Comprehensive Analysis of Combinatorial Regulation using the Transcriptional Regulatory Network of Yeast

Balaji, S.; Babu, M. Madan; Iyer, Lakshminarayan M.; Luscombe, Nicholas M.; Aravind, L.

doi:10.1016/j.jmb.2006.04.029

Cited by 223 publications

(245 citation statements)

References 56 publications

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“…3A; see Materials and Methods). As used in previous studies (14,15), this step only forms the most significant partnership associations.…”

Section: Resultsmentioning

confidence: 99%

“…Various data sources were used for different species: the largest collection of regulatory data obtained from published microarray data for M. tuberculosis (9), regulonDB version 6.2 for E. coli (26), results of genetic and biochemical experiments as used in previous studies for yeast (2,14,15,(27)(28)(29)(30)(31), and Transcriptional Regulatory Element Database database for rat, mouse, and human (as of June 2008) (32). The protein modification network was obtained from the Human Protein Reference Database database (33).…”

Section: Methodsmentioning

confidence: 99%

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Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Bhardwaj

Yan

Gerstein

2010

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

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Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with welldefined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We specify three levels of regulators-top, middle, and bottom-which collectively govern the non-regulator targets lying in the lowest fourth level. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity.coregulatory partnerships | hierarchy | middle managers | autocracy | democracy

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“…3A; see Materials and Methods). As used in previous studies (14,15), this step only forms the most significant partnership associations.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Bhardwaj

Yan

Gerstein

2010

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

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“…31 and references therein). This network consists of 156 TFs and 4,495 TGs comprising of 13,853 interactions.…”

Section: Methodsmentioning

confidence: 99%

Transcriptional regulation constrains the organization of genes on eukaryotic chromosomes

Janga

Collado‐Vides

Babu

2008

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

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Genetic material in eukaryotes is tightly packaged in a hierarchical manner into multiple linear chromosomes within the nucleus. Although it is known that eukaryotic transcriptional regulation is complex and requires an intricate coordination of several molecular events both in space and time, whether the complexity of this process constrains genome organization is still unknown. Here, we present evidence for the existence of a higher-order organization of genes across and within chromosomes that is constrained by transcriptional regulation. In particular, we reveal that the target genes (TGs) of transcription factors (TFs) for the yeast, Saccharomyces cerevisiae, are encoded in a highly ordered manner both across and within the 16 chromosomes. We show that (i) the TGs of a majority of TFs show a strong preference to be encoded on specific chromosomes, (ii) the TGs of a significant number of TFs display a strong preference (or avoidance) to be encoded in regions containing particular chromosomal landmarks such as telomeres and centromeres, and (iii) the TGs of most TFs are positionally clustered within a chromosome. Our results demonstrate that specific organization of genes that allowed for efficient control of transcription within the nuclear space has been selected during evolution. We anticipate that uncovering such higher-order organization of genes in other eukaryotes will provide insights into nuclear architecture, and will have implications in genetic engineering experiments, gene therapy, and understanding disease conditions that involve chromosomal aberrations.gene order ͉ genome ͉ nuclear architecture ͉ systems biology ͉ network A lthough transcription in both prokaryotes and eukaryotes involves the evolutionarily conserved core RNA polymerase subunit, the whole process of transcriptional regulation is fundamentally different. In contrast to prokaryotes where transcription primarily relies on the cis-regulatory DNA sequences alone (1), eukaryotic transcription is regulated at least at three major levels (2, 3). The first is at the level of DNA sequence where the transcription factor (TF) associates with cis-regulatory elements to regulate transcription of the relevant gene (2). The second is at the level of chromatin, which allows segments within a chromosomal arm to switch between different transcriptional states, that is, between a state that suppresses transcription and one that allows for gene activation (2). This involves changes in nucleosome occupancy and chromatin structure, both of which are controlled by the interplay between remodeling complexes, histone modification, DNA methylation, and a variety of repressive and activating mechanisms (4, 5). The third is at the level of nuclear architecture, which includes organization of chromosomes into chromosomal territories, and the dynamic, temporal, and spatial organization of specific chromosomal loci-all of which are known to influence gene expression (6-11). Thus, unlike in prokaryotes, transcription in eukaryotes is an energy-intensive, multi...

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“…Nevertheless, we compare our gene-to-gene regulatory network with some other types of regulatory networks, and it turns out that the overlaps between our network and the ones in the literature, as well as the overlaps among the ones in the literature, are small. Indeed, the overlap between our network and the one in [14] consists of seven edges, between our network and the one in [16] is one edge, and between the ones in [14] and [16] is actually zero edges. p. 5 (28) observations on the relation between lethality and activation/repression.…”

Section: P 4 (28)mentioning

confidence: 88%

“…A recent study by Luscombe and colleagues [8] provided a first step towards an understanding of network dynamics by describing when different sub-networks are active during different cellular conditions in Yeast. A general review of various methods for uncovering the structure of gene regulatory networks from experimental data can be found in [12] and of graph theoretical tools for the analysis in [13,14]. Here we present an exploration of a gene-to-gene regulatory network, obtained through a network identification algorithm using gene expression data [10].…”

Section: Introductionmentioning

confidence: 99%

Genome-wide system analysis reveals stable yet flexible network dynamics in yeast

et al. 2009

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Comprehensive Analysis of Combinatorial Regulation using the Transcriptional Regulatory Network of Yeast

Cited by 223 publications

References 56 publications

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Transcriptional regulation constrains the organization of genes on eukaryotic chromosomes

Genome-wide system analysis reveals stable yet flexible network dynamics in yeast

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