Transcriptome analysis and promoter sequence studies on early adipogenesis in 3T3-L1 cells

Kim, Su Jong; Lee, Ki Hwan; Lee, Yong Sung; Mun, Eun Gyeng; Kwon, Dae Young; Soo, Youn

doi:10.4162/nrp.2007.1.1.19

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…Transcriptomics can be used to decipher the MSC‐differentiation process. Thus, different transcriptomic approaches have been used to study adipogenesis, like microarray and RNA‐seq studies (Li et al, ; Lee et al, ) of animal cells from different species (Kim et al, ; Monaco et al, ), including humans (Ito et al, ; Yin et al, ). Yet, such approaches have not been used to date to analyze human‐MSC differentiation into adipocytes.…”

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confidence: 99%

Transcriptomic Analyses of Adipocyte Differentiation From Human Mesenchymal Stromal‐Cells (MSC)

Casado-Díaz

Anter

Müller

et al. 2016

Journal Cellular Physiology

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Adipogenesis is a physiological process required for fat-tissue development, mainly involved in regulating the organism energetic-state. Abnormal distribution-changes and dysfunctions in such tissue are associated to different pathologies. Adipocytes are generated from progenitor cells, via a complex differentiating process not yet well understood. Therefore, we investigated differential mRNA and miRNA expression patterns of human mesenchymal stromal-cells (MSC) induced and not induced to differentiate into adipocytes by next (second)-generation sequencing. A total of 2,866 differentially expressed genes (101 encoding miRNA) were identified, with 705 (46 encoding miRNA) being upregulated in adipogenesis. They were related to different pathways, including PPARG, lipid, carbohydrate and energy metabolism, redox, membrane-organelle biosynthesis, and endocrine system. Downregulated genes were related to extracellular matrix and cell migration, proliferation, and differentiation. Analyses of mRNA-miRNA interaction showed that repressed miRNA-encoding genes can act downregulating PPARG-related genes; mostly the PPARG activator (PPARGC1A). Induced miRNA-encoding genes regulate downregulated genes related to TGFB1. These results shed new light to understand adipose-tissue differentiation and physiology, increasing our knowledge about pathologies like obesity, type-2 diabetes and osteoporosis. J. Cell. Physiol. 232: 771-784, 2017. © 2016 Wiley Periodicals, Inc.

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confidence: 99%

Transcriptomic Analyses of Adipocyte Differentiation From Human Mesenchymal Stromal‐Cells (MSC)

Casado-Díaz

Anter

Müller

et al. 2016

Journal Cellular Physiology

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“…Amongst the top ranked regulators in adipose were some well-known metabolic targets, including PPARG ( n c = 275), PPARA ( n c = 218), Insulin ( n c = 136) and PPARGC1A ( n c = 105). The top hit as judged by the size of significant regulatees was MYC ( n c = 391) which has been implicated in adipogenesis [30]. It is interesting to note that the well-known transcription factor, MYC, was not co-expressed with its regulatees in the adipose tissue dataset (average correlation coefficient = 0.1161), but a subset of its regulatees were coherently expressed.…”

Section: Resultsmentioning

confidence: 99%

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

et al. 2012

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BackgroundIdentification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators.ResultsWe present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships.ConclusionsCSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

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“…; Kim et al . ). In our study bovine IGFBP6 gene expression was completely suppressed by adipogenic stimulation of the BIP cells, indicating that this gene may be activating the proliferation of preadipocytes.…”

Section: Discussionmentioning

confidence: 97%

Effect of retinoic acid on gene expression profiles of bovine intramuscular preadipocytes during adipogenesis

Mizoguchi

Moriya

Taniguchi

et al. 2013

Animal Science Journal

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To investigate genes involved in intramuscular adipogenesis in ruminants, 16 genes with dramatic variable expression were selected. These were selected from the differentiation- and proliferation-phase libraries of our previous serial analysis of gene expression (SAGE) studies of a clonal bovine intramuscular preadipocyte (BIP) cell line. We harvested the BIP cells over 12 days after adipogenic stimulation with all-trans retinoic acid (ATRA). Quantitative real-time PCR confirmed the earlier SAGE study results of the expression patterns of 15 of the genes. On day 6, TG accumulation increased significantly in the BIP cells but was completely inhibited in the 3T3-L1 cells (the monogastric reference). ATRA enhanced expression levels of six genes whereas it suppressed expression of eight genes on day 3 of adipogenesis in the BIP cells. Forty-eight hours after transfection, the messenger RNA expression level of the adipose differentiation-related protein (ADFP), encoded by one of the upregulated genes, in the ADFP small interference RNA (siRNA)-transfected cells was 3.5% of that in negative control-transfected cells. Also, 6 days after induction the TG level in the ADFP siRNA-transfected cells was 21.8% lower than that in negative control-transfected cells. This analysis of gene expression profiles after ATRA treatment will contribute to our understanding of the molecular mechanisms involved in bovine intramuscular adipogenesis.

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Transcriptome analysis and promoter sequence studies on early adipogenesis in 3T3-L1 cells

Cited by 10 publications

References 31 publications

Transcriptomic Analyses of Adipocyte Differentiation From Human Mesenchymal Stromal‐Cells (MSC)

Transcriptomic Analyses of Adipocyte Differentiation From Human Mesenchymal Stromal‐Cells (MSC)

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

Effect of retinoic acid on gene expression profiles of bovine intramuscular preadipocytes during adipogenesis

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