Golde, T. | Search bioRxiv

bioRxiv2018

High affinity interactions and signal transduction between Aβ oligomers and TREM2

Rare coding variant in the Triggering receptor expressed on myeloid cells 2 (TREM2) are associated with increased risk for Alzheimers disease (AD), but how they confer this risk remains uncertain. We assessed binding of TREM2, AD associated TREM2 variants to various forms of A{beta} and APOE in multiple assays. TREM2 interacts directly with various forms of A{beta}, with highest affinity interactions observed between TREM2 and soluble A{beta}42 oligomers. We confirm the previous interaction between APOE3 and APOE4 and TREM2. High affinity binding of TREM2 to A{beta} oligomers is characterized by very slow dissociation. Pre-incubation with A{beta} is shown to block the interaction of APOE. In cellular assays, AD-associated variants of TREM2 reduced the amount of A{beta}42 internalized, and in NFAT assay the R47H variant decreased NFAT signaling activity in response to A{beta}42. These studies demonstrate i) a high affinity interaction between TREM2 and A{beta} oligomers that can block interaction with another ligand and ii) that AD-associated TREM2 variants bind A{beta} with equivalent affinity but show loss of function in terms of signaling and A{beta} internalization.

cell biology↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗