Drong, A. W. | bioRxiv Science

bioRxiv2023

Mechanism-guided quantification of LINE-1 reveals p53 regulation of both retrotransposition and transcription

Somatic activity of LINE-1 (L1) mobile elements has been implicated in cancer etiology, which may be related to the loss of p53-mediated regulation as a result of TP53 mutations. Quantifying the mechanisms of L1 regulation in cancer has been challenging. Here, we build a statistical model of L1 regulation by simultaneously quantifying L1 retrotransposition, L1 expression, and the fitness costs of mutated TP53 with precision. We first developed Total ReCall, an algorithm specifically tailored to the mechanisms of L1 reintegration, to detect L1 insertions from short-read whole-genome sequencing. Applying Total ReCall to high-quality data consisting of >750 paired tumor and normal samples from The Cancer Genome Atlas (TCGA) shows high L1 insertion heterogeneity among tumor types, with increased retrotransposition burden in lung squamous cell carcinoma, head and neck, and colon cancers. We next assessed the active RNA expression of intact L1 in >9,000 TCGA tumor samples, establishing, for the first time, a clear correlation between L1 expression and retrotransposition. Finally, we integrated the number of L1 insertions, L1 expression and a mathematical model of TP53 fitness into a multi-modal model of p53- mediated mechanisms of L1 regulation. We show that TP53 mutations enable retrotransposition both by disinhibiting L1 expression and enabling its reintegration and quantify the relative weights of this dual regulatory role. We demonstrate how mechanism-based multi-modal modeling applied at scale can statistically disentangle the complex interplay between canonical driver events in tumor evolution and retrotransposon activity.

cancer biology↗

bioRxiv2022

Single-cell multi-omics defines the cell-type specific impact of splicing aberrations in human hematopoietic clonal outgrowths

RNA splicing factors are recurrently affected by alteration-of-function mutations in clonal blood disorders, highlighting the importance of splicing regulation in hematopoiesis. However, our understanding of the impact of dysregulated RNA splicing has been hampered by the inability to distinguish mutant and wildtype cells in primary patient samples, the cell-type complexity of the hematopoietic system, and the sparse and biased coverage of splice junctions by short-read sequencing typically used in single-cell RNA sequencing. To overcome these limitations, we developed GoT-Splice by integrating Genotyping of Transcriptomes (GoT) with enhanced efficiency long-read single-cell transcriptome profiling, as well as proteogenomics (with CITE-seq). This allowed for the simultaneous single-cell profiling of gene expression, cell surface protein markers, somatic mutation status, and RNA splicing. We applied GoT-Splice to bone marrow progenitors from patients with myelodysplastic syndrome (MDS) affected by mutations in the most prevalent mutated RNA splicing factor - the core RNA splicing factor SF3B1. High-resolution mapping of SF3B1mut vs. SF3B1wt hematopoietic progenitors revealed a fitness advantage of SF3B1mut cells in the megakaryocytic-erythroid lineage, resulting in an expansion of SF3B1mut erythroid progenitor (EP) cells. SF3B1mut EP cells exhibited upregulation of genes involved in regulation of cell cycle and mRNA translation. Long-read single-cell transcriptomes revealed the previously reported increase of aberrant 3 splicing site usage in SF3B1mut cells. However, the ability to profile splicing within individual cell populations uncovered distinct cryptic 3 splice site usage across different progenitor populations, as well as stage-specific aberrant splicing during erythroid maturation. Lastly, as splice factor mutations occur in clonal hematopoiesis (CH) with increased risk of neoplastic transformation, we applied GoT-Splice to CH samples. These data revealed that the erythroid lineage bias, as well as cell-type specific cryptic 3 splice site usage in SF3B1mut cells, precede overt MDS. Collectively, we present an expanded multi-omics single-cell toolkit to define the cell-type specific impact of somatic mutations on RNA splicing, from the earliest phases of clonal outgrowths to overt neoplasia, directly in human samples.

cancer biology↗