An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man

Ravasi, Timothy; Suzuki, Harukazu; Cannistraci, Carlo Vittorio; Katayama, Shintaro; Bajić, Vladimir B.; Tan, Kai Soo; Akalin, Altuna; Schmeier, Sebastian; Kanamori-Katayama, Mutsumi; Bertin, Nicolas; Carninci, Piero; Daub, Carsten O.; Gough, Julian; Grimmond, Sean M.; Han, Jung-Hoon; Hashimoto, Takehiro; Hide, Winston; Hofmann, Oliver; Kamburov, Atanas; Kaur, Mandeep; Kawaji, Hideya; Kubosaki, Atsutaka; Lassmann, Timo; Nimwegen, Erik van; MacPherson, Cameron Ross; Ogawa, Chihiro; Radovanović, Aleksandar; Schwartz, Ariel; Teasdale, Rohan D.; Tegnér, Jesper; Lenhard, Boris; Teichmann, Sarah A.; Arakawa, Takahiro; Ninomiya, Noriko; Murakami, Kayoko; Tagami, Michihira; Fukuda, Shiro; Imamura, Kengo; Cui, Kai; Ishihara, Ryoko; Kitazume, Yayoi; Kawai, Jun; Hume, David; Ideker, Trey; Hayashizaki, Yoshihide

doi:10.1016/j.cell.2010.04.004

Cited by 43 publications

(59 citation statements)

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“…Interestingly, Ravasi et al. have recently defined a global interaction map for over 1000 human and mouse transcription factors, and the data from this study indicate the cooperative nature of transcription factor‐mediated fate decisions in tissue development and in the immune system (72). Other studies of hematopoiesis also highlight how one transcription factor can interact with or regulate different partner transcription factors in closely related cell types leading to distinct outcomes of the differentiation program (73).…”

Section: Genomic and Computational Assessment Of The Regulation Of T‐mentioning

confidence: 77%

Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion

Angelosanto

Wherry

2010

Immunological Reviews

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During an infection, antigen-specific CD8+ T cells undergo numerous cellular and transcriptional changes as they develop from naive T cells into effector and memory cells. However, when the antigen persists in a chronic infection, the cellular programs governing effector and memory development are influenced by chronic stimulation, and dysfunctional or exhausted CD8+ T cells are generated. Recently, exhausted CD8+ T cells were found to differ dramatically from naive and functional memory CD8+ T cells on a transcriptional level, demonstrating that exposure to chronic antigen can impact T cells at a fundamental level. While transcriptional changes in CD8+ T cells during memory development is currently a topic of particular interest, the transcriptional changes related to exhaustion and other forms of T-cell dysfunction have received less attention. New computational methods are not only uncovering important transcription factors in these developmental processes but are also going further to define and connect these transcription factors into transcriptional modules that work in parallel to control cell fate and state. Understanding the molecular processes behind the development of CD8+ T-cell memory and exhaustion should not only increase our understanding of the immune system but also could reveal therapeutic targets and treatments for infectious and immunological diseases. Here, we provide a basic overview of acute and chronic viral infections and the transcription factors known to influence the development of virus-specific T cells in both settings. We also discuss recent innovations in genomic and computational tools that could be used to enhance the way we understand the development of T-cell responses to infectious disease.

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Section: Genomic and Computational Assessment Of The Regulation Of T‐mentioning

confidence: 77%

Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion

Angelosanto

Wherry

2010

Immunological Reviews

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“…A manually curated census of 1072 regulatory factors was obtained from Perez-Montarelo et al [104]. This list was elaborated from previous publications [105, 106] and transcription factor databases (http://www.bioguo.org/AnimalTFDB/; http://www.hprd.org/). Out of those 1072 RF, 310 had probes present in the filtered Affymetrix array, and were used for the RIF analysis (Additional file 6).…”

Section: Methodsmentioning

confidence: 99%

Longissimus dorsi transcriptome analysis of purebred and crossbred Iberian pigs differing in muscle characteristics

Óvilo¹,

Benítez²,

Fernández³

et al. 2014

BMC Genomics

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BackgroundThe two main genetic types in Iberian pig production show important phenotypic differences in growth, fattening and tissue composition since early developmental stages. The objective of this work was the evaluation of muscle transcriptome profile in piglets of both genetic types, in order to identify genes, pathways and regulatory factors responsible for their phenotypic differences. Contemporary families coming from pure Iberian pigs (IB) or from crossing with Duroc boars (DU×IB) were generated. Piglets (14 from each genetic type) were slaughtered at weaning (28 days) and longissimus dorsi was sampled for composition and gene expression studies. RNA was obtained and hybridized to Affymetrix Porcine Genechip expression arrays.ResultsLoin muscle chemical composition showed significant differences between genetic types in intramuscular fat content (6.1% vs. 4.3% in IB and DUxIB animals, respectively, P = 0.009) and in saturated (P = 0.019) and monounsaturated fatty acid proportions (P = 0.044). The statistical analysis of gene expression data allowed the identification of 256 differentially expressed (DE) genes between genetic types (FDR < 0.10), 102 upregulated in IB and 154 upregulated in DU×IB. Transcript differences were validated for a subset of DE genes by qPCR. We observed alteration in biological functions related to extracellular matrix function and organization, cellular adhesion, muscle growth, lipid metabolism and proteolysis. Candidate genes with known effects on muscle growth were found among the DE genes upregulated in DU×IB. Genes related to lipid metabolism and proteolysis were found among those upregulated in IB. Regulatory factors (RF) potentially involved in the expression differences were identified by calculating the regulatory impact factors. Twenty-nine RF were found, some of them with known relationship with tissue development (MSTN, SIX4, IRX3), adipogenesis (CEBPD, PPARGC1B), or extracellular matrix processes (MAX, MXI1). Correlation among the expression of these RF and DE genes show relevant differences between genetic types.ConclusionThese results provide valuable information about genetic mechanisms determining the phenotypic differences on growth and meat quality between the genetic types studied, mainly related to the development and function of the extracellular matrix and also to some metabolic processes as proteolysis and lipid metabolism. Transcription factors and regulatory mechanisms are proposed for these altered biological functions.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-413) contains supplementary material, which is available to authorized users.

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“…Surprisingly, genes not only clustered by tissue in the network, but also, tissues clustered together according to their embryonic origin. This fact has already been noted in a mouse and human TF atlas [11] and can be attributed to these tissues being derived from transcriptional alteration of a common precursor and therefore expected to share large sections of expression patterns in common. The GO enrichment analysis provides further evidence about the quality of the inferred network and confirms that indeed it is a good representation of tissue specific regulation.…”

Section: Resultsmentioning

confidence: 56%

“…Most of these methods use a direct function of the ratio between the gene expression in a particular tissue to the sum total expression level across tissues [1], [5]. However other tissue specificity measures that involved more complex components as the relative entropy have been presented [11]. Here, we describe a multi-tiered approach to identify TS genes.…”

Section: Methodsmentioning

confidence: 99%

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Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

et al. 2012

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The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.

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An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man

Abstract: We would like to correct the author list of the above paper as a number of contributors that were on the original submitted version of the manuscript were inadvertently removed in the revision process. We are sorry that we did not detect these omissions in our review of the galley proofs.

Cited by 43 publications

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Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion

Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion

Longissimus dorsi transcriptome analysis of purebred and crossbred Iberian pigs differing in muscle characteristics

Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

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An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man

Abstract: We would like to correct the author list of the above paper as a number of contributors that were on the original submitted version of the manuscript were inadvertently removed in the revision process. We are sorry that we did not detect these omissions in our review of the galley proofs.

Cited by 43 publications

References 0 publications

Transcription factor regulation of CD8+ T‐cell memory and exhaustion

Transcription factor regulation of CD8+ T‐cell memory and exhaustion

Longissimus dorsi transcriptome analysis of purebred and crossbred Iberian pigs differing in muscle characteristics

Porcine Tissue-Specific Regulatory Networks Derived from Meta-Analysis of the Transcriptome

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Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion

Transcription factor regulation of CD8⁺ T‐cell memory and exhaustion