Obesity causes mitochondrial fragmentation and dysfunction in white adipocytes due to RalA activation.

Mitochondrial dysfunction is a characteristic trait of human and rodent obesity, insulin resistance and fatty liver disease. Here we show that high-fat diet (HFD) feeding causes mitochondrial fragmentation in inguinal white adipocytes from male mice, leading to reduced oxidative capacity by a process dependent on the small GTPase RalA. RalA expression and activity are increased in white adipocytes after HFD. Targeted deletion of RalA in white adipocytes prevents fragmentation of mitochondria and diminishes HFD-induced weight gain by increasing fatty acid oxidation. Mechanistically, RalA increases fission in adipocytes by reversing the inhibitory Ser637 phosphorylation of the fission protein Drp1, leading to more mitochondrial fragmentation. Adipose tissue expression of the human homolog of Drp1, DNM1L, is positively correlated with obesity and insulin resistance. Thus, chronic activation of RalA plays a key role in repressing energy expenditure in obese adipose tissue by shifting the balance of mitochondrial dynamics toward excessive fission, contributing to weight gain and metabolic dysfunction.

Analysis of the Drosophila and human DPR elements reveals a distinct human variant whose specificity can be enhanced by machine learning.

The RNA polymerase II core promoter is the site of convergence of the signals that lead to the initiation of transcription. Here, we performed a comparative analysis of the downstream core promoter region (DPR) in Drosophila and humans by using machine learning. These studies revealed a distinct human-specific version of the DPR and led to the use of machine learning models for the identification of synthetic extreme DPR motifs with specificity for human transcription factors relative to Drosophila factors and vice versa. More generally, machine learning models could similarly be used to design synthetic DNA elements with customized functional properties.