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Biology subjects

Günther, C.

Publications and source records attributed to Günther, C..

4 recordsLinked to original sources

TNF-α disrupts the malate-aspartate shuttle, driving metabolic rewiring in iPSC-derived enteric neural lineages from Parkinson's Disease patients

Gastrointestinal (GI) dysfunction emerges years before motor symptoms in Parkinsons disease (PD), implicating the enteric nervous system (ENS) in early disease progression. However, the mechanisms linking the PD hallmark protein, -synuclein (-syn), to ENS dysfunction - and whether these mechanisms are influenced by inflammation - remains elusive. Using iPSC-derived enteric neural lineages from patients with -syn triplications, we reveal that TNF- increases mitochondrial--syn interactions, disrupts the malate-aspartate shuttle, and forces a metabolic shift toward glutamine oxidation. These alterations drive mitochondrial dysfunction, characterizing metabolic impairment under cytokine stress. Interestingly, targeting glutamate metabolism with Chicago Sky Blue 6B restores mitochondrial function, reversing TNF--driven metabolic disruption. Our findings position the ENS as a central player in PD pathogenesis, establishing a direct link between cytokines, -syn accumulation, metabolic stress and mitochondrial dysfunction. By uncovering a previously unrecognized metabolic vulnerability in the ENS, we highlight its potential as a therapeutic target for early PD intervention.

neuroscience↗

Tissue inflammation induced by constitutively active STING is mediated by enhanced TNF signaling

Constitutive activation of STING by gain-of-function mutations triggers manifestation of the systemic autoinflammatory disease STING-associated vasculopathy with onset in infancy (SAVI). In order to investigate the role of signaling by tumor necrosis factor (TNF) in SAVI, we used pharmacological inhibition and genetic inactivation of TNF receptors 1 and 2 in murine SAVI, which is characterized by T cell lymphopenia, inflammatory lung disease and neurodegeneration. Pharmacologic inhibition of TNF signaling improved T cell lymphopenia, but had no effect on interstitial lung disease. Genetic inactivation of TNFR1 and TNFR2, however, rescued the loss of thymocytes, reduced interstitial lung disease and neurodegeneration. Furthermore, genetic inactivation of TNFR1 and TNFR2 blunted transcription of cytokines, chemokines and adhesions proteins, which result from chronic STING activation in SAVI mice. In addition, increased transendothelial migration of neutrophils was ameliorated. Taken together, our results demonstrate a pivotal role of TNFR-signaling in the pathogenesis of SAVI in mice and suggest that available TNFR antagonists could ameliorate SAVI in patients. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=170 HEIGHT=200 SRC="FIGDIR/small/591149v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@177cf0org.highwire.dtl.DTLVardef@b989dorg.highwire.dtl.DTLVardef@150a0c8org.highwire.dtl.DTLVardef@6bd42d_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

hnRNPM and ELAVL1 control type I interferon induction by promoting IRF3 phosphorylation downstream of both cGAS and RIG-I

RIG-I and cGAS are crucial sensors of viral nucleic acids and induce type I IFNs via TBK1/IKK and IRF3. Here, we have identified hnRNPM as a novel positive regulator of IRF3 phosphorylation and type I IFN induction downstream of both cGAS and RIG-I. Combining interactome analysis and genome editing, we further identified ELAVL1 as an immune-relevant interactor of hnRNPM. Depletion of hnRNPM or ELAVL1 impaired type I IFN induction by HSV-1 and SeV. In addition, we found that hnRNPM and ELAVL1 interact with TBK1 and NF-kB p65. Confocal microscopy revealed cytosolic and perinuclear interactions between hnRNPM, ELAVL1, and TBK1. To our knowledge, hnRNPM and ELAVL1 represent the first non-redundant signaling components merging the cGAS-STING and RIG-I-MAVS pathways, thus representing a novel platform that fuels antiviral defense.

immunology↗

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗