Constructive effects of fluctuations in genetic and biochemical regulatory systems

Biochemical and genetic regulatory systems that involve low concentrations of molecules are inherently noisy. This intrinsic stochasticity has received considerable interest recently, leading to new insights about the sources and consequences of noise in complex systems of genetic regulation. However, most prior work was devoted to the reduction of fluctuation and the robustness of cellular function with respect to intrinsic noise. Here, we focus on several scenarios in which the inherent molecular fluctuations are not merely a nuisance, but act constructively and bring about qualitative changes in the dynamics of the system. It will be demonstrated that in many typical situations biochemical and genetic regulatory systems may utilize intrinsic noise to their advantage.     

In vitro recombination of non-homologous genes can result in gene fusions that confer a switching phenotype to cells

Regulation of protein activity is central to the complexity of life. The ability to regulate protein activity through exogenously added molecules has biotechnological/biomedical applications and offers tools for basic science. Such regulation can be achieved by establishing a means to modulate the specific activity of the protein (i.e. allostery). An alternative strategy for intracellular regulation of protein activity is to control the amount of protein through effects on its production, accumulation, and degradation. We have previously demonstrated that the non-homologous recombination of the genes encoding maltose binding protein (MBP) and TEM1 β-lactamase (BLA) can result in fusion proteins in which β-lactamase enzyme activity is allosterically regulated by maltose. Here, through use of a two-tiered genetic selection scheme, we demonstrate that such recombination can result in genes that confer maltose-dependent resistance to β-lactam even though they do not encode allosteric enzymes. These 'phenotypic switch' genes encode fusion proteins whose accumulation is a result of a specific interaction with maltose. Phenotypic switches represent an important class of proteins for basic science and biotechnological applications in vivo.     

Modeling stochastic noise in gene regulatory systems

The Master equation is considered the gold standard for modeling the stochastic mechanisms of gene regulation in molecular detail, but it is too complex to solve exactly in most cases, so approximation and simulation methods are essential. However, there is still a lack of consensus about the best way to carry these out. To help clarify the situation, we review Master equation models of gene regulation, theoretical approximations based on an expansion method due to N.G. van Kampen and R. Kubo, and simulation algorithms due to D.T. Gillespie and P. Langevin. Expansion of the Master equation shows that for systems with a single stable steady-state, the stochastic model reduces to a deterministic model in a first-order approximation. Additional theory, also due to van Kampen, describes the asymptotic behavior of multistable systems. To support and illustrate the theory and provide further insight into the complex behavior of multistable systems, we perform a detailed simulation study comparing the various approximation and simulation methods applied to synthetic gene regulatory systems with various qualitative characteristics. The simulation studies show that for large stochastic systems with a single steady-state, deterministic models are quite accurate, since the probability distribution of the solution has a single peak tracking the deterministic trajectory whose variance is inversely proportional to the system size. In multistable stochastic systems, large fluctuations can cause individual trajectories to escape from the domain of attraction of one steady-state and be attracted to another, so the system eventually reaches a multimodal probability distribution in which all stable steady-states are represented proportional to their relative stability. However, since the escape time scales exponentially with system size, this process can take a very long time in large systems.     

Phenotype sequencing: identifying the genes that cause a phenotype directly from pooled sequencing of independent mutants

Random mutagenesis and phenotype screening provide a powerful method for dissecting microbial functions, but their results can be laborious to analyze experimentally. Each mutant strain may contain 50-100 random mutations, necessitating extensive functional experiments to determine which one causes the selected phenotype. To solve this problem, we propose a "Phenotype Sequencing" approach in which genes causing the phenotype can be identified directly from sequencing of multiple independent mutants. We developed a new computational analysis method showing that 1. causal genes can be identified with high probability from even a modest number of mutant genomes; 2. costs can be cut many-fold compared with a conventional genome sequencing approach via an optimized strategy of library-pooling (multiple strains per library) and tag-pooling (multiple tagged libraries per sequencing lane). We have performed extensive validation experiments on a set of E. coli mutants with increased isobutanol biofuel tolerance. We generated a range of sequencing experiments varying from 3 to 32 mutant strains, with pooling on 1 to 3 sequencing lanes. Our statistical analysis of these data (4099 mutations from 32 mutant genomes) successfully identified 3 genes (acrB, marC, acrA) that have been independently validated as causing this experimental phenotype. It must be emphasized that our approach reduces mutant sequencing costs enormously. Whereas a conventional genome sequencing experiment would have cost $7,200 in reagents alone, our Phenotype Sequencing design yielded the same information value for only $1200. In fact, our smallest experiments reliably identified acrB and marC at a cost of only $110-$340.     

Chromosomal position shift of a regulatory gene alters the bacterial phenotype

Recent studies strongly suggest that in bacterial cells the order of genes along the chromosomal origin-to-terminus axis is determinative for regulation of the growth phase-dependent gene expression. The prediction from this observation is that positional displacement of pleiotropic genes will affect the genetic regulation and hence, the cellular phenotype. To test this prediction we inserted the origin-proximal dusB-fis operon encoding the global regulator FIS in the vicinity of replication terminus on both arms of the Escherichia coli chromosome. We found that the lower fis gene dosage in the strains with terminus-proximal dusB-fis operons was compensated by increased fis expression such that the intracellular concentration of FIS was homeostatically adjusted. Nevertheless, despite unchanged FIS levels the positional displacement of dusB-fis impaired the competitive growth fitness of cells and altered the state of the overarching network regulating DNA topology, as well as the cellular response to environmental stress, hazardous substances and antibiotics. Our finding that the chromosomal repositioning of a regulatory gene can determine the cellular phenotype unveils an important yet unexplored facet of the genetic control mechanisms and paves the way for novel approaches to manipulate bacterial physiology.     

Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.     

Potential energy landscape and robustness of a gene regulatory network: toggle switch

Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers. It leads to the robustness of the biological basins of the gene switches. The technique used here is general and can be applied to explore the potential energy landscape of the gene networks.     

Lighting the shadows: methods that expose nuclear and cytoplasmic gene regulatory control

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It's the machine that matters: Predicting gene function and phenotype from protein networks

Increasing knowledge about the organization of proteins into complexes, systems, and pathways has led to a flowering of theoretical approaches for exploiting this knowledge in order to better learn the functions of proteins and their roles underlying phenotypic traits and diseases. Much of this body of theory has been developed and tested in model organisms, relying on their relative simplicity and genetic and biochemical tractability to accelerate the research. In this review, we discuss several of the major approaches for computationally integrating proteomics and genomics observations into integrated protein networks, then applying guilt-by-association in these networks in order to identify genes underlying traits. Recent trends in this field include a rising appreciation of the modular network organization of proteins underlying traits or mutational phenotypes, and how to exploit such protein modularity using computational approaches related to the internet search algorithm PageRank. Many protein network-based predictions have recently been experimentally confirmed in yeast, worms, plants, and mice, and several successful approaches in model organisms have been directly translated to analyze human disease, with notable recent applications to glioma and breast cancer prognosis.     

PhylDiag: identifying complex synteny blocks that include tandem duplications using phylogenetic gene trees

Background

Extant genomes share regions where genes have the same order and orientation, which are thought to arise from the conservation of an ancestral order of genes during evolution. Such regions of so-called conserved synteny, or synteny blocks, must be precisely identified and quantified, as a prerequisite to better understand the evolutionary history of genomes.

Results

Here we describe PhylDiag, a software that identifies statistically significant synteny blocks in pairwise comparisons of eukaryote genomes. Compared to previous methods, PhylDiag uses gene trees to define gene homologies, thus allowing gene deletions to be considered as events that may break the synteny. PhylDiag also accounts for gene orientations, blocks of tandem duplicates and lineage specific de novo gene births. Starting from two genomes and the corresponding gene trees, PhylDiag returns synteny blocks with gaps less than or equal to the maximum gap parameter gap_max. This parameter is theoretically estimated, and together with a utility to graphically display results, contributes to making PhylDiag a user friendly method. In addition, putative synteny blocks are subject to a statistical validation to verify that they are unlikely to be due to a random combination of genes.

Conclusions

We benchmark several known metrics to measure 2D-distances in a matrix of homologies and we compare PhylDiag to i-ADHoRe 3.0 on real and simulated data. We show that PhylDiag correctly identifies small synteny blocks even with insertions, deletions, incorrect annotations or micro-inversions. Finally, PhylDiag allowed us to identify the most relevant distance metric for 2D-distance calculation between homologies.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-268) contains supplementary material, which is available to authorized users.     

What the papers say: Mutation of N-myc in Mice: What does the phenotype tell us?

Oncogenesis is manifested as uncontrolled cellular proliferation and in some situations a failure of normal differentiation in the transformed cell. This has led to speculation that the normal role of proto-oncogenes during development may be to mediate the relationship between proliferation and differentiation. The advent of gene targeting in ES cells allows the role oncogenes in development to be tested directly. Two recent studies have examined the phenotype of N-myc mutant mice generated by gene targeting^(1,2). In both reports, the mutation is an embryonic lethal at 11.5 days of gestation confirming a critical role for this proto-oncogene in development and the inability of other members of the myc family to substitute functionally for N-myc. Although the phenotypes are similar in general outline, the two reports differ in the specifics of the morphological and histological abnormalities identified. The disparity may result from the mutation created, the genetic background of the mutant mice or the criteria used to determine abnormalities. Assuredly, there is valuable information to be gained about N-myc function from these mutant mice. However, these reports make it clear that morphological and histological abnormalities in N-myc mutant mice serve as a starting point rather than as an endpoint. The challenge now is to link the defect at the cellular level to the abnormalities at the physiological level.