The present study addressed two under-researched questions concerning the QBE: (1) Can the QBE be used to change multiple health behaviors, and (2) does enhancing dissonance during questionnaire completion increase the magnitude of the QBE? Participants (N = 1534) were randomized to one of three conditions  that targeted three health-protective behaviors  and three health-risk behaviors . The dissonance-enhanced intervention comprised a message designed to pressurize participants into forming healthful behavioral intentions. Behavior was assessed via self-reports at four-week follow up. Findings showed significant overall effects of the QBE both in increasing performance of health-protective behaviors (p = .001) and in reducing performance of health-risk behaviors (p = .04). Compared to the standard QBE condition, the dissonance-enhanced QBE intervention increased performance of health-protective behaviors (p = .04) and marginally reduced performance of health-risk behaviors (p = .07). The dissonance-enhanced QBE intervention outperformed the control condition in all analyses. This is the first report that a brief QBE intervention influences performance of multiple health behaviors. Findings supported the idea that magnifying dissonance increases the impact of the QBE.Asking questions about a behavior has been found to influence subsequent performance of that behavior, a phenomenon termed the  Health behaviors are associated with a range of long-term health outcomes . For example, low rates of fruit and vegetable consumption and physical activity, together with high levels of alcohol consumption and smoking are estimated to account for two-thirds of cardiovascular disease, cancer, and all-cause mortality . Improve1d = 0.24). In relation to health behaviors, The question-behavior effect refers to the finding that answering questions about a behavior produces a small-sized change in subsequent performance of that behavior  by highlighting the importance of health and behaving healthily as part of an introductory text presented at the beginning of the questionnaire. This manipulation was designed to powerfully activate norms about health behavior and make participants feel that they should both intend to behave healthily and act in line with their heightened intentions. Thus, the present study had three conditions: A control condition (no questions about target behaviors), a standard QBE condition (questions about target behaviors), and a dissonance-enhanced QBE condition (dissonance-inducing introductory text plus questions about target behaviors); effects were assessed on multiple health-protective and health-risk behaviors measured one month later. The present study offers the first attempt to directly manipulate dissonance in order to maximize the QBE, and thus offers a strong test of the cognitive dissonance explanation of the QBE .31)Asking cognition and behavior questions about increasing health-protective behaviors will promote performance of these behaviors compared to the control condition;2)Asking cognition and behavior questions about reducing health-risk behaviors will decrease performance of these behaviors compared to the control condition;3)Magnifying dissonance will augment the QBE; participants in the dissonance-enhanced QBE condition will perform (a) health-protective behaviors more frequently, and (b) health-risk behaviors less frequently compared to either the standard QBE or control conditions.The present study tested (a) whether the QBE influences rates of performance of three health-protective and three health-risk behaviors, and (b) whether magnifying dissonance increases the behavioral impact of the QBE. We formed the following hypotheses:44.1d = 0.17; p = .05 and 80% power, a total of 1338 participants spread across three conditions was required. Participants were recruited via Prolific Academic, an online database of participants interested in taking part in research from a range of academic areas. In order to allow for drop out we aimed to recruit 2000 participants at baseline . From a database of approximately 26,000 potential participants, a total of 1958 individuals were recruited and completed part 1 of the study. A priori sample size calculations using G*Power were conducted based on average effect sizes for QBE studies conducted for health behaviors and studies using an online setting (average 4.2The study was approved by a University Ethics Committee. Participants first gave informed consent and were then randomized to complete one of six questionnaires (3 conditions × 2 orders of questions). (Conditions are described below.) As questionnaire order had no significant effects and did not interact with condition, this factor is not considered further. Next, participants provided their demographic details before completing the questionnaire relevant to their assigned condition. After four weeks, participants reported their performance of six health behaviors over the previous four weeks. Participants also completed personality scales (after completing the behavior measures) that are not relevant to the present report. Finally, all participants were fully debriefed, thanked, and paid.4.3Participants were randomized to one of three conditions using a random number generator when participants clicked on the study link: (1) Standard QBE condition , (2) Dissonance-enhanced QBE condition , (3) Control condition (questionnaire related to purchasing behavior). Participants and researchers were blinded to condition. The dependent variable was self-reported performance of each of the six health behaviors during the past month at one-month follow-up. All purchase behaviors questioned in the control condition were selected to reduce confounding with health behavior .The questionnaire order was counterbalanced so that participants in the experimental conditions (standard QBE or dissonance-enhanced QBE) were randomly allocated to either receive the questions on health-protective behaviors first or were allocated to receive the health-risk questions first.All respondents first completed demographic questions tapping gender, age (split into <30 versus ≥30 years of age), ethnicity (split into Caucasian versus non-Caucasian), education (split into completed higher education versus not), and employment . Nationality, income and two ladder measures of socioeconomic status  were als4.4Participants in the two QBE conditions received a health behavior questionnaire. There were three health-protective behaviors , and three health-risk behaviors . The questionnaire comprised a total of 102 items, 15 cognition items for each of the six behaviors plus two past behavior items for each of the six behaviors presented at the end of the questionnaire. Each behavior was introduced and defined at the start of the set of questions. Questions were based on guidance for developing Theory of Planned Behavior questionnaires  and askeCognitions were assessed for each behavior as follows: Five intention items ; one self-efficacy item ; one perceived behavioral control item (“How much control do you believe you have over…” No control-complete control); four attitude items ; one injunctive norm item (“Most people important to me think that” I should-I should not …eat five fruit or vegetables a day over the next four weeks); one descriptive norm item (e.g. “I think that most people who are important to meat five fruit and vegetables a day”); one prioritization item ; one context stability item ; and two past behavior items . Items were scored such that higher scores indicated a more positive view of the healthier behavior. The full set of items is available from the corresponding author.4.5After completing demographic details and before completing the health behavior questionnaire, participants in the dissonance-enhanced QBE condition were exposed to a message that emphasized the importance of healthy living and described how much behavior determines one's health. The text explained that up to 1/3 of cancers and 80% of heart disease, stroke and Type 2 diabetes could be prevented if smoking, unhealthy diet, lack of physical activity, and alcohol use were removed as risk factors. The message also emphasized the importance of behaving healthily and encouraged participants to “Make the healthy choice!” The goal of the message was to induce participants to form more healthful intentions than they otherwise might, and evoke dissonance should they fail to realize those intentions.rs = 0.58 to 0.64). We anticipated that scores on this measure would be correlated with how strongly participants intended to perform health-protective behaviors and reduce health-risk behaviors.1,2As dissonance is notoriously difficult to measure directly e.g., , we adop4.6Participants in the control condition completed equivalent items (15 cognition items plus two past behavior items for each of six behaviors) as participants in the experimental conditions but the items related to six purchase behaviors: purchasing groceries, purchasing toiletries and/or cosmetics, purchasing household cleaning items, reducing clothing purchasing, reducing music purchasing , and reducing spending.4.7At 4-week follow-up, all participants were asked three questions about their frequency of performance of each the six health behaviors . The three measures for each behavior were standardized and averaged to form the measure of behavior at follow-up (αs = 0.56–0.95). These were the primary outcome measures.ori.org) and the 21-item personal life investments schedule ; a thirteen item self-monitoring scale , a ten ischedule . None of4.8Representativeness and randomization checks used Chi-square and one-way ANOVA to test for differences on demographic variables. The manipulation check used a mixed-model ANOVA and correlations. The analyses for behavior at follow-up involved a mixed-model hierarchical ANOVA with one overarching factor  and a nested factor representing the Type of QBE (dissonance-enhanced QBE vs. standard QBE), with type of behavior (protective vs. risk) as a within-subjects factor. Interactions with type of behavior were decomposed by examining risk and protective behaviors separately and included specific behavior  as a within-subjects factor.55.1χ2 = 4.79, p = .09, ethnicity χ2 = 3.12, p = .21, education χ2 = 2.06, p = .36, age group χ2 = 4.08, p = .13, or employment status χ2 = 2.43, p = .66.Randomization to condition was successful. There were no significant differences between the three conditions for gender χ2 = 4.50, p = .03, and were more likely to have completed higher education, χ2 = 12.82, p < .001. There were no differences for gender χ2 = 1.60, p = .21, ethnicity χ2 = 0.56, p = .45, or employment status χ2 = 4.60, p = .10.There were significant differences between participants who completed the baseline questionnaire only as compared to participants who completed both the baseline and follow-up questionnaires. Participants who completed both questionnaires were more likely to be in the older age group, 5.2F = 11.41, p < .01, eta2 = 0.009. As predicted, intentions were significantly higher in the dissonance-enhanced QBE condition  than the standard QBE condition . There was a significant effect of behavior, F = 172.46, p < .01, eta2 = 0.118. However, there was no significant interaction between Type of QBE condition and Type of behavior, F = 0.95, p = .45, eta2 = 0.001, indicating that the increase in intention scores was equivalent for all six health behaviors.Two sets of analyses were used to check whether the dissonance manipulation was successful. First, we anticipated that participants in the dissonance-enhanced QBE condition would have higher intentions to perform health-protective behaviors and to reduce health-risk behaviors compared to the standard QBE condition. A 2 (Type of QBE condition: standard QBE vs. dissonance-enhanced QBE) × 6 (Type of behavior) mixed-model ANOVA revealed a significant main effect of Type of QBE condition, r = 0.22, p < .001) in the dissonance-enhanced QBE condition; higher dissonance scores were associated with stronger intentions to perform healthier behaviors. Thus, the dissonance-enhanced QBE intervention passed both manipulation checks.Second, we anticipated that the dissonance-enhanced QBE condition would exhibit increased intention scores precisely because the manipulation made participants feel that they ought to rate their intentions as higher. Consistent with this idea, there was a significant correlation between scores on the dissonance items and intention ratings  = 4.63, p = .003, eta2 = 0.009. No nested effect of the Type of QBE condition (dissonance-enhanced QBE vs. standard QBE) was observed, F = 0.72, p = .54, eta2 = 0.001. However, there was a significant interaction between type of behavior (protective vs. risk behavior) and condition (QBE vs. control), F = 3.87, p = .009, eta2 = 0.008. We decomposed the interaction by conducting separate analyses for health-risk and health-protective behaviors , F = 4.08, p = .04, eta2 = 0.003. There was a marginally significant effect of the nested factor, Type of QBE condition, F = 3.39, p = .07, eta2 = 0.002. Pairwise comparisons again indicated that performance of health-risk behaviors was significantly (p = .008) higher in the control condition compared to the dissonance-enhanced QBE condition, but was not significantly different between the control and the standard QBE condition (p = .41). Risk behaviors were performed marginally significantly (p = .07) less frequently by participants in the dissonance-enhanced QBE condition than the standard QBE condition. Thus, the overall significant effect of the QBE factor was due to the dissonance-enhanced QBE condition. There was no behavior × QBE interaction, F = 1.10, p = .33, eta2 = 0.001 suggesting this QBE effect did not vary across the three different risk behaviors.There was a significant main effect of the overarching QBE vs. control factor indicating lower frequency of performing risk behaviors in the QBE conditions than the control condition , F = 11.02, p = .001, eta2 = 0.007. There was also a significant effect of the nested factor, Type of QBE condition, F = 4.22, p = .04, eta2 = 0.003. Pairwise comparisons again indicated that performance of health-protective behaviors was significantly (p < .001) lower in the control condition compared to the dissonance-enhanced QBE condition, but was not significantly different between the control and the standard QBE condition (p = .07). Protective behaviors were also performed significantly (p = .04) more frequently by participants in the dissonance-enhanced QBE condition than the standard QBE condition. There was no behavior × QBE interaction, F = 1.05, p = .35, eta2 = 0.001 indicating that all three protective behaviors increased as a result of exposure to the dissonance-enhanced QBE condition.There was a significant main effect of the QBE indicating greater frequency of performance of health-protective behaviors in the QBE conditions compared to the control condition (QBE-6question-behavior effect (QBE) has a small effect  in the dissonance-enhanced QBE condition and performance of health-risk behavior was marginally (p = .07) lower. These findings suggest that the magnitude of the QBE can be enhanced by deploying messages designed to evoke dissonance by highlighting potential discrepancies between intentions and health actions. We observed that a dissonance-based message increased behavioral intentions, and led to concomitant changes in subsequent health behaviors. In terms of health significance and potential applicability of these findings, the results support a small effect of the QBE on multiple behaviors. While the effects on behavior were small, the brief nature of the intervention and its online delivery method support its potential wide reach.The fact that the dissonance-enhanced QBE condition increased health-protective behaviors and reduced health-risk behaviors compared to the control condition whereas the standard QBE did not, offers one line of evidence supporting the superiority of the dissonance-enhanced QBE intervention tested here. Comparisons of the dissonance-enhanced QBE and standard QBE conditions also broadly supported this conclusion. Performance of health-protection behaviors was significantly higher  who were equivalent to the sample used here in terms of in gender, ethnicity, and education. Participants were asked whether they believed that completing surveys on protective or risk behaviors would influence their behavior. Given that there are four possible answers , the prior probability of the correct answer (yes to both) is 25%. In fact, 22% of participants got the correct answer which is not significantly different from 25%. Thus, it seems unlikely that participants' expectancies can account for the present findings.The present research has both strengths and limitations. On the plus side, we used a RCT design among a large and diverse sample that was powered BE e.g., , and invBE e.g.,  observedBE e.g.,  and typeBE e.g., . A seconThird, participants who completed both waves of data collection were older and better educated than participants who completed the first wave only. Caution is therefore warranted in making generalizations to other samples. Relatedly, while the use of an online sample is justified in this, the first strong test of the impact of the QBE on multiple behaviors, we acknowledge that further tests using other recruitment strategies are needed in order to better understand the applicability of the QBE. Fourth, completing the questionnaire in the dissonance-enhanced QBE condition may have taken longer than was the case in the standard QBE and control conditions, which could potentially explain the superior impact of this condition. Although we did not measure the time taken in the different conditions, this explanation seems unlikely as the dissonance-enhanced QBE condition only required the reading of a short piece of text plus answering two additional questions compared to the other two conditions. Finally, the study involved just six health behaviors and a 4-week follow-up period. Longer term follow-ups using a greater range of health behaviors would be desirable in order to assess the generality and durability of the QBE on behavior change.in situ and in what contexts does behavior change not occur even though participants were questioned about their future behavior. These shortcomings of the present study suggest valuable directions for future research in exploring how different contextual features might moderate the QBE.Finally, although the present research focused on multiple behavior change, we were unable, in a single study, to address several contextual factors that might influence the strength of the QBE. For instance, the setting in which questions are asked and the mode of question delivery could both be influential. Notwithstanding these limitations, the present study advances understanding of the QBE as a technique for promoting health behavior change. Whereas previous research focused on single health actions , the current study offers new evidence that the QBE can be used to change multiple health behaviors, and can both increase performance of health-protective behaviors and reduce performance of health-risk behaviors. The present study also indicates that standard QBE interventions are not always effective but that effectiveness can be enhanced by using messages designed to enhance dissonance. Based on these findings, further tests of dissonance-enhanced, QBE interventions targeting multiple health behaviors would seem warranted.


There is no consensus regarding the association between maternal obesity or overweight and cerebral palsy (CP) in children.To investigate whether maternal obesity or overweight is associated with CP and identify the factors that explain the differences in the study results.We conducted a meta-analysis of studies published in English with titles or abstracts that discussed the relationships between maternal obesity or overweight and CP before August 23, 2017, using Ovid Medline, EMBASE and Web of Science.Of 2699 initially identified studies, 8 studies that addressed the association between maternal obesity and CP met our final inclusion criteria.Information from the individual studies was abstracted using standardized forms by 2 independent observers who were blinded to the authors’ names and journal titles.I2 = 45.5%, P = 0.103)]; maternal obesity was significantly associated with CP in offspring ; and maternal obesity III was significantly associated with CP in offspring . However, maternal underweight was not significantly associated with CP in offspring . Factors that explained the differences in the meta-analysis results included study design, study location, and whether individual studies adjusted for potential confounders.According to a random effects model, maternal overweight was significantly associated with CP in offspring [RR = 1.29 , heterogeneity (This study suggests that maternal obesity and overweight increase the risk of CP in offspring. Further studies are required to confirm these results and determine the influence of variables across studies. Obesity is becoming an epidemic health problem. This trend is particularly true for maternal obesity and overweight, which may be associated with adverse obstetric complications, 2. MateCerebral palsy (CP), which leads to cognitive impairments and motor deficits, is multTo date, findings regarding this issue have been little known and inconsistent. Thus, we conducted a meta-analysis of human studies investigating the relationship between maternal obesity or overweight and CP in children.We searched Ovid Medline, EMBASE, and Web of Science for articles published before August 23, 2017. We used the following keywords and medical subject headings (MeSH) to search for the first theme: “overweight”, “over-weight”, “body weight”, “obes$”, “body weight changes”, “body fat distribution”, “body mass index”, “overnutrition”, “body weigh$”, “bodyweigh$”, “body mass$”, “bodymass”, “body fat$”, “bodyfat$”, “skinfold thickness”, and “waist-hip ratio”, using “OR” to connect relevant terms within the search field. For the second theme, we used “maternal”, “pregnancy”, “pregnan*”, “prenatal*”, “antenatal*”, “antepart*”, “gestat*”, “fetal development”, “transplacental exposure*”, “fetal programming”, “fetal growth”, “foetal development”, “gestational age”, “fetal age”, and “foetal age”, using “OR” to connect relevant terms within the search field. For the third theme, cerebral palsy, we used MeSH and key words such as “cerebral palsy”, “CP”, “spastic*”, “quadriplegi*”, “quadriplegia”, “tetraplegi*”, “diplegi*”, or “disabled children”. For the fourth theme, etiology, we used “risk”, “mortality”, or “cohort” to acquire the search results. We used “AND” to combine the first theme, the second theme, the third theme and the fourth theme . We rest2≤BMI<30 kg/m2), maternal obesity (BMI≥30 kg/m2), maternal obesity class I (30 kg/m2≤BMI<35 kg/m2), maternal obesity class II (35 kg/m2≤BMI<40 kg/m2), and maternal obesity class III (BMI≥40 kg/m2).The weight categories were defined as follows: maternal overweight (25 kg/mThe study inclusion criteria were as follows: (1) studies that evaluated the association between maternal overweight or obesity and the risk of CP in offspring; (2) a case-control or cohort study design; (3) studies that described the assessment of exposure and outcome; and (4) studies that reported hazard ratios (HRs) and the corresponding 95% confidence intervals (CIs), adjusted and/or unadjusted odds ratios (ORs) and 95% CIs, or adjusted and/or unadjusted relative risk (RR) estimates and 95% CIs for maternal overweight and obesity.The exclusion criteria for the study were as follows: (1) duplicated studies, (2) review or meta-analysis articles, (3) case reports, (4) studies published in a language other than English, (5) animal experiment studies, (6) studies that did not examine the relationship between maternal obesity/overweight and CP in offspring, (7) studies with overlapping data, and (8) studies with unusable data.Data were independently extracted from the studies by two reviewers (Dongqiong Xiao and Lan Huang). The extracted data included the name of the first author; publication year; the country of the participants; study design; sample size; the method for assessing maternal obesity, overweight and CP; primary outcome; and adjusted confounders.The two reviewers (Dongqiong Xiao and Lan Huang) independently used the Newcastle-Ottawa scale (NOS) to examiI2. I2>50% and P<0.1 indicated high heterogeneity. A forest plot was used to display the RR and 95% CIs for each study, as well as the pooled RR and 95% CIs. We conducted subgroup analyses based on study design (case-control or cohort), study location (USA or other), time of maternal BMI measurement (pre-pregnancy or other), and adjusted confounding variables . We performed sensitivity analyses by omitting one study at a time. Publication bias was assessed with Egger’s[The included original studies used ORs, RR, or HRs to assess the association between maternal obesity or overweight and the risk of CP in offspring. Because the P0, which is the incidence of the outcome in the nonexposed group, was very small in our study and because RR = OR/((1-P0)+P0*OR), RR≈OR; h Egger’s and Beggh Egger’s tests. PWe identified 2699 potential studies: 105 from Ovid Medline, 218 from EMBASE, and 2376 from Web of Science. After careful screening, 8 studies were selected for inclusion in this study . The ext2) status as the reference, one study[2) as the reference, and two studies[2) participants as the reference. For the sources of the data used to determine maternal BMI, one study[The included studies were published between 2002 and 2017. All included studies were cohort and case-control studies. The sample sizes varied from a minimum of 439 to a maximum of 6,221,001 patients. In terms of the timing of maternal BMI assessment, four of the included studies, 20,32 ao studies, 19 usedI2 = 0%, P = 0.435); maternal overweight was investigated in 6 studies, with RR = 1.29  and heterogeneity ; maternal obesity was investigated in 6 studies, with RR = 1.45  and heterogeneity ; and maternal obesity III was investigated in 4 studies, with RR = 2.25  and heterogeneity . The results from each study were adjusted for several potentially confounding variables, as shown in The original outcomes of the included studies are presented in A stratified analysis was conducted to investigate possible sources of heterogeneity in studies of maternal BMI and CP risk in offspring . The assThe association between maternal overweight and CP in offspring was inconsistent when stratified by different factors. Stronger associations between maternal overweight and CP in offspring were found in countries other than the USA. Stronger associations between maternal overweight and CP in offspring were found in retrospective cohort studies than in case-control studies. When the results were stratified by certain cofounding variables, stronger associations were found when adjustments were made for maternal age and maternal smoking status than when these confounding variables were not adjusted, while stronger associations were found without adjustments for the child’s sex, maternal race, and maternal diabetes status than when these confounding variables were adjusted.The association between maternal obesity and CP in offspring was consistent when stratified by study location, BMI assessment, maternal smoking and maternal diabetes. Stronger associations between maternal obesity and CP in offspring were found in retrospective cohort studies than in case-control studies. When the results were stratified by certain confounding variables, stronger associations were found with adjustments for maternal age than without adjustments for maternal age; however, stronger associations were found when the child’s sex and maternal race were not adjusted than when these parameters were adjusted.I2 = 24.3%, P = 0.259). For maternal obesity patients, when the study by Crisham et al[I2 = 0%, P = 0.434).Considering the small number of studies in our meta-analysis, we omitted one study at a time to perform a sensitivity analysis. For underweight patients and maternal obesity class III patients, no heterogeneity was observed during the sensitivity analysis. For overweight patients, when the study by Walstab et al was omitham et al was omitPublication bias tests and plots were conducted when the meta-analysis included more than 10 studies; however, these tests were underpowered to detect publication bias when there were only 8 studies included in the meta-analysis .To our knowledge, this study is the first meta-analysis of the relationship between maternal BMI and the risk of CP in offspring. The results of this meta-analysis, which included 8 studies with a total of 7,949,182 participants, showed that maternal overweight and obesity were significantly associated with CP in children. Maternal overweight, maternal obesity, and maternal obesity III were associated with a 29%, 45%, and 125% higher risk of CP in offspring, respectively. Maternal underweight was not associated with CP in offspring.2 doubled from 50 to 100 million between 2000 and 2010[Stronger associations between maternal obesity and CP risk were found in our meta-analysis, especially in the USA and Europe. Maternal obesity has increased globally in recent years, and the incidence of adverse outcomes is increasing. The num and 2010. The eti and 2010, race[17 and 2010, preterm and 2010, and neu and 2010, which m and 2010, which m and 2010.Maternal obesity might affect fetal neurodevelopment through multiple pathways. Maternal obesity has an impact on fetal anomaly screening and birtThe association between maternal overweight and obesity and CP in offspring was inconsistent when the results were stratified by study location, study design, and certain confounding variables. Maternal overweight and obesity could contribute to CP in children via multiple factors, including study location, study design, maternal age, maternal race, maternal smoking status, maternal diabetes status, and child gender. The heterogeneity decreased when the study of Pan et al was omitted, a result that may be related to the adjustment for maternal diabetes. In addition, the source of heterogeneity may be the factors in the stratified analysis that were mentioned above. Although the meta-analysis adjusted for several confounding variables, potential biases due to other factors that contribute to childhood CP cannot be excluded. Other factors may interact with maternal obesity along the pathogenetic pathway that leads to CP in offspring.This meta-analysis has several limitations. First, we may have missed some studies because we included only studies that were published in English and obtained from the Ovid Medline, EMBASE, and Web of Science databases. Furthermore, the pooled results of this meta-analysis should be interpreted with caution because they depend on a small number of studies, and the publication year may be a source of bias. Second, quantitative synthesis cannot eliminate the biases inherent to observational studies. Third, it is very difficult to visually assess publication bias when the number of included studies is only 8. Fourth, meta-analytic methods introduce limitations, especially when the source of heterogeneity is unclear or when publication bias is present. Although we did not find statistical evidence of publication bias, there may be publication bias, errors in data abstraction and incomplete ascertainment of published studies.One of the major advantages of our study is that most of the included original articles used a cohort design, which excludes the possibility of reverse causation. Additionally, the sample size was large, which enhanced the statistical power for precise and reliable estimation.In conclusion, our pooled analyses provide evidence that maternal obesity and overweight are significantly associated with CP in offspring. Additional studies are required to further investigate this question by identifying more risk factors.S1 Checklist(DOC)Click here for additional data file.S1 Data(DOC)Click here for additional data file.


Long-term intelligence and memory outcomes of children post convulsive status epilepticus (CSE) have not been systematically investigated despite evidence of short-term impairments in CSE. The present study aimed to describe intelligence and memory outcomes in children within 10 years of CSE and identify potential risk factors for adverse outcomes. In this cohort study, children originally identified by the population-based North London Convulsive Status Epilepticus in Childhood Surveillance Study (NLSTEPSS) were prospectively recruited between July 2009 and February 2013 and invited for neuropsychological assessments and magnetic resonance imaging (MRI) scans. Full-scale intelligence quotients (FSIQs) were measured using the Wechsler Abbreviated Scales of Intelligence (WASI), and global memory scores (GMS) was assessed using the Children's Memory Scale (CMS). The cohort was analyzed as a whole and stratified into a prolonged febrile seizures (PFS) and non-PFS group. Their performance was compared with population norms and controls. Regression models were fitted to identify predictors of outcomes. With a mean of 8.9 years post-CSE, 28.5% of eligible participants were unable to undertake testing because of their severe neurodevelopmental deficits. Children with CSE who undertook formal testing (N = 94) were shown to have significantly lower FSIQ (p = 0.001) and GMS (p = 0.025) from controls; the PFS group (N = 34) had lower FSIQs (p = 0.022) but similar memory quotients (p = 0.88) with controls. Intracranial volume (ICV), developmental delay at baseline, and active epilepsy at follow-up were predictive of long-term outcomes in the non-PFS group. The relationship between ICV and outcomes was absent in the PFS group despite its presence in the control and non-PFS groups. Post-CSE, survivors reveal significant intelligence and memory impairments, but prognosis differs by CSE type; memory scores are uncompromised in the PFS group despite evidence of their lower FSIQ whereas both are compromised in the non-PFS group. Correlations between brain volumes and outcomes differ in the PFS, non-PFS, and control groups and require further investigation. •Aetiology is the overarching predictor of long term cognitive outcomes with intracranial volume, active epilepsy at follow-up, and developmental delay at baseline CSE being associated with worse outcomes ten years post CSE.•Children who had PFS perform within the average range on standardized intelligence and memory assessments within ten years of CSE.•Long term intelligence outcomes were associated with intracranial volumes in the non PFS and control groups but not the PFS group. Parents volunteered participation of their children. Exclusion criteria for healthy control participation were a diagnosis of seizures/epilepsy and/or the presence of neurodevelopmental delay, as reported by their parents. We obtained written informed consent from all participants' parents/guardians, and where appropriate, we obtained assent from participants themselves. To examine the influence of socioeconomic status (SES) on outcomes, indices of multiple deprivation (IMDs) based on residential postal codes were determined for all participants (2.2All participants were invited for a magnetic resonance imaging (MRI) and neuropsychological assessments at UCL Great Ormond Street Institute of Child Health (ICH) in London. The Wechsler Abbreviated Scales of Intelligence (WASI) https://fsl.fmrib.ox.ac.uk/fsl/fslwiki).Brain MRIs were carried out on an Avanto 1.5 Tesla scanner  using conventional brain MRI sequences. This was done for the following: (a) to assess the presence of structural brain abnormalities in this cohort and (b) to extract hippocampal and intracranial volume (ICV) measurements to investigate their relationship with memory and cognitive indices. Images were reviewed by an experienced pediatric neuroradiologist who determined whether scans were (1) normal, (2) contained a minor abnormality , or (3) contained a major abnormality  2.3Analyses were conducted with Predictive Analytics Software (PASW) version 21  for Windows. Independent sample t-tests and chi-squared tests were used for intergroup comparisons. Univariate analysis of variance (ANOVA) controlling for age and SES was used to compare PFS, non-PFS, and control groups on FSIQ score and GMS; analysis was restricted to FSIQ rather than its constituents VIQ and PIQ as we did not observe any discrepancies between VIQ and PIQ in our sample. One sample t-tests were conducted to compare CSE group means with the population norms. Significant difference in test results was set at p < 0.05.3) in our regression analyses as well as (m) ICV (mm3), which served as a proxy for total brain volume. Bootstrapping of 1000 samples was applied to all our analyses. Any factors significant on univariable analyses at the p < 0.1 level were subsequently included in multivariable modeling (stepwise regression) to identify the factors associated with neurocognitive outcomes after adjustment of others.To identify factors associated with lower intelligence and memory scores according to etiological classification, we performed separate regression analyses for all patients with CSE, PFS, and non-PFS. To assess whether any CSE features were directly related to outcome, we investigated the following in the regression analyses: (a) duration of CSE (min), (b) CSE type (intermittent versus continuous), and (c) CSE loci . In addition, given past literature in this field, we also investigated whether (d) age at CSE, (e) recurrence of CSE, (f) active epilepsy at follow-up, (g) cognitive delay at baseline (yes/no), (h) motor delay at baseline (yes/no), (i) previous seizures at baseline , (j) MRI visible structural abnormalities at follow-up , and (k) prematurity (yes/no) affected outcomes. Antiepileptic drugs were not entered as an independent variable as they are highly correlated with active epilepsy at follow-up. Finally, given the well-established role of the hippocampus in cognition, we also included (l) mean HV . To address this matter, we imputed the minimum value (FSIQ = 50) for each participant with non-PFS that was not assessable on the WASI; a similar strategy has been used in other studies of children with seizures 33.1We identified 183 (90%) of the 203 survivors from the inception CSE cohort for participation in the current study; 20 subjects were lost to follow-up. One hundred and thirty-two children (65% of inception cohort survivors) participated in the study , forty-nine refused study participation altogether, and two provided follow-up data but refused neuropsychological and MRI scanning  (see 2 (1) = 5.04, p = 0.03). In the non-PFS group of children who completed the FSIQ assessment (N = 60), there was a significantly smaller proportion of children with cognitive delay (χ2 (1) = 15.31, p < 0.001) and motor delay at baseline (χ2 (1) = 7.57, p = 0.007) than children who did not participate (N = 87). Moreover, children with non-PFS classified as having any type of seizures  prior to CSE baseline were less likely to participate in our neuropsychological assessments (χ2 (1) = 12.4, p = 0.002).From the 132 participants, 94 children  were assessed with the WASI, a mean of 8.6 years following CSE to derive a FSIQ. From the remaining participants, 38  were not assessed with our neuropsychological instruments, due to their severe developmental delay (2 out of these 38 children had an MRI). Children with PFS who undertook neuropsychological testing (N = 34) were similar to the nonparticipating PFS survivors (N = 22) for all variables except for a larger representation of females in study participants  out of 94 children who were assessed on the WASI were also tested on the CMS. Seventeen non-PFS cases could not be assessed on the CMS because of their severe learning difficulties (mean FSIQ difference between participants able to complete CMS (mean: 94.6) and those unable (mean: 60.2): t (92) = − 9.4, p < 0.001).Seventeen neurologically healthy control subjects without a history of febrile or afebrile seizures  and GMS  scores than controls see .Table 2R3.2.22 = 0.24; power = 1). Post-hoc comparisons with Bonferroni corrections revealed that the PFS group had lower FSIQ scores compared with controls (p = 0.022) but higher FSIQ scores compared with the non-PFS group (p = 0.001). In turn, the non-PFS group, had lower FSIQ scores than the control group (p = 0.001).Mean FSIQ was lowest in children who had non-PFS, higher in children who had PFS, and highest in controls see . Univari2 = 0.13; power = 0.91). The PFS group's mean GMS was not significantly different to controls (p = 0.9), but GMS were higher in the PFS group compared with the non-PFS group (p = 0.002) while the non-PFS group had lower scores than controls (p = 0.002).The mean GMS was also lowest in the non-PFS group, higher in the PFS group, and highest in controls  with uni3.2.3The All CSE group obtained lower scores than the normative mean for FSIQ , VIQ , and PIQ  but did not differ from the test-provided GMS means . The PFS group obtained FSIQ (d = 0.03), VIQ (d = 0.008), and PIQ (d = 0.05) scores that were not significantly different from the normative mean (p > 0.05), nor was there any difference in GMS with the normative mean . The non-PFS group performed significantly worse than the normative mean for FSIQ , PIQ , and VIQ  as well as the GMS .3.32 = 0.51, p < 0.001). Lower GMS were associated with the occurrence of previous seizures at the time of CSE  and cognitive delay at baseline CSE  .The results of the univariable regression analyses can be found in 2 = 0.57, p < 0.001). The same result was obtained in our all-inclusive non-PFS regression analysis . Lower GMS were associated with cognitive delay at baseline  .For the PFS group, no variable was retained in the multivariable model. For the non-PFS group, a smaller ICV at follow-up , active epilepsy at follow-up , and cognitive delay at CSE baseline  were associated with lower FSIQ scores .For the control group, at the multivariable level, ICV was predictive of FSIQ . Moreover, when we included the whole non-PFS group by substituting nonscores with the lowest possible FSIQ score, regression results remained unchanged suggesting that the predictors identified in our study are generalizable to the entire studied population.Thirdly, it could be argued that we recruited a modestly sized control group that were the offspring of hospital and university employees and, consequently, may have been an intrinsically higher functioning group. Nevertheless, the CSE group as a whole was not statistically different in SES, gender, and age to our group of controls. In addition, we controlled for age and SES in all our subgroup analyses. On top of that, our overall findings remained consistent when comparisons were made with the instruments' population normative data arguing against this notion. Using the more recent edition of the WASI  may have been preferable as the older version (WASI-I), which was published in 1999, may have overestimated FSIQs in the present study. Nevertheless, study recruitment was well underway (July 2009–February 2013) when the WASI-II was released in 2011 Finally, with recent advances in the identification of certain genetic mutations , the current classification system may be obscuring further divisions within each group driven by their genetic heterogeneity. Thus, our findings should be considered keeping these limitations in mind.5Children within ten years of CSE reveal intelligence and memory impairments relative to controls and population norms, albeit these are strongly associated with the etiology of CSE. Children classified as non-PFS at baseline have a worse outcome associated with the presence of cognitive delay pre-CSE and ICV at follow-up. Children with a history of PFS reveal no memory impairments on the CMS.


PF) and its regulators in synovial tissues to trace the molecular pathways through which these regulators contribute to RA. A complex protein–protein interaction map (PPIM) of 2,742 genes and 37,032 interactions was constructed from differentially expressed genes (p ≤ 0.05). PPIM was further decomposed into a Regulator Allied Protein Interaction Network (RAPIN) based on the interaction between genes . Pathway network analysis has shown the RA-specific disturbances in the functional connectivity between seed genes  and NKPF members . Interestingly, these genes are known for their involvement in inflammation and immune system  pathways connected to RA. This study, for the first time, reports that SNW1, along with other NK regulatory genes, plays an important role in RA pathogenesis and might act as potential biomarker for RA. Additionally, these genes might play important roles in RA pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative data analysis and network-based methods could accelerate the identification of novel drug targets for RA from high-throughput genomic data.Rheumatoid arthritis (RA) is a chronic synovial autoinflammatory disease that destructs the cartilage and bone, leading to disability. The functional regulation of major immunity-related pathways like nuclear factor kappa B (NF-κB), which is involved in the chronic inflammatory reactions underlying the development of RA, remains to be explored. Therefore, this study has adopted statistical and knowledge-based systemic investigations  to study the gene expression status of NF-κB protein family (NK HLA-DRB1) and non-HLA genes  are the main susceptibility risk factors for RA across multiple ethnic groups (Rheumatoid arthritis (RA) is a complex systemic autoinflammatory disorder causing chronic destructive inflammation in synovial joints resulting in severe physical disability in millions of patients worldwide . Moleculc groups . HoweverOn the contrary, studying genome-wide gene expression provided a deeper insight into the molecular patterns of different autoimmune diseases . Gene exThe NF-κB belongs to a group of transcription factors  which controls the expression of many genes involved in immunity and inflammatory processes . These gIn innate immunity, activation of the NF-κB pathway upregulates the expression of defensins, pro-inflammatory proteins like cytokines , and proteins involved in leukocyte migration (VCAM1 and ICAM1). In adaptive immunity, NF-κB is involved in the proliferation of B- and T-cells as well as in the maturation of dendritic cells . Althougin vitro. For ease of description, regulators of NF-κB protein family are termed as “seed genes” and the NF-κB protein family as “NKPF.”The top 20 known NF-κB pathway regulators controlling the immune response to independent TNF-α and lipopolysaccharide (LPS) antigenic treatments were identified from a large-scale secondary RNAi screening on differentiated human THP-1 monocyte cell lines . These twww.ncbi.nlm.nih.gov/geo). Total number of samples in the dataset was 23  algorithm  of Benjamini and Hochberg with p value ≤ 0.05 was applied on the significant gene data to remove false positives  was constructed using a Cytoscape plugin, Bisogenet, which extracts the interaction among queried genes from the data deposited in the Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), The Molecular Interaction Database (MINT), Database of Interacting Proteins (DIP), Human Protein Reference Database (HPRD), and IntAct database . SelectePIN) was constructed from PPIM by implementing well-established theories like degree centrality (DC) and betweenness centrality (BC) in network biology. From the PPIM, we identified those genes that fit to: a) hubs which are dependent on DC, b) bottlenecks based on BC, and c) NF-κB proteins and regulators. The centrality parameters or network properties were scaled using ‘Network Analyzer’, a Cytoscape plugin (A sub-network of Regulator Allied Protein Interaction Network (RAe plugin .a) Identification of Hub ProteinsWe used a method developed by where Avg is the mean DC of all genes in the biological network PPIM and SD denotes their standard deviation.b) Identification of BottlenecksBC was introduced to scale the bottleneck genes in the interactome. The formula for calculating BC is as follows:s and t are nodes in the network other than n, σst represents the number of shortest paths from s to t, and σst(n) is the number of shortest paths from s to t that n lies on. Genes located in the top 25% of betweenness were extracted as bottleneck genes.where PIN to create a weighted gene correlation map. The Pearson’s correlation coefficient (PCC) of pairs of genes is measured using the following formula:Pearson’s correlation algorithm was applied to the genes of RAx and y are the averages of sample expression values in healthy and RA conditions of the two genes, respectively.where Functional resemblance among two genes is evaluated using prearranged data available in Gene Ontology. To evaluate the functional similarity between two genes, Wang’s measure of semantic similarity was applied to molecular function (MF) hierarchy as MF, which specifically defines a particular gene in terms of functional ontology. The semantic score of functional similarity between genes range from 0 to 1. Higher semantic score between genes represents a stronger functional relationship among the genes . The semTX is the set of all its ancestor ontology as well as ontology X itself and SX(t) represents the contribution of a term t ∈ TX to the semantics of X based on the relative locations of t and X in the graph. A single gene can be annotated by multiple gene ontology (GO) relationships. Best-match average (BMA) approach was implemented integrating semantic similarity of multiple GO annotations and evaluates the mean of all maximum similarities. Based on this model, we used R package, GoSemSim  and co-expressed seed genes in our dataset (Genome-wide association studies (GWAS) are a dominant and broadly used tool to map the susceptibility loci, genes, and genetic markers in complex traits and diseases. GWAS based on pathway analysis is extensively used to unravel novel multigenic functional relationships. In this study, an attempt was made to explore the pathways commonly enriched between RA-GWAS catalog . The normalization plot of standardized genes is given in PF extracted and depicted in High-throughput experimental gene expression profiles of synovial tissues, which are affected in RA, were retrieved from the GEO database. The raw signal intensities of 45,056 probes were standardized by means of RMA algorithm, which bring about 3,573 non-redundant DEGs with a statistically significant PF profiles revealed that majority of the seed genes are downregulated. Overall, of 36 genes, 14 are upregulated and 22 are downregulated. PF category, NFKB1, REL, and RELA are found to be upregulated, with FC ≥ 1.2 in the RA sample, whereas NFKB2 and RELB are downregulated, with FC ≤ −1.2. The most upregulated gene is REL, with FC of 3.04, and the most downregulated gene is KPNA1, with FC of −1.85. It is interesting to note that the most upregulated gene, REL, falls into the NKPF group, whereas most downregulated gene, KPNA1, comes from the seed gene category.Analysis of extracted seed genes and NKSignificant DEGs, 3,573 genes, from the microarray data were used in Bisogenet to build PPIM by extracting all relationships between the queried genes. The PPIM was made stable by eliminating self-loops and repeated edges to calculate the standardized centrality parameters of the whole interactome. Bisogenet produced a complex PPIM which included 2,742 genes and 37,032 interactions, with 13.51 mean edge–node fraction, as given in the supplementary file  and hubs (based on DC) were set based on Eqs. (2) and (1), respectively. A total of 652 genes were categorized as bottlenecks and 131 genes as hubs. Interestingly, seed and NKPF genes like NFKB1, REL, RELA, TLR4, TNFRSF1A, and SNW1 were in the category of hubs and GRIN2B, KPNA1, NFKB2, PRKCG, RELB, CX3CL1, RIPK1, CFLAR, CALCOCO2, ATG7, and ITCH were detected in the bottleneck group  in the normal condition that have critical variation of expression from RA. Similarly, 453 gene pairs (318 genes) in RA had distinct deviation from normal condition. Further, we extracted only the connectivity of seed genes and NKPF from the aforesaid interactome. We found 227 genes co-expressed with NKPF and seed genes in normal condition and 171 co-expressed genes in RA condition , 47 in the category of “protein-containing complex binding” (FDR = 7.59 × 10−16), and nine in the category of “identical protein binding” (FDR = 1.82 × 10−07), which were the top 3 terms of GO molecular function (MF). The top 10 MF among the enriched genes are listed in −09), with a count of 34 genes, followed by “cell junction” (FDR = 7.13 × 10−09) and “neuron part” (FDR = 5.23 × 10−06), with gene counts of 30 and 29, respectively. The top 10 CC among the enriched genes are represented in −15), “positive regulation of gene expression” (FDR = 1.76 × 10−13), and “immune response” (FDR = 4.48 × 10−15) were the most annotated terms, with gene counts of 51, 47, and 45, respectively. The top 10 BP enriched terms are listed in Gene set enrichment analysis of interconnected genes in the normal sample was performed using ToppGene Suite. The gene sets were enriched in few molecular functions, biological processes, cellular components, and pathways. Fifty-four genes fall into the category of “enzyme binding” (FDR = 8.57 × 10−07), “cytokine signaling in immune system” (FDR = 9.88 × 10−08), “adaptive immune system” (FDR = 4.84 × 10−07), “signaling by interleukins” (FDR = 3.04 × 10−05), and “NF kappa B signaling pathway” (FDR = 2.78 × 10−12), with gene counts ranging from 14 to 36. Details of the top 10 pathways enriched by the gene sets are represented in Most of the gene sets are involved in immune- and inflammation-related pathways, like “innate immune system” , “diseases of signal transduction” (FDR = 1 × 10−04), “MAPK signaling pathway” (FDR = 6.4 × 10−05), “Toll-like receptor signaling pathway” (FDR = 2.6 × 10−9), and “TNF signaling pathway” (FDR = 2.2 × 10−5). These pathways are also associated with inflammation and immune systems.By analyzing the pathway interaction network, we observed that seed genes like SNW1 and NFKB2 did not give any result. The GWAS-reported associations for genes of interest are presented in TNFRSF1A, TLR4, NFKB1, REL, RELA, ATG7, FOSB, KPNA1, and TRPC6, significantly associated with RA. The GWAS overlap to these genes and their significant associated terms are represented in Herein, an effort was made to relate susceptibility loci of inflammation-associated disease conditions in GWAS catalog to the driver genes of RA. We extracted the reported traits of these driver genes from GWAS catalog to detect their association to inflammatory conditions Table S2PIN) based on hubs and bottlenecks. Hubs are considered as key features, and they point to crucial intersections among clusters of genes in a network such that the network is disordered when hub nodes are detached  have a critical role in RA by regulating inflammation and immune system , signifying the association of TNFRSF1A to auto-inflammatory diseases. The R92Q mutation in TNFRSF1A could be involved in RA, as was seen in 5.2% of 135 patients with early arthritis  in RA. More interestingly, two genes of the NKPF category, NFKB2 and RELB, are found in the bottleneck group. This implies that both hubs and bottlenecks comprised NKPF gene category.Next, we considered the involvement of bottleneck genes . In order to overcome these limitations, we tried to include protein interaction based on co-expression. Overall, our research analysis has presented the effectiveness of linking gene expression with their functional relationships in the identification of RA candidate genes. Future experimental validation is required to demonstrate the direct or indirect involvement of the novel candidate genes uncovered in the current study in RA.PIN). A detailed parametric downstream analysis based on biological insights highlights 11 candidate genes that can act as potential biomarkers or drug targets for RA. One of the remarkable highlights of this analysis is the identification of SNW1 as potential biomarker for RA. Overall, our research analysis has presented the effectiveness of linking genetic expression with their functional relationship in the identification of RA candidate genes. By experimentally authenticating the results using in vitro and in vivo experiments, this can be further extended in order to pinpoint more selective therapeutic agents.This work systematically and scientifically outlines an integrated bioinformatics pipeline to find the most indispensable key signatures from the interactome Regulators Allied Protein Interaction Network , Riyadh, Saudi Arabia, research grant no. AT-35-140.


Caenorhabditis Intervention Testing Program (CITP) is a multi-institutional, National Institutes of Aging (NIA)-funded consortium charged with identifying chemical compounds that robustly extend lifespan in a genetically diverse panel of Caenorhabditis strains. Compounds are prioritized for screening if they are highly ranked via computational prediction for lifespan or healthspan effects , if they are predicted to engage known lifespan regulating pathways, or if they have previously been reported as extending lifespan or healthspan in model systems . β-guanidinopropionic acid (β-GPA) is a creatine analog (Shields and Whitehair 1973), commonly used as a dietary supplement, and has been shown to extend lifespan in Drosophila under stress via 5′ AMP-activated protein kinase (AMPK) activity . The AMPK pathway is conserved in nematodes and humans  and is involved in multiple pathways affecting stress response and metabolism .The Caenorhabditis species using our previously published workflow . In brief, worms were age-synchronized by timed egg-lays on standard 60 mm diameter Nematode Growth Media (NGM) plates and transferred at a density of 50 individuals per 35 mm treated plate in triplicate when they reached adulthood . β-GPA (Sigma-Aldrich) was dissolved in water and diluted appropriately such that addition of 125 µl of solution to 35 mm diameter plates containing NGM with lawns of E. coli OP50-1 and 51 µm FUdR would generate the following final β-GPA concentrations: 0.1 µM, 1 µM, 10 µM, 50 µM, 100 µM, 500 µM, 1 mM, 10 mM and 300 mM. Worms were maintained at 20 °C and moved to fresh plates on the first, second, and fourth  or fifth (C. elegans and C. briggsae) day of adulthood, then once weekly afterward. Due to our previous experience with compound interventions that potentially alter bacterial viability through transient pH changes upon plate treatment , we investigated the effects of β-GPA on our assay plates. At the above listed concentrations, β-GPA-treated plates had a pH of 6.5 and the bacterial lawns survived treatment when tested by replica plating. Thrice weekly, we observed animals for spontaneous movement or movement after gentle perturbation with a 0.2 mm diameter platinum wire. Death was scored as a lack of movement.We assayed lifespan in response to β-GPA exposure in three invivo, and metabolism by the bacterial food source, we believe that the lack of response in this study was due to a lack of physiological relevance in C. elegans. In Drosophila and mammalian models, β-GPA reduces the level of intracellular phosphocreatine that can be used by creatine kinases to regenerate ATP  resulting in a decreased cellular ATP/AMP ratio, which activates AMPK and ultimately increases lifespan and stress-resistance. Our a priori expectation for lifespan extension in C. elegans was built on the observations that: (1) creatine is reported as detectible in C. elegans ; (2) C. elegans has a creatine-like kinase, ARGK-1, whose activity modulates AMPK signaling ; (3) modulation of AMPK  and ARGK-1  in C. elegans can affect lifespan and stress resistance; and (4) regulation of lifespan by insulin signaling is partially dependent on AMPK signaling in C. elegans . As such, we were surprised to find no changes in lifespan upon treatment with a creatine analog. One possible explanation is that, despite the similarity between ARGK-1 and mammalian creatine kinase, the enzymes’ substrates differ. Biochemical analysis suggests that ARGK-1 uses arginine instead of creatine as a substrate to recharge ADP . Additionally, it has been postulated that the biochemical characterization of C. elegans metabolites may have misidentified creatine, and that creatine is not relevant to C. elegans physiology . Given these caveats, it may not be surprising that β-GPA does not alter Caenorhabditis lifespan.Our results indicate that β-GPA does not extend lifespan in the three nematode species at the concentrations tested here; in fact, in only one instance was an effect detected and, in that case, the compound reduced lifespan . This conclusion is based on one biological replicate per concentration and, as such, could be considered preliminary. While interventions may be ineffective due to a range of causes, including permeability barriers, compound stability


Super-resolution microscopy techniques break the diffraction limit of conventional optical microscopy to achieve resolutions approaching tens of nanometres. The major advantage of such techniques is that they provide resolutions close to those obtainable with electron microscopy while maintaining the benefits of light microscopy such as a wide palette of high specificity molecular labels, straightforward sample preparation and live-cell compatibility. Despite this, the application of super-resolution microscopy to dynamic, living samples has thus far been limited and often requires specialised, complex hardware. Here we demonstrate how a novel analytical approach, Super-Resolution Radial Fluctuations (SRRF), is able to make live-cell super-resolution microscopy accessible to a wider range of researchers. We show its applicability to live samples expressing GFP using commercial confocal as well as laser- and LED-based widefield microscopes, with the latter achieving long-term timelapse imaging with minimal photobleaching. However, the spatial resolution of fluorescence microscopy is generally limited by diffraction to ∼200–300 nm; approximately half the wavelength of the illuminating light. As a result, the study of cellular components on a smaller scale was reliant upon electron microscopy. Electron microscopy achieves resolutions down to single nanometres, but is limited by stringent sample preparation requirements to imaging fixed, dead samples.The development of fluorescence microscopy in the 20st century, a new family of microscopy techniques emerged: super-resolution microscopy. These techniques ‘break’ the diffraction limit and as such allow for fluorescence imaging at resolutions up to ten times higher than in conventional techniques. The major super-resolution microscopy techniques are: Stimulated Emission Depletion (STED) microscopy , which arise from the intrinsic radial symmetry of the microscope PSF. The radiality is a measurement of intensity gradient convergence: for every subpixel, intensity gradient vectors are measured for a ring of nearby surrounding subpixels. The degree of convergence of these vectors at the original central subpixel is then calculated. For a subpixel located close to the true centre of an individual fluorescent molecule, there will be a high degree of convergence and hence a high radiality value . For a subpixel displaced from a fluorescent molecule, the surrounding intensity gradients will display low convergence, as they will either be oriented towards the direction of the true molecule location or, in the case of noise, randomly . Thus, radiality acts as a proxy for the positions of molecules. By performing this radiality measurement for every subpixel across the input stack of images, a radiality stack is generated.Recently, we established a novel analytical approach called Super-Resolution Radial Fluctuations (SRRF)  that hasFig. 1, blue plot) or not (orange plot). The degree of temporal correlations across the radiality stack can then be rendered as the final SRRF image. This temporal correlation analysis is similar to that underpinning Super-Resolution Optical Fluctuation Imaging (SOFI)  . Howevera only displays weak temporal fluctuations in comparison to, for example, an SMLM acquisition with distinct blinking fluorophores. Even so, the final SRRF image achieves ∼70 nm resolution (a 5-fold improvement over the widefield resolution of ∼340 nm) as measured by Fourier Ring Correlation   we assesFig. 1c. A series of 1 s raw image sequences with varying exposure times and numbers of frames were acquired for subsequent SRRF reconstruction. Image sequences were acquired with either 100 x 10 ms frames, 40 x 25 ms frames, 30 x 33 ms frames, 20 x 50 ms frames, 15 x 66 ms frames or 10 x 100 ms frames, and these acquisitions were repeated at 5 different LED intensities. All these modalities therefore allow a SRRF frame rate of 1 Hz. SRRF images were reconstructed for each dataset and their quality  are shown in Fig. 14dSTORM imaging  illumination. In each case, ideal SRRF reconstruction parameters were determined using SQUIRREL to maximise the quality of the final images.To showcase the versatility of SRRF analysis, we imaged live and fixed samples on a range of microscopes. For a traditional SMLM dataset, we performed  imaging  of AlexaArabodopsis, which was unsuccessful with STED due to the rapid bleaching of the fluorescent protein signal . For the data shown here, SRRF resolution was most likely limited due to disruptions in the intensity gradients caused by small phase mismatches in the bidirectional laser scanning and non-contiguous time information within each pixel. However, there are few avenues for super-resolution microscopy in confocal microscopes other than STED, which faces challenges when studying live samples as discussed above. SRRF is thus one of the few approaches for breaking the diffraction limit in live-cell confocal microscopy. For example, confocal-SRRF has been used to investigate PIN protein distribution within the plasma membrane of root epidermis cells of n signal .B. subtilis  . Resolutions for the fixed cell and live-cell data sets are shown in the table in Fig. 2g.For the first time here we showcase the application of SRRF analysis to data acquired using live-cell LED-illuminated widefield microscopy. Fig. 2r (FRET) . The com5We have demonstrated here, in addition to examples present in the literature, that SRRF is a versatile and straightforward method for live-cell super-resolution imaging. It enables routine cell biology imaging experiments to take advantage of super-resolution imaging, without the need for specialised super-resolution microscopes or fluorophores. In the future SRRF will evolve to become more compatible with modern detectors such as sCMOS cameras  and to b66.12 incubator.Cos7 cells were cultured in phenol red-free Dulbecco’s modified Eagle’s medium  supplemented with 10% (v/v) fetal bovine serum , 1% (v/v) penicillin/streptomycin (Thermo Fisher Scientific) and 2 mM L-alanyl-L-glutamine  at 37 °C in a 5% CO6.25 cells/cm2. One day after splitting, cells were transfected with a plasmid encoding the calponin homology domain of utrophin fused to GFP  (2). After fixation cells were permeabilised (1X PEM with 0.25% Triton-X) for 20 min and stained with Phalloidin-AF647  for 30 min.For live-cell imaging, Cos7 cells were seeded on ultraclean  25 mm dihin-GFP)  using Li6.3dSTORM Total Internal Reflection Fluorescence (TIRF) imaging of Alexa Fluor 647-phalloidin in fixed cells  with phalloidin-Alexa Fluor 647 (1 unit/mL).ed cells a was per2 on an Elyra PS.1 inverted microscope (Zeiss) in epifluorescence mode. Images were obtained using a 63x objective  with additional 1.6x magnification which was used to collect fluorescence to an EMCCD camera , resulting in an image pixel size of 158.7 nm. 20,000 frames were acquired continuously with 10 ms exposure and 488 nm laser illumination at 0.5% of maximum intensity. 100 raw images were used to generate each SRRF image, yielding an effective temporal resolution of 1 s.Epifluorescence laser imaging of Utrophin-GFP in live Cos7 cells c was perLaser-based confocal imaging of Utrophin-GFP in live Cos7 cells b was perd–f) was performed at 37 °C and 5% CO2 on a N-STORM microscope (Nikon). A 100x TIRF objective  with additional 1.5x magnification was used to collect fluorescence onto an EMCCD camera , yielding a pixel size of 107 nm. For continuous imaging, frames were acquired for 30 min with 30 ms exposure time and 490 nm LED illumination at 5% of maximum output. 100 raw images were used to generate each SRRF image, yielding an effective temporal resolution of 3 s. For timelapse imaging, settings were as for continuous imaging, except 100 raw frames (30 ms exposure) were acquired once every 10 min with the illumination shutter closed between acquisitions.LED-illumination widefield imaging of Utrophin-GFP in live Cos7 cells d–f was p


DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive.trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation.We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.The online version of this article (10.1186/s13059-018-1601-3) contains supplementary material, which is available to authorized users. Individuals and populations display variable susceptibility to infectious diseases, chronic inflammatory disorders, and autoimmunity , 2. OverAs the immune system is the primary interface with the human pathogenic environment, the study of DNA methylation , 13 offeA few studies, however, have reported that population differences in ancestry, habitat, or lifestyle affect DNA methylation, providing an initial assessment of the contribution of genetic factors and gene-environment (G × E) interactions to population-level epigenetic variation –44. Yet,A growing body of research has reported ancestry-related variation in terms of immune gene expression levels. Two recent studies found marked differences between individuals of African and European ancestry in their transcriptional responses to infectious challenges , 49 and Here, we aimed to broaden our understanding of the mechanistic links between ancestry-related differences in DNA methylation, genetic factors, and immune gene regulation. To do so, we build upon the EvoImmunoPop collection of primary monocytes originating from healthy individuals of African and European ancestry . We profβ value [P value = 10−4; Additional file To assess population differences in DNA methylation of a purified innate immune cell type, we characterized DNA methylation variation at > 850,000 CpG sites across the genome, in monocytes originating from 156 male healthy volunteers: 78 of African descent  and 78 of European descent , all living in Belgium. Note that AFB individuals moved to Belgium between the ages of 6–45 years old (median age = 29 years). After normalization and filtering see “”, we retβ value , see “MaP = 1.42 × 10−224), was independent of the population where hyper-methylation was observed  , as adding these subpopulations as covariates in the model did not alter our results  ~ 2.6, cis-meQTLs), using MatrixEQTL [P < 1 × 10−5). We adjusted for age, two surrogate variables . We first tested for local associations between DNA methylation variation at CpGs and SNPs located within a 100-kb window (trixEQTL  (see “MaP < 1 × 10−326), a trend that was even more pronounced for meQTLs associated with population differences in DNA methylation . Given that multiple linked SNPs can be associated to the same CpG, we kept the best-associated SNP for each meQTL-CpG. However, we also used a fine mapping approach  to detecP < 2.2 × 10−16; Fig. Focusing on ancestry-related differences, we observed that ~ 70.2% of DMS harbor a significant meQTL, with respect to the 12.6% detected genome-wide  the meQTL has a similar effect in both populations but present different allelic frequencies Fig. c, or iid. We theP < 4.1 × 10−10). Stronger enrichments were detected for meQTLs associated with population differences in DNA methylation , in particular for phenotypes related to hematological measurements, neurological disorders, immune system disorders, inflammatory measurements, and digestive system disorders , and has been associated with psoriasis susceptibility [Because DNA methylation and meQTLs have been shown to be largely cell or tissue dependent , 76–81, tibility , 83. Togtibility , 63, 64,trans-meQTLs). To limit the burden of multiple testing, and because trans-meQTLs are enriched in cis-eQTLs for genes encoding transcription factors (TF) [cis-eQTLs for one of 600 TF-coding genes and, more generally, (ii) the 73,561 SNPs located in the vicinity (± 10 kb) of the TSS of these genes. Only associations for which the SNP-CpG distance was higher than 1 Mb were considered, at an FDR of 5% (P < 1 × 10−9). Given the generally low power to map trans-associations, we performed this analysis by considering all individuals together and including ancestry as a covariate.We subsequently searched for the effects of distant genetic variants on DNA methylation variation (ors (TF) , we focutrans-meQTLs that involved 91 independent loci  where local genetic variation was associated with at least 10 different CpGs in trans. Highlighting one pertinent example, a single genetic variant (rs7203742) nearby CTCF—encoding a transcriptional regulator with 11 highly conserved zinc-finger domains—controls the degree of DNA methylation at 30 CpG sites, ~ 29.4% of all CpGs regulated in trans. Furthermore, of the 21 trans-regulated CpGs that were detected as DMS, 12 were controlled by the same CTCF variant. That this variant (T → C) presents high levels of population differentiation  suggests the action of positive selection targeting the derived allele in Europeans. This observation makes of CTCF not only a master regulator of DNA methylation, as previously observed [We identified 133 CpG sites associated with at least one distant SNP, for a total of 672 observed , but alsP value threshold determined using 100 permutations   see “”.We identified 1666 CpGs whose levels of DNA methylation were associated with gene expression (eQTMs), for a total of 811 genes (eQTM-genes) associated with at least one CpG in one population group   and nearby TSS (TSS1500) , as expected following the canonical model. Conversely, positive associations were enriched in sites located nearby UTRs, particularly 3′-UTR  [P = 1.1 × 10−4)  and, importantly, in meQTL-CpGs   × 10−5) , but depBecause the respective roles of genetic and epigenetic factors in transcriptional regulation are not fully understood , we nextM (DNA methylation) on the relationship between an independent variable X (genetics) and a dependent variable Y (gene expression) [We used a Bayesian approach  to assesression) . When coα × β was significantly different from zero, Fig. R2 = 23.4% versus 15.4%, respectively; Wilcoxon P = 3.3 × 10−11), in contrast with the 387 non-mediated cases where we observed the opposite trend (Wilcoxon P = 7.8 × 10−37) , highlighting again the major role of these regions in epigenetic regulatory mechanisms [P = 1.4 × 10−2), which were otherwise enriched in non-mediating CpGs . Notably, 86.6% of mediating CpGs fell directly into a TF-binding site (TFBS), with respect to the expected 76.9% at the genome-wide level . This result suggests that DNA methylation might actively regulate transcriptional activity through the modulation of TF binding, a hypothesis that requires experimental validation.We also found that CpG sites mediating gene expression were preferentially located in enhancers  [CARD9 in EUB and CD1D upon IAV infection in AFB, both genes known to play an important role in host defense   and involved 12,050 sites that were mostly located in genes with functions related to cell periphery or immune response regulation. Previous studies have searched for ancestry-related differences in DNA methylation in various human populations and cell types , 43, 95.trans-meQTL we detected at CTCF, whose local genetic variation has been shown to alter distant DNA methylation patterns in whole blood [CTCF variant (rs7203742) regulates DNA methylation of 30 distant CpGs, 40% of which are differentially methylated between populations. We also found that all CTCF trans-regulated CpGs fall within a TFBS, confirming our initial hypothesis about the mechanism by which a genetic variant might alter DNA methylation at a distant CpG site. Interestingly, 9 out of the 30 CTCF trans-regulated CpGs fall within a TFBS of CTCF, while the remaining 21 fall within a TFBS specific to other TFs such as YY1, ESR1, or ZNF143. This observation is consistent with a model of pioneer transcription factor activity [CTCF acts as a pioneer factor that will generate changes in chromatin state that, in turn, will become accessible for binding of secondary factors.Although variation in past environmental exposures and socioeconomic factors may contribute to population differences in DNA methylation, we found that 70% of differentially methylated sites between African and European ancestry groups were associated with at least one meQTL. This indicates that population differences in DNA methylation are mostly driven by DNA sequence variants , 40–42. le blood . We showactivity  and suggAt the genome-wide level, we find that the quantitative impact of DNA methylation on gene expression variation is lower than that reported by some previous studies, possibly reflecting differences in experimental settings and statistical power  , 84, 89.P < 1 × 10−326, Fig. The detected eQTMs were found to be drastically enriched in genetic control  from two different ancestries (78 of European and 78 of African descent), who were recruited at the Center for Vaccinology from the Ghent University Hospital  . For eacGenomic DNA was extracted from the monocyte fraction using a phenol/chloroform protocol followed by ethanol precipitation. The DNA was then bisulfite converted, and BC-DNA was then processed using the Illumina Infinium MethylationEPIC BeadChip Kit  to obtain the methylation profile of each individual at more than 850,000 CpG sites genome-wide.P value > 10−3, and then, probes with a detection P value > 10−3 in one or more individuals (6833 probes). Following this filtering process, 552,141 of the original 866,836 sites on the array were retained.In total, 184 samples were hybridized with the EPIC array, including 172 unique samples and 12 technical replicates. We removed any technically unreliable probes: (i) potentially cross-hybridizing probes , (ii) those located on the X and Y chromosomes , and (iii) probes overlapping SNPs that present a frequency higher than 1% in at least one of the studied populations 206,998 probes). These SNPs were chosen based on our own genotyping dataset, as well as on the 1000 Genomes project [8 probes.M value has been shown to provide better detection sensitivity than β values at extreme levels of modification [M value to run all statistical analysis unless otherwise stated. Note that in some instances of the text and figures, β values are reported for ease of clarity and interpretation. M values were then adjusted for background noise with the normal-exponential using out-of-band probes (noob) from the R Bioconductor minfi package [lumiMethyC with the “quantile” method, and for methylated/unmethylated intensity variation using the lumiMethyN with the “ssn” method [sva function from the sva Bioconductor package [We calculated methylation levels from raw data, using the R Bioconductor lumi package . Given tfication , we used package . Next, n” method . Finally” method . To corr package  with ageM value ~ population + age + two surrogate variables + error, and next applied an empirical Bayes smoothing to the standard errors using the R Bioconductor limma pipeline [P values were adjusted using the Benjamini and Hochberg method. DMS were extracted using a threshold of adjusted P value (< 0.01) and a difference in the mean β value of each population |Δβ| > 5%.To detect CpG sites presenting statistically different levels of DNA methylation between AFB and EUB, we fitted a linear regression model for each CpG site: pipeline . P valuesva R package, were used as covariates in the linear model.All individuals were genotyped for a total of 4,301,332 SNPs on the Illumina HumanOmni5-Quad BeadChips and went through whole-exome sequencing with the Nextera Rapid Capture Expanded Exome kit, on the Illumina HiSeq 2000 platform, with 100-bp paired-end reads. Details of the processing of genotyping and whole-exome sequencing data, together with imputation using the 1000 Genomes Project imputation panel , are repcis, we regressed out genotypes of all primary cis-meQTLs and then performed cis-meQTL mapping on the regressed methylation data to find secondary cis-meQTLs. We repeated this process in a stepwise fashion until no additional independent cis-meQTLs were detected. This allowed us to refine our local meQTL mapping by detecting all possible independent SNP-CpG associations.We mapped meQTLs using the statistical framework implemented in the MatrixEQTL R package . For loctrans-acting associations , we restricted our analysis to SNPs located in the vicinity of transcription factor (TF) coding genes, to limit the burden of multiple testing. Specifically, we selected (i) all SNPs located less than 10 kb to the TSS of any expressed TF in our dataset and (ii) SNPs detected as cis-eQTLs for these TFs. For each SNP, we only investigated CpG sites that mapped at least 1 Mb from the SNP or located on other chromosomes, using a Kruskal-Wallis rank test.For distant, cis- and trans-meQTLs, FDR was computed by mapping meQTLs on 100 datasets with the M values permuted within each population. We then kept, after each permutation, the most significant P value per CpG site, across populations (probe-level FDR). Finally, we computed the FDR associated with different P value thresholds for cis or trans, and subsequently selected the P value threshold that provided a 5% FDR: P = 1 × 10−5 and P = 1 × 10−9 for cis- and trans-meQTLs, respectively.For both β reflecting the effect of the derived allele of the meQTL on methylation, ΔDAF the difference in allelic frequencies between Europeans and Africans (DAFEUB − DAFAFB), and ΔMeth the observed difference in the mean levels of DNA methylation between European and African individuals .We aimed at refining our meQTL mapping by detecting population-specific meQTL effects . To do so, we used a Bayesian model selection approach to identify specific and shared effects for each of the 69,702 CpGs that we detected as being associated with at least one genetic variant. Specifically, for each CpG-SNP pair, we computed the likelihood of three models:EUB coded 0,1,2 in EUB individuals and 0 in AFB individuals, and SNPAFB coded 0,1,2 in AFB individuals and 0 in EUB individuals. We next calculated the posterior probability of each model assuming that all models are equally likely a priori. We then set a threshold of 0.9 to consider one of the models as supported by the data. Thus, a meQTL is classified as EUB-specific if the posterior probability of model (ii) is higher than 0.9, or AFB-specific if the probability of model (iii) is higher than 0.9.with SNPP < 1 × 10−8. Using this set of GWAS hits, we next extracted all SNPs in LD with each of these hits (R2 > 0.8) and classified the resulting final set of 166,248 SNPs according to their parental Experimental Factor Ontology (EFO) term [We used the NHGRI GWAS catalog  to firstFO) term .P value threshold corresponding to FDR 5% in our initial mapping, and filtered out meQTLs that were in LD (R2 > 0.8) keeping one SNP per independent loci . For the resampling set, we considered all SNPs that were initially used for the meQTL mapping and pruned them for LD (R2 > 0.8), yielding a final set of 921,466 SNPs. Resampling was performed using bins of allelic frequencies at intervals of 5%.We then selected all meQTLs in our dataset that passed the P values associated to the fold enrichment were calculated by fitting a normal distribution to the empirical distribution of our 10,000 resampled sets of SNPs. Confidence intervals were computed using 10,000 resamples by bootstrap. The same procedure was applied when searching for enrichments of meQTLs specifically in GWAS hits related to the 268 traits of the “Immune system disorder” EFO parental term.Finally, we tested for fold enrichments of meQTLs in GWAS hits, for each of the 17 parental EFO categories . The folTo identify associations between DNA methylation levels and gene expression of nearby genes, we leveraged RNA-sequencing data obtained from the same individuals, both at the non-stimulated state (NS) and in response to four immune stimuli . BrieflyP values and the coefficients of correlation between methylation profiles and gene expression were obtained using Spearman’s rank correlation. FDR was computed by mapping eQTMs on 100 datasets with the M values permuted, and kept, after each permutation, the most significant P value per gene (gene-level FDR). We selected the P value threshold that provided a 5% FDR (P = 5 × 10−5).Using the RNA-sequencing data from the NS condition, we mapped eQTMs  in a window of 100 kb around the TSS of each gene . The associated 2 of expression values between the stimulated and non-stimulated states, corrected for the effect of low values of FPKM, for each gene expressed in at least one of the two conditions.We also mapped eQTMs in the context of the response to the various stimulations, namely response-QTMs (reQTMs). To do so, the same procedure explained above for the eQTM mapping was followed, using the fold change of expression upon stimulation as a measure of the host response to infection. Specifically, we calculated the difference of the logFor the mapping of eQTMs and reQTMs, we conducted two separate analyses: merging all individuals and including ancestry as a covariate, or keeping the two populations separately.We mapped expression quantitative trait loci (eQTLs) using the MatrixEQTL R package , leveragP value per gene, across populations (gene-level FDR). Finally, we computed the FDR associated with different P value thresholds for eQTLs or reQTLs, and subsequently selected the P value threshold that provided a 5% FDR: P = 5 × 10−5 and P = 5 × 10−6 for eQTLs and reQTLs, respectively.For both eQTLs and reQTLs, FDR was computed by mapping eQTLs/reQTLs on 100 datasets with the expression values permuted within each population. We then kept, after each permutation, the most significant (i)(ii)(iii)(iv)where Gi is the genotype of the ith sampled variant and Gi_std the standardized value of its genotype; Mi is the simulated methylation data and Mi_std its standardized methylation value; Ei is the simulated gene expression data; αi is the proportion of variance of Mi that is explained by Gi, and γ is a noise parameter that corresponds to the total proportion of variance of Ei that is explained by Gi and Mi. βi and τi are the proportions of explained variance that are attributable to Gi and Mi respectively (satisfying βi + τi = 1). Finally, εi and ζi are random, normally distributed residuals. Note that in the simulation presented in Fig. We simulated different scenarios to infer causal relationships between DNA methylation and gene expression. For each scenario, we started by randomly selecting genomic blocks of 1 Mb each along the genome to keep realistic expectations of genetic structure. We next randomly sampled SNPs in these blocks, which we used to simulate methylation and gene expression data. For example, in a scenario where a genetic variant influences DNA methylation variation that, in turn, actively regulates gene expression see Fig. a, we folglmnet function from the R package glmnet [α of 0.5. The strength of the penalty λ1se was chosen as the largest value of lambda such that the error was within 1 standard deviation of the minimum lambda, when performing k-fold cross validation with the cv.glmnet function. Finally, the generic R function predict was used to build the optimal linear predictor in each case. For the trios presenting more than one SNP, we also used a predictor of gene expression based on genetic variability, as summary variable for the genetic variability, and found no differences in our simulation-based mediation results when compared to building the summary variable from a predictor of DNA methylation (data not shown).To infer causality between regulatory loci and gene expression variation, we considered eQTLs that were also detected as meQTLs, and, out of this subset, we kept only those for which the meQTL-CpG had previously been identified as an eQTM of the eQTL-gene  on the relationship between an independent variable X (genetics) and a dependent variable Y (gene expression). In this scenario, the global effect of X on Y can be written as ρX → Y = τ + α · β, where τ is the specific effect of X on Y, α the specific effect of X on M, and β the specific effect of M on Y. With this, the product α·β represents the mediation effect of G on Y, through M. The mediate function of the mediation R package was used to compute point estimates for average causal mediation effects, as well as 1000 simulation draws of average causal mediation effects. The empirical distribution of simulated effects was used to fit a normal distribution, which was subsequently used to compute empirical P values for the H0 hypothesis “α·β = 0.” We used the R function p.adjust with method “fdr” to correct at a FDR = 5%.For conducting causal mediation analyses, we used a Bayesian approach as implemented in the mediation R package . Brieflypcor.test function from the R package ppcor [P values of the partial correlation between simulated expression and methylation data.For comparison purposes with the mediation analyses, we conducted on simulated data a partial correlation approach to test for independence between expression and methylation levels when accounting for genetic variability. We used the ge ppcor  to compuAdditional file 1:Figure S1. Overview of the EvoImmunoPop experimental setting. Figure S2. Exploring the non-linear effects of age on DNA methylation. Figure S3. Mono-DMS were detected using the same approach as described in the Figure S4. PCA of the genetic data, based on 151,419 SNPs, for Africans  and Europeans . Figure S5. Fine mapping of meQTLs. Figure S6. Histogram of physical proximity of cis-meQTLs. Figure S7. Genomic location of CpG sites associated with a meQTL. Figure S8. Proportions of population differences in DNA methylation accounted for by genetics. Figure S9. Fold enrichment of meQTLs in GWAS hits. Figure S10. Fold enrichment of meQTLs associated with DMS in GWAS hits related to “immune system disorder”. Figure S11. Rationale for the detection of trios to be used for causality inference. Figure S12. Cartoons of the various simulated scenarios. Figure S13. Heat map of correlation between the first ten PCs of expression and DNA methylation. Figure S14. Genomic location of eQTMs (NS) and reQTMs . Figure S15. Number of reQTM-genes, per condition, according to the direction of their association with DNA methylation. Figure S16. Causality inference upon immune stimulation. (PDF 3088 kb)Additional file 2:Notes 1–2. (PDF 94 kb)Additional file 3:Table S1. (XLSX 868 kb)Additional file 4:Table S2. (XLSX 75 kb)Additional file 5:Table S3. (XLSX 157 kb)Additional file 6:Table S4. (XLSX 36 kb)


There is increasing evidence that consumption of polyphenol and phenolic-rich foods and beverages have the potential to reduce the risk of developing diabetes type 2, with coffee a dominant example according to epidemiological evidence. One of the proposed mechanisms of action is the inhibition of carbohydrate-digesting enzymes leading to attenuated post-prandial blood glucose concentrations, as exemplified by the anti-diabetic drug, acarbose. We determined if the phenolic, 5-caffeoylquinic acid, present in coffee, apples, potatoes, artichokes and prunes, for example, and also selected free phenolic acids , could inhibit human salivary α-amylase and rat intestinal maltase activities, digestive enzymes involved in the degradation of starch and malto-oligosaccharides. Using validated assays, we show that phenolic acids, both free and linked to quinic acid, are poor inhibitors of these enzymes, despite several publications that claim otherwise. 5-CQA inhibited human α-amylase only by <20% at 5 mM, with even less inhibition of rat intestinal maltase. The most effective inhibition was with 3,4-dimethoxycinnamic acid (plateau at maximum 32% inhibition of human α-amylase at 0.6 mM), but this compound is found in coffee in the free form only at very low concentrations. Espresso coffee contains the highest levels of 5-CQA among all commonly consumed foods and beverages with a typical concentration of ~5 mM, and much lower levels of free phenolic acids. We therefore conclude that inhibition of carbohydrate-digesting enzymes by chlorogenic or phenolic acids from any food or beverage is unlikely to be sufficient to modify post-prandial glycaemia, and so is unlikely to be the mechanism by which chlorogenic acid-rich foods and beverages such as coffee can reduce the risk of developing type 2 diabetes.  •Chlorogenic and phenolic acids weakly inhibit human α-amylase activity.•Chlorogenic acids weakly inhibit rat maltase activity.•Free phenolic acids exhibit no inhibition of rat maltase activity. In foodin vitro studies  and caffeic acid were alleged to be inhibitors of both α-amylase (porcine) and α-glucosidase enzymes with IC50 values <100 μM  and 3 mL (60 mg) were purchased from Waters Ltd., Milford, MA, U.S.A. All the reagents were of the highest purity and standards were ≥98 %. The colour reagent was prepared by mixing 20 mL of 96 mM of 3,5-dinitrosalicylic acid with 8 mL of 5.3 M (12 g in 8 mL of 2 M sodium hydroxide) and 12 mL Millipore water. Human salivary amylase type IX-A stock concentration of 1.25 U/mL was prepared in PBS  to give 0.5 U/mL in the assay according to the optimized assay .2.2Amylose (1 mg/mL) was used as the substrate and the assay was conducted according to the optimized assay . A total% Inhibition = ((Abs Control − Abs sample)/Abs control) x 100.50 was calculated graphically by dose-dependent inhibition.Where inhibition was obtained above 50%, IC2.3The method used to assess rat α-glucosidase inhibition was adapted from 2.4p ≤ .05). The data are expressed as the mean ± standard deviation (n = 3).Statistical analysis was performed by one-way analysis of variance using the Number Cruncher Statistical System version 6.0 software . Significant differences were assessed with Tukey-Kramer multiple comparison test  tested concentration A. As a pectively B and C, ectively .Fig. 2Inp ≥ .05). Caffeic acid and ferulic acid both showed inhibition of <20% at the highest concentration tested of 1 mM. None of the free phenolic acids inhibited rat intestinal maltase activity , did not attenuate post-pranidal blood glucose after consumption of bread as a carbohydrate-rich food (The aim of the present study was to determine whether the antidiabetic properties attributed to drinking coffee (and possibly to other foods or beverages containing chlorogenic acids) are due to an acarbose-like action, a drug used to attenuate hyperglycaemia through inhibition of carbohydrate-digesting enzymes. In a detailed survey of espresso coffees, the strongest form of coffee normally consumed and commercially available in a major city of the UK, a typical serving of espresso was in the range of 24–422 mg per serving in an average serving size of 43 mL . Since 5ich food . Accordiich food , filter ich food , at ~2 mich food . Raw potich food . Free phich food  and in mich food .50 value of 0.08 mM for 5-CQA  rather than a synthesized dye-linked substrate, which would have markedly different affinity for the enzyme, have also used a human source of α-amylase, and have ensured that the phenolic acids do not interfere in the DNS product determination. Several studies using the porcine pancreatic enzyme  have repor 5-CQA  and of 0or 5-CQA . It is nor 5-CQA  that theor 5-CQA  can yielin vivo. We therefore conclude that the anti-diabetic effects of coffee consumption are not due to inhibition of carbohydrate-hydrolysing enzymes. However, several studies have shown that consumption of coffee reduces postprandial blood glucose levels. There was a significant reduction in total area under the glucose curve in a rat model after consumption of a standardised meal containing carbohydrate with chlorogenic acid (via modulation of glucose metabolism, other mechanisms may be involved which include inhibition of intestinal glucose transport. In this respect, when consumed with a glucose bolus, coffee exhibited some effect on post-prandial glycemia, although not directly on plasma glucose concentration (For inhibition of rat intestinal maltase, 5-CQA was a weak inhibitor, but none of the free phenolics showed inhibition. Other research (Iwai, Kim, Onodera, & Matsue, 2006nic acid . In healnic acid . It was nic acid  and in anic acid , that contration , and thi