Unfolding single RNA molecules: bridging the gap between equilibrium and non-equilibrium statistical thermodynamics

During the last 15 years, scientists have developed methods that permit the direct mechanical manipulation of individual molecules. Using this approach, they have begun to investigate the effect of force and torque in chemical and biochemical reactions. These studies span from the study of the mechanical properties of macromolecules, to the characterization of molecular motors, to the mechanical unfolding of individual proteins and RNA. Here I present a review of some of our most recent results using mechanical force to unfold individual molecules of RNA. These studies make it possible to follow in real time the trajectory of each molecule as it unfolds and characterize the various intermediates of the reaction. Moreover, if the process takes place reversibly it is possible to extract both kinetic and thermodynamic information from these experiments at the same time that we characterize the forces that maintain the three-dimensional structure of the molecule in solution. These studies bring us closer to the biological unfolding processes in the cell as they simulate in vitro, the mechanical unfolding of RNAs carried out in the cell by helicases. If the unfolding process occurs irreversibly, I show here that single-molecule experiments can still provide equilibrium, thermodynamic information from non-equilibrium data by using recently discovered fluctuation theorems. Such theorems represent a bridge between equilibrium and non-equilibrium statistical mechanics. In fact, first derived in 1997, the first experimental demonstration of the validity of fluctuation theorems was obtained by unfolding mechanically a single molecule of RNA. It is perhaps a sign of the times that important physical results are these days used to extract information about biological systems and that biological systems are being used to test and confirm fundamental new laws in physics.     

Peptidomics: bridging the gap between proteome and metabolome

Studies of naturally occurring peptides and protein profiling by 'classical' proteomics are linked by common analytical objectives and methodologies. The first international workshop "Peptidomics: methods and applications" held on 6-7th September 2005 at Royal Holloway University of London confirmed that the science of peptidomics is a rapidly developing activity of high interest to both academia and industry. This meeting featured talks by over 20 leading international scientists detailing methods and typical applications, including newly-developed capabilities for protein and peptide analyses. It provided a definition of the scope of the subject in terms of current and future technologies together with applications ranging from studies of defined biological extracts to complete ecosystems. The proceedings of this meeting, speakers' contact details and other relevant information can be accessed at: www.rhul.ac.uk/biosci/meetings.     

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.     

Telomerase-mediated telomere addition in vivo requires DNA primase and DNA polymerases alpha and delta

To better understand the requirements for telomerase-mediated telomere addition in vivo, we developed an assay in S. cerevisiae that creates a chromosome end immediately adjacent to a short telomeric DNA tract. The de novo end acts as a telomere: it is protected from degradation in a CDC13-dependent manner, telomeric sequences are added efficiently, and addition occurs at a faster rate in mutant strains that have long telomeres. Telomere addition was detected in M phase arrested cells, which permitted us to determine that the essential DNA polymerases alpha and delta and DNA primase were required. This indicates that telomeric DNA synthesis by telomerase is tightly coregulated with the production of the opposite strand. Such coordination prevents telomerase from generating excessively long single-stranded tails, which may be deleterious to chromosome stability in S. cerevisiae.     

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.     

Immunological relationships between Artemia RNA polymerases and between RNA polymerases II from different eukaryotic organisms

Summary Rabbit antibodies against Artemia RNA polymerase II have been raised and utilized to study the immunological relationships between the subunits from RNA polymerases I, II and III from this organism and RNA polymerase II from other eukaryotes. We describe here for the first time the subunit structure of Artemia RNA polymerases I and III. These enzymes have 9 and 13 subunits respectively. The anti-RNA polymerase II antibodies recognize two subunits of 19.4 and 18 kDa common to the three enzymes, and another subunit of 25.6 kDa common to RNA polymerases II and III. The antibodies against Artemia RNA polymerase II also react with the subunits of high molecular weight and with subunits of around 25 and 33 kDa of RNA polymerase II from other eukaryotes (Drosophila melanogaster, Chironomus thummi, triticum (wheat) and Rattus (rat)). This interspecies relatedness is a common feature of eukaryotic RNA polymerases.Abbreviations RNAp RNA polymerase - DPT diazophenylthioether - SDS sodium dodecylsulfate     

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.     

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.     

The DNA database search controversy revisited: bridging the Bayesian-frequentist gap

Two different quantities have been suggested for quantification of evidence in cases where a suspect is found by a search through a database of DNA profiles. The likelihood ratio, typically motivated from a Bayesian setting, is preferred by most experts in the field. The so-called np rule has been suggested through frequentist arguments and has been suggested by the American National Research Council and Stockmarr (1999, Biometrics55, 671-677). The two quantities differ substantially and have given rise to the DNA database search controversy. Although several authors have criticized the different approaches, a full explanation of why these differences appear is still lacking. In this article we show that a P-value in a frequentist hypothesis setting is approximately equal to the result of the np rule. We argue, however, that a more reasonable procedure in this case is to use conditional testing, in which case a P-value directly related to posterior probabilities and the likelihood ratio is obtained. This way of viewing the problem bridges the gap between the Bayesian and frequentist approaches. At the same time it indicates that the np rule should not be used to quantify evidence.     

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.     

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.     

Manufacturing flexibility and performance: bridging the gap between theory and practice

How firms scan and interpret their environments has implications for the flexibility strategy that they choose, as well as for the performance of that strategy. We extend Daft and Weick’s (Acad Manage Rev 9(2):284–295, 1984) model of firms as interpretation systems into a theoretical model of flexibility performance through extended iterations between observations of a failed flexibility initiative and relevant literature. We test the model using well-known teaching cases. We argue that the use of an iterative process that involves cases and theory both stimulates creativity in integrating theory and lays an initial foundation for evidence-based practice.     

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.