Biology subjects

Michael P Snyder

Publications and source records attributed to Michael P Snyder.

3 recordsLinked to original sources

bioRxiv2020

KLF4 Recruits SWI/SNF to Increase Chromatin Accessibility and Reprogram the Endothelial Enhancer Landscape under Laminar Shear Stress

Physiologic laminar shear stress (LSS) induces an endothelial gene expression profile that is vasculo-protective. In this report, we delineate how LSS mediates changes in the epigenetic landscape to promote this beneficial response. We show that under LSS, KLF4 interacts with the SWI/SNF nucleosome remodeling complex to increase accessibility at enhancer sites that promote expression of homeostatic endothelial genes. By combining molecular and computational approaches we discovered enhancers that loop to promoters of known and novel KLF4- and LSS-responsive genes that stabilize endothelial cells and suppress inflammation, such as BMPR2 and DUSP5. By linking enhancers to genes that they regulate under physiologic LSS, our work establishes a foundation for interpreting how non-coding DNA variants in these regions might disrupt protective gene expression to influence vascular disease.

molecular biology↗

bioRxiv2016

A Common Class of Transcripts with 5’-Intron Depletion, Distinct Early Coding Sequence Features, and N1-Methyladenosine Modification

Introns are found in 5 untranslated regions (5UTRs) for 35% of all human transcripts. These 5UTR introns are not randomly distributed: genes that encode secreted, membrane-bound and mitochondrial proteins are less likely to have them. Curiously, transcripts lacking 5UTR introns tend to harbor specific RNA sequence elements in their early coding regions. To model and understand the connection between coding-region sequence and 5UTR intron status, we developed a classifier that can predict 5UTR intron status with >80% accuracy using only sequence features in the early coding region. Thus, the classifier identifies transcripts with 5 proximal-intron-minus-like-coding regions (\"5IM\" transcripts). Unexpectedly, we found that the early coding sequence features defining 5IM transcripts are widespread, appearing in 21% of all human RefSeq transcripts. The 5IM class of transcripts is enriched for non-AUG start codons, more extensive secondary structure both preceding the start codon and near the 5 cap, greater dependence on eIF4E for translation, and association with ER-proximal ribosomes. 5IM transcripts are bound by the Exon Junction Complex (EJC) at non-canonical 5 proximal positions. Finally, N1-methyladenosines are specifically enriched in the early coding regions of 5IM transcripts. Taken together, our analyses point to the existence of a distinct 5IM class comprising [~]20% of human transcripts. This class is defined by depletion of 5 proximal introns, presence of specific RNA sequence features associated with low translation efficiency, N1-methyladenosines in the early coding region, and enrichment for non-canonical binding by the Exon Junction Complex.

Bioinformatics↗

bioRxiv2015

Integrative analysis of RNA, translation and protein levels reveals distinct regulatory variation across humans

Elucidating the consequences of genetic differences between humans is essential for understanding phenotypic diversity and personalized medicine. Although variation in RNA levels, transcription factor binding and chromatin have been explored, little is known about global variation in translation and its genetic determinants. We used ribosome profiling, RNA sequencing, and mass spectrometry to perform an integrated analysis in lymphoblastoid cell lines from a diverse group of individuals. We find significant differences in RNA, translation, and protein levels suggesting diverse mechanisms of personalized gene expression control. Combined analysis of RNA expression and ribosome occupancy improves the identification of individual protein level differences. Finally, we identify genetic differences that specifically modulate ribosome occupancy - many of these differences lie close to start codons and upstream ORFs. Our results reveal a new level of gene expression variation among humans and indicate that genetic variants can cause changes in protein levels through effects on translation.

Genomics↗