Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Identification and characterization of chemotherapy resistant high-risk neuroblastoma persister cells.

Relapse rates in high-risk neuroblastoma remain exceedingly high. The malignant cells that are responsible for relapse have not been identified, and mechanisms of therapy resistance remain poorly understood. Here, we used single nucleus RNA sequencing and bulk whole genome sequencing to identify and characterize the residual malignant persister cells that survive chemotherapy from a cohort of 20 matched diagnosis and definitive surgery tumor samples from patients treated with high-risk neuroblastoma induction chemotherapy. We show that persister cells share common mechanisms of chemotherapy escape including suppression of MYCN activity and activation of NF-κB signaling, the latter is further enhanced by cell-cell communication between the malignant cells and the tumor microenvironment. Overall, our work dissects the transcriptional landscape of cellular persistence in high-risk neuroblastoma and paves the way to the development of new therapeutic strategies to prevent disease relapse.