Directed molecular evolution: bridging the gap between genomics leads and commercial products

Agricultural crops, engineered to express transgenic traits, have been rapidly adopted by farmers since the initial commercialization of this technology in 1996. However, despite nearly 20 years of research in agricultural biotechnology, only two product categories have achieved commercial success: plants containing transgenes conferring tolerance to herbicides and plants containing insecticidal protein genes derived from Bacillus thuringensis. A number of transgenic concepts, while exhibiting promising phenotypes in laboratory experiments, have failed to generate commercially viable crops. Many of the leads produced by modern integrative approaches to understanding plant biology will need further optimization to deliver economically viable crops. Directed molecular evolution represents a powerful technology to optimize newly discovered leads towards product objectives. In this review, we show by example how directed molecular evolution can be used to develop enabling technologies for plant biologists; how genes can be optimized to generate improved input traits such as those conferring insect tolerance, disease control and herbicide tolerance; and how plant quality can be altered to improve yield, produce novel industrial feedstocks and improve nutritional qualities.     

Toward bridging the gap between life and physics

Examination of the scale properties of living organisms and the electronic configuration of crystalline structures suggests that related modeling may be used for both. This paper comments on individual and common properties of the two systems and draws a comparison between them. Both exhibit multiple ‘scales’ separated by complex or forbidden regions and a global ‘overview’ of their scale properties. We conclude that the analogy may provide a fruitful route toward extension of the modeling of both living organisms and electronic materials, by permitting bootstrapping cross-modeling between them.     

Soft networks for bridging the gap between research and practice: illuminative evaluation of CHAIN

Objectives To explore the process of knowledge exchange in an informal email network for evidence based health care, to illuminate the value of the service and its critical success factors, and to identify areas for improvement.Design Illuminative evaluation.Setting Targeted email and networking service for UK healthcare practitioners and researchers.Participants 2800 members of a networking service.Main outcome measures Tracking of email messages, interviews with core staff, and a qualitative analysis of messages, postings from focus groups, and invited and unsolicited feedback to the service.Results The informal email network helped to bridge the gap between research and practice by serving as a rich source of information, providing access to members'' experiences, suggestions, and ideas, facilitating cross boundary collaboration, and enabling participation in networking at a variety of levels. Ad hoc groupings and communities of practice emerged spontaneously as members discovered common areas of interest.Conclusion This study illuminated how knowledge for evidence based health care can be targeted, personalised, and made meaningful through informal social processes. Critical success factors include a broad based membership from both the research and service communities; a loose and fluid network structure; tight targeting of messages based on members'' interests; the presence of a strong network identity and culture of reciprocity; and the opportunity for new members to learn through passive participation.     

Biodiversity: bridging the gap between condition and conservation

The aim of this study is to create a two-tiered assessment combining restoration and conservation, both needed for biodiversity management. The first tier of this approach assesses the condition of a site using a standard bioassessment method, AUSRIVAS, to determine whether significant loss of biodiversity has occurred because of human activity. The second tier assesses the conservation value of sites that were determined to be unimpacted in the first step against a reference database. This ensures maximum complementarity without having to set a priori target areas. Using the reference database, we assign site-specific and comparable coefficients for both restoration (Observed/Expected taxa with >50% probability of occurrence) and conservation values (O/E taxa with <50%, rare taxa). In a trial on 75 sites on rivers around Sydney, NSW, Australia we were able to identify three regions: (1) an area that may need restoration; (2) an area that had a high conservation value and; (3) a region that was identified as having significant biodiversity loss but with high potential to respond to rehabilitation and become a biodiversity hotspot. These examples highlight the use of the new framework as a comprehensive system for biodiversity assessment.     

Livestock genomics: bridging the gap between mice and men

Dissecting the genetic control of variation in complex traits, such as disease resistance and agricultural-product quality, remains very challenging. Farm animals are now well placed to bridge the gap between human biology and traditional model species. Livestock species share with model species the benefits of controlled breeding, and their biology is often much closer to that of humans. Genetic research in model species focuses on differences between homogenous lines, whereas genetic research in humans focuses on genetic variation within populations. Livestock genetics has the strengths of both human and model-species genetics because researchers can exploit both the abundant genetic variation between divergent breeds and the variation that is segregating within breeds. Therefore, livestock genomics fills the void where the genetics of model species proves intractable or where model species are not a good proxy for human biology.     

Tissue microarrays: bridging the gap between research and the clinic

Tissue microarrays are a high-throughput method for the investigation of biomarkers in multiple tissue specimens at once. This technique allows for the analysis of up to 500 tissue samples in a single experiment using immunohistochemistry and in situ hybridization. Recently, cell lines and xenografts have been reduced to a tissue microarray format and are being applied to preclinical drug development. In clinical research, tissue microarrays are applied at multiple levels: comprehensive analysis of samples in the context of a clinical trial or across a population. Tissue microarrays play a central role in translational research, facilitating the discovery of molecules that have potential roles in the diagnosis, prognosis and prediction of response to therapy.     

Tissue microarrays: bridging the gap between research and the clinic

Tissue microarrays are a high-throughput method for the investigation of biomarkers in multiple tissue specimens at once. This technique allows for the analysis of up to 500 tissue samples in a single experiment using immunohistochemistry and in situ hybridization. Recently, cell lines and xenografts have been reduced to a tissue microarray format and are being applied to preclinical drug development. In clinical research, tissue microarrays are applied at multiple levels: comprehensive analysis of samples in the context of a clinical trial or across a population. Tissue microarrays play a central role in translational research, facilitating the discovery of molecules that have potential roles in the diagnosis, prognosis and prediction of response to therapy.     

Evolution of complex life cycles of amphibians: bridging the gap between metapopulation dynamics and life history evolution

Current evolutionary models for amphibian life cycles reflect tradeoffs in size-specific growth and mortality rates between the aquatic and terrestrial stages. A limitation of these models is that they do not incorporate evolutionary phenomena that are associated with metapopulation structure. In this work I address components of the evolution of complex life cycles (CLCs) that are tied to the metapopulation dynamics of amphibians that use seasonal wetlands that vary in hydroperiod. In particular, I describe how selection for the minimum length of the larval period affects metapopulation viability and the selection/migration equilibrium. Selection to increase the minimum length of the larval period functionally reduces the number of viable breeding sites on the landscape, increases the average distance between neighboring sites, and increases the risk of metapopulation extinction. Within a metapopulation, asymmetric gene flow between populations that are adapted to different hydroperiods tends to swamp local selection for long larval periods at sites with long hydroperiods. The evolutionary stability of CLCs of many species with metapopulation structure may reflect the fact that extremely small metamorphs cannot survive on land, while lineages with long larval periods incur a high risk of metapopulation extinction. I encourage theorists to more carefully consider how life history traits and metapopulation viability are related for these and other taxa.     

Self-complementarity within proteins: bridging the gap between binding and folding

Complementarity, in terms of both shape and electrostatic potential, has been quantitatively estimated at protein-protein interfaces and used extensively to predict the specific geometry of association between interacting proteins. In this work, we attempted to place both binding and folding on a common conceptual platform based on complementarity. To that end, we estimated (for the first time to our knowledge) electrostatic complementarity (Em) for residues buried within proteins. Em measures the correlation of surface electrostatic potential at protein interiors. The results show fairly uniform and significant values for all amino acids. Interestingly, hydrophobic side chains also attain appreciable complementarity primarily due to the trajectory of the main chain. Previous work from our laboratory characterized the surface (or shape) complementarity (Sm) of interior residues, and both of these measures have now been combined to derive two scoring functions to identify the native fold amid a set of decoys. These scoring functions are somewhat similar to functions that discriminate among multiple solutions in a protein-protein docking exercise. The performances of both of these functions on state-of-the-art databases were comparable if not better than most currently available scoring functions. Thus, analogously to interfacial residues of protein chains associated (docked) with specific geometry, amino acids found in the native interior have to satisfy fairly stringent constraints in terms of both Sm and Em. The functions were also found to be useful for correctly identifying the same fold for two sequences with low sequence identity. Finally, inspired by the Ramachandran plot, we developed a plot of Sm versus Em (referred to as the complementarity plot) that identifies residues with suboptimal packing and electrostatics which appear to be correlated to coordinate errors.     

Relations as patterns: bridging the gap between OBO and OWL

Background  

Most biomedical ontologies are represented in the OBO Flatfile Format, which is an easy-to-use graph-based ontology language. The semantics of the OBO Flatfile Format 1.2 enforces a strict predetermined interpretation of relationship statements between classes. It does not allow flexible specifications that provide better approximations of the intuitive understanding of the considered relations. If relations cannot be accurately expressed then ontologies built upon them may contain false assertions and hence lead to false inferences. Ontologies in the OBO Foundry must formalize the semantics of relations according to the OBO Relationship Ontology (RO). Therefore, being able to accurately express the intended meaning of relations is of crucial importance. Since the Web Ontology Language (OWL) is an expressive language with a formal semantics, it is suitable to de ne the meaning of relations accurately.     

Archaeal primase: bridging the gap between RNA and DNA polymerases

In the evolution of life, DNA replication is a fundamental process, by which species transfer their genetic information to their offspring. DNA polymerases, including bacterial and eukaryotic replicases, are incapable of de novo DNA synthesis. DNA primases are required for this function, which is sine qua non to DNA replication. In Escherichia coli, the DNA primase (DnaG) exists as a monomer and synthesizes a short RNA primer. In Eukarya, however, the primase activity resides within the DNA polymerase alpha-primase complex (Pol alpha-pri) on the p48 subunit, which synthesizes the short RNA segment of a hybrid RNA-DNA primer. To date, very little information is available regarding the priming of DNA replication in organisms in Archaea. Available sequenced genomes indicate that the archaeal DNA primase is a homolog of the eukaryotic p48 subunit. Here, we report investigations of a p48-like DNA primase from Pyrococcus furiosus, a hyperthermophilic euryarchaeote. P. furiosus p48-like protein (Pfup41), unlike hitherto-reported primases, does not catalyze by itself the synthesis of short RNA primers but preferentially utilizes deoxynucleotides to synthesize DNA fragments up to several kilobases in length. Pfup41 is the first DNA polymerase that does not require primers for the synthesis of long DNA strands.     

Something from nothing: bridging the gap between constraint-based and kinetic modelling

Two divergent modelling methodologies have been adopted to increase our understanding of metabolism and its regulation. Constraint-based modelling highlights the optimal path through a stoichiometric network within certain physicochemical constraints. Such an approach requires minimal biological data to make quantitative inferences about network behaviour; however, constraint-based modelling is unable to give an insight into cellular substrate concentrations. In contrast, kinetic modelling aims to characterize fully the mechanics of each enzymatic reaction. This approach suffers because parameterizing mechanistic models is both costly and time-consuming. In this paper, we outline a method for developing a kinetic model for a metabolic network, based solely on the knowledge of reaction stoichiometries. Fluxes through the system, estimated by flux balance analysis, are allowed to vary dynamically according to linlog kinetics. Elasticities are estimated from stoichiometric considerations. When compared to a popular branched model of yeast glycolysis, we observe an excellent agreement between the real and approximate models, despite the absence of (and indeed the requirement for) experimental data for kinetic constants. Moreover, using this particular methodology affords us analytical forms for steady state determination, stability analyses and studies of dynamical behaviour.