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Guo, E.

Publications and source records attributed to Guo, E..

2 recordsLinked to original sources

BEN-solo factors partition active chromatin to ensure proper gene activation in Drosophila

The Drosophila genome encodes three BEN-solo proteins including Insensitive (Insv), Elba1 and Elba2 that possess activities in both transcriptional repression and chromatin insulation. These proteins all have a DNA binding BEN domain. A fourth protein Elba3 bridges Elba1 and Elba2 to form a heterotrimeric complex ELBA. Here we report comprehensive investigation on the in vivo functions of these proteins in Drosophila embryos. We generate ChIP-seq data of all these factors from all cognate and non-cognate mutants to assess common and distinct binding locations of Insv and ELBA, and genetic interdependencies. Notably, while most Elba1 and Elba2 binding requires the full ELBA complex, the adapter protein Elba3 can associate with chromatin and repress gene expression independently of Elba1 and Elba2. We also employ high-resolution ChIP-nexus mapping to show that Insv binds to DNA in a symmetric configuration while the ELBA complex binds asymmetrically in vivo. We observe that motifs of known insulator proteins are enriched in ELBA and Insv ChIP peaks and demonstrate that ELBA collaborates with other insulator factors to regulate developmental patterning in embryos. To differentiate the insulator function of ELBA and Insv from their repressor activity, we determined real-time transcription change in mutant embryos using precision nuclear run-on sequencing. ELBA factor mutants dampen expression differences between pairs of ELBA-bound neighboring genes. Finally, transgenic reporters confirm insulation activity of ELBA- and Insv-bound sites. Altogether, these findings define ELBA and Insv as general insulator proteins in Drosophila and demonstrate the functional importance of insulators in partitioning transcription units.

developmental biology

An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Algorithmic classifiers are crucial components of clinical decision support (CDS) systems needed to advance healthcare delivery. Robust CDS systems must be derived and validated via creation of multi-reviewer adjudicated gold standard datasets. Manual annotation of physiologic data such as mechanical ventilator waveform data (VWD) can be time-consuming, and lacks methodological consistency in dataset development. To address these issues, we have created a system for annotating and adjudicating VWD called the Annotation PipeLine (APL) to optimize VWD annotation by expert reviewers. APL combines visual assessment of waveform characteristics with metadata display, enabling inclusion of quantitative thresholds into annotation decisions by reviewers. APL also includes specific features for resolving multi-reviewer disagreements and generating gold standard data sets. APLs unique combination of methods and open source framework may accelerate the creation of CDS algorithms for ventilator management, and may serve as a model for future research into physiologic waveform annotation systems.

physiology