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Anecdotes, data and regulatory modules.

ABSTRACT: Beginning in the late 1980s, Eric Davidson's group at Cal Tech developed a modularity hypothesis of developmental gene regulation, showing that in an expanding number of cases, particular aspects of development were governed by compact 'modules' of transcription factor binding sites (TFBSs), and that these modules were separable, complex and interconnected. Davidson made no attempt to further generalize the hypothesis, but others took up the idea, transported it out of development and extended it to a general rule of clustering. Despite such misbegotten origins, the 'extended' modularity hypothesis--that TFBSs in general tend to come in compact clusters--has been highly productive, yet it has never been challenged with a large, diverse and unbiased dataset to see how universal it actually is. The aim of the present paper is to do so. Applying human-mouse-rat phylogenetic footprinting to neighbourhoods of a diverse set of TFBSs, including both developmental and non-developmental signals, we find that the extended hypothesis holds in at least 93.5% of cases. Based on this particular sample, we found a mean module length of 609 nucleotides containing, on an average, 24.5 presumptive regulatory signals of length greater than 5 and averaging 8.5 nucleotides each.

SUBMITTER: Balmer JE

PROVIDER: S-EPMC1686202 | biostudies-literature | 2006 Sep

REPOSITORIES: biostudies-literature

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Anecdotes, data and regulatory modules.

Balmer James E JE Blomhoff Rune R

Biology letters 20060901 3

Beginning in the late 1980s, Eric Davidson's group at Cal Tech developed a modularity hypothesis of developmental gene regulation, showing that in an expanding number of cases, particular aspects of development were governed by compact 'modules' of transcription factor binding sites (TFBSs), and that these modules were separable, complex and interconnected. Davidson made no attempt to further generalize the hypothesis, but others took up the idea, transported it out of development and extended i ...[more]

PMID: 17148423

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Project description:In this study we used the maize (Zea mays) inflorescence to investigate gene networks that modulate determinacy, specifically the decision to allow branch growth. We characterized developmental transitions by associating spatiotemporal expression profiles with morphological changes resulting from genetic perturbations that disrupt steps in a pathway controlling branching. These are the RNA-seq datasets used in this study. We profiled changes in gene expression during normal maize ear and tassel development and in developing maize ear primordia upon genetic perturbation of the RAMOSA branching pathway. For the wild-type ear and tassel developmental series, greenhouse-grown B73 inbred plants were used. 10mm ears were collected and sectioned as follows from tip to base along the developmental gradient: tip 1mm sampled (tip; Inflorescence Meristem/Spikelet Pair Meristem), next 1mm discarded, next 1mm sampled (mid; Spikelet Meristem), next 2mm discarded, next 2 mm sampled (base; Floral Meristem), and immediately frozen in liquid nitrogen. Sections from ~30 sampled ears were pooled for each of 2 biological replicates to represent tip, mid, and base stages. Tassels were hand-dissected, measured, separated by stage: 1-2mm (stg1), 3-4mm (stg2), and 5-7mm (stg3), and immediately frozen in liquid N. For each stage, ~20-30 tassels were pooled for each of 2 biological replicates. For ramosa mutant series, segregating families (1:1) of ra1-R, ra2-R, and ra3-fea1 mutant alleles, all introgressed at least 6 times into the B73 inbred background, were grown at CSHL Uplands Farm. Field-grown plants were genotyped and collected 6-7 weeks after germination (V7-V8 stage). First and second ear primordia were immediately hand-dissected, measured, and frozen in liquid nitrogen. For ra1, ra2 and ra3 mutants and wild-type controls, ears were pooled into two size classes: 1) 1mm class included a range of 0.7-1.5mm sized ears and nine ears were pooled for each of 2 biological replicates; 2) 2mm class included a range of 1.8-2.5mm sized ears and six ears were pooled for each of three biological replicates. Wild-type samples were proportional mixtures of heterozygote siblings segregating in ra1, ra2, and ra3 populations. Variability factors (e.g. ear size within class, ear rank on the plant, and time of collection) were distributed evenly across pooled samples.

Project description:BackgroundPrecise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.Results and principal findingsWe propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.ConclusionsWe established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.

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Anecdotes, data and regulatory modules.

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Anecdotes, data and regulatory modules.

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