Quantitative kinetic analysis of the bacteriophage λ genetic network

Using a module exchange approach, we have tested a longstanding model for the role of Cro repressor in prophage induction. This epigenetic switch from lysogeny to the lytic state occurs on activation of the host SOS system, which leads to specific cleavage of CI repressor. It has been proposed that Cro repressor, which operates during lytic growth and which we shall term the lytic repressor, is crucial to prophage induction. In this view, Cro binds to the O R3 operator, thereby repressing the cI gene and making the switch irreversible. Here we tested this model by replacing Cro with a dimeric form of Lac repressor and adding several lac operators. This approach allowed us to regulate the function of the lytic repressor at will and to prevent it from repressing cI, because lac repressor could not repress P RM in our constructs. Repression of cI by the lytic repressor was not required for prophage induction to occur. However, our evidence suggests that this binding can make induction more efficient, particularly at intermediate levels of DNA damage that otherwise cause induction of only a fraction of the population. These results indicate that this strategy of module exchange will have broad applications for analysis of gene regulatory circuits.circuit design ͉ epigenetic switch ͉ gene regulation ͉ systems biology ͉ threshold behavior A cell infected with can follow either of two exclusive pathways (1, 2). In the lytic pathway, a temporal pattern of gene expression occurs, the viral DNA replicates, and Ϸ100 new virions are made and released by cell lysis. In the alternative lysogenic pathway, the phage DNA is integrated into the host genome, and its lytic genes are repressed by CI repressor, resulting in a stable association with the host termed the lysogenic state. The initial choice between these two pathways is largely determined by the level of a viral regulatory protein, CII. If CII levels are high, it stimulates three promoters that act in various ways (3) to favor the lysogenic pathway. CII is unstable but is stabilized by another viral protein, CIII. Infection with several phages favors high CII levels and the lysogenic pathway (1, 4); poorly understood physiological factors also likely help to regulate CII levels.The lysogenic state is highly stable; however, it can switch to the lytic state in an epigenetic switch called prophage induction (2, 5). Upon induction of the host SOS system (6) by DNA damage, such as from UV irradiation, RecA protein is activated to a form that mediates specific cleavage of CI (7, 8), inactivating CI and allowing expression of lytic genes and the lytic pathway.The central events that control and stabilize the regulatory states occur in the O R region (Fig. 1A). This region contains two promoters: the lytic promoter P R , from which cro and several early lytic genes are expressed, and P RM , from which cI is expressed in a lysogen. O R also contains three operator sites to which both Cro and CI bind. In addition, both proteins bind to three operators in the O L region; binding ...

Section: Formation Of Stable Lysogens and The Lysis-lysogeny Decisionsupporting

confidence: 63%

“…25; see also ref. 3), suggesting that a low level of CI function is needed to substitute for the partial repression function of Cro.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Role of the lytic repressor in prophage induction of phage λ as analyzed by a module-replacement approach

Atsumi

Little²

2006

“…For example, CNV can cause statistically significant changes in concentrations of RNA associated with growth rate changes in bacteria (1,2) and enzyme concentrations associated with nutrient intake in humans (3,4). The copy number of viral genomes undergoes dynamical changes during multiple infection of bacteria by phages, leading to qualitative changes in gene regulation that may lead to alternative modes of exploitation (5,6). The duplication of a gene can facilitate subsequent diversification-a mechanism considered to be a dominant cause of phenotypic innovation (7)(8)(9)(10).…”

mentioning

confidence: 99%

Small-scale copy number variation and large-scale changes in gene expression

Mileyko¹,

Joh

Weitz

2008

The expression dynamics of interacting genes depends, in part, on the structure of regulatory networks. Genetic regulatory networks include an overrepresentation of subgraphs commonly known as network motifs. In this article, we demonstrate that gene copy number is an omnipresent parameter that can dramatically modify the dynamical function of network motifs. We consider positive feedback, bistable feedback, and toggle switch motifs and show that variation in gene copy number, on the order of a single or few copies, can lead to multiple orders of magnitude change in gene expression and, in some cases, switches in deterministic control. Further, small changes in gene copy number for a 3-gene motif with successive inhibition (the ''repressilator'') can lead to a qualitative switch in system behavior among oscillatory and equilibrium dynamics. In all cases, the qualitative change in expression is due to the nonlinear nature of transcriptional feedback in which duplicated motifs interact via common pools of transcription factors. We are able to implicitly determine the critical values of copy number which lead to qualitative shifts in system behavior. In some cases, we are able to solve for the sufficient condition for the existence of a bifurcation in terms of kinetic rates of transcription, translation, binding, and degradation. We discuss the relevance of our findings to ongoing efforts to link copy number variation with cell fate determination by viruses, dynamics of synthetic gene circuits, and constraints on evolutionary adaptation.gene duplication ͉ gene regulation ͉ network motifs ͉ nonlinear dynamics

“…These efforts have focused on characterizing the components and interactions for specific biological networks and their dynamical behaviors, by using standard genetic and biochemical approaches in conjunction with mathematical analysis of discovered circuits (1)(2)(3)(4)(5)(6)(7). Valuable insights into certain design features of biological networks have emerged through these efforts (8)(9)(10)(11)(12)(13) and are used to guide the design of synthetic systems (14)(15)(16). The choice of which synthetic circuits to build is often a matter of careful hand-picking guided by experimental restrictions.…”

mentioning

confidence: 99%

Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output

Nochomovitz

2006

Deciphering the design principles for regulatory networks is fundamental to an understanding of biological systems. We have explored the mapping from the space of network topologies to the space of dynamical phenotypes for small networks. Using exhaustive enumeration of a simple model of three-and four-node networks, we demonstrate that certain dynamical phenotypes can be generated by an atypically broad spectrum of network topologies. Such dynamical outputs are highly designable, much like certain protein structures can be designed by an unusually broad spectrum of sequences. The network topologies that encode a highly designable dynamical phenotype possess two classes of connections: a fully conserved core of dedicated connections that encodes the stable dynamical phenotype and a partially conserved set of variable connections that controls the transient dynamical flow. By comparing the topologies and dynamics of the three-and four-node network ensembles, we observe a large number of instances of the phenomenon of ''mutational buffering,'' whereby addition of a fourth node suppresses phenotypic variation amongst a set of three-node networks.designability ͉ dynamical phenotype ͉ enumeration ͉ mutational buffering ͉ regulatory network D iscerning the structure and function of cellular networks is essential to the development of a true understanding of biological systems. Experimental and theoretical studies are steadily advancing our knowledge of the wiring and input-output characteristics of a variety of natural and designed biological networks. These efforts have focused on characterizing the components and interactions for specific biological networks and their dynamical behaviors, by using standard genetic and biochemical approaches in conjunction with mathematical analysis of discovered circuits (1-7). Valuable insights into certain design features of biological networks have emerged through these efforts (8-13) and are used to guide the design of synthetic systems (14-16). The choice of which synthetic circuits to build is often a matter of careful hand-picking guided by experimental restrictions.Reverse-engineering and modeling of specific experimental systems on a case-by-case basis is necessary and meritorious. However, the space of networks and associated dynamics is potentially very large, and parallel approaches that consider broad ensembles of networks may advance our understanding of general design principles in ways that the serial strategies may have difficulty revealing. Therefore, we have chosen to explore general design principles by using a global strategy. We expect that exploration of an entire ensemble of networks and associated dynamics will reveal statistical signatures connecting network architectures to categories of dynamical phenotypes. In particular, we analyze the relationship between the space of topology and the space of dynamics by employing an analogy to the protein ''designability principle,'' which states that compact protein structures that can be encoded by a wide array of ...