Hydrolysis of fish oil by hyperactivated rhizomucor miehei lipase immobilized by multipoint anion exchange

Rhizomucor miehei lipase (RML) is greatly hyperactivated (around 20‐ to 25‐fold toward small substrates) in the presence of sucrose laurate. Hyperactivation appears to be an intramolecular process because it is very similar for soluble enzymes and covalently immobilized derivatives. The hyperactivated enzyme was immobilized (in the presence of sucrose laurate) on cyanogen bromide‐activated Sepharose (very mild covalent immobilization through the amino terminal residue), on glyoxyl Sepharose (intense multipoint covalent immobilization through the region with the highest amount of Lys residues), and on different anion exchangers (by multipoint anionic exchange through the region with the highest density of negative charges). Covalent immobilization does not promote the fixation of the hyperactivated enzyme, but immobilization on Sepharose Q retains the hyperactivated enzyme even in the absence of a detergent. The hydrolysis of fish oils by these hyperactivated enzyme derivatives was sevenfold faster than by covalently immobilized derivatives and three and a half times faster than by the enzyme hyperactivated on octyl‐Sepharose. The open structure of the hyperactivated lipase is fairly exposed to the medium, and no steric hindrance should interfere with the hydrolysis of large substrates. These new hyperactivated derivatives seem to be more suitable for hydrolysis of oils by RML immobilized inside porous supports. In addition, the hyperactivated derivatives are fairly stable against heat and organic cosolvents. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2011     

Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients

Understanding drivers of biodiversity patterns is of prime importance in this era of severe environmental crisis. More diverse plant communities have been postulated to represent a larger functional trait‐space, more likely to sustain a diverse assembly of herbivore species. Here, we expand this hypothesis to integrate environmental, functional and phylogenetic variation of plant communities as factors explaining the diversity of lepidopteran assemblages along elevation gradients in the Swiss Western Alps. According to expectations, we found that the association between butterflies and their host plants is highly phylogenetically structured. Multiple regression analyses showed the combined effect of climate, functional traits and phylogenetic diversity in structuring butterfly communities. Furthermore, we provide the first evidence that plant phylogenetic beta diversity is the major driver explaining butterfly phylogenetic beta diversity. Along ecological gradients, the bottom up control of herbivore diversity is thus driven by phylogenetically structured turnover of plant traits as well as environmental variables.     

Ecological assembly rules in plant communities—approaches,patterns and prospects

Understanding how communities of living organisms assemble has been a central question in ecology since the early days of the discipline. Disentangling the different processes involved in community assembly is not only interesting in itself but also crucial for an understanding of how communities will behave under future environmental scenarios. The traditional concept of assembly rules reflects the notion that species do not co‐occur randomly but are restricted in their co‐occurrence by interspecific competition. This concept can be redefined in a more general framework where the co‐occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive. Here we present a survey and meta‐analyses of 59 papers that compare observed patterns in plant communities with null models simulating random patterns of species assembly. According to the type of data under study and the different methods that are applied to detect community assembly, we distinguish four main types of approach in the published literature: species co‐occurrence, niche limitation, guild proportionality and limiting similarity. Results from our meta‐analyses suggest that non‐random co‐occurrence of plant species is not a widespread phenomenon. However, whether this finding reflects the individualistic nature of plant communities or is caused by methodological shortcomings associated with the studies considered cannot be discerned from the available metadata. We advocate that more thorough surveys be conducted using a set of standardized methods to test for the existence of assembly rules in data sets spanning larger biological and geographical scales than have been considered until now. We underpin this general advice with guidelines that should be considered in future assembly rules research. This will enable us to draw more accurate and general conclusions about the non‐random aspect of assembly in plant communities.     

Preparation of new lipases derivatives with high activity–stability in anhydrous media: adsorption on hydrophobic supports plus hydrophilization with polyethylenimine

A novel method to prepare immobilized lipases derivatives is hereby proposed. Lipases are firstly adsorbed on supports having large internal surfaces covered by hydrophobic groups (e.g. polyacrylic resins covered by C18 moieties). Then, immobilized lipases are incubated in the presence of polyethyleneimine (PEI) at a pH value over the isoelectric point of the enzyme in order to cover the lipase surface with this polymer. In this way, we try to minimize all possible direct interactions between immobilized lipase and organic solvents when using these derivatives in anhydrous media.

Lipases from Rhizomucor miehie (RML) and Candida rugosa (CRL) were immobilized according to the proposed protocol. These derivatives were very active and very stable when catalyzing esterifications and transesterifications in anhydrous media. For example, RML derivatives exhibited a very high synthetic activity (more than 1000 Units/g immobilized biocatalyst) even when catalyzing the esterification of lauric acid with octanol at water activity values very close to zero. On the contrary, covalently immobilized derivatives exhibited a much lower synthetic activity under similar conditions (less than 10 Units/g of immobilized biocatalyst). Moreover, these new RML derivatives preserve 100% activity after incubation for 3 days in anhydrous butanone in the presence of molecular sieves. Under the same conditions, commercial immobilized RML lost more than 90% of activity in less than 10 min.     

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence–absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size ( n  < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.     

Immobilization and stabilization of a bimolecular aggregate of the lipase from Pseudomonas fluorescens by multipoint covalent attachment

The soluble lipase from Pseudomonas fluorescens (PFL) forms bimolecular aggregates in which the hydrophobic active centers of the enzyme monomers are in close contact. This bimolecular aggregate could be immobilized by multipoint covalent linkages on glyoxyl supports at pH 8.5. The monomer of PFL obtained by incubation of the soluble enzyme in the presence of detergent (0.5% TRITON X-100) could not be immobilized under these conditions. The bimolecular aggregate has two amino terminal residues in the same plane. A further incubation of the immobilized derivative under more alkaline conditions (e.g., pH 10.5) allows a further multipoint attachment of lysine (Lys) residues located in the same plane as the amino terminal residues. Monomeric PFL was immobilized at pH 10.5 in the presence of 0.5% TRITON X-100. The properties of both PFL derivatives were compared. In general, the bimolecular derivatives were more active, more selective and more stable both in water and in organic solvents than the monomolecular ones. The bimolecular derivative showed twice the activity and a much higher selectivity (100 versus 20) for the hydrolysis of R,S-2-hydroxy-4-phenylbutyric acid ethyl ester (HPBEt) in aqueous media at pH 5.0 compared to the monomeric derivative. In experiments measuring thermal inactivation at 75 °C, the bimolecular derivative was 5-fold more stable than the monomeric derivative (and 50-fold more stable than a one-point covalently immobilized PFL derivative), and it had a half-life greater than 4 h. In organic solvents (cyclohexane and tert-amyl alcohol), the bimolecular derivative was much more stable and more active than the monomeric derivative in catalyzing the transesterification of olive oil with benzyl alcohol.     

GLM versus CCA spatial modeling of plant species distribution

Despite the variety of statistical methods available for static modeling of plant distribution, few studies directly compare methods on a common data set. In this paper, the predictive power of Generalized Linear Models (GLM) versus Canonical Correspondence Analysis (CCA) models of plant distribution in the Spring Mountains of Nevada, USA, are compared. Results show that GLM models give better predictions than CCA models because a species-specific subset of explanatory variables can be selected in GLM, while in CCA, all species are modeled using the same set of composite environmental variables (axes). Although both techniques can be readily ported to a Geographical Information System (GIS), CCA models are more readily implemented for many species at once. Predictions from both techniques rank the species models in the same order of quality; i.e. a species whose distribution is well modeled by GLM is also well modeled by CCA and vice-versa. In both cases, species for which model predictions have the poorest accuracy are either disturbance or fire related, or species for which too few observations were available to calibrate and evaluate the model. Each technique has its advantages and drawbacks. In general GLM will provide better species specific-models, but CCA will provide a broader overview of multiple species, diversity, and plant communities.     

Spatial modelling of soil water holding capacity improves models of plant distributions in mountain landscapes

Plant and Soil - This study explored the effects of Epichloë gansuensis endophyte on photosynthetic ability, the activity of nitrogen metabolism enzymes, nitrogen use efficiency, and biomass...     

Predicting global change impacts on plant species’ distributions: Future challenges

Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity. Even though increasing evidence shows that recent human-induced environmental changes have already triggered species’ range shifts, changes in phenology and species’ extinctions, accurate projections of species’ responses to future environmental changes are more difficult to ascertain. This is problematic, since there is a growing awareness of the need to adopt proactive conservation planning measures using forecasts of species’ responses to future environmental changes.

There is a substantial body of literature describing and assessing the impacts of various scenarios of climate and land-use change on species’ distributions. Model predictions include a wide range of assumptions and limitations that are widely acknowledged but compromise their use for developing reliable adaptation and mitigation strategies for biodiversity. Indeed, amongst the most used models, few, if any, explicitly deal with migration processes, the dynamics of population at the “trailing edge” of shifting populations, species’ interactions and the interaction between the effects of climate and land-use.

In this review, we propose two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species’ distribution in response to any global change phenomena. Deliberately focusing on plant species, we first explore the different ways to incorporate species’ migration in the existing modelling approaches, given data and knowledge limitations and the dual effects of climate and land-use factors. Secondly, we explore the mechanisms and processes happening at the trailing edge of a shifting species’ distribution and how to implement them into a modelling approach. We finally conclude this review with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.     

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.