Biology subjects

Wolf, A.

Publications and source records attributed to Wolf, A..

21 records · Page 2Linked to original sources

bioRxiv2020

Microbial regulation of hexokinase 2 links mitochondrial metabolism and cell death in colitis

Hexokinases (HK) catalyze the first step of glycolysis and thereby limit its pace. HK2 is highly expressed in the gut epithelium, plays a role in immune responses and is upregulated in inflammation and ulcerative colitis 1-3. Here, we examined the microbial regulation of HK2 and its impact on intestinal inflammation by generating mice lacking HK2 specifically in intestinal epithelial cells (Hk2{Delta}IEC). Hk2{Delta}IEC mice were less susceptible to acute intestinal inflammation upon challenge with dextran sodium sulfate (DSS). Analyzing the epithelial transcriptome from Hk2{Delta}IEC mice during acute colitis revealed downregulation of cell death signaling and mitochondrial dysfunction dependent on loss of HK2. Using intestinal organoids derived from Hk2{Delta}IEC mice and Caco-2 cells lacking HK2, we identified peptidyl-prolyl cis-trans isomerase (PPIF) as a key target of HK2-mediated regulation of mitochondrial permeability and repression of cell-death during intestinal inflammation. The microbiota strongly regulated HK2 expression and activity. The microbially-derived short-chain fatty acid (SCFA) butyrate repressed HK2 expression and oral supplementation protected wildtype but not Hk2{Delta}IEC mice from DSS colitis. Our findings define a novel mechanism how butyrate may act as a protective factor for intestinal barrier homeostasis and suggest targeted HK2 inhibition as a promising therapeutic avenue in intestinal inflammation.

immunology↗

bioRxiv2020

CITEseq analysis of non-small-cell lung cancer lesions reveals an axis of immune cell activation associated with tumor antigen load and TP53 mutations

Immunotherapy is becoming a mainstay in the treatment of NSCLC. While tumor mutational burden (TMB) has been shown to correlate with response to immunotherapy, little is known about the relation of the baseline immune response with the tumor genotype. Here, we profiled 35 early stage NSCLC lesions using multiscale single cell sequencing. Unsupervised clustering identified in a subset of patients a key cellular module consisting of PDCD1+ CXCL13+ activated T cells, IgG+ plasma cells, and SPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). Transcriptional data from two NSCLC cohorts confirmed a subset of patients with LCAMhi enrichment, which was independent of overall immune cell content. The LCAMhi module strongly correlated with TMB, expression of cancer testis antigens, and with TP53 mutations in smokers and non-smokers. These data establish LCAM as a key mode of immune cell activation associated with high tumor antigen load and driver mutations.

immunology↗

bioRxiv2017

Assessment of batch-correction methods for scRNA-seq data with a new test metric

Single-cell transcriptomics is a versatile tool for exploring heterogeneous cell populations. As with all genomics experiments, batch effects can hamper data integration and interpretation. The success of batch effect correction is often evaluated by visual inspection of dimension-reduced representations such as principal component analysis. This is inherently imprecise due to the high number of genes and non-normal distribution of gene expression. Here, we present a k-nearest neighbour batch effect test (kBET, https://github.com/theislab/kBET) to quantitatively measure batch effects. kBET is easier to interpret, more sensitive and more robust than visual evaluation and other measures of batch effects. We use kBET to assess commonly used batch regression and normalisation approaches, and quantify the extent to which they remove batch effects while preserving biological variability. Our results illustrate that batch correction based on log-transformation or scran pooling followed by ComBat reduced the batch effect while preserving structure across data sets. Finally we show that kBET can pinpoint successful data integration methods across multiple data sets, in this case from different publications all charting mouse embryonic development. This has important implications for future data integration efforts, which will be central to projects such as the Human Cell Atlas where data for the same tissue may be generated in multiple locations around the world.\n\n[Before final publication, we will upload the R package to Bioconductor]

bioinformatics↗