Systems pathology analysis identifies neurodegenerative nature of age‐related vitreoretinal interface diseases

Aging is a phenomenon that is associated with profound medical implications. Idiopathic epiretinal membrane (iEMR) and macular hole (MH) are the major vision‐threatening vitreoretinal diseases affecting millions of aging people globally, making these conditions an important public health issue. iERM is characterized by fibrous tissue developing on the surface of the macula, which leads to biomechanical and biochemical macular damage. MH is a small breakage in the macula and is associated with many ocular conditions. Although several individual factors and pathways are suggested, a systems pathology level understanding of the molecular mechanisms underlying these disorders is lacking. Therefore, we performed mass spectrometry‐based label‐free quantitative proteomics analysis of the vitreous proteomes from patients with iERM and MH to identify the key proteins, as well as the multiple interconnected biochemical pathways, contributing to the development of these diseases. We identified a total of 1,014 unique proteins, many of which are linked to inflammation and the complement cascade, revealing the inflammation processes in retinal diseases. Additionally, we detected a profound difference in the proteomes of iEMR and MH compared to those of diabetic retinopathy with macular edema and rhegmatogenous retinal detachment. A large number of neuronal proteins were present at higher levels in the iERM and MH vitreous, including neuronal adhesion molecules, nervous system development proteins, and signaling molecules, pointing toward the important role of neurodegenerative component in the pathogenesis of age‐related vitreoretinal diseases. Despite them having marked similarities, several unique vitreous proteins were identified in both iERM and MH, from which candidate targets for new diagnostic and therapeutic approaches can be provided.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

Atp1a3‐deficient heterozygous mice show lower rank in the hierarchy and altered social behavior

Atp1a3 is the Na‐pump alpha3 subunit gene expressed mainly in neurons of the brain. Atp1a3‐deficient heterozygous mice (Atp1a3^+/?) show altered neurotransmission and deficits of motor function after stress loading. To understand the function of Atp1a3 in a social hierarchy, we evaluated social behaviors (social interaction, aggression, social approach and social dominance) of Atp1a3^+/? and compared the rank and hierarchy structure between Atp1a3^+/? and wild‐type mice within a housing cage using the round‐robin tube test and barbering observations. Formation of a hierarchy decreases social conflict and promote social stability within the group. The hierarchical rank is a reflection of social dominance within a cage, which is heritable and can be regulated by specific genes in mice. Here we report: (1) The degree of social interaction but not aggression was lower in Atp1a3^+/? than wild‐type mice, and Atp1a3^+/? approached Atp1a3^+/? mice more frequently than wild type. (2) The frequency of barbering was lower in the Atp1a3^+/? group than in the wild‐type group, while no difference was observed in the mixed‐genotype housing condition. (3) Hierarchy formation was not different between Atp1a3^+/? and wild type. (4) Atp1a3^+/? showed a lower rank in the mixed‐genotype housing condition than that in the wild type, indicating that Atp1a3 regulates social dominance. In sum, Atp1a3^+/? showed unique social behavior characteristics of lower social interaction and preference to approach the same genotype mice and a lower ranking in the hierarchy.     

COMT Val158Met moderates the link between rank and aggression in a non‐human primate

The COMT Val¹⁵⁸Met polymorphism is one of the most widely studied genetic polymorphisms in humans implicated in aggression and the moderation of stressful life event effects. We screened a wild primate population for polymorphisms at the COMT Val¹⁵⁸Met site and phenotyped them for aggression to test whether the human polymorphism exists and is associated with variation in aggressive behavior. Subjects were all adults from 4 study groups (37 males, 40 females) of Assamese macaques (Macaca assamensis) in their natural habitat (Phu Khieo Wildlife Sanctuary, Thailand). We collected focal animal behavioral data (27 males, 36 females, 5964 focal hours) and fecal samples for non‐invasive DNA analysis. We identified the human COMT Val¹⁵⁸Met polymorphism (14 Met/Met, 41 Val/Met and 22 Val/Val). Preliminary results suggest that COMT genotype and dominance rank interact to influence aggression rates. Aggression rates increased with rank in Val/Val, but decreased in Met/Met and Val/Met individuals, with no significant main effect of COMT genotype on aggression. Further support for the interaction effect comes from time series analyses revealing that when changing from lower to higher rank position Val/Val individuals decreased, whereas Met/Met individuals increased their aggression rate. Contradicting the interpretation of earlier studies, we show that the widely studied Val¹⁵⁸Met polymorphism in COMT is not unique to humans and yields similar behavioral phenotypes in a non‐human primate. This study represents an important step towards understanding individual variation in aggression in a wild primate population and may inform human behavioral geneticists about the evolutionary roots of inter‐individual variation in aggression.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Targeted subfield switchgrass integration could improve the farm economy,water quality,and bioenergy feedstock production

Progress on reducing nutrient loss from annual croplands has been hampered by perceived conflicts between short‐term profitability and long‐term stewardship, but these may be overcome through strategic integration of perennial crops. Perennial biomass crops like switchgrass can mitigate nitrate‐nitrogen (NO₃‐N) leaching, address bioenergy feedstock targets, and – as a lower‐cost management alternative to annual crops (i.e., corn, soybeans) – may also improve farm profitability. We analyzed publicly available environmental, agronomic, and economic data with two integrated models: a subfield agroecosystem management model, Landscape Environmental Assessment Framework (LEAF), and a process‐based biogeochemical model, DeNitrification‐DeComposition (DNDC). We constructed a factorial combination of profitability and NO₃‐N leaching thresholds and simulated targeted switchgrass integration into corn/soybean cropland in the agricultural state of Iowa, USA. For each combination, we modeled (i) area converted to switchgrass, (ii) switchgrass biomass production, and (iii) NO₃‐N leaching reduction. We spatially analyzed two scenarios: converting to switchgrass corn/soybean cropland losing >US$ 100 ha^?1 and leaching >50 kg ha^?1 (‘conservative’ scenario) or losing >US$ 0 ha^?1 and leaching >20 kg ha^?1 (‘nutrient reduction’ scenario). Compared to baseline, the ‘conservative’ scenario resulted in 12% of cropland converted to switchgrass, which produced 11 million Mg of biomass and reduced leached NO₃‐N 18% statewide. The ‘nutrient reduction’ scenario converted 37% of cropland to switchgrass, producing 34 million Mg biomass and reducing leached NO₃‐N 38% statewide. The opportunity to meet joint goals was greatest within watersheds with undulating topography and lower corn/soybean productivity. Our approach bridges the scales at which NO₃‐N loss and profitability are usually considered, and is informed by both mechanistic and empirical understanding. Though approximated, our analysis supports development of farm‐level tools that can identify locations where both farm profitability and water quality improvement can be achieved through the strategic integration of perennial vegetation.     

Patterning of diverse mammalian cell types in serum free medium with photoablation

Integration of living cells with novel microdevices requires the development of innovative technologies for manipulating cells. Chemical surface patterning has been proven as an effective method to control the attachment and growth of diverse cell populations. Patterning polyelectrolyte multilayers through the combination of layer‐by‐layer self‐assembly technique and photolithography offer a simple, versatile, and silicon compatible approach that overcomes chemical surface patterning limitations, such as short‐term stability and low‐protein adsorption resistance. In this study, direct photolithographic patterning of two types of multilayers, PAA (poly acrylic acid)/PAAm (poly acryl amide) and PAA/PAH (poly allyl amine hydrochloride), were developed to pattern mammalian neuronal, skeletal, and cardiac muscle cells. For all studied cell types, PAA/PAAm multilayers behaved as a cytophobic surface, completely preventing cell attachment. In contrast, PAA/PAH multilayers have shown a cell‐selective behavior, promoting the attachment and growth of neuronal cells (embryonic rat hippocampal and NG108‐15 cells) to a greater extent, while providing little attachment for neonatal rat cardiac and skeletal muscle cells (C2C12 cell line). PAA/PAAm multilayer cellular patterns have also shown a remarkable protein adsorption resistance. Protein adsorption protocols commonly used for surface treatment in cell culture did not compromise the cell attachment inhibiting feature of the PAA/PAAm multilayer patterns. The combination of polyelectrolyte multilayer patterns with different adsorbed proteins could expand the applicability of this technology to cell types that require specific proteins either on the surface or in the medium for attachment or differentiation, and could not be patterned using the traditional methods. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009