Shear Stress Modulation of Smooth Muscle Cell Marker Genes in 2-D and 3-D Depends on Mechanotransduction by Heparan Sulfate Proteoglycans and ERK1/2

During vascular injury, vascular smooth muscle cells (SMCs) and fibroblasts/myofibroblasts (FBs/MFBs) are exposed to altered luminal blood flow or transmural interstitial flow. We investigate the effects of these two types of fluid flows on the phenotypes of SMCs and MFBs and the underlying mechanotransduction mechanisms.Exposure to 8 dyn/cm(2) laminar flow shear stress (2-dimensional, 2-D) for 15 h significantly reduced expression of alpha-smooth muscle actin (alpha-SMA), smooth muscle protein 22 (SM22), SM myosin heavy chain (SM-MHC), smoothelin, and calponin. Cells suspended in collagen gels were exposed to interstitial flow (1 cmH(2)O, approximately 0.05 dyn/cm(2), 3-D), and after 6 h of exposure, expression of SM-MHC, smoothelin, and calponin were significantly reduced, while expression of alpha-SMA and SM22 were markedly enhanced. PD98059 (an ERK1/2 inhibitor) and heparinase III (an enzyme to cleave heparan sulfate) significantly blocked the effects of laminar flow on gene expression, and also reversed the effects of interstitial flow on SM-MHC, smoothelin, and calponin, but enhanced interstitial flow-induced expression of alpha-SMA and SM22. SMCs and MFBs have similar responses to fluid flow. Silencing ERK1/2 completely blocked the effects of both laminar flow and interstitial flow on SMC marker gene expression. Western blotting showed that both types of flows induced ERK1/2 activation that was inhibited by disruption of heparan sulfate proteoglycans (HSPGs).The results suggest that HSPG-mediated ERK1/2 activation is an important mechanotransduction pathway modulating SMC marker gene expression when SMCs and MFBs are exposed to flow. Fluid flow may be involved in vascular remodeling and lesion formation by affecting phenotypes of vascular wall cells. This study has implications in understanding the flow-related mechanobiology in vascular lesion formation, tumor cell invasion, and stem cell differentiation

Particles, air quality, policy and health

The diversity of ambient particle size and chemical composition considerably complicates pinpointing the specific causal associations between exposure to particles and adverse human health effects, the contribution of different sources to ambient particles at different locations, and the consequent formulation of policy action to most cost-effectively reduce harm caused by airborne particles. Nevertheless, the coupling of increasingly sophisticated measurements and models of particle composition and epidemiology continue to demonstrate associations between particle components and sources (and at lower concentrations) and a wide range of adverse health outcomes. This article reviews the current approaches to source apportionment of ambient particles and the latest evidence for their health effects, and describes the current metrics, policies and legislation for the protection of public health from ambient particles. A particular focus is placed on particles in the ultrafine fraction. The review concludes with an extended evaluation of emerging challenges and future requirements in methods, metrics and policy for understanding and abating adverse health outcomes from ambient particles

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

Marrow adipose tissue: trimming the fat

Marrow adipose tissue (MAT) is a unique fat depot, located in the skeleton, that has the potential to contribute to both local and systemic metabolic processes. In this review we highlight several recent conceptual developments pertaining to the origin and function of MAT adipocytes; consider the relationship of MAT to beige, brown, and white adipose depots; explore MAT expansion and turnover in humans and rodents; and discuss future directions for MAT research in the context of endocrine function and metabolic disease. MAT has the potential to exert both local and systemic effects on metabolic homeostasis, skeletal remodeling, hematopoiesis, and development of bone metastases. The diversity of these functions highlights the breadth of MAT’s potential impact on health and disease

The role of neural impulse control mechanisms for dietary success in obesity

Deficits in impulse control are discussed as key mechanisms for major worldwide health problems such as drug addiction and obesity. For example, obese subjects have difficulty controlling their impulses to overeat when faced with food items. Here, we investigated the role of neural impulse control mechanisms for dietary success in middle-aged obese subjects. Specifically, we used a food-specific delayed gratification paradigm and functional magnetic resonance imaging to measure eating-related impulse-control in middle-aged obese subjects just before they underwent a twelve-week low calorie diet. As expected, we found that subjects with higher behavioral impulse control subsequently lost more weight. Furthermore, brain activity before the diet in VMPFC and DLPFC correlates with subsequent weight loss. Additionally, a connectivity analysis revealed that stronger functional connectivity between these regions is associated with better dietary success and impulse control. Thus, the degree to which subjects can control their eating impulses might depend on the interplay between control regions (DLPFC) and regions signaling the reward of food (VMPFC). This could potentially constitute a general mechanism that also extends to other disorders such as drug addiction or alcohol abuse

Is cannabis a gateway to hard drugs?

Gateway hypothesis, Illicit drugs, Duration analysis, Latent class models, I12, I18,

Diagnostic performance of Clock Drawing Test by CLOX in an Asian Chinese population

10.1159/000107080Dementia and Geriatric Cognitive Disorders243193-200DGCD