Chunquan Li

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

Background & AimsLong non-coding RNAs (lncRNAs) are expressed in tissue-specific pattern, but it is not clear how these are regulated. We aimed to identify squamous cell carcinoma (SCC)-specific lncRNAs and investigate mechanisms that control their expression and function.MethodsWe studied expression patterns and functions of 4 SCC-specific lncRNAs. We obtained 113 esophageal SCC (ESCC) and matched non-tumor esophageal tissues from a hospital in Shantou City, China, and performed quantitative reverse transcription polymerase chain reaction assays to measure expression levels of LINC01503. We collected clinical data from patients and compared expression levels with survival times. LINC01503 was knocked down using small interfering RNAs and oligonucleotides in TE7, TE5, and KYSE510 cell lines and overexpressed in KYSE30 cells. Cells were analyzed by chromatin immunoprecipitation sequencing, luciferase reporter assays, colony formation, migration and invasion, and mass spectrometry analyses. Cells were injected into nude mice and growth of xenograft tumors was measured. LINC01503 interaction with proteins was studied using fluorescence in situ hybridization, RNA pulldown, and RNA immunoprecipitation analyses.ResultsWe identified a lncRNA, LINC01503, which is regulated by a super enhancer and is expressed at significantly higher levels in esophageal and head and neck SCCs than in non-tumor tissues. High levels in SCCs correlated with shorter survival times of patients. The transcription factor TP63 bound to the super enhancer at the LINC01503 locus and activated its transcription. Expression of LINC01503 in ESCC cell lines increased their proliferation, colony formation, migration, and invasion. Knockdown of LINC01503 in SCC cells reduced their proliferation, colony formation, migration, and invasion, and the growth of xenograft tumors in nude mice. Expression of LINC01503 in ESCC cell lines reduced ERK2 dephosphorylation by DUSP6, leading to activation of ERK signaling via MAPK. LINC01503 disrupted the interaction between EBP1 and the p85 subunit of PI3K, increasing AKT signaling.ConclusionsWe identified an lncRNA, LINC01503, which is increased in SCC cells compared with non-tumor cells. Increased expression of LINC01503 promotes ESCC cell proliferation, migration, invasion, and growth of xenograft tumors. It might be developed as a biomarker of aggressive SCCs in patients. Long non-coding RNAs (lncRNAs) are expressed in tissue-specific pattern, but it is not clear how these are regulated. We aimed to identify squamous cell carcinoma (SCC)-specific lncRNAs and investigate mechanisms that control their expression and function. We studied expression patterns and functions of 4 SCC-specific lncRNAs. We obtained 113 esophageal SCC (ESCC) and matched non-tumor esophageal tissues from a hospital in Shantou City, China, and performed quantitative reverse transcription polymerase chain reaction assays to measure expression levels of LINC01503. We collected clinical data from patients and compared expression levels with survival times. LINC01503 was knocked down using small interfering RNAs and oligonucleotides in TE7, TE5, and KYSE510 cell lines and overexpressed in KYSE30 cells. Cells were analyzed by chromatin immunoprecipitation sequencing, luciferase reporter assays, colony formation, migration and invasion, and mass spectrometry analyses. Cells were injected into nude mice and growth of xenograft tumors was measured. LINC01503 interaction with proteins was studied using fluorescence in situ hybridization, RNA pulldown, and RNA immunoprecipitation analyses. We identified a lncRNA, LINC01503, which is regulated by a super enhancer and is expressed at significantly higher levels in esophageal and head and neck SCCs than in non-tumor tissues. High levels in SCCs correlated with shorter survival times of patients. The transcription factor TP63 bound to the super enhancer at the LINC01503 locus and activated its transcription. Expression of LINC01503 in ESCC cell lines increased their proliferation, colony formation, migration, and invasion. Knockdown of LINC01503 in SCC cells reduced their proliferation, colony formation, migration, and invasion, and the growth of xenograft tumors in nude mice. Expression of LINC01503 in ESCC cell lines reduced ERK2 dephosphorylation by DUSP6, leading to activation of ERK signaling via MAPK. LINC01503 disrupted the interaction between EBP1 and the p85 subunit of PI3K, increasing AKT signaling. We identified an lncRNA, LINC01503, which is increased in SCC cells compared with non-tumor cells. Increased expression of LINC01503 promotes ESCC cell proliferation, migration, invasion, and growth of xenograft tumors. It might be developed as a biomarker of aggressive SCCs in patients.

BACKGROUND: MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. RESULTS: We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. CONCLUSIONS: In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.

Squamous cell carcinomas (SCCs) are aggressive malignancies. Previous report demonstrated that master transcription factors (TFs) TP63 and SOX2 exhibited overlapping genomic occupancy in SCCs. However, functional consequence of their frequent co-localization at super-enhancers remains incompletely understood. Here, epigenomic profilings of different types of SCCs reveal that TP63 and SOX2 cooperatively and lineage-specifically regulate long non-coding RNA (lncRNA) CCAT1 expression, through activation of its super-enhancers and promoter. Silencing of CCAT1 substantially reduces cellular growth both in vitro and in vivo, phenotyping the effect of inhibiting either TP63 or SOX2. ChIRP analysis shows that CCAT1 forms a complex with TP63 and SOX2, which regulates EGFR expression by binding to the super-enhancers of EGFR, thereby activating both MEK/ERK1/2 and PI3K/AKT signaling pathways. These results together identify a SCC-specific DNA/RNA/protein complex which activates TP63/SOX2-CCAT1-EGFR cascade and promotes SCC tumorigenesis, advancing our understanding of transcription dysregulation in cancer biology mediated by master TFs and super-enhancers.

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.