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The WRKY gene family is a widely distributed and highly conserved transcription factor (TF) family in plants, with its members playing key roles in plant growth and development, stress response, and metabolism. Although WRKY TFs have been extensively studied in many plant species, research on the WRKY gene family in Vaccinium bracteatum Thunb. remains limited. Therefore, integrating molecular biology and bioinformatics approaches to further explore the WRKY gene family in V. bracteatum is of considerable scientific importance. In this study, we employed various online tools to obtain genomic and expression data, which were subsequently analyzed to determine the composition, evolutionary relationships, and functions of WRKY family genes in V. bracteatum. A total of 66 WRKY genes (VaWRKY) were identified, named based on homology alignment. Phylogenetic analysis classified the 66 VaWRKYs into three major clades and seven subclades. Sequence and structural analyses of VaWRKY genes provided insights into their evolutionary and functional characteristics. Expression profile analysis revealed significant differences in the expression of 12 VaWRKY genes at various stages of fruit development. Protein interaction analysis further indicated that VaWRKY genes are functionally diverse, playing important roles in stress response, seed germination regulation, and plant growth and development. In summary, we have a deeper understanding of VaWRKY genes, and systematic analysis of structure, evolutionary characteristics, and expression patterns plays an important role in analyzing its biological functions, molecular breeding, and enhancing economic value.

Background Statins are prescribed to lower LDL cholesterol. Clinical guidelines recommend 30–50% reduction within 3 months, yet many patients do not achieve this. We investigated predictors of LDL-c reduction, treatment adherence, and adverse clinical outcomes in a sample of UK Biobank participants. Methods We analysed 76,000 UK Biobank participants prescribed atorvastatin or simvastatin in primary care: 41,000 had LDL-c measurements before statin initiation (median = 16 days prior, IQR = 28) and within a year of starting treatment (median = 89 days, IQR = 125). Adherence was defined as the “proportion of days covered” (PDC). We estimated associations between PDC within 1 year of statin initiation, genetic factors, post-treatment LDL-c reduction, and clinical adverse outcomes. For 13,000 patients with ≥ 3 LDL-c measures, we used inverse probability of treatment weighting methods to estimate the effect of sustained adherence intervention on LDL-c reduction longitudinally. Results LDL-c reduction following statin initiation was predicted by time until the 1st measurement (up to 26% greater reduction if returned ≤ 3 months vs > 3 months), PDC (up to 38% reduction when PDC > 95% [high] vs. 15% when PDC < 50% [low]), and the pharmacogenetic variant SLCO1B1 *5 (lowest reduction in CC-allele: 37% versus TT-allele: 39.5%). Longitudinal causal modelling showed that the most recent PDC measure exerted the largest influence on overall LDL-c reduction, followed by the initial PDC. Genetic predictors of reduced PDC included liability to schizophrenia (Coef top 20%  − 1.94, 95%CI − 2.69 to − 1.19), while genetic liability to cardiovascular diseases increased PDC (Coef top 20% 1.30, 95%CI 0.55 to 2.05). High PDC was associated with increased risk of incident iron deficiency anaemia (HR 1.30, 95%CI 1.09–1.54) and cataract (HR 1.20, 95%CI 1.07–1.34), and decreased risk of incident coronary heart disease (HR 0.78, 95%CI 0.73–0.84). Conclusions We identify substantial variability in the time to first on-treatment LDL-c measurements and also in adherence to statin medication, highlighting a gap between NHS guidelines, LDL-c monitoring, and statin adherence. We show its subsequent impact on long-term health, demonstrating the potential effect of targeted interventions to improve adherence. We identify important predictors of reduced statin effectiveness, including pharmacogenetic variants, polygenic scores, but most of all, adherence. Tailored statin therapy strategies with patient education on statin indication and adherence could optimize treatment efficacy, safety, and long-term clinical outcomes.

In this thesis, we consider the problem of instrumental variable (IV) selection when we have a large number of available instruments. We allow that some of these candidate instruments may be invalid in the sense that they may violate the exclusion restriction and enter the model as explanatory variables. We propose three methods for selecting the valid IVs from the candidates. The first method is the Confidence Interval (CI) method. It selects as valid the largest group of instruments where all the confidence intervals of their instrument-specific causal estimates mutually overlap with each other. It can achieve consistent IV selection under the plurality rule, which assumes that all the valid instruments form the largest group, where instruments form a group if their instrument-specific estimators converge to the same value. We apply this method to estimate the effect of Body Mass Index (BMI) on diastolic blood pressure using 96 SNPs as candidate instruments. The second method is the adaptive Lasso IV selection method, which contributes to the literature by allowing for two endogenous regressors. Under the assumption that the number of invalid instruments is smaller than half of the total number of candidate instruments minus one, we develop a median-of-medians estimator, which is √n-consistent for the causal effects. Adaptive Lasso using the median-of-medians estimator as penalty weights can select valid instruments consistently. We apply this method to estimate the direct effects of educational attainment and cognitive ability on BMI. The third method combines the agglomerative hierarchical clustering (AHC) algorithm, a commonly used statistical learning method for clustering analysis, with the downward testing procedure based on the Sargan-Hansen test for overidentifying restrictions. Under the plurality assumption, the AHC method can select valid instruments consistently. The main advantage of this method is that it performs well in the presence of weak instruments, can be extended to allow for multiple endogenous regressors, and can be used to detect potential heterogeneous causal effects. We apply this method to estimate the short- and long-term effects of immigration on wages in the US labor market.

Background Multimorbidity, the co-occurrence of multiple long-term conditions (LTCs), is an increasingly important clinical problem, but little is known about the underlying causes. We investigate the role of a critical multimorbidity risk factor, obesity, as measured by body mass index (BMI), in explaining shared genetics amongst 71 common LTCs. Methods In a population of northern Europeans, we estimated genetic correlation, between LTCs and partial genetic correlations after adjustment for the genetics of BMI. We used multiple causal inference methods to confirm that BMI causally affects individual LTCs, and their co-occurrence. Finally, we quantified the population-level impact of intervening and lowering BMI on the prevalence of 15 key common multimorbid LTC pairs. Results BMI partially explains some of the shared genetics for 740 LTC pairs (30% of all pairs considered). For a further 161 LTC pairs, the genetic similarity between the LTCs was entirely accounted for by BMI genetics. This list included diabetes and osteoarthritis and gout and osteoarthritis: Causal inference methods confirmed that higher BMI acts as a common risk factor for a subset of these pairs, and therefore BMI-lowering interventions would likely reduce their prevalence. For example, we estimated that a 1 standard deviation or 4.5 unit decrease in BMI would result in 17 fewer people with both chronic kidney disease and osteoarthritis per 1000 who currently have both LTCs. Conclusions Our genetics-centred approach quantifies the contribution of obesity to multi-morbidity. Our method for calculating full and partial genetic correlations is published as an R package {partialLDSC} . Plain language summary More than half of people over 65 have several long-term health conditions at the same time. This is becoming a bigger issue in the UK, but we don’t fully understand why some people develop many conditions. We looked at how body weight, measured by body mass index (BMI), affects the shared genetic risks for 71 common health problems such as diabetes, heart disease, arthritis and depression. Using data from people with northern European ancestry, we studied how much the same genes are linked to different conditions — both before and after taking the genetics of BMI into account. We found that BMI explains some of the shared genetic risks between many health conditions, and all of the shared risk for some, such as diabetes and osteoarthritis. Our results suggest that helping people lower their BMI could reduce the number of long-term health problems they experience, allowing more people to live longer and healthier lives. Mounier et al., analyse whether obesity, measured by body mass index (BMI) affects the shared genetic risk between 71 long-term health conditions including diabetes, heart disease and arthritis. Health interventions that help to lower BMI can reduce multimorbidity and promote longer and healthier lives.

We propose a procedure which combines hierarchical clustering with a test of overidentifying restrictions for selecting valid instrumental variables (IV) from a large set of IVs. Some of these IVs may be invalid in that they fail the exclusion restriction. We show that if the largest group of IVs is valid, our method achieves oracle properties. Unlike existing techniques, our work deals with multiple endogenous regressors. Simulation results suggest an advantageous performance of the method in various settings. The method is applied to estimating the effect of immigration on wages.

Medication adherence is essential to ensure treatment effectiveness, but too often in routine care non-adherence compromises the desired outcome. We explore longitudinal causal modelling using observational data to estimate the time-varying effects of continuous drug adherence measures on health outcomes over a sustained period. The goal of such analyses is to quantify the potential impact of interventions to improve adherence on long-term health. We consider two established longitudinal causal approaches designed to handle time-varying confounding under the ``no unmeasured confounding'' (NUC) assumption: G-estimation and inverse probability of treatment weighting (IPTW). In randomized controlled trial, NUC-based methods have been applied to address non-adherence as an intercurrent event, and instrumental variable (IV) extensions of G-estimation have also been introduced for settings where the NUC assumption may fail. We adapt these methods to observational data settings and illustrate their use for assessing how adherence over time impacts health outcomes. We align the causal parameters across methods and show they can target the same causal estimand: the average effect among treated individuals of full adherence versus zero adherence. We set out the identification conditions for IPTW and G-estimation under NUC, and for an IV-based extension that has specific utility when the NUC assumption is implausible. We assess the statistical properties, strengths and weaknesses of each approach through Monte Carlo simulations designed to reflect longitudinal studies with a continuous exposure. We demonstrate these methods by quantifying the effect of full statin adherence on LDL cholesterol control in 13,000 UK Biobank participants with linked primary care data.

In a linear instrumental variables (IV) setting for estimating the causal effects of multiple confounded exposure/treatment variables on an outcome, we investigate the adaptive Lasso method for selecting valid instrumental variables from a set of available instruments that may contain invalid ones. An instrument is invalid if it fails the exclusion conditions and enters the model as an explanatory variable. We extend the results as developed in Windmeijer et al. (2019) for the single exposure model to the multiple exposures case. In particular we propose a median-of-medians estimator and show that the conditions on the minimum number of valid instruments under which this estimator is consistent for the causal effects are only moderately stronger than the simple majority rule that applies to the median estimator for the single exposure case. The adaptive Lasso method which uses the initial median-of-medians estimator for the penalty weights achieves consistent selection with oracle properties of the resulting IV estimator. This is confirmed by some Monte Carlo simulation results. We apply the method to estimate the causal effects of educational attainment and cognitive ability on body mass index (BMI) in a Mendelian Randomization setting.

In a linear instrumental variables (IV) setting for estimating the causal effects of multiple confounded exposure/treatment variables on an outcome, we investigate the adaptive Lasso method for selecting valid instrumental variables from a set of available instruments that may contain invalid ones. An instrument is invalid if it fails the exclusion conditions and enters the model as an explanatory variable. We extend the results as developed in Windmeijer et al. (2019) for the single exposure model to the multiple exposures case. In particular we propose a median-of-medians estimator and show that the conditions on the minimum number of valid instruments under which this estimator is consistent for the causal effects are only moderately stronger than the simple majority rule that applies to the median estimator for the single exposure case. The adaptive Lasso method which uses the initial median-of-medians estimator for the penalty weights achieves consistent selection with oracle properties of the resulting IV estimator. This is confirmed by some Monte Carlo simulation results. We apply the method to estimate the causal effects of educational attainment and cognitive ability on body mass index (BMI) in a Mendelian Randomization setting.

The STEP 1 randomized trial evaluated the effect of taking semaglutide vs placebo on body weight over a 68 week duration. As with any study evaluating an intervention delivered over a sustained period, non-adherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing non-adherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realised had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this paper we propose an alternative Instrumental Variable method to adjust for non-adherence which does not rely on the same `unconfoundedness' assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where non-adherence is guaranteed). Unlike many previous Instrumental Variable approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they both suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.

Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial secondary messenger, with diverse functions, many of which are yet to be uncovered. Stemming from an Escherichia coli proteome microarray, we found that c-di-GMP bound to 23S rRNA methyltransferases (RlmI and RlmE). rRNA methylation assays showed that c-di-GMP inhibits RlmI activity, thereby modulating ribosome assembly. Based on molecular dynamic simulation and mutagenesis studies, we found that c-di-GMP binds to RlmI at residues R64, R103, G114, and K201. Structural simulation revealed that c-di-GMP quenches RlmI activity by inducing the closure of the catalytic pocket. Furthermore, we revealed that c-di-GMP promotes antibiotic tolerance by regulating RlmI activity, which played a role in antibiotic-resistant strains. Finally, the binding and methylation assays showed that the effect of c-di-GMP on RlmI is conserved, at least in various pathogenic bacteria. This study discovered an unexpected functional role of c-di-GMP in regulating ribosome assembly by inhibiting rRNA methylases. This study identified an unexpected but crucial member among the c-di-GMP effectors. c-di-GMP regulates ribosome assembly in Escherichia coli.c-di-GMP inhibits rRNA methylation activity of RlmI by inducing catalytic pocket closure.c-di-GMP promotes antibiotic resistance by regulating ribosome assembly. c-di-GMP regulates ribosome assembly in Escherichia coli. c-di-GMP inhibits rRNA methylation activity of RlmI by inducing catalytic pocket closure. c-di-GMP promotes antibiotic resistance by regulating ribosome assembly. biorxiv;2024.06.05.597503v1/UFIG1F1ufig1