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RNA-Seq Analysis Reveals a Negative Role of KLF16 in Adipogenesis.

ABSTRACT: In this study, we performed high throughput RNA sequencing at the preadipocyte (D0) and differentiated adipocyte (D7) stages of primary brown preadipocyte differentiation in order to characterize the transcriptional events regulating differentiation and function. Compared to the preadipocyte stage (D0), 6,668 genes were identified as differentially expressed genes (DEGs) with a fold change of ? 2.0 at the differentiated adipocyte stage (D7). Several adipogenic genes including peroxisome proliferator-activated receptor-? (PPAR?) and CCAAT/enhancer-binding protein-? (C/EBP?), and Krüppel-like factor (KLF) family genes were differentially expressed at D0 and D7. Since KLF16 gene expression was downregulated at day 7 and its adipogenic function has not been characterized, we investigated its role in adipogenesis. Knockdown of KLF16 stimulated the differentiation of both brown and 3T3-L1 preadipocytes, and led to increased PPAR? expression. However, overexpression of KLF16 had opposite effects. Furthermore, KLF16 downregulated PPAR? expression in brown adipocytes and inhibited its promoter activity. These results indicate that KLF16 inhibits adipogenesis through downregulation of PPAR? expression.

SUBMITTER: Jang MK

PROVIDER: S-EPMC5017575 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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RNA-Seq Analysis Reveals a Negative Role of KLF16 in Adipogenesis.

Jang Min-Kyung MK Lee Sunwoo S Jung Myeong Ho MH

PloS one 20160909 9

In this study, we performed high throughput RNA sequencing at the preadipocyte (D0) and differentiated adipocyte (D7) stages of primary brown preadipocyte differentiation in order to characterize the transcriptional events regulating differentiation and function. Compared to the preadipocyte stage (D0), 6,668 genes were identified as differentially expressed genes (DEGs) with a fold change of ≥ 2.0 at the differentiated adipocyte stage (D7). Several adipogenic genes including peroxisome prolifer ...[more]

PMID: 27611969

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Project description:BackgroundRNA-sequencing (RNA-Seq) has become a powerful technology to characterize gene expression profiles because it is more accurate and comprehensive than microarrays. Although statistical methods that have been developed for microarray data can be applied to RNA-Seq data, they are not ideal due to the discrete nature of RNA-Seq data. The Poisson distribution and negative binomial distribution are commonly used to model count data. Recently, Witten (Annals Appl Stat 5:2493-2518, 2011) proposed a Poisson linear discriminant analysis for RNA-Seq data. The Poisson assumption may not be as appropriate as the negative binomial distribution when biological replicates are available and in the presence of overdispersion (i.e., when the variance is larger than or equal to the mean). However, it is more complicated to model negative binomial variables because they involve a dispersion parameter that needs to be estimated.ResultsIn this paper, we propose a negative binomial linear discriminant analysis for RNA-Seq data. By Bayes' rule, we construct the classifier by fitting a negative binomial model, and propose some plug-in rules to estimate the unknown parameters in the classifier. The relationship between the negative binomial classifier and the Poisson classifier is explored, with a numerical investigation of the impact of dispersion on the discriminant score. Simulation results show the superiority of our proposed method. We also analyze two real RNA-Seq data sets to demonstrate the advantages of our method in real-world applications.ConclusionsWe have developed a new classifier using the negative binomial model for RNA-seq data classification. Our simulation results show that our proposed classifier has a better performance than existing works. The proposed classifier can serve as an effective tool for classifying RNA-seq data. Based on the comparison results, we have provided some guidelines for scientists to decide which method should be used in the discriminant analysis of RNA-Seq data. R code is available at http://www.comp.hkbu.edu.hk/~xwan/NBLDA.R or https://github.com/yangchadam/NBLDA.

Project description:Breast cancer is the most commonly diagnosed cancer in the world, with triple-negative breast cancer (TNBC) making up 12% of these diagnoses. TNBC tumours are highly heterogeneous in both inter-tumour and intra-tumour gene expression profiles, where they form subclonal populations of varying levels of aggressiveness. These aspects make it difficult to study and treat TNBC, requiring further research into tumour heterogeneity as well as potential therapeutic targets and biomarkers. Recently, it was discovered that the majority of the transcribed genome comprises non-coding RNAs, in particular long non-coding RNAs (lncRNAs). LncRNAs are transcripts of >200 nucleotides in length that do not encode a protein. They have been characterised as regulatory molecules and their expression can be associated with a malignant phenotype. We set out to explore TNBC tumour heterogeneity in vivo at a single cell level to investigate whether lncRNA expression varies across different cells within the tumour, even if cells are coming from the same cell line, and whether lncRNA expression is sufficient to define cellular subpopulations. We applied single-cell expression profiling due to its ability to capture expression signals of lncRNAs expressed in small subpopulations of cells. Overall, we observed most lncRNAs to be expressed at low, but detectable levels in TNBC xenografts, with a median of 25 lncRNAs detected per cell. LncRNA expression alone was insufficient to define a subpopulation of cells, and lncRNAs showed highly heterogeneous expression patterns, including ubiquitous expression, subpopulation-specific expression, and a hybrid pattern of lncRNAs expressed in several, but not all subpopulations. These findings reinforce that transcriptionally defined tumour cell subpopulations can be identified in cell-line derived xenografts, and uses single-cell RNA-seq (scRNA-seq) to detect and characterise lncRNA expression across these subpopulations in xenografted tumours. Future studies will aim to investigate the spatial distribution of lncRNAs within xenografts and patient tissues, and study the potential of subclone-specific lncRNAs as new therapeutic targets and/or biomarkers.

Dataset Information

RNA-Seq Analysis Reveals a Negative Role of KLF16 in Adipogenesis.

Publications

RNA-Seq Analysis Reveals a Negative Role of KLF16 in Adipogenesis.

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