Angiotensin-Converting Enzyme (ACE) Gene Insertion/Deletion Polymorphism and ACE Inhibitor-Related Cough: A Meta-Analysis

Objective: An insertion/deletion (I/D) variant in the angiotensin-converting enzyme (ACE) gene was associated with ACE inhibitor (ACEI)-related cough in previous studies. However, the results were inconsistent. Our objective was to assess the relationship between the ACE I/D polymorphism and ACEI-related cough by meta-analysis and to summarize all studies that are related to ACE I/D polymorphism and ACEI-cough and make a summary conclusion to provide reference for the researchers who attempt to conduct such a study. Methods: Databases including PubMed, EMbase, Cochrane Library, and China National Knowledge Infrastructure, were searched for genetic association studies. Data were extracted by two independent authors and pooled odds ratio (OR) with 95% confidence interval (CI) was calculated. Metaregression and subgroup analyses were performed to identify the source of heterogeneity. Results: Eleven trials, including 906 cases (ACEI-related cough) and 1,175 controls, were reviewed in the present meta-analysis. The random effects pooled OR was 1.16 (95% CI: 0.78-1.74, p = 0.46) in the dominant model and 1.61 (95% CI: 1.18-2.20, p = 0.003) in the recessive model. Heterogeneity was found among and within studies. Metaregression indicated that the effect size was positively associated with age and negatively associated with follow-up duration of ACEI treatment. Subgroup analysis revealed a significant association between ACE I/D polymorphism and ACEI-related cough in studies with mean age >60 y, but not in studies with mean age 2 mo or in studies in Caucasians. No heterogeneity was detected in these two subgroups. Conclusions: Synthesis of the available evidence supports ACE I/D polymorphism as an age-dependent predictor for risk of ACEI-related cough

A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

<p>Abstract</p> <p>Background</p> <p>The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.</p> <p>Methods</p> <p>Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.</p> <p>Results</p> <p>Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (<it>P </it>< 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).</p> <p>Conclusions</p> <p>Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.</p

Plakophilin3 Loss Leads to an Increase in PRL3 Levels Promoting K8 Dephosphorylation, Which Is Required for Transformation and Metastasis

The desmosome anchors keratin filaments in epithelial cells leading to the formation of a tissue wide IF network. Loss of the desmosomal plaque protein plakophilin3 (PKP3) in HCT116 cells, leads to an increase in neoplastic progression and metastasis, which was accompanied by an increase in K8 levels. The increase in levels was due to an increase in the protein levels of the Phosphatase of Regenerating Liver 3 (PRL3), which results in a decrease in phosphorylation on K8. The increase in PRL3 and K8 protein levels could be reversed by introduction of an shRNA resistant PKP3 cDNA. Inhibition of K8 expression in the PKP3 knockdown clone S10, led to a decrease in cell migration and lamellipodia formation. Further, the K8 PKP3 double knockdown clones showed a decrease in colony formation in soft agar and decreased tumorigenesis and metastasis in nude mice. These results suggest that a stabilisation of K8 filaments leading to an increase in migration and transformation may be one mechanism by which PKP3 loss leads to tumor progression and metastasis

Regulation of the Fruit-Specific PEP Carboxylase SlPPC2 Promoter at Early Stages of Tomato Fruit Development

The SlPPC2 phosphoenolpyruvate carboxylase (PEPC; EC 4.1.1.31) gene from tomato (Solanum lycopersicum) is differentially and specifically expressed in expanding tissues of developing tomato fruit. We recently showed that a 1966 bp DNA fragment located upstream of the ATG codon of the SlPPC2 gene (GenBank AJ313434) confers appropriate fruit-specificity in transgenic tomato. In this study, we further investigated the regulation of the SlPPC2 promoter gene by analysing the SlPPC2 cis-regulating region fused to either the firefly luciferase (LUC) or the β-glucuronidase (GUS) reporter gene, using stable genetic transformation and biolistic transient expression assays in the fruit. Biolistic analyses of 5′ SlPPC2 promoter deletions fused to LUC in fruits at the 8th day after anthesis revealed that positive regulatory regions are mostly located in the distal region of the promoter. In addition, a 5′ UTR leader intron present in the 1966 bp fragment contributes to the proper temporal regulation of LUC activity during fruit development. Interestingly, the SlPPC2 promoter responds to hormones (ethylene) and metabolites (sugars) regulating fruit growth and metabolism. When tested by transient expression assays, the chimeric promoter:LUC fusion constructs allowed gene expression in both fruit and leaf, suggesting that integration into the chromatin is required for fruit-specificity. These results clearly demonstrate that SlPPC2 gene is under tight transcriptional regulation in the developing fruit and that its promoter can be employed to drive transgene expression specifically during the cell expansion stage of tomato fruit. Taken together, the SlPPC2 promoter offers great potential as a candidate for driving transgene expression specifically in developing tomato fruit from various tomato cultivars

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field