Biology subjects

Knaack, S.

Publications and source records attributed to Knaack, S..

3 recordsLinked to original sources

bioRxiv2025

Single-nucleus analysis reveals dysregulated oxidative phosphorylation in Down syndrome basal forebrain at birth

INTRODUCTIONBasal forebrain cholinergic neurons (BFCNs) are integral to learning, attention, and memory, and are prone to degeneration in Down syndrome (DS), Alzheimers disease, and other neurodegenerative diseases. However, the mechanisms that lead to the degeneration of these neurons are not known. METHODSSingle-nucleus gene expression and ATAC sequencing were performed on postmortem human basal forebrain from unaffected control and DS tissue samples at 0-2 years of age (n=4 each). RESULTSSequencing analysis of postmortem human basal forebrain identifies gene expression differences in DS early in life. Genes encoding proteins associated with energy metabolism pathways, specifically oxidative phosphorylation and glycolysis, and genes encoding antioxidant enzymes are upregulated in DS BFCNs. DISCUSSIONMultiomic analyses reveal that energy metabolism may be disrupted in DS BFCNs by birth. Increased oxidative phosphorylation and the accumulation of reactive oxygen species byproducts may be early contributors to DS BFCN neurodegeneration.

neuroscience↗

bioRxiv2020

Dynamic regulatory module networks for inference of cell type specific transcriptional networks

Multi-omic datasets with parallel transcriptomic and epigenomic measurements across time or cell types are becoming increasingly common. However, integrating these data to infer regulatory network dynamics is a major challenge. We present Dynamic Regulatory Module Networks (DRMNs), a novel approach that uses multi-task learning to infer cell type-specific cis-regulatory networks dynamics. Compared to existing approaches, DRMN integrates expression, chromatin state and accessibility, accurately predicts cis-regulators of context-specific expression and models network dynamics across linearly and hierarchically related contexts. We apply DRMN to three dynamic processes of different experimental designs and predict known and novel regulators driving cell type-specific expression patterns.

bioinformatics↗

bioRxiv2018

In silico prediction of high-resolution Hi-C interaction matrices

The three-dimensional organization of the genome plays an important role in gene regulation by enabling distal sequence elements to control the expression level of genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to measure the contact count of pairs of genomic loci needed to study three-dimensional organization. Due to experimental costs high resolution Hi-C datasets are available only for a handful of cell lines. Computational prediction of Hi-C contact counts can offer a scalable and inexpensive approach to examine three-dimensional genome organization across many cellular contexts. Here we present HiC-Reg, a novel approach to predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. HiC-Reg exploits the signal from the region spanning two interacting regions and from across multiple cell lines to generalize to new contexts. Using existing feature importance measures and a new matrix factorization based approach, we found CTCF and chromatin marks, especially repressive and elongation marks, as important for predictive performance. Predicted counts from HiC-Reg identify topologically associated domains as well as significant interactions that are enriched for CTCF bi-directional motifs and agree well with interactions identified from complementary long-range interaction assays. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions as well as higher-order organizational units of the genome.

bioinformatics↗