Hysteresis,oscillations, and pattern formation in realistic immobilized enzyme systems

Summary Hysteresis, oscillations, and pattern formation in realistic biochemical systems governed by P.D.E.s are considered from both numerical and mathematical points of view. Analysis of multiple steady states in the case of hysteresis, and bifurcation theory in the cases of oscillations and pattern formation, account for the observed numerical results. The possibility to realize these systems experimentally is their main interest, thus bringing further arguments in favor of theories explaining basic biological phenomena by diffusion and reaction.     

Studies on the binary and ternary complexes formed by a neurospora glutamate dehydrogenase and its substrates

The NADP⁺ specific glutamate dehydrogenase from wild-type $\text{Neurospora crassa}$ forms a stable binary complex with NADPH. This can combine with L-glutamate, α-ketoglutarate or the substrate analogue D-glutamate to form ternary complexes which can be distinguished by their different fluorescence properties. The affinity of the enzyme for NADPH diminishes with increases in pH or ionic strength of the solution. Experimental data obtained using modified glutamate dehydrogenases from mutant strains of $\text{N. crassa}$ suggest that the reduced-coenzyme binding sites observed fluorimetrically are the same as those observed by enzyme kinetics.     

Alleviation of Both Water and Nutrient Limitations is Necessary to Accelerate Ecological Restoration of Waste Rock Tips

Hard rock quarries are commonly located close to national parks and special areas of conservation and are generally regarded as visually intrusive. Consequently, restoration strategies that effectively accelerate natural plant regeneration processes are required. Slate waste tips present extreme conditions for plant establishment with multiple potential limiting factors (e.g., lack of organic matter, nutrients, and poor water retention). In this study, we investigated ecological strategies to accelerate natural regeneration at the largest slate quarry in Europe. A field experiment was conducted to assess ecosystem restoration using a contrasting set of native woody species. Treatments included amendments of waste tips with: polyacrylamide gel to increase water‐holding capacity; mineral fertilizer to increase nutrient supply; and two treatments that increased both (organic waste or boulder clay addition). Ecosystem recovery was evaluated through above‐ and below‐ground productivity (plant and microbial, respectively) and soil analyses. Neither increasing nutrient supply (with mineral fertilizer) nor water‐holding capacity (with polyacrylamide gel) was sufficient, alone, to improve plant establishment. However, both boulder clay and organic waste amendment significantly enhanced plant growth. There was a marked positive interaction in the effects on tree growth of the amendment with organic waste and boulder clay. Large interactions occurred between tree species and substrate amendments. The growth of N₂‐fixing species was strongly favored over non‐fixers where there was no addition of material increasing soil nitrogen supply, whereas the growth advantage of pioneer species over non‐pioneers was greatest with fertilizer, organic waste, or clay additions. Organic waste addition had the greatest positive impact on soil processes.     

Quantitative Properties of Random Amplified Polymorphic DNA (RAPD)

Random Amplified Polymorphic DNA analysis (RAPD) is a methodology that has been used as a tool for monitoring microbial communities. To be useful in this application RAPD, and any other methodology, must show properties that allows for the detection of quantitative changes in composition of the microbiota. Therefore, the objective of this study was to establish whether RAPD possesses such properties. The strategy was to use genomic DNA, extracted from a set of tertiary bacterial mixtures defined according to an experimental mixture design, and containing varying proportions of Escherichia coli, Bacillus subtilis, and Pseudomonas CF600. RAPD-PCR was performed on the mixed DNA extracts and the amplified DNA fragments were separated on sequencing gels to produce genomic fingerprints that were digitized and modeled by Partial Least Squares regression (PLS). Significant predictions were obtained using an external test set for validation, with Root Mean Square Error of Predictions (RMSEP) of 0.21, 0.19 and 0.20 for the proportion of E. coli, B. subtilis and Pseudomonas CF600 respectively. Taken together, the results showed that RAPD patterns quantitatively represented the initial mixture proportions. Therefore, the view that RAPD could be useful for whole microbial community monitoring was strengthened.     

Design and characterization of a prototype enzyme microreactor: Quantification of immobilized transketolase kinetics

In this work, we describe the design of an immobilized enzyme microreactor (IEMR) for use in transketolase (TK) bioconversion process characterization. The prototype microreactor is based on a 200‐μm ID fused silica capillary for quantitative kinetic analysis. The concept is based on the reversible immobilization of His₆‐tagged enzymes via Ni‐NTA linkage to surface derivatized silica. For the initial microreactor design, the mode of operation is a stop‐flow analysis which promotes higher degrees of conversion. Kinetics for the immobilized TK‐catalysed synthesis of L ‐erythrulose from substrates glycolaldehyde (GA) and hydroxypyruvate (HPA) were evaluated based on a Michaelis–Menten model. Results show that the TK kinetic parameters in the IEMR (V_max(app) = 0.1 ± 0.02 mmol min^–1, K_m(app) = 26 ± 4 mM) are comparable with those measured in free solution. Furthermore, the k_cat for the microreactor of 4.1 × 10⁵ s^?1 was close to the value for the bioconversion in free solution. This is attributed to the controlled orientation and monolayer surface coverage of the His₆‐immobilized TK. Furthermore, we show quantitative elution of the immobilized TK and the regeneration and reuse of the derivatized capillary over five cycles. The ability to quantify kinetic parameters of engineered enzymes at this scale has benefits for the rapid and parallel evaluation of evolved enzyme libraries for synthetic biology applications and for the generation of kinetic models to aid bioconversion process design and bioreactor selection as a more efficient alternative to previously established microwell‐based systems for TK bioprocess characterization. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Purification and Characterization of an Endopolygalacturonase Produced by Sclerotinia Sclerotiorum

An endopolygalacturonase (endo-PG), was purified from the culture medium of a local isolate of Sclerotinia sclerotiorum with ammonium sulphate precipitation, cation exchange chromatography and gel filtration. The purified endo-PG had a molecular mass of approximately 18 kDa estimated by gel filtration. The isoelectric point was determined by isoelectric focusing to be approximately 8, suggesting that PG II possesses a net positive charge at physiological pHs. The pH optimum for the enzyme was at pH 4.5. The endo-PG showed essentially the same affinity for pectin and polygalacturonic acid as substrates. This revised version was published online in July 2006 with corrections to the Cover Date.     

Distinguishing enzyme structures from non-enzymes without alignments

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.