High-Fat Diet Promotes Acute Promyelocytic Leukemia through PPARδ-Enhanced Self-renewal of Preleukemic Progenitors.

Risk and outcome of acute promyelocytic leukemia (APL) are particularly worsened in obese-overweight individuals, but the underlying molecular mechanism is unknown. In established mouse APL models (Ctsg-PML::RARA), we confirmed that obesity induced by high-fat diet (HFD) enhances leukemogenesis by increasing penetrance and shortening latency, providing an ideal model to investigate obesity-induced molecular events in the preleukemic phase. Surprisingly, despite increasing DNA damage in hematopoietic stem cells (HSC), HFD only minimally increased mutational load, with no relevant impact on known cancer-driving genes. HFD expanded and enhanced self-renewal of hematopoietic progenitor cells (HPC), with concomitant reduction in long-term HSCs. Importantly, linoleic acid, abundant in HFD, fully recapitulates the effect of HFD on the self-renewal of PML::RARA HPCs through activation of peroxisome proliferator-activated receptor delta, a central regulator of fatty acid metabolism. Our findings inform dietary/pharmacologic interventions to counteract obesity-associated cancers and suggest that nongenetic factors play a key role. PREVENTION RELEVANCE: Our work informs interventions aimed at counteracting the cancer-promoting effect of obesity. On the basis of our study, individuals with a history of chronic obesity may still significantly reduce their risk by switching to a healthier lifestyle, a concept supported by evidence in solid tumors but not yet in hematologic malignancies. See related Spotlight, p. 47.

Discovering sequence and structure landscapes in RNA interaction motifs.

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.