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Mathios, D.

Publications and source records attributed to Mathios, D..

2 recordsLinked to original sources

RAPPL-Seq Enables Rapid, High-Sensitivity Ribosome Profiling of Scarce Samples

Ribosome profiling is a powerful method for transcriptome-wide monitoring of translation activity in vivo, but its use is limited by lengthy protocols, reliance on specialized equipment and inefficient recovery of ribosome-protected fragments (RPFs). To overcome these challenges, we developed RAPPL-Seq, a magnetic bead-based affinity capture method that enables fast and efficient RPF isolation. RAPPL-Seq achieves high recovery efficiency, short processing time, and robust performance with limited input material. It is broadly applicable across multiple sample types, including cell lines, primary tissues, and human biospecimens. RAPPL-Seq can also be paired with translation inhibitors to characterize inhibitor- and tissue-specific mRNA translation. RAPPL-Seq efficiently captured translatomes of glioblastoma (GBM) and adjacent non-tumor brain tissues from patient biopsies. Together, these findings establish RAPPL-Seq as a straightforward, easily accessible, and highly sensitive approach that extends translatome studies to scarce experimental and clinical samples, opening new avenues for mechanistic insight and therapeutic discovery.

molecular biology↗

Integrative analysis of DNA methylation suggests down-regulation of oncogenic pathways and reduced de-novo mutation in survival outliers of glioblastoma

The study of survival outliers of glioblastoma (GBM) can have important implications on gliomagenesis as well as in the identification of ways to alter clinical course on this almost uniformly lethal cancer type. However, current studied epigenetic and genetic signatures of the GBM outliers have failed to identify unifying criteria to characterize this unique group of patients. In this study, we profiled the global DNA methylation pattern of mainly IDH1 wild type survival outliers of glioblastoma and performed comprehensive enrichment analyses with genomic and epigenomic signatures. We found that the genome of long-term survivors in glioblastoma is differentially methylated relative to short-term survivor patients depending on CpG density: hypermethylation near CpG islands (CGIs) and hypomethylation far from CGIs. Interestingly, these two patterns are associated with distinct oncogenic aspects in gliomagenesis. The hypomethylation pattern at the region distant from CGI is associated with lower rates of de novo mutations while the hypermethylation at CGIs correlates with transcriptional downregulation of genes involved in cancer progression pathways. These results extend our understanding of DNA methylation of survival outliers in glioblastoma in a genome-wide level, and provide insight on the potential impact of DNA hypomethylation in cancer genome.

cancer biology↗