Genomic regulatory regions: insights from comparative sequence analysis

Comparative sequence analysis is contributing to the identification and characterization of genomic regulatory regions with functional roles. It is effective because functionally important regions tend to evolve at a slower rate than do less important regions. The choice of species for comparative analysis is crucial: shared ancestry of a clade of species facilitates the discovery of genomic features important to that clade, whereas increased sequence divergence improves the resolution at which features can be discovered. Recent studies suggest that comparative analyses are useful for all branches of life and that, in the near future, large-scale mammalian comparative sequence analysis will provide the best approach for the comprehensive discovery of human regulatory elements.     

Functional analysis of regulatory single-nucleotide polymorphisms

PURPOSE OF REVIEW: The identification of regulatory polymorphisms has become a key problem in human genetics. In the past few years there has been a conceptual change in the way in which regulatory single-nucleotide polymorphisms are studied. We revise the new approaches and discuss how gene expression studies can contribute to a better knowledge of the genetics of common diseases. RECENT FINDINGS: New techniques for the association of single-nucleotide polymorphisms with changes in gene expression have been recently developed. This, together with a more comprehensive use of the old in-vitro methods, has produced a great amount of genetic information. When added to current databases, it will help to design better tools for the detection of regulatory single-nucleotide polymorphisms. SUMMARY: The identification of functional regulatory single-nucleotide polymorphisms cannot be done by the simple inspection of DNA sequence. In-vivo techniques, based on primer-extension, and the more recently developed 'haploChIP' allow the association of gene variants to changes in gene expression. Gene expression analysis by conventional in-vitro techniques is the only way to identify the functional consequences of regulatory single-nucleotide polymorphisms. The amount of information produced in the last few years will help to refine the tools for the future analysis of regulatory gene variants.     

Yeast regulatory protein LEU3: a structure-function analysis.

Eleven mutations resulting in partially deleted or truncated LEU3 protein were generated by linker insertion or other modifications at restriction sites, deletion of restriction fragments, or oligonucleotide-directed mutagenesis. Functional studies of these mutants showed the following: (i) A specific DNA binding region is contained within the 173 N-terminal residues, but other regions of the protein are required for optimal binding. (ii) Activation of LEU2 expression depends on the C-terminal 113 residues of the LEU3 protein. (iii) Deletion of part or all of a central section of LEU3 eliminates the ability of the LEU3 protein to respond to the co-activator alpha-isopropylmalate, i.e. creates an unmodulated activator. (iv) Overproduction of unmodulated activator slows down cell growth. (v) Specific deletion of two short acidic regions, including one with net charge - 19, has only minor effects on activation and modulation.     

Functional analysis of the proteasome regulatory particle

We have developed S. cerevisiae as a model system for mechanistic studies of the 26S proteasome. The subunits of the yeast 19S complex, or regulatory particle (RP), have been defined, and are closely related to those of mammalian proteasomes. The multiubiquitin chain binding subunit (S5a/Mcb1/Rpn10) was found, surprisingly, to be nonessential for the degradation of a variety of ubiquitin-protein conjugates in vivo. Biochemical studies of proteasomes from rpn10 mutants revealed the existence of two structural subassemblies within the RP, the lid and the base. The lid and the base are both composed of 8 subunits. By electron microscopy, the base and the lid correspond to the proximal and distal masses of the RP, respectively. The base is sufficient to activate the 20S core particle for degradation of peptides, but the lid is required for ubiquitin-dependent degradation. The lid subunits share sequence motifs with components of the COP9/signalosome complex, suggesting that these functionally diverse particles have a common evolutionary ancestry. Analysis of equivalent point mutations in the six ATPases of the base indicate that they have well-differentiated functions. In particular, mutations in one ATPase gene, RPT2, result in an unexpected defect in peptide hydrolysis by the core particle. One interpretation of this result is that Rpt2 participates in gating of the channel through which substrates enter the core particle.     

Modelling and analysis of gene regulatory networks

Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.     

Modular response analysis of cellular regulatory networks

The sheer complexity of intracellular regulatory networks, which involve signal transducing, metabolic, and genetic circuits, hampers our ability to carry out a quantitative analysis of their functions. Here, we describe an approach that greatly simplifies this type of analysis by capitalizing on the modular organization of such networks. Steady-state responses of the network as a whole are accounted for in terms of intermodular interactions between the modules alone; processes operating solely within modules need not be considered when analysing signal transfer through the entire network. The intermodular interactions are quantified through (local) response coefficients which populate an interaction map (matrix). This matrix can be derived from a biochemical or molecular biological analysis of (macro) molecular interactions that constitute the regulatory network. The approach is illustrated by two examples: (i) mitogenic signalling through the mitogen-activated protein kinase cascade in the epidermal growth factor receptor network and (ii) regulation of ammonium assimilation in Escherichia coli.     

Mutational analysis of the regulatory mechanism of PKN: the regulatory region of PKN contains an arachidonic acid-sensitive autoinhibitory domain.

PKN is a fatty acid- and Rho GTPase-activated protein kinase whose catalytic domain in the carboxyl terminus is homologous to those of protein kinase C (PKC) family members. The amino terminal region of PKN is suggested to function as a regulatory domain, since tryptic cleavage or the binding of Rho GTPase to this region results in protein kinase activation of PKN. The structural basis for the regulation of PKN was investigated by analyzing the activity of a series of deletion/site-directed mutants expressed in insect cells. The amino-terminally truncated form of PKN (residue 455-942) showed low basal activity similar to that of the wild-type enzyme, and was arachidonic acid-dependent. However, further deletion (residue 511-942) resulted in a marked increase in the basal activity and a decrease in the arachidonic acid dependency. A (His)(6)-tagged protein comprising residues 455-511 of PKN (designated His-Ialpha) inhibited the kinase activity of the catalytic fragment of PKN in a concentration-dependent manner in competition with substrate (K(i) = 0.6+/-0.2 microM). His-Ialpha also inhibited the activity of the catalytic fragment of PRK2, an isoform of PKN, but had no inhibitory effect on protein kinase A or protein kinase Cdelta. The IC(50) value obtained in the presence of 40 microM arachidonic acid was two orders of magnitude greater than that in the absence of the modifier. These results indicate that this protein fragment functions as a specific inhibitor of PKN and PRK2, and that arachidonic acid relieves the catalytic activity of wild-type PKN from autoinhibition by residues 455-511 of PKN. Autophosphorylation of wild-type PKN increased the protein kinase activity, however, substitution of Thr64, Ser374, or Thr531 in the regulatory region of PKN with alanine, abolished this effect. Substitution of Thr774 in the activation loop of the catalytic domain of PKN with alanine completely abolished the protein kinase activity. These results suggest that these phosphorylation sites are also important in the regulation of the PKN kinase activity. Potential differences in the mechanism of activation between the catalytic regions of PKN and PRK2 are also discussed.     

Multivariate analysis of noise in genetic regulatory networks

Stochasticity is an intrinsic property of genetic regulatory networks due to the low copy numbers of the major molecular species, such as, DNA, mRNA, and regulatory proteins. Therefore, investigation of the mechanisms that reduce the stochastic noise is essential in understanding the reproducible behaviors of real organisms and is also a key to design synthetic genetic regulatory networks that can reliably work. We use an analytical and systematic method, the linear noise approximation of the chemical master equation along with the decoupling of a stoichiometric matrix. In the analysis of fluctuations of multiple molecular species, the covariance is an important measure of noise. However, usually the representation of a covariance matrix in the natural coordinate system, i.e. the copy numbers of the molecular species, is intractably complicated because reactions change copy numbers of more than one molecular species simultaneously. Decoupling of a stoichiometric matrix, which is a transformation of variables, significantly simplifies the representation of a covariance matrix and elucidates the mechanisms behind the observed fluctuations in the copy numbers. We apply our method to three types of fundamental genetic regulatory networks, that is, a single-gene autoregulatory network, a two-gene autoregulatory network, and a mutually repressive network. We have found that there are multiple noise components differently originating. Each noise component produces fluctuation in the characteristic direction. The resulting fluctuations in the copy numbers of the molecular species are the sum of these fluctuations. In the examples, the limitation of the negative feedback in noise reduction and the trade-off of fluctuations in multiple molecular species are clearly explained. The analytical representations show the full parameter dependence. Additionally, the validity of our method is tested by stochastic simulations.     

Comparative analysis indicates regulatory neofunctionalization of yeast duplicates

Background  

Gene duplication provides raw material for the generation of new functions, but most duplicates are rapidly lost due to the initial redundancy in gene function. How gene function diversifies following duplication is largely unclear. Previous studies analyzed the diversification of duplicates by characterizing their coding sequence divergence. However, functional divergence can also be attributed to changes in regulatory properties, such as protein localization or expression, which require only minor changes in gene sequence.