LncRNA Ctcflos orchestrates transcription and alternative splicing in thermogenic adipogenesis.

The recruitment of thermogenic brite adipocytes within white adipose tissue attenuates obesity and metabolic comorbidities, arousing interest in understanding the underlying regulatory mechanisms. The molecular network of brite adipogenesis, however, remains largely unresolved. In this light, long noncoding RNAs (lncRNAs) emerged as a versatile class of modulators that control many steps within the differentiation machinery. Leveraging the naturally varying propensities of different inbred mouse strains for white adipose tissue browning, we identify the nuclear lncRNA Ctcflos as a pivotal orchestrator of thermogenic gene expression during brite adipocyte differentiation. Mechanistically, Ctcflos acts as a pleiotropic regulator, being essential for the transcriptional recruitment of the early core thermogenic regulatory program and the modulation of alternative splicing to drive brite adipogenesis. This is showcased by Ctcflos-regulated gene transcription and splicing of the key browning factor Prdm16 toward the isoform that is specific for the thermogenic gene program. Conclusively, our findings emphasize the mechanistic versatility of lncRNAs acting at several independent levels of gene expression for effective regulation of key differentiation factors to direct cell fate and function.

Accounting for Latent Covariates in Average Effects from Count Regressions.

The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.

Average treatment effects on binary outcomes with stochastic covariates.

When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment-covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.

Subgroup discovery in structural equation models.

Structural equation modeling is one of the most popular statistical frameworks in the social and behavioral sciences. Often, detection of groups with distinct sets of parameters in structural equation models (SEM) are of key importance for applied researchers, for example, when investigating differential item functioning for a mental ability test or examining children with exceptional educational trajectories. In the present article, we present a new approach combining subgroup discovery-a well-established toolkit of supervised learning algorithms and techniques from the field of computer science-with structural equation models termed SubgroupSEM. We provide an overview and comparison of three approaches to modeling and detecting heterogeneous groups in structural equation models, namely, finite mixture models, SEM trees, and SubgroupSEM. We provide a step-by-step guide to applying subgroup discovery techniques for structural equation models, followed by a detailed and illustrated presentation of pruning strategies and four subgroup discovery algorithms. Finally, the SubgroupSEM approach will be illustrated on two real data examples, examining measurement invariance of a mental ability test and investigating interesting subgroups for the mediated relationship between predictors of educational outcomes and the trajectories of math competencies in 5th grade children. The illustrative examples are accompanied by examples of the R package subgroupsem, which is a viable implementation of our approach for applied researchers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Treatment effects on count outcomes with non-normal covariates.

The effects of a treatment or an intervention on a count outcome are often of interest in applied research. When controlling for additional covariates, a negative binomial regression model is usually applied to estimate conditional expectations of the count outcome. The difference in conditional expectations under treatment and under control is then defined as the (conditional) treatment effect. While traditionally aggregates of these conditional treatment effects (e.g., average treatment effects) are computed by averaging over the empirical distribution, a recently proposed moment-based approach allows for computing aggregate effects as a function of distribution parameters. The moment-based approach makes it possible to control for (latent) multivariate normally distributed covariates and provides more reliable inferences under certain conditions. In this paper we propose three different ways to account for non-normally distributed continuous covariates in this approach: an alternative, known non-normal distribution; a plausible factorization of the joint distribution; and an approximation using finite Gaussian mixtures. A saturated model is used for categorical covariates, making a distributional assumption obsolete. We further extend the moment-based approach to allow for multiple treatment conditions and the computation of conditional effects for categorical covariates. An illustrative example highlighting the key features of our extension is provided.

A MAFG-lncRNA axis links systemic nutrient abundance to hepatic glucose metabolism.

Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcripts with elusive functions in metabolism. Here we show that a high fraction of lncRNAs, but not protein-coding mRNAs, are repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation induced lncRNAs in mouse liver. Similarly, lncRNAs are lost in diabetic humans. LncRNA promoter analyses, global cistrome and gain-of-function analyses confirm that increased MAFG signaling during DIO curbs lncRNA expression. Silencing Mafg in mouse hepatocytes and obese mice elicits a fasting-like gene expression profile, improves glucose metabolism, de-represses lncRNAs and impairs mammalian target of rapamycin (mTOR) activation. We find that obesity-repressed LincIRS2 is controlled by MAFG and observe that genetic and RNAi-mediated LincIRS2 loss causes elevated blood glucose, insulin resistance and aberrant glucose output in lean mice. Taken together, we identify a MAFG-lncRNA axis controlling hepatic glucose metabolism in health and metabolic disease.

Average Effects Based on Regressions with a Logarithmic Link Function: A New Approach with Stochastic Covariates.

Researchers often use regressions with a logarithmic link function to evaluate the effects of a treatment on a count variable. In order to judge the average effectiveness of the treatment on the original count scale, they compute average treatment effects, which are defined as the average difference between the expected outcomes under treatment and under control. Current practice is to evaluate the expected differences at every observation and use the sample mean of these differences as a point estimate of the average effect. The standard error for this average effect estimate is based on the implicit assumption that covariate values are fixed, i.e., do not vary across different samples. In this paper, we present a new way of analytically computing average effects based on regressions with log link using stochastic covariates and develop new formulas to obtain standard errors for the average effect. In a simulation study, we evaluate the statistical performance of our new estimator and compare it with the traditional approach. Our findings suggest that the new approach gives unbiased effect estimates and standard errors and outperforms the traditional approach when strong interaction and/or a skewed covariate is present.

LincRNA H19 protects from dietary obesity by constraining expression of monoallelic genes in brown fat.

Increasing brown adipose tissue (BAT) thermogenesis in mice and humans improves metabolic health and understanding BAT function is of interest for novel approaches to counteract obesity. The role of long noncoding RNAs (lncRNAs) in these processes remains elusive. We observed maternally expressed, imprinted lncRNA H19 increased upon cold-activation and decreased in obesity in BAT. Inverse correlations of H19 with BMI were also observed in humans. H19 overexpression promoted, while silencing of H19 impaired adipogenesis, oxidative metabolism and mitochondrial respiration in brown but not white adipocytes. In vivo, H19 overexpression protected against DIO, improved insulin sensitivity and mitochondrial biogenesis, whereas fat H19 loss sensitized towards HFD weight gains. Strikingly, paternally expressed genes (PEG) were largely absent from BAT and we demonstrated that H19 recruits PEG-inactivating H19-MBD1 complexes and acts as BAT-selective PEG gatekeeper. This has implications for our understanding how monoallelic gene expression affects metabolism in rodents and, potentially, humans.

The COMT Val/Met polymorphism modulates effects of tDCS on response inhibition.

BACKGROUND: Transcranial direct current stimulation (tDCS) is increasingly discussed as a new option to support the cognitive rehabilitation in neuropsychiatric disorders. However, the therapeutic impact of tDCS is limited by high inter-individual variability. Genetic factors most likely contribute to this variability by modulating the effects of tDCS. OBJECTIVES: We aimed to investigate the influence of the COMT Val(108/158)Met polymorphism on cathodal tDCS effects on executive functioning. METHODS: Cathodal tDCS was applied to the left dorsolateral prefrontal cortex (dlPFC) during the performance of a parametric Go/No-Go test. RESULTS: We demonstrate an impairing effect of cathodal tDCS to the dlPFC on response inhibition. This effect was only found in individuals homozygous for the Val-allele of the COMT Val(108/158)Met polymorphism. No effects of stimulation on executive functions in Met-allele carriers were detected. CONCLUSION: Our data indicate that i) cathodal, excitability reducing tDCS, interferes with inhibitory cognitive control, ii) the left dlPFC is critically involved in the neuronal network underlying the control of response inhibition, and iii) the COMT Val(108/158)Met polymorphism modulates the impact of cathodal tDCS on inhibitory control. Together with our previous finding that anodal tDCS selectively impairs set-shifting abilities in COMT Met/Met homozygous individuals, these results indicate that genetic factors modulate effects of tDCS on cognitive performance. Therefore, future tDCS research should account for genetic variability in the design and analysis of neurocognitive as well as therapeutic applications to reduce the variability of results and facilitate individualized neurostimulation approaches.