Mapping phenotypic landscapes using DNA micro-arrays

Inverse metabolic engineering is a useful approach for engineering phenotypes in biological systems. The overarching objective of this approach is to combine the power of evolutionary engineering approaches with the precision of constructive metabolic engineering strategies. Often the difficulty in this approach is elucidating the genetic basis of the phenotypes that emerge as a result of evolutionary mechanisms. As a result of advances in genomics technologies, several techniques now exist that substantially improve researchers ability to identify such genes. Metabolic engineers now have the ability to map phenotypic landscapes of considerable genetic diversity, which should improve understanding of the relationships that exist among phenotype, genotype, and environment. In this mini-review, we will discuss several of such genomics tools that may be useful in developing inverse metabolic engineering strategies and, in particular, mapping phenotypic landscapes.     

Mapping folding energy landscapes with theory and experiment

The detailed characterization of the overall free energy landscape associated with the folding process of a protein is the ultimate goal in protein folding studies. Modern experimental techniques and all-atom simulations provide a way to obtain accurate thermodynamic and kinetic measurements, but they are oftentimes restricted to probe limited regions of a protein landscape. Although simplified protein models can access larger regions of the landscape, they are built on assumptions and approximations that can affect the accuracy of the results. We review here recent promising approaches that allow to combine the complementary strengths of theory and experiment for a more complete characterization of a protein folding landscape at multiple resolutions. Recent results and possible applications are discussed.     

Mapping the dynamic organization of the nuclear pore complex inside single living cells

Most cellular activities are executed by multi-protein complexes that form the basic functional modules of their molecular machinery. Proteomic approaches can provide an evermore detailed picture of their composition, but do not reveal how these machines are organized dynamically to accomplish their biological function. Here, we present a method to determine the dissociation rates of protein subunits from complexes that have a traceable localization inside single living cells. As a case study, we systematically analysed the dynamic organization of vertebrate nuclear pore complexes (NPCs), large supramolecular complexes of about 30 different polypeptides. NPC components exhibited a wide range of residence times covering five orders of magnitude from seconds to days. We found the central parts of the NPC to be very stable, consistent with a function as a structural scaffold, whereas more peripheral components exhibited more dynamic behaviour, suggesting adaptor as well as regulatory functions. The presented strategy can be applied to many multi-protein complexes and will help to characterize the dynamic behaviour of complex networks of proteins in live cells.     

Turing patterns inside cells

Concentration gradients inside cells are involved in key processes such as cell division and morphogenesis. Here we show that a model of the enzymatic step catalized by phosphofructokinase (PFK), a step which is responsible for the appearance of homogeneous oscillations in the glycolytic pathway, displays Turing patterns with an intrinsic length-scale that is smaller than a typical cell size. All the parameter values are fully consistent with classic experiments on glycolytic oscillations and equal diffusion coefficients are assumed for ATP and ADP. We identify the enzyme concentration and the glycolytic flux as the possible regulators of the pattern. To the best of our knowledge, this is the first closed example of Turing pattern formation in a model of a vital step of the cell metabolism, with a built-in mechanism for changing the diffusion length of the reactants, and with parameter values that are compatible with experiments. Turing patterns inside cells could provide a check-point that combines mechanical and biochemical information to trigger events during the cell division process.     

Mapping viability loci using molecular markers

In genetic mapping experiments, some molecular markers often show distorted segregation ratios. We hypothesize that these markers are linked to some viability loci that cause the observed segregation ratios to deviate from Mendelian expectations. Although statistical methods for mapping viability loci have been developed for line-crossing experiments, methods for viability mapping in outbred populations have not been developed yet. In this study, we develop a method for mapping viability loci in outbred populations using a full-sib family as an example. We develop a maximum likelihood (ML) method that uses the observed marker genotypes as data and the proportions of the genotypes of the viability locus as parameters. The ML solutions are obtained via the expectation-maximization algorithm. Application and efficiencies of the method are demonstrated and tested using a set of simulated data. We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits.     

Peptoid conformational free energy landscapes from implicit-solvent molecular simulations in AMBER

To test the accuracy of existing AMBER force field models in predicting peptoid conformation and dynamics, we simulated a set of model peptoid molecules recently examined by Butterfoss et al. (JACS 2009, 131, 16798-16807) using QM methods as well as three peptoid sequences with experimentally determined structures. We found that AMBER force fields, when used with a Generalized Born/Surface Area (GBSA) implicit solvation model, could accurately reproduce the peptoid torsional landscape as well as the major conformers of known peptoid structures. Enhanced sampling by replica exchange molecular dynamics (REMD) using temperatures from 300 to 800 K was used to sample over cis-trans isomerization barriers. Compared to (Nrch)5 and cyclo-octasarcosyl, the free energy of N-(2-nitro-3-hydroxyl phenyl)glycine-N-(phenyl)glycine has the most "foldable" free energy landscape, due to deep trans-amide minima dictated by N-aryl sidechains. For peptoids with (S)-N (1-phenylethyl) (Nspe) side chains, we observe a discrepancy in backbone dihedral propensities between molecular simulations and QM calculations, which may be due to force field effects or the inability to capture n --> n* interactions. For these residues, an empirical phi-angle biasing potential can "rescue" the backbone propensities seen in QM. This approach can serve as a general strategy for addressing force fields without resorting to a complete reparameterization. Overall, this study demonstrates the utility of implicit-solvent REMD simulations for efficient sampling to predict peptoid conformational landscapes, providing a potential tool for first-principles design of sequences with specific folding properties.     

An inside look at hippocampal silent cells

In this issue of Neuron, Greenberg and colleagues revise our understanding of how activity-dependent MeCP2 phosphorylation regulates distinct aspects of brain development and circuit function. The study also suggests a prominent role for MeCP2 in the regulation of global chromatin state in?vivo.     

Imaging proteins inside cells with fluorescent tags

Watching biological molecules provides clues to their function and regulation. Some of the most powerful methods of labeling proteins for imaging use genetically encoded fluorescent fusion tags. There are four standard genetic methods of covalently tagging a protein with a fluorescent probe for cellular imaging. These use (i) autofluorescent proteins, (ii) self-labeling enzymes, (iii) enzymes that catalyze the attachment of a probe to a target sequence, and (iv) biarsenical dyes that target tetracysteine motifs. Each of these techniques has advantages and disadvantages. In this review, we cover new developments in these methods and discuss practical considerations for their use in imaging proteins inside living cells.     

Pattern generation in molecular evolution: Exploitation of the variation in RNA landscapes

Evolution of RNA secondary structure is studied using simulation techniques and statistical analysis of fitness landscapes. The transition from RNA sequence to RNA secondary structure leads to fitness landscapes that have local variations in their ruggedness. Evolution exploits these variations. In stable environments it moves the quasispecies toward relatively flat peaks, where not only the master sequence but also its mutants have a high fitness. In a rapidly changing environment, the situation is reversed; evolution moves the quasispecies to a region where the correlation between secondary structures of neighboring RNA sequences is relatively low. In selection for simple secondary structures the movement toward flat peaks leads to pattern generation in the RNA sequences. Patterns are generated at the level of polynucleotide frequencies and the distribution of purines and pyrimidines. The patterns increase the modularity of the sequence. They thereby prevent the formation of alternative secondary structures after mutations. The movement of the quasispecies toward relatively rugged parts of the landscape results in pattern generation at the level of the RNA secondary structure. The base-pairing frequency of the sequences increases. The patterns that are generated in the RNA sequences and the RNA secondary structures are not directly selected for and can be regarded as a side effect of the evolutionary dynamics of the system. Correspondence to: M.A. Huynen     

Looking inside molecular bonds at biological interfaces with dynamic force spectroscopy

Weak non-covalent interactions between large molecules govern interfacial structure and adhesion in biology. Because of thermal activation, these bonds have modest lifetimes and bond lifetimes are progressively shortened under application of external force. Theory predicts that bond survival time depends on how fast the force is applied and the expected survival time specifies the most likely breakage force (strength) at a given loading rate (force/time). Plotted as a function of log(e) (loading rate), the dynamic spectrum of bond strength provides an image of the prominent barriers traversed in the energy landscape along the unbinding pathway, which establishes a direct link between measurements of bond force and molecular-scale chemistry. Experimentally, the challenge is to measure bond strength over several orders of magnitude in loading rate. With a recently designed probe technique, we have measured strengths of single receptor-ligand bonds and receptor-membrane anchoring over an enormous range of loading rates from 10(-1) pN/s to 10(5) pN/s, which reveals an inner view of the complexity of these interactions.     

Mapping quantitative trait loci using molecular marker linkage maps

Summary High-density restriction fragment length polymorphism (RFLP) and allozyme linkage maps have been developed in several plant species. These maps make it technically feasible to map quantitative trait loci (QTL) using methods based on flanking marker genetic models. In this paper, we describe flanking marker models for doubled haploid (DH), recombinant inbred (RI), backcross (BC), F₁ testcross (F₁TC), DH testcross (DHTC), recombinant inbred testcross (RITC), F₂, and F₃ progeny. These models are functions of the means of quantitative trait locus genotypes and recombination frequencies between marker and quantitative trait loci. In addition to the genetic models, we describe maximum likelihood methods for estimating these parameters using linear, nonlinear, and univariate or multivariate normal distribution mixture models. We defined recombination frequency estimators for backcross and F₂ progeny group genetic models using the parameters of linear models. In addition, we found a genetically unbiased estimator of the QTL heterozygote mean using a linear function of marker means. In nonlinear models, recombination frequencies are estimated less efficiently than the means of quantitative trait locus genotypes. Recombination frequency estimation efficiency decreases as the distance between markers decreases, because the number of progeny in recombinant marker classes decreases. Mean estimation efficiency is nearly equal for these methods.     

VEGF signaling inside vascular endothelial cells and beyond

Vascular endothelial growth factor-A (VEGF-A) has long been recognized as the key regulator of vascular development and function in health and disease. VEGF is a secreted polypeptide that binds to transmembrane tyrosine kinase VEGF receptors on the plasma membrane, inducing their dimerization, activation and assembly of a membrane-proximal signaling complex. Recent studies have revealed that many key events of VEGFR signaling occur inside the endothelial cell and are regulated by endosomal receptor trafficking. Plasma membrane VEGFR interacting molecules, including vascular guidance receptors Neuropilins and Ephrins also regulate VEGFR endocytosis and trafficking. VEGF signaling is increasingly recognized for its roles outside of the vascular system, notably during neural development, and blood vessels regulate epithelial branching morphogenesis. We review here recent advances in our understanding of VEGF signaling and its biological roles.     

Development of nitroxides for selective localization inside cells

The use of nitroxides to measure intracellular phenomena, especially oxygen concentrations, is a new and potentially important approach to a number of physiological and pathophysiological studies. This study provides data indicating the feasibility of developing nitroxides that localize selectively in the intracellular compartment; it is based on the use of readily hydrolysed ester linkages, such that the nitroxides become converted intracellularly to ionic derivatives that do not cross cell membranes readily. Up to 120-fold increased concentrations of intracellular nitroxides (and their one electron reduction product, the hydroxylamines) were obtained. The ESR spectra of the intracellular nitroxides were consistent with their conversion to the ionic species. Preliminary studies indicate that these nitroxides have the properties needed for their use as probes of intracellular concentrations of oxygen and that it should be feasible to synthesize nitroxides that will be even more effective for this purpose.