Biotransformation of 4‐fluoro‐N‐(1‐{2‐[(propan‐2‐yl)phenoxy]ethyl}‐8‐azabicyclo[3.2.1]octan‐3‐yl)‐benzenesulfonamide,a novel potent 5‐HT7 receptor antagonist with antidepressant‐like and anxiolytic properties: In vitro and in silico approach

The aim of the study was to investigate the metabolism of 4‐fluoro‐N‐(1‐{2‐[(propan‐2‐yl)phenoxy]ethyl}‐8‐azabicyclo[3.2.1]octan‐3‐yl)‐benzenesulfonamide (PZ‐1150), a novel 5‐HT₇ receptor antagonist with antidepressant‐like and anxiolytic properties, by the following three ways: in vitro with microsomes; in vitro employing Cunninghamella echinulata, and in silico using MetaSite. Biotransformation of PZ‐1150 with microsomes resulted in five metabolites, while transformation with C. echinulata afforded two metabolites. In both models, the predominant metabolite occurred due to hydroxylation of benzene ring. In silico data coincide with in vitro experiments, as three MetaSite metabolites matched compounds identified in microsomal samples. In human liver microsomes PZ‐1150 exhibited in vitro half‐life of 64 min, with microsomal intrinsic clearance of 54.1 μL/min/mg and intrinsic clearance of 48.7 mL/min/kg. Therefore, PZ‐1150 is predicted to be a high‐clearance agent. The study demonstrated the applicability of using microsomal model coupled with microbial model to elucidate the metabolic pathways of compounds and comparison with in silico metabolite predictions.     

Testing hypotheses driving genetic structure in the cooperatively breeding Brown‐headed Nuthatch Sitta pusilla

Habitat fragmentation has often been implicated in the decline of many species. For habitat specialists and/or sedentary species, loss of habitat can result in population isolation and lead to negative genetic effects. However, factors other than fragmentation can often be important and also need to be considered when assessing the genetic structure of a species. We genotyped individuals from 13 populations of the cooperatively breeding Brown‐headed Nuthatch Sitta pusilla in Florida to test three alternative hypotheses regarding the effects that habitat fragmentation might have on genetic structure. A map of potential habitat developed from recent satellite imagery suggested that Brown‐headed Nuthatch populations in southern Florida occupied smaller and more isolated habitat patches (i.e. were more fragmented) than populations in northern Florida. We also genotyped individuals from a small, isolated Brown‐headed Nuthatch population on Grand Bahama Island. We found that populations associated with more fragmented habitat in southern Florida had lower allelic richness than populations in northern Florida (P = 0.02), although there were no differences in heterozygosity. Although pairwise estimates of F_ST were low overall, values among southern populations were generally higher than northern populations. Population assignment tests identified K = 3 clusters corresponding to a northern cluster, a southern cluster and a unique population in southeast Florida; using sampling localities as prior information revealed K = 7 clusters, with greater structure only among southern Florida populations. The Bahamas population showed moderate to high differentiation compared with Florida populations. Overall, our results suggest that fragmentation could affect gene flow in Brown‐headed Nuthatch populations and is likely to become more pronounced over time.     

PEGylation of lysine residues improves the proteolytic stability of fibronectin while retaining biological activity

Excessive proteolysis of fibronectin (FN) impairs tissue repair in chronic wounds. Since FN is essential in wound healing, our goal is to improve its proteolytic stability and at the same time preserve its biological activity. We have previously shown that reduced FN conjugated with polyethylene glycol (PEG) at cysteine residues is more proteolytically stable than native FN. Cysteine‐PEGylated FN supported cell adhesion and migration to the same extent as native FN. However, unlike native FN, cysteine‐PEGylated FN was not assembled into an extracellular matrix (ECM) when immobilized. Here, we present an alternative approach in which FN is preferentially PEGylated at lysine residues using different molecular weight PEGs. We show that lysine PEGylation does not perturb FN secondary structure. PEG molecular weight, from 2 to 10 kDa, positively correlates with FN–PEG proteolytic stability. Cell adhesion, cell spreading, and gelatin binding decrease with increasing molecular weight of PEG. The 2‐kDa FN–PEG conjugate shows comparable cell adhesion to native FN and binds gelatin. Moreover, immobilized FN–PEG is assembled into ECM fibrils. In summary, lysine PEGylation of FN can be used to stabilize FN against proteolytic degradation with minimal perturbation to FN structure and retained biological activity.     

Distribution and diversity of palms in a tropical biodiversity hotspot (Thailand) assessed by species distribution modeling

Species distribution modeling has been widely used to address questions related to ecology, biogeography and species conservation on global and regional scales. Here, we study palms (Arecaceae) in a tropical biodiversity hotspot (Thailand) using species distribution modeling to assess range‐limiting factors and estimate distribution and diversity patterns based on a comprehensive compilation of occurrence records. We focused on palms as a model group due to their key‐stone importance for ecosystem functioning and socio‐economics. Different combinations of climatic, non‐climatic environmental and spatial predictors were used. The most accurate models as indicated by the ‘area under the receiver operating characteristic curve’ (AUC) statistic were those that combined all predictors. The four strongest single predictors of palm species distributions were, in decreasing order of importance, 1) latitude, 2) precipitation of driest quarter, 3) annual precipitation, and 4) minimum temperature of the coldest month, suggesting rainfall patterns and latitudinal spatial constraints as the main range determinants. Overlaying the predicted distributions revealed that potential palm hotspots are situated in the provinces of Satun and Yala in southern Thailand where vast areas remain relatively open to the discovery of new palm records and perhaps even new species.     

The plant glycosyltransferase clone collection for functional genomics

The glycosyltransferases (GTs) are an important and functionally diverse family of enzymes involved in glycan and glycoside biosynthesis. Plants have evolved large families of GTs which undertake the array of glycosylation reactions that occur during plant development and growth. Based on the Carbohydrate‐Active enZymes (CAZy) database, the genome of the reference plant Arabidopsis thaliana codes for over 450 GTs, while the rice genome (Oryza sativa) contains over 600 members. Collectively, GTs from these reference plants can be classified into over 40 distinct GT families. Although these enzymes are involved in many important plant specific processes such as cell‐wall and secondary metabolite biosynthesis, few have been functionally characterized. We have sought to develop a plant GTs clone resource that will enable functional genomic approaches to be undertaken by the plant research community. In total, 403 (88%) of CAZy defined Arabidopsis GTs have been cloned, while 96 (15%) of the GTs coded by rice have been cloned. The collection resulted in the update of a number of Arabidopsis GT gene models. The clones represent full‐length coding sequences without termination codons and are Gateway^® compatible. To demonstrate the utility of this JBEI GT Collection, a set of efficient particle bombardment plasmids (pBullet) was also constructed with markers for the endomembrane. The utility of the pBullet collection was demonstrated by localizing all members of the Arabidopsis GT14 family to the Golgi apparatus or the endoplasmic reticulum (ER). Updates to these resources are available at the JBEI GT Collection website http://www.addgene.org/ .     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.