Hutchings, G. | bioRxiv Science

bioRxiv2026

The obesity associated BDNF intron-3 cis-regulatory element, BE5.1, modulates the effects of early-life stress on weight gain, anxiety and gene expression in a sex dependent manner.

Early-life stress (ELS) has been associated with increases in obesity and anxiety and affects males and females in different ways. Animal and human studies indicate that ELS has maladaptive effects on brain development due, in part, to disruptions in the expression of genes that include brain-derived neurotrophic factor (BDNF). Our previous studies identified a cis-regulatory element (CRE, BE5.1) within BDNF intron-3 that harbours a SNP associated with obesity and anxiety. We used maternal separation (MS) to examine the role of BE5.1 in controlling resilience to the maladaptive effects of ELS. At 9-11 weeks, BE5.1KO mice experience a delay in weight gain compared to WT mice, a phenotype partly reversed by MS in BE5.1KO females. The effects of either MS or BE5.1KO decreased anxiety-like behaviour in males whereas MS had little effect in females who displayed increased anxiety-like behaviour in animals lacking BE5.1. We also found that social behaviours are influenced by BE5.1 deletion in females but are more influenced by MS in males. Furthermore, deletion of BE5.1 and MS together significantly decreased marble burying behaviour in male mice, an effect negated by diazepam. RNA-seq analyses suggest that, in hypothalamus, BE5.1 is required to stabilize or down-regulate the transcription of 26 key genes, with diverse neuroregulatory roles, in response to MS as these 26 genes are strongly upregulated in the absence of BE5.1. We also provide evidence that three of these genes; Prkcg, Foxg1 and Mef2c, modulate BE5.1 activity in an allele-specific manner. Taken together, these results suggest that the BE5.1CRE acts as a buffer element that protects against the maladaptive effects of MS on gene expression and later life behaviours in a sex-dependent manner.

molecular biology↗

bioRxiv2026

MIDFA: Scalable Bayesian Factor Analysis for Mixed and Incomplete Data

Probabilistic latent variable models are a powerful tool for uncovering structure in high-dimensional datasets, particularly in biomedical applications. The increasing availability of large-scale epidemiological studies, such as the UK Biobank, poses important modelling challenges, including mixed data types, high dimensionality, and structured missingness. Existing approaches address some of these issues, but few provide a unified and scalable framework for handling them simultaneously. Here, we propose a scalable Bayesian factor analysis framework designed to address these challenges. Our method combines a semi-parametric Gaussian copula model with a continuous spike-and-slab prior to induce sparse and interpretable factor loadings. The number of latent dimensions is learned nonparametrically from the data using an Indian buffet process prior. For model fitting, we develop an expectation-maximisation algorithm that naturally accommodates missing data. We validate the proposed method through comprehensive simulation studies. In addition, we showcase the proposed model using the Novartis-Oxford Multiple Sclerosis dataset in two ways. First, we identify latent dimensions shared across MS clinical and neuroimaging variables, characterising disease structure while demonstrating the models ability to handle multiple data types and structured missingness. Second, we use the model for dimensionality reduction of structural MRI data, extracting features for downstream analysis that go beyond traditional whole-brain summary statistics. In those applications, our method identifies sparse latent structures and provides insights beyond those obtained from traditional approaches.

neuroscience↗