Search bioRxiv⌕ Search

Biology subjects

Cebeci, M.

Publications and source records attributed to Cebeci, M..

2 recordsLinked to original sources

SETD2 suppresses tumorigenesis in a KRASG12C-driven lung cancer model and its catalytic activity is regulated by histone acetylation

Histone H3 trimethylation at lysine 36 (H3K36me3) is a key chromatin modification that regulates fundamental physiologic and pathologic processes. In humans, SETD2 is the only known enzyme that catalyzes H3K36me3 in somatic cells and is implicated in tumor suppression across multiple cancer types. While there is considerable crosstalk between the SETD2-H3K36me3 axis and other epigenetic modifications, much remains to be understood. Here, we show that SETD2 functions as a potent tumor suppressor in a KRASG12C-driven lung adenocarcinoma (LUAD) mouse model, and that acetylation enhances SETD2 in vitro methylation of H3K36 on nucleosome substrates. In vivo, SETD2 ablation accelerates lethality in an autochthonous KRASG12C-driven LUAD mouse tumor model. Biochemical analyses reveal that polyacetylation of histone tails in a nucleosome context promote H3K36 methylation by SETD2. In addition, monoacetylation exerts position-specific effects to stimulate SETD2 methylation activity. In contrast, mono-ubiquitination at various histone sites, including at H2AK119 and H2BK120, does not affect SETD2 methylation of nucleosomes. Together, these findings provide insight into how SETD2 integrates histone modification signals to regulate H3K36 methylation and highlights the potential role of SETD2-associated epigenetic crosstalk in cancer pathogenesis.

molecular biology↗

PASV: Automatic protein partitioning and validation using conserved residues

BackgroundIncreasingly, researchers use protein-coding genes from targeted PCR amplification or direct metagenomic sequencing in community and population ecology. Analysis of protein-coding genes presents different challenges from those encountered in traditional SSU rRNA studies. Most protein-coding sequences are annotated based on homology to other computationally-annotated sequences, which can lead to inaccurate annotations. Therefore, the results of sensitive homology searches must be validated to remove false-positives and assess functionality. Multiple lines of in silico evidence can be gathered by examining conserved domains and residues identified through biochemical investigations. However, manually validating sequences in this way can be time consuming and error prone, especially in large environmental studies. ResultsAn automated pipeline for protein active site validation (PASV) was developed to improve validation and partitioning accuracy for protein-coding sequences, combining multiple sequence alignment with expert domain knowledge. PASV was tested using commonly misannotated proteins: ribonucleotide reductase (RNR), alternative oxidase (AOX), and plastid terminal oxidase (PTOX). PASV partitioned 9,906 putative Class I alpha and Class II RNR sequences from bycatch in a global viral metagenomic investigation with >99% true positive and true negative rates. PASV predicted the class of 2,579 RNR sequences in >98% agreement with manual annotations. PASV correctly partitioned all 336 tested AOX and PTOX sequences. ConclusionsPASV provides an automated and accurate way to address post-homology search validation and partitioning of protein-coding marker genes. Source code is released under the MIT license and is found with documentation and usage examples on GitHub at https://github.com/mooreryan/pasv.

bioinformatics↗