Body fat distribution is a heritable risk factor for cardiovascular and metabolic disease. In humans, rare Inhibin beta E ( INHBE , activin E) loss-of-function variants are associated with a lower waist-to-hip ratio and protection from type 2 diabetes. Hepatic fatty acid sensing promotes INHBE expression during fasting and in obese individuals, yet it is unclear how the hepatokine activin E governs body shape and energy metabolism. Here, we uncover activin E as a regulator of adipose energy storage. By suppressing β-agonist-induced lipolysis, activin E promotes fat accumulation and adipocyte hypertrophy and contributes to adipose dysfunction in mice. Mechanistically, we demonstrate that activin E elicits its effect on adipose tissue through ACVR1C, activating SMAD2/3 signaling and suppressing PPARG target genes. Conversely, loss of activin E or ACVR1C in mice increases fat utilization, lowers adiposity, and drives PPARG-regulated gene signatures indicative of healthy adipose function. Our studies identify activin E–ACVR1C as a metabolic rheostat promoting liver–adipose cross talk to restrain excessive fat breakdown and preserve fat mass during prolonged fasting, a mechanism that is maladaptive in obese individuals.

The growing complexity and volume of genomic and omics data present critical challenges for storage, transfer, and analysis in edge–cloud platforms. Existing compression techniques often involve trade-offs between efficiency and speed, requiring innovative approaches that ensure scalability and cost-effectiveness. This paper introduces a lossless compression method that integrates Trie-based shared dictionaries within an edge–cloud architecture. It presents a software-centric scientific research process of the design and evaluation of the proposed compression method. By enabling localized preprocessing at the edge, our approach reduces data redundancy before cloud transmission, thereby optimizing both storage and network efficiency. A global shared dictionary is constructed using N-gram analysis to identify and prioritize repeated sequences across multiple files. A lightweight index derived from this dictionary is then pushed to edge nodes, where Trie-based sequence replacement is applied to eliminate redundancy locally. The preprocessed data are subsequently transmitted to the cloud, where advanced compression algorithms, such as Zstd, GZIP, Snappy, and LZ4, further compress them. Evaluation on real patient omics datasets from B-cell Acute Lymphoblastic Leukemia (B-ALL) and Chronic Lymphocytic Leukemia (CLL) demonstrates that edge preprocessing significantly improves compression ratios, reduces upload times, and enhances scalability in hybrid cloud frameworks.

Finding insights in large and complex high-dimensional numerical genomic data with many numerical values of the gene expression or protein marker is challenging. Machine learning (ML) explainer SHapley Additive exPlanations (SHAP) is an explainable artificial intelligence (XAI) approach which provides insights into how each feature influences the model's predictions and enhances transparency and trust in ML models. Visualisations are typically used to support XAI, but there is limited research that utilises XAI to enhance visualisation outcomes. This paper presents a novel method that utilises an interactive visualisation dashboard, incorporating explainer SHAP, to select more relevant genomic features and enhance the visual analytics dashboard outcomes. This is crucial for genomic data analytics due to the high number of numerical gene features, which is not effective in analysing and visualising with the limited space and human cognitive load. We demonstrate the effectiveness of our new method on two case studies with Rhabdomyosarcoma (RMS) datasets and Acute Lymphoblastic Leukaemia (ALL) with over 200 selected gene features. The ML explainer SHAP method ranks and finds essential features from a high-dimensional numerical genomic dataset, and the ranked results are applied to the heatmap on the interactive visualisation dashboard to reduce cognitive clutter.