Insights into the biochemical strategies of adaptation to heat stress in the hyperthermophilic archaeon Pyrococcus furiosus

Organisms that thrive optimally at temperatures above 80°C are called hyperthermophiles. These prokaryotes have been isolated from a variety of hot environments, such as marine geothermal areas, hence they are usually slightly halophilic. Like other halophiles, marine hyperthermophiles have to cope with fluctuations in the salinity of the external medium and generally use low-molecular mass organic compounds to adjust cell turgor pressure. These compounds can accumulate to high levels without interfering with cell metabolism, thereby deserving the designation of compatible solutes. Curiously, the accumulation of compatible solutes also occurs in response to supraoptimal temperatures.(...)Fundação para a Ciência e a Tecnologi

Meta-omics reveals genetic flexibility of diatom nitrogen transporters in response to environmental changes

Diatoms (Bacillariophyta), one of the most abundant and diverse groups of marine phytoplankton, respond rapidly to the supply of new nutrients, often out-competing other phytoplankton. Herein, we integrated analyses of the evolution, distribution and expression modulation of two gene families involved in diatom nitrogen uptake (DiAMT1 and DiNRT2), in order to infer the main drivers of divergence in a key functional trait of phytoplankton. Our results suggest that major steps in the evolution of the two gene families reflected key events triggering diatom radiation and diversification. Their expression is modulated in the contemporary ocean by seawater temperature, nitrate and iron concentrations. Moreover, the differences in diversity and expression of these gene families throughout the water column hint at a possible link with bacterial activity. This study represents a proof-of-concept of how a holistic approach may shed light on the functional biology of organisms in their natural environment

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Mechanical unfolding of the cytosolic domain from hHCN2 channel by using single molecule force spectroscopy

Atomic force microscopy (AFM)-based single molecule force spectroscopy (SMFS) has emerged into a reliable technique for probing structural and mechanical properties of biological samples at molecular level. It has been successfully applied to investigate the mechanical unfolding of soluble proteins as well as membrane proteins. In contrast with the traditional structural techniques it provides a direct mechanical interaction with targets molecule in their near-physiological environment. The possibility to sample one molecule at a time, made the techniques extremely valuable for studying complex dynamic behaviors, unveiling rare events that are usually averaged in large data distribution from big populations of molecules. Here we investigate the mechanical unfolding of the purified cytosolic c-linker and cyclic nucelotide binding domain (CNBD) domain from the human hyperpolarization-activated and cyclic nucleotide-gated (hHCN)2 channel, and characterize the ligand-depended differences in the unfolding behaviour of the molecule. In parallel, we develop an all-in-one environment for Force vs distance (F-d) curves analysis, containing informatics tools to handle the reproducibility and automation required for exploring stochastic processes like folding and unfolding of complex proteins

Modelling of expert nurses' pressure sore risk assessment skills as an expert system for in-service training

In the nursing literature to date there have been no reported applications of `cognitive simulation' nor of intelligent Computer Assisted Learning. In Chapter 1 of this thesis a critical review of existing nurse education by computer is used to establish a framework within which to explore the possibility of simulation of thinking processes of nurses on computer. One conclusion from this review which is offered concerns the importance of firstly undertaking reliable study of nursing cognition. The crucial issue is that an understanding must be gained of how expert nurses mentally represent their patients in order that a valid model might be constructed on computer. The construction of a valid computer based cognitive model proves to be an undertaking which occupies the remainder of this thesis. The approach has been to gradually raise the specificity of analysis of the knowledge base of expert and proficient nurses while seeking concurrently to evaluate validity of the findings. Reported in Chapter 2, therefore, are the several experimental stages of a knowledge acquisition project which begins the process of constructing this knowledge base. Discussed firstly is the choice of the skill domain to be studied - pressure sore risk assessment. Subsequently, the method of eliciting from nurses top-level and micro-level descriptors of patients is set out. This account of knowledge acquisition ends with scrutiny of the performance of nurse subjects who performed a comprehensive simulated patient assessment task in order that two groups might be established - one Expert and one Proficient with respect to the nursing task. In Chapter 3, an extensive analysis of the data provided by the simulated assessment experiment is undertaken. This analysis, as the most central phase of the project, proceeds by degrees. Hence, the aim is to `explain' progressively more of the measured cognitive behaviour of the Expert nurses while incorporating the most powerful explanations into a developing cognitive model. More specifically, explanations are sought of the role of `higher' cognition, of whether attribute importance is a feature of cognition, of the point at which a decision can be made, and of the process of deciding between competing patient judgements. Interesting findings included several reliable differences which were found to exist between the cognition of subjects deemed to be proficient and those taken as expert. In the final part of this thesis, Chapter 4, a more formal evaluation of the computer based cognitive model which was constructed and predictions made by it was undertaken. The first phase involved analysis in terms of process and product of decision making of the cognitive model in comparison to two alternative models; one derived from Discriminant Function Analysis and the other from Automated Rule Induction. The cognitive model was found to most closely approximate to the process of decision making of the human subjects and also to perform most accurately with a test set of unseen patients. The second phase reports some experimental support for the prediction made by the model that nurses represent their patients around action-related `care concepts' rather than in terms of diagnostic categories based on superficial features. The thesis concludes by offering some general conclusions and recommendations for further research

Stability and aggregation-prone conformations of an antibody fragment antigen-binding (Fab)

Antibody-based products have become the main drug class of approved biopharmaceuticals, with over 60 drugs on the market and many more in clinical development. However, many never reach the market because protein aggregates form during manufacturing and storage, which lower the efficacy of the product and may cause immune responses in patients. To date, very little is known about the structural conformers that initiate aggregation. Stability of the humanized fragment antigen-binding (Fab) A33 was first studied using molecular dynamic (MD) simulations under two stresses, low pH and high temperature. Results revealed different unfolding pathways, with CL domain partially unfolding at low pH, and CL and VH at high temperature. These conformational changes exposed different predicted aggregation-prone regions (APR), to suggest different aggregation mechanisms. Further salt bridge analysis provided insights into the ionizable residues likely to get protonated first. Mutational study with FoldX and Rosetta predicted that the constant domain interface can be stabilized further, backed by packing density calculations. To experimentally characterize the aggregation-prone conformers, solution structures of Fab A33 under different conditions of pH and salt concentration, were solved using small angle X-ray scattering (SAXS). SAXS revealed an expanded conformation at pH 5.5 and below, with an Rg increase of 2.2% to 4.1%, that correlated with accelerated aggregation. Scattering data were fitted using 45,000 structures obtained from the atomistic MD simulations under the same conditions, to locate the conformational change at low pH to the CL domain. The approach was then validated using intra-molecular single-molecule FRET with a dual-labelled Fab as an orthogonal detection method. The conformational changes were found to expose a predicted APR, which forms a mechanistic basis for subsequent aggregation. Overall, these findings provide a means by which aggregation-prone conformers can be determined experimentally, and thus potentially used to guide protein engineering, or ligand binding strategies, with the aim of stabilizing the protein against aggregation

Marine diatoms change their gene expression profile when exposed to microscale turbulence under nutrient replete conditions

Diatoms are a fundamental microalgal phylum that thrives in turbulent environments. Despite several experimental and numerical studies, if and how diatoms may profit from turbulence is still an open question. One of the leading arguments is that turbulence favours nutrient uptake. Morphological features, such as the absence of flagella, the presence of a rigid exoskeleton and the micrometre size would support the possible passive but beneficial role of turbulence on diatoms. We demonstrate that in fact diatoms actively respond to turbulence in non-limiting nutrient conditions. TURBOGEN, a prototypic instrument to generate natural levels of microscale turbulence, was used to expose diatoms to the mechanical stimulus. Differential expression analyses, coupled with microscopy inspections, enabled us to study the morphological and transcriptional response of Chaetoceros decipiens to turbulence. Our target species responds to turbulence by activating energy storage pathways like fatty acid biosynthesis and by modifying its cell chain spectrum. Two other ecologically important species were examined and the occurrence of a morphological response was confirmed. These results challenge the view of phytoplankton as unsophisticated passive organisms

Diversification of DNA-Binding Specificity by Permissive and Specificity-Switching Mutations in the ParB/Noc Protein Family

Specific interactions between proteins and DNA are essential to many biological processes. Yet, it remains unclear how the diversification in DNA-binding specificity was brought about, and the mutational paths that led to changes in specificity are unknown. Using a pair of evolutionarily related DNA-binding proteins, each with a different DNA preference (ParB [Partitioning Protein B] and Noc [Nucleoid Occlusion Factor], which both play roles in bacterial chromosome maintenance), we show that specificity is encoded by a set of four residues at the protein-DNA interface. Combining X-ray crystallography and deep mutational scanning of the interface, we suggest that permissive mutations must be introduced before specificity-switching mutations to reprogram specificity and that mutational paths to new specificity do not necessarily involve dual-specificity intermediates. Overall, our results provide insight into the possible evolutionary history of ParB and Noc and, in a broader context, might be useful for understanding the evolution of other classes of DNA-binding proteins