Analysis of Differential Expressions of Long Non-coding RNAs in Nasopharyngeal Carcinoma Using Next-generation Deep Sequencing.
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ABSTRACT:
SUBMITTER: Li XX
PROVIDER: S-EPMC5995947 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionLong non-coding RNA (lncRNA) have proven to play key roles in cell physiology from nuclear organization and epigenetic remodeling to post-transcriptional regulation. Last decade, gene expression based-classifications have been developed in clear-cell renal cell carcinoma (ccRCC) to identify distinct subtypes of disease and predict patient's outcome. However, there are no current lncRNA comprehensive characterizations in ccRCC.Patients and methodsRNA-sequencing profiles of 475 primary ccRCC samples from the Cancer Genome Atlas (TCGA) were used to assess expressed lncRNA and identify lncRNA-based classification. In addition, integrative analysis was performed to correlate tumor subtypes with copy-number alterations and somatic mutations.ResultsUsing stringent criteria, we identified 1934 expressed lncRNA and assessed their chromatin marks. Unsupervised clustering unravels four lncRNA subclasses in ccRCC associated with distinct clinicopathological and genomic features of this disease. Cluster C2 (23.4%) defines the most aggressive tumours, with the highest Fuhrman grade and stage and the worst overall survival time. Furthermore, cluster C2 is enriched for 9p deletion and chromatin remodeler BAP1 somatic mutations. Interestingly, cluster C4 (7.8%) is related to a tumor subtype arising from the distal tubules of the nephron. Consistent with its distinct ontogeny, cluster C4 is devoid of classical alterations seen in ccRCC, bears frequent 1p deletion and 17q gain, and is enriched for MiTF/TFE translocations. In addition, reexaminations of copy-number data from one side and tumor histology by pathologists from the other side reveal misclassified tumors within C4 cluster including chromophobe RCC and clear cell papillary RCC.ConclusionThis study establishes a foundation for categorizing lncRNA subclasses, which may contribute to understand tumor ontogeny and help predicting patients' outcome in ccRCC.
| S-EPMC4516270 | biostudies-literature
Project description:Small non-coding RNAs (sncRNAs) play important roles in health and disease. Next Generation Sequencing (NGS) technologies are considered as the most powerful and versatile methodologies to explore small RNA (sRNA) transcriptomes in diverse experimental and clinical studies. Small RNA-Seq (sRNA-Seq) data analysis proved to be challenging due to non-unique genomic origin, short length, and abundant post-transcriptional modifications of sRNA species. Here, we present Manatee, an algorithm for the quantification of sRNA classes and the detection of novel expressed non-coding loci. Manatee combines prior annotation of sRNAs with reliable alignment density information and extensive rescue of usually neglected multimapped reads to provide accurate transcriptome-wide sRNA expression quantification. Comparison of Manatee against state-of-the-art implementations using real and simulated data demonstrates its high accuracy across diverse sRNA classes. Manatee also goes beyond common pipelines by identifying and quantifying expression from unannotated loci and microRNA isoforms (isomiRs). It is user-friendly, can be easily incorporated in pipelines, and provides a simplified output suitable for direct usage in downstream analyses and functional studies.
| S-EPMC6971259 | biostudies-literature
Project description:Myelodysplastic Syndromes (MDSS) are pre-leukemic disorders with increasing incident rates worldwide, but very limited treatment options. Little is known about small regulatory RNAs and how they contribute to pathogenesis, progression and transcriptome changes in MDS.Patients' primary marrow cells were screened for short RNAs (RNA-seq) using next generation sequencing. Exon arrays from the same cells were used to profile gene expression and additional measures on 98 patients obtained. Integrative bioinformatics algorithms were proposed, and pathway and ontology analysis performed.In low-grade MDS, observations implied extensive post-transcriptional regulation via microRNAs (miRNA) and the recently discovered Piwi interacting RNAs (piRNA). Large expression differences were found for MDS-associated and novel miRNAs, including 48 sequences matching to miRNA star (miRNA*) motifs. The detected species were predicted to regulate disease stage specific molecular functions and pathways, including apoptosis and response to DNA damage. In high-grade MDS, results suggested extensive post-translation editing via transfer RNAs (tRNAs), providing a potential link for reduced apoptosis, a hallmark for this disease stage. Bioinformatics analysis confirmed important regulatory roles for MDS linked miRNAs and TFs, and strengthened the biological significance of miRNA*. The "RNA polymerase II promoters" were identified as the tightest controlled biological function. We suggest their control by a miRNA dominated feedback loop, which might be linked to the dramatically different miRNA amounts seen between low and high-grade MDS.The presented results provide novel findings that build a basis of further investigations of diagnostic biomarkers, targeted therapies and studies on MDS pathogenesis.
| S-EPMC3060843 | biostudies-other
Project description:BACKGROUND: The advent of large-scale gene expression technologies has helped to reveal in eukaryotic cells, the existence of thousands of non-coding transcripts, whose function and significance remain mostly poorly understood. Among these non-coding transcripts, long non-coding RNAs (lncRNAs) are the least well-studied but are emerging as key regulators of diverse cellular processes. In the present study, we performed a survey in bovine Longissimus thoraci of lincRNAs (long intergenic non-coding RNAs not overlapping protein-coding transcripts). To our knowledge, this represents the first such study in bovine muscle. RESULTS: To identify lincRNAs, we used paired-end RNA sequencing (RNA-Seq) to explore the transcriptomes of Longissimus thoraci from nine Limousin bull calves. Approximately 14-45 million paired-end reads were obtained per library. A total of 30,548 different transcripts were identified. Using a computational pipeline, we defined a stringent set of 584 different lincRNAs with 418 lincRNAs found in all nine muscle samples. Bovine lincRNAs share characteristics seen in their mammalian counterparts: relatively short transcript and gene lengths, low exon number and significantly lower expression, compared to protein-encoding genes. As for the first time, our study identified lincRNAs from nine different samples from the same tissue, it is possible to analyse the inter-individual variability of the gene expression level of the identified lincRNAs. Interestingly, there was a significant difference when we compared the expression variation of the 418 lincRNAs with the 10,775 known selected protein-encoding genes found in all muscle samples. In addition, we found 2,083 pairs of lincRNA/protein-encoding genes showing a highly significant correlated expression. Fourteen lincRNAs were selected and 13 were validated by RT-PCR. Some of the lincRNAs expressed in muscle are located within quantitative trait loci for meat quality traits. CONCLUSIONS: Our study provides a glimpse into the lincRNA content of bovine muscle and will facilitate future experimental studies to unravel the function of these molecules. It may prove useful to elucidate their effect on mechanisms underlying the genetic variability of meat quality traits. This catalog will complement the list of lincRNAs already discovered in cattle and therefore will help to better annotate the bovine genome.
| S-EPMC4073507 | biostudies-literature
Project description:BackgroundSmall ncRNAs (sncRNAs) offer great hope as biomarkers of disease and response to treatment. This has been highlighted in the context of several medical conditions such as cancer, liver disease, cardiovascular disease, and central nervous system disorders, among many others. Here we assessed several steps involved in the development of an ncRNA biomarker discovery pipeline, ranging from sample preparation to bioinformatic processing of small RNA sequencing data.MethodsA total of 45 biological samples were included in the present study. All libraries were prepared using the Illumina TruSeq Small RNA protocol and sequenced using the HiSeq2500 or MiSeq Illumina sequencers. Small RNA sequencing data was validated using qRT-PCR. At each stage, we evaluated the pros and cons of different techniques that may be suitable for different experimental designs. Evaluation methods included quality of data output in relation to hands-on laboratory time, cost, and efficiency of processing.ResultsOur results show that good quality sequencing libraries can be prepared from small amounts of total RNA and that varying degradation levels in the samples do not have a significant effect on the overall quantification of sncRNAs via NGS. In addition, we describe the strengths and limitations of three commercially available library preparation methods: (1) Novex TBE PAGE gel; (2) Pippin Prep automated gel system; and (3) AMPure XP beads. We describe our bioinformatics pipeline, provide recommendations for sequencing coverage, and describe in detail the expression and distribution of all sncRNAs in four human tissues: whole-blood, brain, heart and liver.ConclusionsUltimately this study provides tools and outcome metrics that will aid researchers and clinicians in choosing an appropriate and effective high-throughput sequencing quantification method for various study designs, and overall generating valuable information that can contribute to our understanding of small ncRNAs as potential biomarkers and mediators of biological functions and disease.
| S-EPMC4487992 | biostudies-literature
Project description:Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addressing questions such as variant calling, metagenomic classification and quantification, genomic feature detection, or downstream analysis in larger biological or medical contexts. In addition to classical algorithmic approaches, machine-learning (ML) techniques are often used for such tasks. In particular, deep learning (DL) methods that use multilayered artificial neural networks (ANNs) for supervised, semisupervised, and unsupervised learning have gained significant traction for such applications. Here, we highlight important network architectures, application areas, and DL frameworks in a NGS context.
| S-EPMC7550123 | biostudies-literature
Project description:BACKGROUND:Bronchopulmonary dysplasia (BPD) in premature infants is a predominantly secondary occurrence to intrauterine inflammation/infection and postpartum mechanical ventilation; The purpose of this study is to explore the biological roles of lincRNA in the pathogenesis of BPD. METHODS:Newborn rats were randomly assigned to hyperoxia (85% O2) or the control group: the normoxia group (21% O2). Lung tissues were collected on days 1-14. The BPD animal model was validated using HE staining, Masson staining, and real-time RT-PCR. Deep Illumina sequencing was used to reveal the differential expression of long non-coding RNAs in hyperoxia bronchopulmonary dysplasia rat models. KEGG and GO functions were predicted. Nine possible BPD-related target lincRNAs were verified by RTq-PCR. RESULTS:The histopathologic changes in lung tissues manifested as hyperaemia, edema, hemorrhage, and inflammation cell infiltration after continuous exposure to hyperoxia for 3 days, and became aggravated after 7 days of hyperoxic exposure. The above lung tissue inflammatory manifestations were alleviated and taken over by pulmonary interstitia hyperplasia and fibrocyte proliferation after 14 days of hyperoxic exposure. The expressions of lincRNA differed between the hyperoxia bronchopulmonary dysplasia model group and the normoxia group. 1175 different lincRNAs were detected in the hyperoxia group and the normoxia group, of which 544 were up-regulated and 631 were down-regulated. 673 moleculars related to GO functions were enriched, including cell location and biological process. Pathway enrichment analysis showed that lincRNA was involved in 257 KEGG pathways. 9 lincRNA were validated in the sample, and the difference was statistically significant. CONCLUSION:LincRNAs were identified differently between the BPD model and the normoxia group. Many target genes were involved in the developmental process, including cell component biogenesis, biological regulation, transcription regulator, and translation regulator. The BPD might be caused by the activation of the pathways of the EMC-receptor interaction, cytokine-cytokine receptor interaction, cell cycle, and cell adhesion molecules. The present study provides new insight into the pathogenesis mechanism of BPD.
| S-EPMC5752920 | biostudies-literature
Project description:BackgroundDeep RNA sequencing (RNAseq) has opened a new horizon for understanding global gene expression. The functional annotation of non-model mammalian genomes including bovines is still poor compared to that of human and mouse. This particularly applies to tissues without direct significance for milk and meat production, like skin, in spite of its multifunctional relevance for the individual. Thus, applying an RNAseq approach, we performed a whole transcriptome analysis of pigmented and nonpigmented bovine skin to describe the comprehensive transcript catalogue of this tissue.ResultsA total of 39,577 unique primary skin transcripts were mapped to the bovine reference genome assembly. The majority of the transcripts were mapped to known transcriptional units (65%). In addition to the reannotation of known genes, a substantial number (10,884) of unknown transcripts (UTs) were discovered, which had not previously been annotated. The classification of UTs was based on the prediction of their coding potential and comparative sequence analysis, subsequently followed by meticulous manual curation. The classification analysis and experimental validation of selected UTs confirmed that RNAseq data can be used to amend the annotation of known genes by providing evidence for additional exons, untranslated regions or splice variants, by approving genes predicted in silico and by identifying novel bovine loci. A large group of UTs (4,848) was predicted to potentially represent long noncoding RNA (lncRNA). Predominantly, potential lncRNAs mapped in intergenic chromosome regions (4,365) and therefore, were classified as potential intergenic lncRNA. Our analysis revealed that only about 6% of all UTs displayed interspecies conservation and discovered a variety of unknown transcripts without interspecies homology but specific expression in bovine skin.ConclusionsThe results of our study demonstrate a complex transcript pattern for bovine skin and suggest a possible functional relevance of novel transcripts, including lncRNA, in the modulation of pigmentation processes. The results also indicate that the comprehensive identification and annotation of unknown transcripts from whole transcriptome analysis using RNAseq data remains a tremendous future challenge.
| S-EPMC3833843 | biostudies-literature
Project description:Background and aimsAlthough long non-coding RNAs (lncRNAs) have been linked to many diseases including asthma, little is known about lncRNA transcriptomes of CD4+ T cells in asthma. The present study aimed to explore the lncRNAs profile in the CD4+T cells from the mouse model of acute asthma.MethodsNext generation sequencing for lncRNAs and mRNAs was performed on CD4+ T cells from asthma and control mice. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed to predict the functions and signal pathways for the aberrant lncRNAs. The selected lncRNAs were further measured using quantitative real-time PCR (polymerase chain reaction) and observed in the fluorescence in situ hybridization (FISH). The lncRNA-mRNA co-expression network was constructed via Pearson's correlation coefficient and Cytoscape 3.6.ResultsNext generation sequencing revealed 36 up-regulated lncRNAs and 98 down-regulated lncRNAs in acute asthma compared with controls. KEGG pathway analysis showed that cytokine-cytokine receptor interaction had the highest enrichment scores. A co-expression network was constructed in which 23 lncRNAs and 301 mRNAs altered formed a total of 12424 lncRNA and mRNA pairs. To validate the RNA sequencing results, we measured the 4 different lncRNAs using qPCR. The lncRNA fantom3_9230106C11 was significantly reduced in CD4+ T cells of asthma. Bioinformatics analysis showed that lncRNA fantom3_9230106C11 had the potential to interact with many miRNAs and transcription factors related to Th2 differentiation.ConclusionThis study provided the first evidence for different expression of lncRNAs of CD4+T cells in asthma and may serve as a template for further, larger functional in-depth analyses regarding asthma molecular lncRNAs.
| S-EPMC6565949 | biostudies-literature
Project description:Reverse transcriptase (RT) enzymes are indispensable tools for interrogating diverse aspects of RNA metabolism and transcriptome composition. Due to the growing interest in sequence and structural complexity of long RNA molecules, processive RT enzymes are now required for preserving linkage and information content in mixed populations of transcripts, and the low-processivity RT enzymes that are commercially available cannot meet this need. MarathonRT is encoded within a eubacterial group II intron, and it has been shown to efficiently copy highly structured long RNA molecules in a single pass. In this work, we systematically characterize MarathonRT as a tool enzyme and optimize its performance in a variety of applications that include single-cycle reverse transcription of long RNAs, dimethyl sulfate mutational profiling (DMS-MaP), selective 2'-hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP), using ultra-long amplicons and the detection of natural RNA base modifications. By diversifying MarathonRT reaction protocols, we provide an upgraded suite of tools for cutting-edge RNA research and clinical application.
| S-EPMC7556701 | biostudies-literature