Metabolic changes of rhizobia in legume nodules

Bacteria have evolved a wide variety of metabolic strategies to cope with varied environments. Some are specialists and only able to survive in restricted environments; others are generalists and able to cope with diverse environmental conditions. Rhizolbia (e.g. Rhizobium, Sinorhizobium, Bradyrhizobium, Mesorhizobium and Azorhizobium species) can survive and compete for nutrients in soil and the plant rhizosphere but can also form a beneficial symbiosis with legumes in a highly specialized plant cell environment. Inside the legume-root nodule, the bacteria (bacteroids) reduce dinitrogen to ammonium, which is secreted to the plant in exchange for a carbon and energy source. A new and challenging aspect of nodule physiology is that nitrogen fixation requires the cycling of amino acids between the bacteroid and plant. This review aims to summarize the metabolic plasticity of rhizobia and the importance of amino acid cycling.

De novo alanine synthesis by bacteroids of Mesorhizobium loti is not required for nitrogen transfer in the determinate nodules of Lotus corniculatus

Deletion of both alanine dehydrogenase genes (aldA) in Mesorhizobium loti resulted in the loss of AldA enzyme activity from cultured bacteria and bacteroids but had no effect on the symbiotic performance of Lotus corniculatus plants. Thus, neither indeterminate pea nodules nor determinate L. corniculatus nodules export alanine as the sole nitrogen secretion product.

Regulation of L-alanine dehydrogenase in Rhizobium leguminosarum bv. viciae and its role in pea nodules

Alanine dehydrogenase (AldA) is the principal enzyme with which pea bacteroids synthesize alanine de novo. In free-living culture, AMA activity is induced by carboxylic acids (succinate, malate, and pyruvate), although the best inducer is alanine. Measurement of the intracellular concentration of alanine showed that AldA contributes to net alanine synthesis in laboratory cultures. Divergently transcribed from aldA is an AsnC type regulator, aldR. Mutation of aldR prevents induction of AldA activity. Plasmid-borne gusA fusions showed that aldR is required for transcription of both aldA and aldR; hence, AldR is autoregulatory. However, plasmid fusions containing the aldA-aldR intergenic region could apparently titrate out AldR, sometimes resulting in a complete loss of AldA enzyme activity. Therefore, integrated aldR::gusA and aldA::gusA fusions, as well as Northern blotting, were used to confirm the induction of aldA activity. Both aldA and aldR were expressed in the II/III interzone and zone III of pea nodules. Overexpression of aldA in bacteroids did not alter the ability of pea plants to fix nitrogen, as measured by acetylene reduction, but caused a large reduction in the size and dry weight of plants. This suggests that overexpression of aldA impairs the ability of bacteroids to donate fixed nitrogen that the plant can productively assimilate. We propose that the role of AldA may be to balance the alanine level for optimal functioning of bacteroid metabolism rather than to synthesize alanine as the sole product of N-2 reduction.

Synthesis, structural analysis, and magnetic behaviour of three fumarate bridged coordination polymers: five-fold interpenetrated diamond-like net of Ni-II, sheets of Ni-II and Co-II

The hydrothermal reactions of Ni(NO3)(2).6H(2)O, disodium fumarate (fum) and 1,2-bis(4-pyridyl)ethane (bpe)/1,3-bis(4-pyridyl) propane (bpp) in aqueous-methanol medium yield one 3-D and one 2-D metal-organic hybrid material, [Ni(fum)(bpe)] (1) and [Ni(fum)(bpp)(H2O)] (2), respectively. Complex 1 possesses a novel unprecedented structure, the first example of an "unusual mode" of a five-fold distorted interpenetrated network with metal-ligand linkages where the four six-membered windows in each adamantane-type cage are different. The structural characterization of complex 2 evidences a buckled sheet where nickel ions are in a distorted octahedral geometry, with two carboxylic groups, one acting as a bis-chelate, the other as a bis-monodentate ligand. The metal ion completes the coordination sphere through one water molecule and two bpp nitrogens in cis position. Variable-temperature magnetic measurements of complexes 1 and 2 reveal the existence of very weak antiferromagnetic intramolecular interactions and/or the presence of single-ion zero field splitting (D) of isolated Ni-II ions in both the compounds. Experimentally, both the J parameters are close, comparable and very small. Considering zero-field splitting of Ni-II, the calculated D values are in agreement with values reported in the literature for Ni-II ions. Complex 3, [{Co(phen)}(2)(fum)(2)] (phen=1,10-phenanthroline) is obtained by diffusing methanolic solution of 1,10-phenanthroline on an aqueous layer of disodium fumarate and Co(NO3)(2).6H(2)O. It consists of dimeric Co-II(phen) units, doubly bridged by carboxylate groups in a distorted syn-syn fashion. These fumarate anions act as bis-chelates to form corrugated sheets. The 2D layer has a (4,4) topology, with the nodes represented by the centres of the dimers. The magnetic data were fitted ignoring the very weak coupling through the fumarate pathway and using a dimer model.

Improving sequence-based fold recognition by using 3D model quality assessment

Motivation: The ability of a simple method (MODCHECK) to determine the sequence–structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAPs) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detect native-likemodels. Results: We show that compared with the other three methods tested MODCHECK is the most reliable method for consistently performing the best top model selection and for ranking the models. In addition, we show that the choice of model similarity score used to assess a model's similarity to the experimental structure can influence the overall performance of these tools. Although these MQAP methods fail to improve the model selection performance for methods that already incorporate protein three dimension (3D) structural information, an improvement is observed for methods that are purely sequence-based, including the best profile–profile methods. This suggests that even the best sequence-based fold recognition methods can still be improved by taking into account the 3D structural information.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM-based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets.

Improvement of the GenTHREADER method for genomic fold recognition

Motivation: In order to enhance genome annotation, the fully automatic fold recognition method GenTHREADER has been improved and benchmarked. The previous version of GenTHREADER consisted of a simple neural network which was trained to combine sequence alignment score, length information and energy potentials derived from threading into a single score representing the relationship between two proteins, as designated by CATH. The improved version incorporates PSI-BLAST searches, which have been jumpstarted with structural alignment profiles from FSSP, and now also makes use of PSIPRED predicted secondary structure and bi-directional scoring in order to calculate the final alignment score. Pairwise potentials and solvation potentials are calculated from the given sequence alignment which are then used as inputs to a multi-layer, feed-forward neural network, along with the alignment score, alignment length and sequence length. The neural network has also been expanded to accommodate the secondary structure element alignment (SSEA) score as an extra input and it is now trained to learn the FSSP Z-score as a measurement of similarity between two proteins. Results: The improvements made to GenTHREADER increase the number of remote homologues that can be detected with a low error rate, implying higher reliability of score, whilst also increasing the quality of the models produced. We find that up to five times as many true positives can be detected with low error rate per query. Total MaxSub score is doubled at low false positive rates using the improved method.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

What are the baselines for protein fold recognition?

What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?

Fold-change detection in a whole-pathway model of Escherichia coli chemotaxis

There has been recent interest in sensory systems that are able to display a response which is proportional to a fold change in stimulus concentration, a feature referred to as fold-change detection (FCD). Here, we demonstrate FCD in a recent whole-pathway mathematical model of Escherichia coli chemotaxis. FCD is shown to hold for each protein in the signalling cascade and to be robust to kinetic rate and protein concentration variation. Using a sensitivity analysis, we find that only variations in the number of receptors within a signalling team lead to the model not exhibiting FCD. We also discuss the ability of a cell with multiple receptor types to display FCD and explain how a particular receptor configuration may be used to elucidate the two experimentally determined regimes of FCD behaviour. All findings are discussed in respect of the experimental literature.