A Pair of Dopamine Neurons Target the D1-Like Dopamine Receptor DopR in the Central Complex to Promote Ethanol-Stimulated Locomotion in Drosophila

Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol

Maternal corticotropin-releasing hormone is associated with LEP DNA methylation at birth and in childhood: an epigenome-wide study in Project Viva

BackgroundCorticotropin-releasing hormone (CRH) plays a central role in regulating the secretion of cortisol which controls a wide range of biological processes. Fetuses overexposed to cortisol have increased risks of disease in later life. DNA methylation may be the underlying association between prenatal cortisol exposure and health effects. We investigated associations between maternal CRH levels and epigenome-wide DNA methylation of cord blood in offsprings and evaluated whether these associations persisted into mid-childhood.MethodsWe investigated mother-child pairs enrolled in the prospective Project Viva pre-birth cohort. We measured DNA methylation in 257 umbilical cord blood samples using the HumanMethylation450 Bead Chip. We tested associations of maternal CRH concentration with cord blood cells DNA methylation, adjusting the model for maternal age at enrollment, education, maternal race/ethnicity, maternal smoking status, pre-pregnancy body mass index, parity, gestational age at delivery, child sex, and cell-type composition in cord blood. We further examined the persistence of associations between maternal CRH levels and DNA methylation in children's blood cells collected at mid-childhood (n = 239, age: 6.7-10.3 years) additionally adjusting for the children's age at blood drawn.ResultsMaternal CRH levels are associated with DNA methylation variability in cord blood cells at 96 individual CpG sites (False Discovery Rate <0.05). Among the 96 CpG sites, we identified 3 CpGs located near the LEP gene. Regional analyses confirmed the association between maternal CRH and DNA methylation near LEP. Moreover, higher maternal CRH levels were associated with higher blood-cell DNA methylation of the promoter region of LEP in mid-childhood (P < 0.05, β = 0.64, SE = 0.30).ConclusionIn our cohort, maternal CRH was associated with DNA methylation levels in newborns at multiple loci, notably in the LEP gene promoter. The association between maternal CRH and LEP DNA methylation levels persisted into mid-childhood

Recommendations for a core outcome set for measuring standing balance in adult populations: a consensus-based approach

Standing balance is imperative for mobility and avoiding falls. Use of an excessive number of standing balance measures has limited the synthesis of balance intervention data and hampered consistent clinical practice.To develop recommendations for a core outcome set (COS) of standing balance measures for research and practice among adults.A combination of scoping reviews, literature appraisal, anonymous voting and face-to-face meetings with fourteen invited experts from a range of disciplines with international recognition in balance measurement and falls prevention. Consensus was sought over three rounds using pre-established criteria.The scoping review identified 56 existing standing balance measures validated in adult populations with evidence of use in the past five years, and these were considered for inclusion in the COS.Fifteen measures were excluded after the first round of scoring and a further 36 after round two. Five measures were considered in round three. Two measures reached consensus for recommendation, and the expert panel recommended that at a minimum, either the Berg Balance Scale or Mini Balance Evaluation Systems Test be used when measuring standing balance in adult populations.Inclusion of two measures in the COS may increase the feasibility of potential uptake, but poses challenges for data synthesis. Adoption of the standing balance COS does not constitute a comprehensive balance assessment for any population, and users should include additional validated measures as appropriate.The absence of a gold standard for measuring standing balance has contributed to the proliferation of outcome measures. These recommendations represent an important first step towards greater standardization in the assessment and measurement of this critical skill and will inform clinical research and practice internationally

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

<p>Abstract</p> <p>Background</p> <p>Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).</p> <p>Results</p> <p>In this paper, we further explore the differences and links between the F_∞and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞model. A new way of linking the Cockerham and F_∞model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞models are applied.</p> <p>Conclusion</p> <p>The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞model can then be calculated by translating formulas derived for the Cockerham models.</p

Clinical Usefulness of Measuring Red Blood Cell Distribution Width in Patients with Hepatitis B

BACKGROUND: Red blood cell distribution width (RDW), an automated measure of red blood cell size heterogeneity (e.g., anisocytosis) that is largely overlooked, is a newly recognized risk marker in patients with cardiovascular diseases, but its role in persistent viral infection has not been well-defined. The present study was designed to investigate the association between RDW values and different disease states in hepatitis B virus (HBV)-infected patients. In addition, we analyzed whether RDW is associated with mortality in the HBV-infected patients. METHODOLOGY/PRINCIPAL FINDINGS: One hundred and twenty-three patients, including 16 with acute hepatitis B (AHB), 61 with chronic hepatitis B (CHB), and 46 with chronic severe hepatitis B (CSHB), and 48 healthy controls were enrolled. In all subjects, a blood sample was collected at admission to examine liver function, renal function, international normalized ratio and routine hematological testing. All patients were followed up for at least 4 months. A total of 10 clinical chemistry, hematology, and biochemical variables were analyzed for possible association with outcomes by using Cox proportional hazards and multiple regression models. RDW values at admission in patients with CSHB (18.30±3.11%, P<0.001), CHB (16.37±2.43%, P<0.001) and AHB (14.38±1.72%, P<0.05) were significantly higher than those in healthy controls (13.03±1.33%). Increased RDW values were clinically associated with severe liver disease and increased 3-month mortality rate. Multivariate analysis demonstrated that RDW values and the model for end-stage liver disease score were independent predictors for mortality (both P<0.001). CONCLUSION: RDW values are significantly increased in patients with hepatitis B and associated with its severity. Moreover, RDW values are an independent predicting factor for the 3-month mortality rate in patients with hepatitis B

A distortion of very--high--redshift galaxy number counts by gravitational lensing

The observed number counts of high-redshift galaxy candidates have been used to build up a statistical description of star-forming activity at redshift z >~ 7, when galaxies reionized the Universe. Standard models predict that a high incidence of gravitational lensing will probably distort measurements of flux and number of these earliest galaxies. The raw probability of this happening has been estimated to be ~ 0.5 percent, but can be larger owing to observational biases. Here we report that gravitational lensing is likely to dominate the observed properties of galaxies with redshifts of z >~ 12, when the instrumental limiting magnitude is expected to be brighter than the characteristic magnitude of the galaxy sample. The number counts could be modified by an order of magnitude, with most galaxies being part of multiply imaged systems, located less than 1 arcsec from brighter foreground galaxies at z ~ 2. This lens-induced association of high-redshift and foreground galaxies has perhaps already been observed among a sample of galaxy candidates identified at z ~ 10.6. Future surveys will need to be designed to account for a significant gravitational lensing bias in high-redshift galaxy samples.Comment: Nature, Jan. 13, 2011 issue (in press

Identifying Modules of Coexpressed Transcript Units and Their Organization of Saccharopolyspora erythraea from Time Series Gene Expression Profiles

BACKGROUND: The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. METHODOLOGY/PRINCIPAL FINDINGS: In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. CONCLUSIONS: This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms

Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients