Dataset Information

Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine.

ABSTRACT: Plant long non-coding RNA (lncRNA) undergoes dynamic regulation and acts in developmental and stress regulation. In this study, we surveyed the expression dynamics of lncRNAs in grapevine (Vitis vinifera L.) under cold stress using high-throughput sequencing. Two-hundred and three known lncRNAs were significantly up-regulated and 144 known lncRNAs were significantly down-regulated in cold-treated grapevine. In addition, 2 088 novel lncRNA transcripts were identified in this study, with 284 novel lncRNAs significantly up-regulated and 182 novel lncRNAs significantly down-regulated in cold-treated grapevine. Two-hundred and forty-two differentially expressed grapevine lncRNAs were predicted to target 326 protein-coding genes in a cis-regulatory relationship. Many differentially expressed grapevine lncRNAs targeted stress response-related genes, such as CBF4 transcription factor genes, late embryogenesis abundant protein genes, peroxisome biogenesis protein genes, and WRKY transcription factor genes. Sixty-two differentially expressed grapevine lncRNAs were predicted to target 100 protein-coding genes in a trans-regulatory relationship. The expression of overall target genes in both cis and trans-regulatory relationships were positively related to the expression of lncRNAs in grapevines under cold stress. We identified 31 known lncRNAs as 34 grapevine micro RNA (miRNA) precursors and some miRNAs may be derived from multiple lncRNAs. We found 212 lncRNAs acting as targets of miRNAs in grapevines, involving 150 miRNAs; additionally, 120 grapevine genes were predicted as targets of grapevine miRNAs and lncRNAs. We found one gene cluster that was up-regulated and showed the same expression trend. In this cluster, many genes may be involved in abiotic stress response such as WRKY, Hsf, and NAC transcription factor genes.

SUBMITTER: Wang P

PROVIDER: S-EPMC6488645 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine.

Wang Pengfei P Dai Lingmin L Ai Jun J Wang Yongmei Y Ren Fengshan F

Scientific reports 20190429 1

Plant long non-coding RNA (lncRNA) undergoes dynamic regulation and acts in developmental and stress regulation. In this study, we surveyed the expression dynamics of lncRNAs in grapevine (Vitis vinifera L.) under cold stress using high-throughput sequencing. Two-hundred and three known lncRNAs were significantly up-regulated and 144 known lncRNAs were significantly down-regulated in cold-treated grapevine. In addition, 2 088 novel lncRNA transcripts were identified in this study, with 284 novel ...[more]

PMID: 31036931

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Project description:Cold stimulation reduces the quality of animal products and increases animal mortality, causing huge losses to the livestock industry in cold regions. Long non-coding RNAs (lncRNAs) take part in many biological processes through transcriptional regulation, intracellular material transport, and chromosome remodeling. Although cold stress-related lncRNAs have been reported in plants, no research is available on the characteristic and functional analysis of lncRNAs after cold stress in rats. Here, we built a cold stress animal model firstly. Six SPF male Wistar rats were randomly divided to the acute cold stress group (4?°C, 12?h) and the normal group (24?°C, 12?h). lncRNA libraries were constructed by high-throughput sequencing (HTS) using rat livers. 2,120 new lncRNAs and 273 differentially expressed (DE) lncRNAs were identified in low temperature environments. The target genes of DElncRNA were predicted by cis and trans, and then functional and pathway analysis were performed to them. GO and KEGG analysis revealed that lncRNA targets were mainly participated in the regulation of nucleic acid binding, cold stimulation reaction, metabolic process, immune system processes, PI3K-Akt signaling pathway and pathways in cancer. Next, a interaction network between lncRNA and its targets was constructed. To further reveal the mechanism of cold stress, DElncRNA and DEmRNA were extracted to reconstruct a co-expression sub-network. We found the key lncRNA MSTRG.80946.2 in sub-network. Functional analysis of key lncRNA targets showed that targets were significantly enriched in fatty acid metabolism, the PI3K-Akt signaling pathway and pathways in cancer under cold stress. qRT-PCR confirmed the sequencing results. Finally, hub lncRNA MSTRG.80946.2 was characterized, and verified its relationship with related mRNAs by antisense oligonucleotide (ASO) interference and qRT-PCR. Results confirmed the accuracy of our analysis. To sum up, our work was the first to perform detailed characterization and functional analysis of cold stress-related lncRNAs in rats liver. lncRNAs played crucial roles in energy metabolism, growth and development, immunity and reproductive performance in cold stressed rats. The MSTRG.80946.2 was verified by network and experiments to be a key functional lncRNA under cold stress, regulating ACP1, TSPY1 and Tsn.

Project description:BackgroundCancer-associated metabolic reprogramming promotes cancer cell differentiation, growth, and influences the tumor immune microenvironment (TIME) to promote hepatocellular carcinoma (HCC) progression. However, the clinical significance of metabolism-related lncRNA remains largely unexplored.MethodsBased on The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset, we identified characteristic prognostic long non-coding RNAs (lncRNAs) and construct metabolism-related lncRNA prognostic signature for HCC. Gender, age, grade, stage and TP53 status were used as covariates were used to assess the prognostic capacity of the characteristic lncRNA signature. Subsequently, the molecular and immune characteristics and drug sensitivity in metabolism-related lncRNA signature defined subgroups were analyzed.ResultsWe identified 34 metabolism-related lncRNAs significantly associated with the prognosis of HCC (P<0.05). Subsequently, we constructed a multigene signature based on 9 characteristics prognostic lncRNAs and classified HCC patients into high- and low-risk groups based on cutoff values. We found the lncRNA signature [hazard ratio (HR) =3.55 (2.44-5.15), P<0.001] to be significantly associated with survival. The receiver operating characteristic curve (ROC) curves area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.811, 0.773, and 0.753, respectively. In univariate and multivariate Cox regression analyses, prognostic characteristic lncRNAs were the most crucial prognostic factor besides the stage. The prognostic signature was subsequently validated in the test set. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses revealed potential biological features and signaling pathways associated with the prognostic signature. We constructed a nomogram including risk groups and clinical parameters (age, gender, grade, and stage). Calibration plots and decision curve analysis (DCA) showed that our nomogram had a good predictive performance. Finally, we found reduced expression of immune-activated cells in the high-risk group.ConclusionsThe metabolism-related lncRNA signature is a promising biomarker to distinguish the prognosis and an immune characteristic in HCC.

Project description:BackgroundLong non-coding RNAs (lncRNAs) exhibit remarkable cell-type specificity and disease association. LncRNA's functional versatility includes epigenetic modification, nuclear domain organization, transcriptional control, regulation of RNA splicing and translation, and modulation of protein activity. However, most lncRNAs remain uncharacterized due to a shortage of predictive tools available to guide functional experiments.ResultsTo address this gap for lymphoma-associated lncRNAs identified in our studies, we developed a new computational method, Predicting LncRNA Activity through Integrative Data-driven 'Omics and Heuristics (PLAIDOH), which has several unique features not found in other methods. PLAIDOH integrates transcriptome, subcellular localization, enhancer landscape, genome architecture, chromatin interaction, and RNA-binding (eCLIP) data and generates statistically defined output scores. PLAIDOH's approach identifies and ranks functional connections between individual lncRNA, coding gene, and protein pairs using enhancer, transcript cis-regulatory, and RNA-binding protein interactome scores that predict the relative likelihood of these different lncRNA functions. When applied to 'omics datasets that we collected from lymphoma patients, or to publicly available cancer (TCGA) or ENCODE datasets, PLAIDOH identified and prioritized well-known lncRNA-target gene regulatory pairs (e.g., HOTAIR and HOX genes, PVT1 and MYC), validated hits in multiple lncRNA-targeted CRISPR screens, and lncRNA-protein binding partners (e.g., NEAT1 and NONO). Importantly, PLAIDOH also identified novel putative functional interactions, including one lymphoma-associated lncRNA based on analysis of data from our human lymphoma study. We validated PLAIDOH's predictions for this lncRNA using knock-down and knock-out experiments in lymphoma cell models.ConclusionsOur study demonstrates that we have developed a new method for the prediction and ranking of functional connections between individual lncRNA, coding gene, and protein pairs, which were validated by genetic experiments and comparison to published CRISPR screens. PLAIDOH expedites validation and follow-on mechanistic studies of lncRNAs in any biological system. It is available at https://github.com/sarahpyfrom/PLAIDOH .

Dataset Information

Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine.

Publications

Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine.

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