Metabolic regulation of ApoB mRNA editing is associated with phosphorylation of APOBEC-1 complementation factor

Apolipoprotein B (apoB) mRNA editing is a nuclear event that minimally requires the RNA substrate, APOBEC-1 and APOBEC-1 Complementation Factor (ACF). The co-localization of these macro-molecules within the nucleus and the modulation of hepatic apoB mRNA editing activity have been described following a variety of metabolic perturbations, but the mechanism that regulates editosome assembly is unknown. APOBEC-1 was effectively co-immunoprecipitated with ACF from nuclear, but not cytoplasmic extracts. Moreover, alkaline phosphatase treatment of nuclear extracts reduced the amount of APOBEC-1 co-immunoprecipitated with ACF and inhibited in vitro editing activity. Ethanol stimulated apoB mRNA editing was associated with a 2- to 3-fold increase in ACF phosphorylation relative to that in control primary hepatocytes. Significantly, phosphorylated ACF was restricted to nuclear extracts where it co-sedimented with 27S editing competent complexes. Two-dimensional phosphoamino acid analysis of ACF immunopurified from hepatocyte nuclear extracts demonstrated phosphorylation of serine residues that was increased by ethanol treatment. Inhibition of protein phosphatase I, but not PPIIA or IIB, stimulated apoB mRNA editing activity coincident with enhanced ACF phosphorylation in vivo. These data demonstrate that ACF is a metabolically regulated phosphoprotein and suggest that this post-translational modification increases hepatic apoB mRNA editing activity by enhancing ACF nuclear localization/retention, facilitating the interaction of ACF with APOBEC-1 and thereby increasing the probability of editosome assembly and activity

The harvest plot: A method for synthesising evidence about the differential effects of interventions

<p>Abstract</p> <p>Background</p> <p>One attraction of meta-analysis is the forest plot, a compact overview of the essential data included in a systematic review and the overall 'result'. However, meta-analysis is not always suitable for synthesising evidence about the effects of interventions which may influence the wider determinants of health. As part of a systematic review of the effects of population-level tobacco control interventions on social inequalities in smoking, we designed a novel approach to synthesis intended to bring aspects of the graphical directness of a forest plot to bear on the problem of synthesising evidence from a complex and diverse group of studies.</p> <p>Methods</p> <p>We coded the included studies (n = 85) on two methodological dimensions (suitability of study design and quality of execution) and extracted data on effects stratified by up to six different dimensions of inequality (income, occupation, education, gender, race or ethnicity, and age), distinguishing between 'hard' (behavioural) and 'intermediate' (process or attitudinal) outcomes. Adopting a hypothesis-testing approach, we then assessed which of three competing hypotheses (positive social gradient, negative social gradient, or no gradient) was best supported by each study for each dimension of inequality.</p> <p>Results</p> <p>We plotted the results on a matrix ('harvest plot') for each category of intervention, weighting studies by the methodological criteria and distributing them between the competing hypotheses. These matrices formed part of the analytical process and helped to encapsulate the output, for example by drawing attention to the finding that increasing the price of tobacco products may be more effective in discouraging smoking among people with lower incomes and in lower occupational groups.</p> <p>Conclusion</p> <p>The harvest plot is a novel and useful method for synthesising evidence about the differential effects of population-level interventions. It contributes to the challenge of making best use of all available evidence by incorporating all relevant data. The visual display assists both the process of synthesis and the assimilation of the findings. The method is suitable for adaptation to a variety of questions in evidence synthesis and may be particularly useful for systematic reviews addressing the broader type of research question which may be most relevant to policymakers.</p

Development and application of the DePtH framework for categorising the agentic demands of population health interventions [Pre-print]

The ‘agentic demand’ of population health interventions may influence intervention effectiveness and equity, yet the absence of an adequate framework to classify agentic demands limits the fields’ advancement. We systematically developed the DEmands for PopulaTion Health Interventions (DePtH) framework identifying three constructs influencing agentic demand - exposure (initial contact with intervention), mechanism of action (how the intervention enables or discourages behaviour), and engagement (recipient response), combined into twenty classifications. We conducted expert qualitative feedback and reliability testing, revised the framework and applied it in a proof-of-concept review, combining it with data on overall effectiveness and equity of dietary and physical activity interventions. Intervention components were concentrated in a small number of classifications; DePtH classification appeared to be related to intervention equity but not effectiveness. This framework holds potential for future research, policy and practice, facilitating the design, selection, evaluation and synthesis of evidence

Development and application of the Demands for Population Health Interventions (Depth) framework for categorising the agentic demands of population health interventions

Background: The ‘agentic demand’ of population health interventions (PHIs) refers to the capacity, resources and freedom to act that interventions demand of their recipients to benefit, which have a socio-economical pattern. Highly agentic interventions, e.g. information campaigns, rely on recipients noticing and responding to the intervention and thus might affect intervention effectiveness and equity. The absence of an adequate framework to classify agentic demands limits the fields’ ability to systematically explore these associations. Methods: We systematically developed the Demands for Population Health Interventions (Depth) framework using an iterative approach: (1) Developing the Depth framework by systematically identifying examples of PHIs aiming to promote healthier diets and physical activity, coding of intervention actors and actions and synthesising the data to develop the framework; (2) Testing the Depth framework in online workshops with academic and policy experts and a quantitative reliability assessment. We applied the final framework in a proof-of-concept review, extracting studies from three existing equity focused systematic reviews on framework category, overall effectiveness and differential socioeconomic effects and visualised the findings in Harvest Plots. Results: The Depth framework identifies three constructs influencing agentic demand: exposure - initial contact with intervention (2 levels), mechanism of action - how the intervention enables or discourages behaviour (5 levels), and engagement - recipient response (2 levels). When combined, these constructs form a matrix of twenty possible classifications. In the proof-of-concept review, we classified all components of 31 interventions according to the Depth framework. Intervention components were concentrated in a small number of Depth classifications; Depth classification appeared to be related to intervention equity but not effectiveness. Conclusions: This framework holds potential for future research, policy and practice, facilitating the design, selection and evaluation of interventions and evidence synthesis

Data from a pre-publication independent replication initiative examining ten moral judgement effects

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science

The pipeline project: Pre-publication independent replications of a single laboratory's research pipeline

This crowdsourced project introduces a collaborative approach to improving the reproducibility of scientific research, in which findings are replicated in qualified independent laboratories before (rather than after) they are published. Our goal is to establish a non-adversarial replication process with highly informative final results. To illustrate the Pre-Publication Independent Replication (PPIR) approach, 25 research groups conducted replications of all ten moral judgment effects which the last author and his collaborators had “in the pipeline” as of August 2014. Six findings replicated according to all replication criteria, one finding replicated but with a significantly smaller effect size than the original, one finding replicated consistently in the original culture but not outside of it, and two findings failed to find support. In total, 40% of the original findings failed at least one major replication criterion. Potential ways to implement and incentivize pre-publication independent replication on a large scale are discussed

Crowdsourcing hypothesis tests: Making transparent how design choices shape research results

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer fiveoriginal research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were then randomly assigned to complete one version of each study. Effect sizes varied dramatically across different sets of materials designed to test the same hypothesis: materials from different teams renderedstatistically significant effects in opposite directions for four out of five hypotheses, with the narrowest range in estimates being d = -0.37 to +0.26. Meta-analysis and a Bayesian perspective on the results revealed overall support for two hypotheses, and a lack of support for three hypotheses. Overall, practically none of the variability in effect sizes was attributable to the skill of the research team in designing materials, while considerable variability was attributable to the hypothesis being tested. In a forecasting survey, predictions of other scientists were significantly correlated with study results, both across and within hypotheses. Crowdsourced testing of research hypotheses helps reveal the true consistency of empirical support for a scientific claim.</div

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Data from a pre-publication independent replication initiative examining ten moral judgement effects

We present the data from a crowdsourced project seeking to replicate findings in independent laboratories before (rather than after) they are published. In this Pre-Publication Independent Replication (PPIR) initiative, 25 research groups attempted to replicate 10 moral judgment effects from a single laboratory's research pipeline of unpublished findings. The 10 effects were investigated using online/lab surveys containing psychological manipulations (vignettes) followed by questionnaires. Results revealed a mix of reliable, unreliable, and culturally moderated findings. Unlike any previous replication project, this dataset includes the data from not only the replications but also from the original studies, creating a unique corpus that researchers can use to better understand reproducibility and irreproducibility in science.Link_to_subscribed_fulltex