Playing Dissymmetry in Action Research: The Role of Power and Differences in Promoting Participative Knowledge and Change

This article explores the importance of power and dissymmetry in promoting participative knowledge and change in action research. Based on the analysis of two action research cases, the paper builds its argument by analyzing two key aspects: the construction of the action research setting and its maintenance during the process. It does so by highlighting the decisions assumed with respect to the relationship between researchers and participants and with respect to power issues. The findings indicate that promoting a functional dissymmetry in internal relationships allows distribution of the necessary types of power that make the participants use their authority and knowledge to invest in change. Thus, the distributed leadership is essential every time an organization needs to create a realistic and workable change of roles and responsibilities inside its boundaries. The article discusses some key factors in employing dissymmetry for sustained learning and knowledge-sharing.     

Glucose sensing by POMC neurons regulates glucose homeostasis and is impaired in obesity

A subset of neurons in the brain, known as 'glucose-excited' neurons, depolarize and increase their firing rate in response to increases in extracellular glucose. Similar to insulin secretion by pancreatic beta-cells, glucose excitation of neurons is driven by ATP-mediated closure of ATP-sensitive potassium (K(ATP)) channels. Although beta-cell-like glucose sensing in neurons is well established, its physiological relevance and contribution to disease states such as type 2 diabetes remain unknown. To address these issues, we disrupted glucose sensing in glucose-excited pro-opiomelanocortin (POMC) neurons via transgenic expression of a mutant Kir6.2 subunit (encoded by the Kcnj11 gene) that prevents ATP-mediated closure of K(ATP) channels. Here we show that this genetic manipulation impaired the whole-body response to a systemic glucose load, demonstrating a role for glucose sensing by POMC neurons in the overall physiological control of blood glucose. We also found that glucose sensing by POMC neurons became defective in obese mice on a high-fat diet, suggesting that loss of glucose sensing by neurons has a role in the development of type 2 diabetes. The mechanism for obesity-induced loss of glucose sensing in POMC neurons involves uncoupling protein 2 (UCP2), a mitochondrial protein that impairs glucose-stimulated ATP production. UCP2 negatively regulates glucose sensing in POMC neurons. We found that genetic deletion of Ucp2 prevents obesity-induced loss of glucose sensing, and that acute pharmacological inhibition of UCP2 reverses loss of glucose sensing. We conclude that obesity-induced, UCP2-mediated loss of glucose sensing in glucose-excited neurons might have a pathogenic role in the development of type 2 diabetes.     

A second generation human haplotype map of over 3.1 million SNPs

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.     

Genome-wide detection and characterization of positive selection in human populations

With the advent of dense maps of human genetic variation, it is now possible to detect positive natural selection across the human genome. Here we report an analysis of over 3 million polymorphisms from the International HapMap Project Phase 2 (HapMap2). We used 'long-range haplotype' methods, which were developed to identify alleles segregating in a population that have undergone recent selection, and we also developed new methods that are based on cross-population comparisons to discover alleles that have swept to near-fixation within a population. The analysis reveals more than 300 strong candidate regions. Focusing on the strongest 22 regions, we develop a heuristic for scrutinizing these regions to identify candidate targets of selection. In a complementary analysis, we identify 26 non-synonymous, coding, single nucleotide polymorphisms showing regional evidence of positive selection. Examination of these candidates highlights three cases in which two genes in a common biological process have apparently undergone positive selection in the same population:LARGE and DMD, both related to infection by the Lassa virus, in West Africa;SLC24A5 and SLC45A2, both involved in skin pigmentation, in Europe; and EDAR and EDA2R, both involved in development of hair follicles, in Asia.     

The ribosome uses two active mechanisms to unwind messenger RNA during translation

The ribosome translates the genetic information encoded in messenger RNA into protein. Folded structures in the coding region of an mRNA represent a kinetic barrier that lowers the peptide elongation rate, as the ribosome must disrupt structures it encounters in the mRNA at its entry site to allow translocation to the next codon. Such structures are exploited by the cell to create diverse strategies for translation regulation, such as programmed frameshifting, the modulation of protein expression levels, ribosome localization and co-translational protein folding. Although strand separation activity is inherent to the ribosome, requiring no exogenous helicases, its mechanism is still unknown. Here, using a single-molecule optical tweezers assay on mRNA hairpins, we find that the translation rate of identical codons at the decoding centre is greatly influenced by the GC content of folded structures at the mRNA entry site. Furthermore, force applied to the ends of the hairpin to favour its unfolding significantly speeds translation. Quantitative analysis of the force dependence of its helicase activity reveals that the ribosome, unlike previously studied helicases, uses two distinct active mechanisms to unwind mRNA structure: it destabilizes the helical junction at the mRNA entry site by biasing its thermal fluctuations towards the open state, increasing the probability of the ribosome translocating unhindered; and it mechanically pulls apart the mRNA single strands of the closed junction during the conformational changes that accompany ribosome translocation. The second of these mechanisms ensures a minimal basal rate of translation in the cell; specialized, mechanically stable structures are required to stall the ribosome temporarily. Our results establish a quantitative mechanical basis for understanding the mechanism of regulation of the elongation rate of translation by structured mRNAs.     

Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies

Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease. Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion (pi(samples)) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone. We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >or= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >or= 0.01).