Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.     

The Q-soluble N-Ethylmaleimide-sensitive Factor Attachment Protein Receptor (Q-SNARE) SNAP-47 Regulates Trafficking of Selected Vesicle-associated Membrane Proteins (VAMPs)

SNAREs constitute the core machinery of intracellular membrane fusion, but vesicular SNAREs localize to specific compartments via largely unknown mechanisms. Here, we identified an interaction between VAMP7 and SNAP-47 using a proteomics approach. We found that SNAP-47 mainly localized to cytoplasm, the endoplasmic reticulum (ER), and ERGIC and could also shuttle between the cytoplasm and the nucleus. SNAP-47 preferentially interacted with the trans-Golgi network VAMP4 and post-Golgi VAMP7 and -8. SNAP-47 also interacted with ER and Golgi syntaxin 5 and with syntaxin 1 in the absence of Munc18a, when syntaxin 1 is retained in the ER. A C-terminally truncated SNAP-47 was impaired in interaction with VAMPs and affected their subcellular distribution. SNAP-47 silencing further shifted the subcellular localization of VAMP4 from the Golgi apparatus to the ER. WT and mutant SNAP-47 overexpression impaired VAMP7 exocytic activity. We conclude that SNAP-47 plays a role in the proper localization and function of a subset of VAMPs likely via regulation of their transport through the early secretory pathway.     

Correlation between EGFR Amplification and the Expression of MicroRNA-200c in Primary Glioblastoma Multiforme

Extensive infiltration of the surrounding healthy brain tissue is a critical feature in glioblastoma. Several miRNAs have been related to gliomagenesis, some of them related with the EGFR pathway. We have evaluated whole-genome miRNA expression profiling associated with different EGFR amplification patterns, studied by fluorescence in situ hybridization in tissue microarrays, of 30 cases of primary glioblastoma multiforme, whose clinicopathological and immunohistochemical features have also been analyzed. MicroRNA-200c showed a very significant difference between tumors having or not EGFR amplification. This microRNA plays an important role in epithelial-mesenchymal transition, but its implication in the behavior of glioblastoma is largely unknown. With respect to EGFR status our cases were categorized into three groups: high level EGFR amplification, low level EGFR amplification, and no EGFR amplification. Our results showed that microRNA-200c and E-cadherin expression are down-regulated, while ZEB1 is up-regulated, when tumors showed a high level of EGFR amplification. Conversely, ZEB1 mRNA expression levels were significantly lower in the group of tumors without EGFR amplification. Tumors with a low level of EGFR amplification showed ZEB1 expression levels comparable to those detected in the group with a high level of amplification. In this study we provide what is to our knowledge the first report of association between microRNA-200c and EGFR amplification in glioblastomas.     

Imaging Cell Membrane Injury and Subcellular Processes Involved in Repair

The ability of injured cells to heal is a fundamental cellular process, but cellular and molecular mechanisms involved in healing injured cells are poorly understood. Here assays are described to monitor the ability and kinetics of healing of cultured cells following localized injury. The first protocol describes an end point based approach to simultaneously assess cell membrane repair ability of hundreds of cells. The second protocol describes a real time imaging approach to monitor the kinetics of cell membrane repair in individual cells following localized injury with a pulsed laser. As healing injured cells involves trafficking of specific proteins and subcellular compartments to the site of injury, the third protocol describes the use of above end point based approach to assess one such trafficking event (lysosomal exocytosis) in hundreds of cells injured simultaneously and the last protocol describes the use of pulsed laser injury together with TIRF microscopy to monitor the dynamics of individual subcellular compartments in injured cells at high spatial and temporal resolution. While the protocols here describe the use of these approaches to study the link between cell membrane repair and lysosomal exocytosis in cultured muscle cells, they can be applied as such for any other adherent cultured cell and subcellular compartment of choice.     

Modulation of P450 enzymes by Cuban natural products rich in polyphenolic compounds in rat hepatocytes

This paper reports cytotoxic effects and changes in the P450 system after exposing rat hepatocytes to four polyphenol-rich products widely used in Cuban traditional medicine (Mangifera indica L. (MSBE), Thalassia testudinum (Tt), Erythroxylum minutifolium and confusum extracts). Effects of mangiferin, the main polyphenol in MSBE, were also evaluated. Cytotoxicity was assayed by the MTT test after exposure of cells to the products (50-1000 microg/mL) for 24 or 72 h. The results showed that 500 microg/mL MSBE was moderately cytotoxic after 72 h, while mangiferin was not. Marked reductions in cell viability were produced by Erythroxylum extracts at concentrations > or = 200 microg/mL, whereas only moderate effects were induced by 1000 microg/mL Tt. Seven specific P450 activities were evaluated after 48 h exposure of cells to the products. MSBE reduced phenacetin O-deethylation (POD; CYP1A2) activity in a concentration-dependent manner (IC(50)=190 microg/mL). No decreases were observed in other activities. In contrast, mangiferin produced reductions in five P450 activities: IC(50) values of 132, 194, >200, 151 and 137 microg/ml for POD (CYP1A2), midazolam 1'-hydroxylation (M1OH; CYP3A1), diclofenac 4'-hydroxylation (D4OH; CYP2C6), S-mephenytoin 4'-hydroxylation (SM4OH), and chlorzoxazone 6-hydroxyaltion (C6OH; CYP2E1), respectively. E. minutifolium, E. confusum and Tt extracts produced small reductions in SM4OH and C6OH activities, but no significant changes were noted in the other P450 activities. On the other hand, all the products increased the benzyloxyresorufin O-debenzylation (BROD; CYP2B1) activity, with MSBE, mangiferin or E. minutifolium showing the highest effects (about 2-fold over control). Our results showed in vitro effects of these natural products on P450 systems, possibly leading to potential metabolic-based interactions.     

Modeling the in vitro 20S proteasome activity: the effect of PA28-alphabeta and of the sequence and length of polypeptides on the degradation kinetics

Proteasomes are fundamental for the degradation of intracellular proteins, having a key role in several important metabolic and signaling pathways, in the cell cycle and in antigen presentation. In vitro proteasomal digestion assays are widely used in molecular biology and immunology. We developed a model, ProteaMAlg (proteasome modeling algorithm) that describes the kinetics of specific protein fragments generated by 20S proteasomes in different conditions, once the substrate cleavage strengths are provided. ProteaMAlg was tested on a variety of data available in the literature as well as on new degradation experiments performed with polypeptides of different sequences and lengths. The comparison between in vitro and in silico experiments was used to quantify the effect on degradation of the sequence and the length of target polypeptides, of the presence of regulatory molecules such as PA28-αβ, and of the type of 20S proteasome (constitutive- or immunoproteasome). The model showed that the effect of the PA28 regulatory subunit results in a modification of the gating functions of the proteasome core particle. Immunoproteasome digestion experiments suggested that this form of proteasome, which is involved in generating MHC-class I epitopes, presents modified cleavage and gating activities. Our analysis improves the current understanding of the kinetics of proteasome functioning, and provides a tool to quantify and predict the effect of key parameters during in vitro digestion. ProteaMAlg is publicly available on the web (http://www.proteamalg.com).     

Cross‐validation strategies for data with temporal,spatial, hierarchical,or phylogenetic structure

Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross‐validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross‐validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also provides ample opportunity for overfitting with non‐causal predictors. This problem can persist even if remedies such as autoregressive models, generalized least squares, or mixed models are used. Block cross‐validation, where data are split strategically rather than randomly, can address these issues. However, the blocking strategy must be carefully considered. Blocking in space, time, random effects or phylogenetic distance, while accounting for dependencies in the data, may also unwittingly induce extrapolations by restricting the ranges or combinations of predictor variables available for model training, thus overestimating interpolation errors. On the other hand, deliberate blocking in predictor space may also improve error estimates when extrapolation is the modelling goal. Here, we review the ecological literature on non‐random and blocked cross‐validation approaches. We also provide a series of simulations and case studies, in which we show that, for all instances tested, block cross‐validation is nearly universally more appropriate than random cross‐validation if the goal is predicting to new data or predictor space, or for selecting causal predictors. We recommend that block cross‐validation be used wherever dependence structures exist in a dataset, even if no correlation structure is visible in the fitted model residuals, or if the fitted models account for such correlations.