Biology subjects

Walch, A.

Publications and source records attributed to Walch, A..

5 recordsLinked to original sources

bioRxiv2022

Predicting the HER2 status in esophageal cancer from tissue microarrays using convolutional neural networks

BackgroundFast and accurate diagnostics are key for personalized medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies which can prolong lives. In this work we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalized therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. MethodsOur method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1,602 patient samples and tested on an independent set of 307 patient samples. We incorporated an attention mechanism in the CNN architecture to identify the tissue regions in these patient cases which the network has detected as important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridization (ISH) tests. ResultsWe show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve, requiring additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical center. ConclusionsWe demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology.

bioinformatics↗

bioRxiv2022

INFLAMMATION INDUCED DOWNREGULATION OF SUCCINATE DEHYDROGENASE REGULATES ADRENOCORTICAL FUNCTION

The hypothalamus-pituitary-adrenal (HPA) axis is activated in response to inflammation leading to increased production of anti-inflammatory glucocorticoids by the adrenal cortex, thereby representing an endogenous feedback loop. However, severe inflammation reduces the responsiveness of the adrenal gland to adrenocorticotropic hormone (ACTH) although the underlying mechanisms are poorly understood. Here, we show by transcriptomic, proteomic and metabolomic analyses that LPS-induced systemic inflammation triggers profound metabolic changes in steroidogenic adrenocortical cells, including downregulation of the TCA cycle and oxidative phosphorylation. Inflammation disrupts the TCA cycle at the level of succinate dehydrogenase (SDH) leading to succinate accumulation and disturbed steroidogenesis. Mechanistically, IL-1{beta} reduces SDHB expression through upregulation of DNA methyltransferase 1 (DNMT1) and methylation of the SDHB promoter. Consequently, increased succinate levels impair oxidative phosphorylation and ATP synthesis, leading to reduced steroidogenesis. Together, we demonstrate that the IL-1{beta}-DNMT1-SDHB-succinate axis disrupts steroidogenesis. Our findings not only provide a mechanistic explanation for the adrenal dysfunction in severe inflammation but also a potential target for therapeutic intervention.

immunology↗

bioRxiv2021

Functional metabolic phenotyping of human pancreatic ductal adenocarcinoma

Pancreatic Ductal Adenocarcinoma (PDAC) lacks targeted treatment options. Although subtypes with transcriptome-based distinct lineage and differentiation features have been identified, deduced clinically actionable targets remain elusive. We here investigate functional metabolic features of the classical and QM (quasi-mesenchymal)/basal-like PDAC subtypes potentially exploitable for non-invasive subtype differentiation and therapeutic intervention. A collection of human PDAC cell lines, primary patient derived cells (PDC), patient derived xenografts (PDX) and patient PDAC samples were transcriptionally stratified into the classical and QM subtype. Functional metabolic analyses including targeted and non-targeted metabolite profiling (matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI)), seahorse metabolic flux assays and metabolic drug targeting were performed. Hyperpolarized 13C-magnetic resonance spectroscopy (HP-MRS) of PDAC xenografts was used for in vivo detection of intra-tumoral [1-13C]pyruvate and [1-13C]lactate metabolism. We identified glycolysis and lipid metabolism/fatty acid oxidation as transcriptionally preserved metabolic pathways in QM and classical PDAC subtype respectively. However, these metabolic cues were not unambiguously functionally linked to one subtype. Striking functional metabolic heterogeneity was observed especially in primary patient derived cells with only individual samples representing high dependence on glycolysis or mitochondrial oxidation. Of note, QM cells actively use the glycolytic product lactate as oxidative mitochondrial fuel. Using HP-MRS, we were able to non-invasively differentiate glycolytic tumor xenografts with high intratumoral [1-13C]pyruvate to [1-13C]lactate conversion in vivo. Although PDAC transcriptomes indicate molecular subtype-associated distinct metabolic pathways, we found substantial functional metabolic heterogeneity independent of the molecular subtype. Non-invasive identification of highly glycolytic tumors by [1-13C]pyruvate/lactate HP-MRS support individualized metabolic targeting approaches.

cancer biology↗

bioRxiv2020

Identification and characterization of distinct murine brown adipocyte lineages

Brown adipose tissue (BAT) plays an important role in the regulation of body weight and glucose homeostasis. While increasing evidence supports white adipose tissue heterogeneity, little is known about heterogeneity within murine BAT. Using single cell RNA sequencing of the stromal vascular fraction of murine BAT and analysis of 67 brown preadipocyte and adipocyte clones we unravel heterogeneity within brown preadipocytes. Statistical analysis of gene expression profiles from these clones identifies markers distinguishing brown adipocyte lineages. We confirm the presence of distinct brown adipocyte populations in vivo using three identified markers; Eif5, Tcf25, and Bin1. Functionally, we demonstrate that loss of Bin1 enhances UCP1 expression and mitochondrial respiration, suggesting that Bin1 marks a dormant brown adipocyte type. The existence of multiple brown adipocyte lineages suggests distinct functional properties of BAT depending on its cellular composition, with potentially distinct function in thermogenesis and the regulation of whole body energy homeostasis.

cell biology↗

bioRxiv2019

Iron-sequestering nanocompartments as multiplexed Electron Microscopy gene reporters

Multi-colored gene reporters such as fluorescent proteins are indispensable for biomedical research, but equivalent tools for electron microscopy (EM), a gold standard for deciphering mechanistic details of cellular processes1,2 and uncovering the network architecture of cell-circuits3,4, are still sparse and not easily multiplexable. Semi-genetic EM reporters are based on the precipitation of exogenous chemicals5-9 which may limit spatial precision and tissue penetration and can affect ultrastructure due to fixation and permeabilization. The latter technical constraints also affect EM immunolabeling techniques10-13 which may furthermore be complicated by limited epitope accessibility. The fully genetic iron storage protein ferritin generates contrast via its electron-dense iron core14-16, but its small size complicates differentiation of individual ferritin particles from cellular structures. To enable multiplexed gene reporter imaging via conventional transmission electron microscopy (TEM), we here introduce the encapsulin system of Quasibacillus thermotolerans (Qt) as a fully genetic iron-biomineralizing nanocompartment. We reveal by cryo-electron reconstructions that the Qt monomers (QtEnc) self-assemble to nanospheres with T=4 icosahedral symmetry and an ~44 nm diameter harboring two putative pore regions at the fivefold and threefold axes. We furthermore show that the native cargo (QtlMEF) auto-targets to the inner surface of QtEnc and exhibits ferroxidase activity leading to efficient iron sequestration inside mammalian cells. We then demonstrate that QtEnc can be robustly differentiated from the non-intermixing encapsulin of Myxococcus xanthus17 (Mx, ~32 nm) via a deep-learning model, thus enabling automated multiplexed EM gene reporter imaging in mammalian cells.

synthetic biology↗