathway.net/sedb) that aimed to provide a large number of available resources on human super-enhancers. The database was annotated with potential functions of super-enhancers in the gene regulation. The current version of SEdb documented a total of 331 601 super-enhancers from 542 samples. Especially, unlike existing super-enhancer databases, we manually curated and classified 410 available H3K27ac samples from >2000 ChIP-seq samples from NCBI GEO/SRA. Furthermore, SEdb provides detailed genetic and epigenetic annotation information on super-enhancers. Information includes common SNPs, motif changes, expression quantitative trait locus (eQTL), risk SNPs, transcription factor binding sites (TFBSs), CRISPR/Cas9 target sites and Dnase I hypersensitivity sites (DHSs) for in-depth analyses of super-enhancers. SEdb will help elucidate super-enhancer-related functions and find potential biological effects.

SEanalysis: a web tool for super-enhancer associated regulatory analysis.

Super-enhancers (SEs) have prominent roles in biological and pathological processes through their unique transcriptional regulatory capability. To date, several SE databases have been developed by us and others. However, these existing databases do not provide downstream or upstream regulatory analyses of SEs. Pathways, transcription factors (TFs), SEs, and SE-associated genes form complex regulatory networks. Therefore, we designed a novel web server, SEanalysis, which provides comprehensive SE-associated regulatory network analyses. SEanalysis characterizes SE-associated genes, TFs binding to target SEs, and their upstream pathways. The current version of SEanalysis contains more than 330 000 SEs from more than 540 types of cells/tissues, 5042 TF ChIP-seq data generated from these cells/tissues, DNA-binding sequence motifs for ∼700 human TFs and 2880 pathways from 10 databases. SEanalysis supports searching by either SEs, samples, TFs, pathways or genes. The complex regulatory networks formed by these factors can be interactively visualized. In addition, we developed a customizable genome browser containing >6000 customizable tracks for visualization. The server is freely available at http://licpathway.net/SEanalysis.

Atlas of interactions between SARS-CoV-2 macromolecules and host proteins.

The proteins and RNAs of viruses extensively interact with host proteins after infection. We collected and reanalyzed all available datasets of protein-protein and RNA-protein interactions related to SARS-CoV-2. We investigated the reproducibility of those interactions and made strict filters to identify highly confident interactions. We systematically analyzed the interaction network and identified preferred subcellular localizations of viral proteins, some of which such as ORF8 in ER and ORF7A/B in ER membrane were validated using dual fluorescence imaging. Moreover, we showed that viral proteins frequently interact with host machinery related to protein processing in ER and vesicle-associated processes. Integrating the protein- and RNA-interactomes, we found that SARS-CoV-2 RNA and its N protein closely interacted with stress granules including 40 core factors, of which we specifically validated G3BP1, IGF2BP1, and MOV10 using RIP and Co-IP assays. Combining CRISPR screening results, we further identified 86 antiviral and 62 proviral factors and associated drugs. Using network diffusion, we found additional 44 interacting proteins including two proviral factors previously validated. Furthermore, we showed that this atlas could be applied to identify the complications associated with COVID-19. All data are available in the AIMaP database (https://mvip.whu.edu.cn/aimap/) for users to easily explore the interaction map.

Alterations of RNA splicing patterns in esophagus squamous cell carcinoma.

Alternative splicing (AS) is an important biological process for regulating the expression of various isoforms from a single gene and thus to promote proteome diversity. In this study, RNA-seq data from 15 pairs of matched esophageal squamous cell carcinoma (ESCC) and normal tissue samples as well as two cell lines were analyzed. AS events with significant differences were identified between ESCC and matched normal tissues, which were re-annotated to find protein coding genes or non-coding RNAs. A total of 45,439 AS events were found. Of these, 6019 (13.25%) significant differentially AS events were identified. Exon skipping (SE) events occupied the largest proportion of abnormal splicing events. Fifteen differential splicing events with the same trends of ΔΨ values in ESCC tissues, as well in the two cell lines were found. Four pathways and 20 biological processes related to pro-metastasis cell junction and migration were significantly enriched for the differentially spliced genes. The upregulated splicing factor SF3B4, which regulates 92 gene splicing events, could be a potential prognostic factor of ESCC. Differentially spliced genes, including HNRNPC, VCL, ZNF207, KIAA1217, TPM1 and CALD1 are shown with a sashimi plot. These results suggest that cell junction- and migration-related biological processes are influenced by AS abnormalities, and aberrant splicing events can be affected by splicing factor expression changes. The involved splicing factor SF3B4 was found to be a survival-related gene in ESCC and is presumed to regulate AS in multiple cancers. In summary, we identified significant differentially expressed AS events which may be related to the development of ESCC.

Coinfection with influenza A virus enhances SARS-CoV-2 infectivity.

The upcoming flu season in the Northern Hemisphere merging with the current COVID-19 pandemic raises a potentially severe threat to public health. Through experimental coinfection with influenza A virus (IAV) and either pseudotyped or live SARS-CoV-2 virus, we found that IAV preinfection significantly promoted the infectivity of SARS-CoV-2 in a broad range of cell types. Remarkably, in vivo, increased SARS-CoV-2 viral load and more severe lung damage were observed in mice coinfected with IAV. Moreover, such enhancement of SARS-CoV-2 infectivity was not observed with several other respiratory viruses, likely due to a unique feature of IAV to elevate ACE2 expression. This study illustrates that IAV has a unique ability to aggravate SARS-CoV-2 infection, and thus, prevention of IAV infection is of great significance during the COVID-19 pandemic.

Integrative Epigenomic Analysis of Transcriptional Regulation of Human CircRNAs.

Circular RNAs (circRNAs) are evolutionarily conserved and abundant non-coding RNAs whose functions and regulatory mechanisms remain largely unknown. Here, we identify and characterize an epigenomically distinct group of circRNAs (TAH-circRNAs), which are transcribed to a higher level than their host genes. By integrative analysis of cistromic and transcriptomic data, we find that compared with other circRNAs, TAH-circRNAs are expressed more abundantly and have more transcription factors (TFs) binding sites and lower DNA methylation levels. Concordantly, TAH-circRNAs are enriched in open and active chromatin regions. Importantly, ChIA-PET results showed that 23-52% of transcription start sites (TSSs) of TAH-circRNAs have direct interactions with cis-regulatory regions, strongly suggesting their independent transcriptional regulation from host genes. In addition, we characterize molecular features of super-enhancer-driven circRNAs in cancer biology. Together, this study comprehensively analyzes epigenomic characteristics of circRNAs and identifies a distinct group of TAH-circRNAs that are independently transcribed via enhancers and super-enhancers by TFs. These findings substantially advance our understanding of the regulatory mechanism of circRNAs and may have important implications for future investigations of this class of non-coding RNAs.

Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients.

Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients' lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

ReCirc: prediction of circRNA expression and function through probe reannotation of non-circRNA microarrays.

Growing evidence shows that circular RNAs (circRNAs) play important roles in physiological and pathological processes, but our knowledge about the function of circRNAs in diseases is still limited. CircRNA functions are closely related to their expression levels. We developed a probe reannotating program named ReCirc, which is based on sequence alignment between microarray probes and circRNAs, to reannotate circRNAs from non-circRNA microarrays (any microarray that was not designed to profile circRNAs) with microarray probe sequences that were aligned to the body and back-spliced junction sequences of circRNAs to identify circRNAs. Through ReCirc, we obtained 39 818 reannotated probe set-circRNA pairs, which involved 5388 circRNAs, from an Affymetrix human exon array. We evaluated our method by comparing circRNAs obtained by us with golden standard RNase R-resistant (RNase R+) circRNAs, predicted by an RNA-seq-based method find_circ, in the HeLa cell line. The results showed that ReCirc circRNAs, especially those with higher expression level, were partially present in RNase R+ data. In addition to RNA-seq, a circRNA microarray, such as the Agilent-069978 Arraystar Human CircRNA microarray, was also applied to predict and profile circRNAs. Thus, we compared the circRNA profile obtained from ReCirc with that from the circRNA microarray. The results showed that circRNA expression is similar between ReCirc and circRNA microarray in samples from the same tissue. We also evaluated ReCirc, by comparing ReCirc with the find_circ program, in their abilities to compute circRNA expression variation in multiple cell lines and performed molecular verification in the HeLaS3 cell line for those circRNAs that got good performance. As a result, 5 of the 9 randomly selected circRNAs were successfully verified. Functional analysis of identified circRNAs in 4 different cancers indicated that circRNAs may be crucial biomarkers for cancer diagnosis and prognosis. Thus, ReCirc allows us to identify circRNAs from any non-circRNA microarray, and to back-annotate old microarray data from public data sets, which would facilitate re-utilization of the wealth of microarray data sets, to enable the characterization of circRNAs in tissues and cell lines. Here we state that our method is designed only for microarrays and cannot be used for RNAseq data.

Increased levels of the long noncoding RNA, HOXA-AS3, promote proliferation of A549 cells.

Many long noncoding RNAs (lncRNAs) have been identified as powerful regulators of lung adenocarcinoma (LAD). However, the role of HOXA-AS3, a novel lncRNA, in LAD is largely unknown. In this study, we showed that HOXA-AS3 was significantly upregulated in LAD tissues and A549 cells. After knockdown of HOXA-AS3, cell proliferation, migration, and invasion were inhibited. Xenografts derived from A549 cells transfected with shRNA/HOXA-AS3 had significantly lower tumor weights and smaller tumor volumes. We also demonstrated that HOXA-AS3 increased HOXA6 mRNA stability by forming an RNA duplex. In addition, HOXA6 promoted cell proliferation, migration, and invasion in vitro. Using a RNA pull-down assay, we found that HOXA-AS3 bonded with NF110, which regulated the cell localization of HOXA-AS3. Moreover, histone acetylation was involved in upregulation of HOXA-AS3. These results demonstrate that HOXA-AS3 was activated in LAD and supported cancer cell progression. Therefore, inhibition of HOXA-AS3 could be an effective targeted therapy for patients with LAD.