Phenotypic and molecular characterization of the onset of neuropathy in rodent models of diabetes mellitus

Abstract : The principal focus of this work was to study the molecular changes leading to the development of diabetic peripheral neuropathy (DPN). DPN is the most common complication associated with both type I and II diabetes mellitus (DM). This pathology is the leading cause of non-traumatic amputations. Even though the pathological and morphological changes underlying DPN are relatively well described, the implicated molecular mechanisms remain poorly understood. The following two approaches were developed to study the development of DPN in a rodent model of DM type I. As a first approach, we studied the implication of lipid metabolism in DPN phenotype, concentrating on Sterol Response Element Binding Protein (SREBP)-lc which is the key regulator of storage lipid metabolism. We showed that SREBP-1c was expressed in peripheral nerves and that its expression profile followed the expression of genes involved in storage lipid metabolism. In addition, the expression of SREBP-1c in the endoneurium of peripheral nerves was dependant upon nutritional status and this expression was also perturbed in type I diabetes. In line with this, we showed that insulin elevated the expression of SREBP-1c in primary cultured Schwann cells by activating the SREBP-1c promoter. Taken together, these findings reveal that SREBP-1c expression in Schwann cells responds to metabolic stimuli including insulin and that this response is affected in type I diabetes mellitus. This suggests that disturbed SREBP-1c regulated lipid metabolism may contribute to the pathophysiology of DPN. As a second approach, we performed a comprehensive analysis of the molecular changes associated with DPN in the Akital~1~+ mouse which is a model of spontaneous early-onset type I diabetes mellitus. This mouse expresses a mutated non-functional isoform of insulin, leading to hypoinsulinemia and hyperglycaemia. To determine the onset of DPN, weight, blood glucose and motor nerve conduction velocity (MNCV) were measured in Akital+/+ mice during the first three months of life. A decrease in MNCV was evident akeady one week after the onset of hyperglycemia. To explore the molecular changes associated with the development of DPN in these mice, we performed gene expression profiling using sciatic nerve endoneurium and dorsal root ganglia (DRG) isolated from early diabetic male Akita+/+ mice and sex-matched littermate controls. No major transcriptional changes were detected either in the DRG or in the sciatic nerve endoneurium. This experiment indicates that the phenotypic changes observed during the development of DPN are not correlated with major transcriptional alterations, but mainly with alterations at the protein level. Résumé Lors ce travail, nous nous sommes intéressés aux changements moléculaires aboutissant aux neuropathies périphériques dues au diabète (NPD). Les NPD sont la complication la plus commune du diabète de type I et de type II. Cette pathologie est une cause majeure d'amputations. Même si les changements pathologiques et morphologiques associés aux NPD sont relativement bien décrits, les mécanismes moléculaires provoquant cette pathologie sont mal connus. Deux approches ont principalement été utilisées pour étudier le développement des NPD dans des modèles murins du diabète de type I. Nous avons d'abord étudié l'impact du métabolisme des lipides sur le développement des NPD en nous concentrant sur Sterol Response Element Binding Protein (SREBP)-1 c qui est un régulateur clé des lipides de stockage. Nous avons montré que SREBP-1 c est exprimé dans les nerfs périphériques et que son profil d'expression suit celui de gènes impliqués dans le métabolisme des lipides de stockage. De plus, l'expression de SREBP-1c dans l'endoneurium des nerfs périphériques est dépendante du statut nutritionnel et est dérégulée lors de diabète de type I. Nous avons également pu montrer que l'insuline augmente l'expression de SREBP-1c dans des cultures primaires de cellules de Schwann en activant le promoteur de SREBP-1c. Ses résultats démontrent que l'expression de SREBP-1c dans les cellules de Schwann est contrôlée par des stimuli métaboliques comme l'insuline et que cette réponse est affectée dans le cas d'un diabète de type I. Ces données suggèrent que la dérégulation de l'expression de SREBP-1c lors du diabète pourrait affecter le métabolisme des lipides et ainsi contribuer à la pathophysiologie des NPD. Comme seconde approche, nous avons réalisé une analyse globale des changements moléculaires associés au développement des NPD chez les souris Akita+/+, un modèle de diabète de type I. Cette souris exprime une forme mutée et non fonctionnelle de l'insuline provoquant une hypoinsulinémie et une hyperglycémie. Afin de déterminer le début du développement de la NPD, le poids, le niveau de glucose sanguin et la vitesse de conduction nerveuse (VCN) ont été mesurés durant les 3 premiers mois de vie. Une diminution de la VCN a été détectée une semaine seulement après le développement de l'hyperglycémie. Pour explorer les changements moléculaires associés avec le développement des NPD, nous avons réalisé un profil d'expression de l'endoneurium du nerf sciatique et des ganglions spinaux isolés à partir de souris Akital+/+ et de souris contrôles Akita+/+. Aucune altération transcriptionnelle majeure n'a été détectée dans nos échantillons. Cette expérience suggère que les changements phénotypiques observés durant le développement des NPD ne sont pas corrélés avec des changements importants au niveau transcriptionnel, mais plutôt avec des altérations au niveau protéique. Résumé : Lors ce travail, nous nous sommes intéressés aux changements moléculaires aboutissant aux neuropathies périphériques dues au diabète (NPD). Les NPD sont la complication la plus commune du diabète de type I et de type II. Cette pathologie est une cause majeure d'amputations. Même si les changements pathologiques et morphologiques associés aux NPD sont relativement bien décrits, les mécanismes moléculaires provoquant cette pathologie sont mal connus. Deux approches ont principalement été utilisées pour étudier le développement des NPD dans des modèles murins du diabète de type I. Nous avons d'abord étudié l'impact du métabolisme des lipides sur le développement des NPD en nous concentrant sur Sterol Response Element Binding Protein (SREBP)-1c qui est un régulateur clé des lipides de stockage. Nous avons montré que SREBP-1 c est exprimé dans les nerfs périphériques et que son profil d'expression suit celui de gènes impliqués dans le métabolisme des lipides de stockage. De plus, l'expression de SREBP-1c dans l'endoneurium des nerfs périphériques est dépendante du statut nutritionnel et est dérégulée lors de diabète de type I. Nous avons également pu montrer que l'insuline augmente l'expression de SREBP-1c dans des cultures primaires de cellules de Schwann en activant le promoteur de SREBP-1c. Ses résultats démontrent que l'expression de SREBP-1c dans les cellules de Schwann est contrôlée par des stimuli métaboliques comme l'insuline et que cette réponse est affectée dans le cas d'un diabète de type I. Ces données suggèrent que la dérégulation de l'expression de SREBP-1c lors du diabète pourrait affecter le métabolisme des lipides et ainsi contribuer à la pathophysiologie des NPD. Comme seconde approche, nous avons réalisé une analyse globale des changements moléculaires associés au développement des NPD chez les souris Akita~~Z~+, un modèle de diabète de type I. Cette souris exprime une forme mutée et non fonctionnelle de l'insuline provoquant une hypoinsulinémie et une hyperglycémie. Afin de déterminer le début du développement de la NPD, le poids, le niveau de glucose sanguin et la vitesse de conduction nerveuse (VCN) ont été mesurés durant les 3 premiers mois de vie. Une diminution de la VCN a été détectée une semaine seulement après le développement de l'hyperglycémie. Pour explorer les changements moléculaires associés avec le développement des NPD, nous avons réalisé un profil d'expression de l'endoneurium du nerf sciatique et des ganglions spinaux isolés à partir de souris Akital+/+ et de souris contrôles Akita+/+. Aucune altération transcriptionnelle majeure n'a été détectée dans nos échantillons. Cette expérience suggère que les changements phénotypiques observés durant le développement des NPD ne sont pas corrélés avec des changements importants au niveau transcriptionnel, mais plutôt avec des altérations au niveau protéique.

Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: a test with plants in an arid environment

Aim To evaluate the effects of using distinct alternative sets of climatic predictor variables on the performance, spatial predictions and future projections of species distribution models (SDMs) for rare plants in an arid environment. . Location Atacama and Peruvian Deserts, South America (18º30'S - 31º30'S, 0 - 3 000 m) Methods We modelled the present and future potential distributions of 13 species of Heliotropium sect. Cochranea, a plant group with a centre of diversity in the Atacama Desert. We developed and applied a sequential procedure, starting from climate monthly variables, to derive six alternative sets of climatic predictor variables. We used them to fit models with eight modelling techniques within an ensemble forecasting framework, and derived climate change projections for each of them. We evaluated the effects of using these alternative sets of predictor variables on performance, spatial predictions and projections of SDMs using Generalised Linear Mixed Models (GLMM). Results The use of distinct sets of climatic predictor variables did not have a significant effect on overall metrics of model performance, but had significant effects on present and future spatial predictions. Main conclusion Using different sets of climatic predictors can yield the same model fits but different spatial predictions of current and future species distributions. This represents a new form of uncertainty in model-based estimates of extinction risk that may need to be better acknowledged and quantified in future SDM studies.

Integrating nuclear receptor mobility in models of gene regulation.

The mode of action of nuclear receptors in living cells is an actively investigated field but much remains hypothetical due to the lack, until recently, of methods allowing the assessment of molecular mechanisms in vivo. However, these last years, the development of fluorescence microscopy methods has allowed initiating the dissection of the molecular mechanisms underlying gene regulation by nuclear receptors directly in living cells or organisms. Following our analyses on peroxisome proliferator activated receptors (PPARs) in living cells, we discuss here the different models arising from the use of these tools, that attempt to link mobility, DNA binding or chromatin interaction, and transcriptional activity.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium.

Global habitat suitability models of terrestrial mammals.

Detailed large-scale information on mammal distribution has often been lacking, hindering conservation efforts. We used the information from the 2009 IUCN Red List of Threatened Species as a baseline for developing habitat suitability models for 5027 out of 5330 known terrestrial mammal species, based on their habitat relationships. We focused on the following environmental variables: land cover, elevation and hydrological features. Models were developed at 300 m resolution and limited to within species' known geographical ranges. A subset of the models was validated using points of known species occurrence. We conducted a global, fine-scale analysis of patterns of species richness. The richness of mammal species estimated by the overlap of their suitable habitat is on average one-third less than that estimated by the overlap of their geographical ranges. The highest absolute difference is found in tropical and subtropical regions in South America, Africa and Southeast Asia that are not covered by dense forest. The proportion of suitable habitat within mammal geographical ranges correlates with the IUCN Red List category to which they have been assigned, decreasing monotonically from Least Concern to Endangered. These results demonstrate the importance of fine-resolution distribution data for the development of global conservation strategies for mammals.

Using niche-based models to improve the sampling of rare species

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.

Are niche-based species distribution models transferable in space?

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.

Thymic stromal lymphopoietin plays divergent roles in murine models of atopic and nonatopic airway inflammation.

BACKGROUND: Thymic stromal lymphopoietin (TSLP) is a cytokine primarily produced by epithelial cells, which has been shown to be a potent inducer of T-helper 2 (Th2)-type responses. However, TSLP has pleiotropic effects upon immune cells, and although extensively studied in the context of atopic asthma, its relevance as a therapeutic target and its role in the pathogenesis of nonatopic asthma remains unknown. We sought to investigate the role of TSLP in atopic, nonatopic and viral-induced exacerbations of pulmonary inflammation. METHODS: Using stringently defined murine models of atopic, nonatopic and virally exacerbated forms of pulmonary inflammation, we compared inflammatory responses of C57BL/6 wild-type (WT) and TSLP receptor-deficient (TSLPR KO) mice. RESULTS: Thymic stromal lymphopoietin receptor (TSLPR) signaling was crucial for the development of atopic asthma. Specifically, TSLPR signaling to lung recruited CD4+ T cells enhanced eosinophilia, goblet cell hyperplasia, and overall inflammation within the airways. In contrast, the absence of TSLPR signaling was associated with strikingly exaggerated pulmonary neutrophilic inflammation in a nonatopic model of airway inflammation. The inflammation was associated with excessive levels of interleukin (IL)-17A in the lungs, indicating that TSLP negatively regulates IL-17A. In addition, in a model of influenza-induced exacerbation of atopic airway inflammation, the absence of TSLPR signaling also led to exaggerated neutrophilic inflammation. CONCLUSION: Thymic stromal lymphopoietin plays divergent roles in the pathogenesis of atopic and nonatopic asthma phenotypes by either enhancing Th2 responses or curtailing T-helper 17 responses. These findings raise important caveats for the design of therapeutic interventions targeting TSLP in asthma.

Role of dendritic cells in the lung: in vitro models, animal models and human studies

Dendritic cells (DCs) are the most potent antigen-presenting cells in the human lung and are now recognized as crucial initiators of immune responses in general. They are arranged as sentinels in a dense surveillance network inside and below the epithelium of the airways and alveoli, where thet are ideally situated to sample inhaled antigen. DCs are known to play a pivotal role in maintaining the balance between tolerance and active immune response in the respiratory system. It is no surprise that the lungs became a main focus of DC-related investigations as this organ provides a large interface for interactions of inhaled antigens with the human body. During recent years there has been a constantly growing body of lung DC-related publications that draw their data from in vitro models, animal models and human studies. This review focuses on the biology and functions of different DC populations in the lung and highlights the advantages and drawbacks of different models with which to study the role of lung DCs. Furthermore, we present a number of up-to-date visualization techniques to characterize DC-related cell interactions in vitro and/or in vivo.

Dynamic models of energy allocation and investment

In dynamic models of energy allocation, assimilated energy is allocated to reproduction, somatic growth, maintenance or storage, and the allocation pattern can change with age. The expected evolutionary outcome is an optimal allocation pattern, but this depends on the environment experienced during the evolutionary process and on the fitness costs and benefits incurred by allocating resources in different ways. Here we review existing treatments which encompass some of the possibilities as regards constant or variable environments and their predictability or unpredictability, and the ways in which production rates and mortality rates depend on body size and composition and age and on the pattern of energy allocation. The optimal policy is to allocate resources where selection pressures are highest, and simultaneous allocation to several body subsystems and reproduction can be optimal if these pressures are equal. This may explain balanced growth commonly observed during ontogeny. Growth ceases at maturity in many models; factors favouring growth after maturity include non-linear trade-offs, variable season length, and production and mortality rates both increasing (or decreasing) functions of body size. We cannot yet say whether these are sufficient to account for the many known cases of growth after maturity and not all reasonable models have yet been explored. Factors favouring storage are also reviewed.

Climate change and plant distribution: local models predict high-elevation persistence

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolution and geographic extent. Here, we assess whether climate-change induced habitat losses predicted at the European scale (10x10' grid cells) are also predicted from local scale data and modeling (25x25m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10x10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Do stacked species distribution models reflect altitudinal diversity patterns?

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.