T-REX17 is a transiently expressed non-coding RNA essential for human endoderm formation

Abstract

Long non-coding RNAs (lncRNAs) have emerged as fundamental regulators in various biological processes, including embryonic development and cellular differentiation. Despite much progress over the past decade, the genome-wide annotation of lncRNAs remains incomplete and many known non-coding loci are still poorly characterized. Here, we report the discovery of a previously unannotated lncRNA that is transcribed 230 kb upstream of the SOX17 gene and located within the same topologically associating domain. We termed it T-REX17 (Transcript Regulating Endoderm and activated by soX17) and show that it is induced following SOX17 activation but its expression is more tightly restricted to early definitive endoderm. Loss of T-REX17 affects crucial functions independent of SOX17 and leads to an aberrant endodermal transcriptome, signaling pathway deregulation and epithelial to mesenchymal transition defects. Consequently, cells lacking the lncRNA cannot further differentiate into more mature endodermal cell types. Taken together, our study identified and characterized T-REX17 as a transiently expressed and essential non-coding regulator in early human endoderm differentiation.

Enhancers are genes that express organizational RNAs

“…Enhancers must act transiently at almost every stage of ontogeny [see, e.g. (Bachu et al, 2022;Landshammer et al, 2023)], most of which have not been polled. Humans contain an estimated 30 trillion cells (excluding the microbiome) (Bianconi et al, 2013;Sender et al, 2016), which means that −6 × 10 13 binary cell fate (differentiate and/or divide) decisions must be made with high precision and reproducibility to ensure the correct formation of an adult with its myriad of architecturally distinct and correctly wired muscles, bones and organs, exemplified by the phenotypic congruence of monozygotic twins (Mattick and Amaral, 2022).…”

Developmental trajectories and cooperating genomic events define molecular subtypes of BCR::ABL1-positive ALL

“…Machine learning–based gene expression analysis 15 was used to define in our GMALL cohort BCR::ABL1- ALL gene expression clusters, reproducibly validated in 3 external cohorts. 12 , 13 , 14 Long-read RNA-Seq 17 and single-cell assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-Seq)/RNA-Seq (10× Genomics, Pleasanton, CA) were used to confirm cluster-specific gene isoform expressions. Immunophenotyping and measurement of minimal residual disease were performed in central reference laboratories.…”