Influence of mass transfer limitations on the enzymatic synthesis of β-lactam antibiotics catalyzed by penicillin G acylase immobilized on glioxil-agarose

Mass transfer effects were investigated for the synthesis of ampicillin and amoxicillin, at pH 6.5 and 25 degrees C, catalyzed by penicillin G acylase immobilized on agarose. The influence of external mass transfer was analysed using different stirring rates, ranging form 200 to 800 rpm. Above 400 rpm, the film resistance may be neglected. Intra-particle diffusion limitation was investigated using biocatalysts prepared with different enzyme loads and agarose with different mean pore diameters. When agarose with 6, 8 and 10% of crosslinking were used, for the same enzyme load, substrates and products concentration profiles presented no expressive differences, suggesting pore diameter is not important parameter. An increase on enzyme load showed that when more than 90 IU of enzyme activity were used per mL of support, the system was influenced by intra-particle mass transfer. A reactive-diffusive model was used to estimate effective diffusivities of substrates and products.     

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.     

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.     

Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?

Many studies have investigated the potential impacts of climate change on the distribution of plant species, but few have attempted to constrain projections through plant dispersal limitations. Instead, most studies published so far have simplified dispersal as either unlimited or null. However, depending on the dispersal capacity of a species, landscape fragmentation, and the rate of climatic change, these assumptions can lead to serious over- or underestimation of the future distribution of plant species.
To quantify the discrepancies between simulations accounting for dispersal or not, we carried out projections of future distribution over the 21st century for 287 mountain plant species in a study area of the western Swiss Alps. For each species, simulations were run for four dispersal scenarios (unlimited dispersal, no dispersal, realistic dispersal, and realistic dispersal with long-distance dispersal events) and under four climate change scenarios.
Although simulations accounting for realistic dispersal limitations did significantly differ from those considering dispersal as unlimited or null in terms of projected future distribution, the unlimited dispersal simplification did nevertheless provide good approximations for species extinctions under more moderate climate change scenarios. Overall, simulations accounting for dispersal limitations produced, for our mountainous study area, results that were significantly closer to unlimited dispersal than to no dispersal. Finally, analysis of the temporal pattern of species extinctions over the entire 21st century revealed that important species extinctions for our study area might not occur before the 2080–2100 period, due to the possibility of a large number of species shifting their distribution to higher elevation.     

Adsorption behavior of bovine serum albumin on lowly activated anionic exchangers suggests a new strategy for solid-phase proteomics

Diluted solutions of bovine serum albumin (BSA) (e.g., 0.1 mg /mL) do not form detectable protein large aggregates. Using gel-filtration experiments, we determined that a diluted solution of BSA is 97% monomeric BSA and 3% dimeric. The adsorption of this diluted BSA on highly activated anionic exchangers (e,g., having 40 micromol/wet g) keeps this mainly monomeric form. When supports activated with 2 micromol/wet g are used, only dimers become adsorbed to the support, accounting for 100% of the offered BSA. When the diluted BSA solution is offered to very mildly activated anionic exchangers (even only 0.125 micromol/wet g), an unexpected adsorption of most of the BSA on the support was also observed. These very slightly activated supports are only able to adsorb very large proteins or very large protein-protein complexes, larger than BSA dimers. In fact, a rapid cross-linking of the adsorbed BSA with dextran-aldehyde reveals the formation of very large BSA-BSA complexes with molecular mass higher than 500 000 Da, complexes that may be observed for soluble BSA with very high concentrations but are not detectable at 0.1 mg/mL. Moreover, the size of the aggregates strongly depends on the concentration of the ionized groups on the support: the less activated the supports are, the higher the sizes of the complexes. It seems that the interaction of the BSA molecules on the margins of the BSA aggregate with the groups on the support may stabilize the whole protein aggregate, although some components are not interacting with the support. Aggregates could account for more than 40% of the BSA in the solution after 50 h of incubation. However, only these large BSA aggregates were adsorbed in the support.     

One-step purification, covalent immobilization, and additional stabilization of poly-His-tagged proteins using novel heterofunctional chelate-epoxy supports.

Epoxy supports covalently immobilize proteins following a two-step mechanism; that is, the protein is physically adsorbed and then the covalent reaction takes place. This mechanism has been exploited to combine the selectivity of metal chelate affinity chromatography with the covalent immobilization capacity of epoxy supports. In this way, it has been possible to accomplish, in a simple manner, the purification, immobilization, and stabilization of a poly-His-tagged protein. To fulfill this objective we developed a new kind of multifunctional epoxy support (chelate epoxy support [CES]), which was tested using a poly-His-tagged glutaryl acylase as a model protein (an alphabeta-heterodimeric enzyme of significant industrial interest). The selectivity of the immobilization in CES toward poly-His-tagged proteins was dependent to a large extent on the density and nature of the chelated metal. The highest selectivity was achieved by using low-density chelate groups (e.g., 5 micromol/g) and metals with a low affinity (e.g., Co). However, the rate of covalent immobilization of the protein by its reaction with the epoxy groups on the support significantly increased at alkaline pH values. The multipoint attachment to the CES also depended on the reaction time. The immobilization of both glutaryl acylase subunits was achieved by incubation of the enzyme derivative at pH 10 for 24 h, with the best enzyme derivative 100-fold more stable than the soluble enzyme. By taking advantage of the selectivity properties of the novel support, we were able to immobilize up to 30 mg of protein per gram of modified Eupergit 250 using either pure enzyme or a very crude enzyme extract.     

The adsorption of multimeric enzymes on very lowly activated supports involves more enzyme subunits: Stabilization of a glutamate dehydrogenase from Thermus thermophilus by immobilization on heterofunctional supports

Glutamate dehydrogenase (GDH) from Thermus thermophilus is a homotrimeric enzyme that tends to dissociate at acidic pH values. GDH is readily adsorbed on highly activated anionic exchangers (HAAE), but hardly adsorbed on lowly activated supports (LAAE) or on highly activated epoxy supports. When using amino-epoxy supports, GDH immobilized on HAAE-epoxy and more slowly on LAAE-epoxy supports. Both immobilized biocatalysts were incubated at pH 10 for different times to increase the multipoint covalent attachment. LAAE-epoxy-GDH was stable at pH 4 and 25 °C, the enzyme stability did not depend on the enzyme concentration and did not release any subunit to the supernatant, in opposition to the results obtained using HAAE-epoxy supports. The general application of this strategy to stabilize multimeric enzymes was verified by immobilizing a crude protein extract. It seems that proteins adsorb on LAAE by the larger region of their surface (that is the one that involves the highest number of enzyme subunits), since it is the only area large enough to permit a multipoint ionic exchange on this LAAE. On the contrary, using HAAE, some proteins may become adsorbed by clusters that were rich in anionic groups and located in a corner of the multimer, involving only some of the subunits in the enzyme immobilization. That way, a careful design of the design of the support permits to take full advantage of the immobilization on heterofunctional supports.     

Climate‐based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate‐based species distribution models (S‐SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate‐based S‐SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate‐based S‐SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S‐SDMs were more accurate in plant‐rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate‐based S‐SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate‐based S‐SDMs.