Making Sense of Transcription Networks

Sorrells, Trevor R.; Johnson, Alexander D.

doi:10.1016/j.cell.2015.04.014

Cited by 131 publications

(123 citation statements)

References 89 publications

(116 reference statements)

Supporting

Mentioning

116

Contrasting

Order By: Relevance

“…NPM1 also coimmunoprecipitated with PU.1 in the reverse pull-down (Supplemental Figure 1). PU.1 is essential for monocyte differentiation, contributes to granulocyte differentiation (23,(42)(43)(44)(45)(46)(47), and is considered a master transcription factor, commanding other transcription factors and hundreds of genes to dictate cell fates (32). Critically, the transcription factors identified as interacting with NPM1 in nuclei of NPM1-WT AML cells were observed in cytoplasm of NPM1-mutated AML cells ( Figure 1A).…”

Section: Resultsmentioning

confidence: 99%

Leukemogenic nucleophosmin mutation disrupts the transcription factor hub that regulates granulomonocytic fates

Gu¹,

Ebrahem²,

Mahfouz³

et al. 2018

Journal of Clinical Investigation

103

115

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Leukemogenic nucleophosmin mutation disrupts the transcription factor hub that regulates granulomonocytic fates

Gu¹,

Ebrahem²,

Mahfouz³

et al. 2018

Journal of Clinical Investigation

103

115

View full text Add to dashboard Cite

show abstract

“…Although the number of interactions at individual nodes may reflect functional importance in some networks (14), this rule does not apply to developmental GRNs. In GRNs, a high number of regulatory interactions may control the expression of a gene of little regulatory significance, such as a differentiation gene, whereas a regulatory gene upstream in the GRN hierarchy with important regulatory functions may only be connected through few regulatory interactions.…”

Section: Regulatory Information At the Single-node Levelmentioning

confidence: 99%

Assessing regulatory information in developmental gene regulatory networks

Peter

Davidson

2017

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Gene regulatory networks (GRNs) provide a transformation function between the static genomic sequence and the primary spatial specification processes operating development. The regulatory information encompassed in developmental GRNs thus goes far beyond the control of individual genes. We here address regulatory information at different levels of network organization, from single node to subcircuit to large-scale GRNs and discuss how regulatory design features such as network architecture, hierarchical organization, and cisregulatory logic contribute to the developmental function of network circuits. Using specific subcircuits from the sea urchin endomesoderm GRN, for which both circuit design and biological function have been described, we evaluate by Boolean modeling and in silico perturbations the import of given circuit features on developmental function. The examples include subcircuits encoding positive feedback, mutual repression, and coherent feedforward, as well as signaling interaction circuitry. Within the hierarchy of the endomesoderm GRN, these subcircuits are organized in an intertwined and overlapping manner. Thus, we begin to see how regulatory information encoded at individual nodes is integrated at all levels of network organization to control developmental process.developmental GRN | network topology | circuit function | network hierarchy | Boolean modeling D evelopmental process is controlled by gene regulatory networks (GRNs), the regulatory interactions between genes encoding transcription factors and signaling interactions that determine developmental gene expression throughout the genome (1-3). At the level of a single node, the information captured in GRNs is intuitively accessible. However, the information encompassed in GRNs is not just to regulate individual genes. During development, GRNs control the differential specification of cell fates and determine the organization of body parts, organs, and cell types within the animal body plan. As more and more GRNs become experimentally solved, the question arises as to how network features can be recognized that carry information for more complex developmental transactions.Comparison of different regulatory networks shows that particular constellations of regulatory interactions among a few genes, so-called subcircuits, are recurrently deployed in very different biological contexts (1, 4, 5). These subcircuits are composed of different regulatory genes, but nevertheless encode similar regulatory functions. Specific subcircuit topologies include, for example, positive feedback circuitry, leading to stabilization of gene expression, or mutual-repression circuits that lead to the exclusion of regulatory states (6-9). Several types of network subcircuits have been identified so far, each associated with specific regulatory functions (1, 4, 5). It appears, furthermore, that given types of subcircuits are often found at given positions within the GRN hierarchy, although the number of experimentally solved largescale networks is so far still sma...

show abstract

“…Today, with approaches such as single-cell RNA sequencing, we are beginning to learn how whole transcriptomes are regulated in the context of complex cellular behaviors. However, the signaling networks that occur in nature have been generated by natural selection -a process hypothesized to have generated networks with many non-functional interactions and redundancies (Sorrells and Johnson, 2015). Such functional and non-functional complexity would make it difficult to extract the relevant information from transcriptome data.…”

Section: Resultsmentioning

confidence: 99%

“…During cell differentiation, many transcription factors interact to build up a self-sustaining network that gives the cell its properties and locks its identity in place (Sorrells and Johnson, 2015;Holmberg and Perlmann, 2012). Such a transcriptional network is established sequentially: early sets of transcription factors activate, and are often replaced by later sets, which, in turn, activate still later sets, and so on.…”

Section: The Extracellular Environment Of Neural Stem Cells and Parenmentioning

confidence: 99%

Stars from the darkest night: unlocking the neurogenic potential of astrocytes in different brain regions

Magnusson

Frisén

2016

Development

View full text Add to dashboard Cite

In a few regions of the adult brain, specialized astrocytes act as neural stem cells capable of sustaining life-long neurogenesis. In other, typically non-neurogenic regions, some astrocytes have an intrinsic capacity to produce neurons when provoked by particular conditions but do not use this ability to replace neurons completely after injury or disease. Why do astrocytes display regional differences and why do they not use their neurogenic capacity for brain repair to a greater extent? In this Review, we discuss the neurogenic potential of astrocytes in different brain regions and ask what stimulates this potential in some regions but not in others. We discuss the transcriptional networks and environmental cues that govern cell identity, and consider how the activation of neurogenic properties in astrocytes can be understood as the de-repression of a latent neurogenic transcriptional program.

show abstract

Making Sense of Transcription Networks

Cited by 131 publications

References 89 publications

Leukemogenic nucleophosmin mutation disrupts the transcription factor hub that regulates granulomonocytic fates

Leukemogenic nucleophosmin mutation disrupts the transcription factor hub that regulates granulomonocytic fates

Assessing regulatory information in developmental gene regulatory networks

Stars from the darkest night: unlocking the neurogenic potential of astrocytes in different brain regions

Contact Info

Product

Resources

About