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Placental PPAR? regulates spatiotemporally diverse genes and a unique metabolic network.

ABSTRACT: The nuclear receptor peroxisome proliferator-activated receptor ? (PPAR?) is essential for placental development. For insights into its functions in the placenta, we screened for PPAR?-regulated genes by integrating expression profiles of Pparg-null and Rxra-null placentas with those of WT and Pparg-null trophoblast stem cells differentiated in the presence or absence of a PPAR? agonist. Intersection of these paradigms identified high-probability PPAR? target genes. A few of these genes were previously reported as PPAR? targets in other tissues, but most are new in the context of either PPAR? or placental biology. Transcriptional profiling demonstrated a widespread role for the coactivator NCOA6/AIB3, but not MED1/PBP, in PPAR?-dependent placental gene expression. Spatial and temporal expression analyses revealed that PPAR? impacts genes in diverse trophoblast lineages and during different stages of differentiation. We further validated the Ldhb gene, which encodes the H isoform of lactate dehydrogenase, as a robust PPAR? target in trophoblasts, and propose a hypothetical model that integrates it with a network of PPAR?-regulated genes into a novel pathway of placental fuel metabolism. These findings offer insights not only into the placental functions of PPAR?, but also into unique, previously unsuspected biosynthetic functions of trophoblasts.

SUBMITTER: Shalom-Barak T

PROVIDER: S-EPMC3479343 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Placental PPARγ regulates spatiotemporally diverse genes and a unique metabolic network.

Shalom-Barak Tali T Zhang Xiaowen X Chu Tianjiao T Timothy Schaiff W W Reddy Janardan K JK Xu Jianming J Sadovsky Yoel Y Barak Yaacov Y

Developmental biology 20120829 1

The nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ) is essential for placental development. For insights into its functions in the placenta, we screened for PPARγ-regulated genes by integrating expression profiles of Pparg-null and Rxra-null placentas with those of WT and Pparg-null trophoblast stem cells differentiated in the presence or absence of a PPARγ agonist. Intersection of these paradigms identified high-probability PPARγ target genes. A few of these genes were pre ...[more]

PMID: 22967998

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Project description:Machine learning approaches to predict essential genes have gained a lot of traction in recent years. These approaches predominantly make use of sequence and network-based features to predict essential genes. However, the scope of network-based features used by the existing approaches is very narrow. Further, many of these studies focus on predicting essential genes within the same organism, which cannot be readily used to predict essential genes across organisms. Therefore, there is clearly a need for a method that is able to predict essential genes across organisms, by leveraging network-based features. In this study, we extract several sets of network-based features from protein-protein association networks available from the STRING database. Our network features include some common measures of centrality, and also some novel recursive measures recently proposed in social network literature. We extract hundreds of network-based features from networks of 27 diverse organisms to predict the essentiality of 87000+ genes. Our results show that network-based features are statistically significantly better at classifying essential genes across diverse bacterial species, compared to the current state-of-the-art methods, which use mostly sequence and a few 'conventional' network-based features. Our diverse set of network properties gave an AUROC of 0.847 and a precision of 0.320 across 27 organisms. When we augmented the complete set of network features with sequence-derived features, we achieved an improved AUROC of 0.857 and a precision of 0.335. We also constructed a reduced set of 100 sequence and network features, which gave a comparable performance. Further, we show that our features are useful for predicting essential genes in new organisms by using leave-one-species-out validation. Our network features capture the local, global and neighbourhood properties of the network and are hence effective for prediction of essential genes across diverse organisms, even in the absence of other complex biological knowledge. Our approach can be readily exploited to predict essentiality for organisms in interactome databases such as the STRING, where both network and sequence are readily available. All codes are available at https://github.com/RamanLab/nbfpeg.

Dataset Information

Placental PPAR? regulates spatiotemporally diverse genes and a unique metabolic network.

Publications

Placental PPARγ regulates spatiotemporally diverse genes and a unique metabolic network.

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