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Hahnefeld, L.

Publications and source records attributed to Hahnefeld, L..

4 recordsLinked to original sources

Nerve injury triggers nociceptive hypersensitivity with interhemispheric divergence in haplodeficient GAD67-GFP mice

Nerve injury causes an imbalance of glutamatergic excitation over GABAergic inhibition, contributing thereby to lasting neuropathic pain. Transgenic GAD67-GFP knock-in reporter mice were developed to visualize GABAergic interneurons. The knock-in into glutamate decarboxylase (GAD67) causes haploinsufficiency that manifest in low GABA levels. In this model, we studied if diminished GABA exacerbates neuropathic pain after nerve injury. Adolescent male and female GAD67-GFP knock-in mice were subjected to Spared Sciatic Nerve Injury (SNI). At baseline, nociception and thermal preferences were equal but after SNI, GAD67-GFP mice developed thermal allodynia which was not detected in wildtype littermates. At the electrophysiology level, SNI caused a partial decrease in the excitability in layer 2/3 pyramidal neurons in the projection-hemisphere in wildtype mice. This effect was exacerbated in GAD67-GFP, affecting both sides, and was accompanied with imbalance of field-potential (FP) amplitudes between projection and non-projection hemisphere, which did not occur in wildtype mice. The results suggest that GABA deficiency can be compensated to protect from hyperexcitability at baseline, but it cannot be further upscaled, ultimately leading to network hyperactivity after injury. Metabolomic studies confirmed the moderate loss of GABA in ipsi- and contralateral cortex and lumbar spinal cord of GAD67-GFP mice and failure to raise GABA in the ipsilateral dorsal horn after injury. Carnosine, cystathionine, and glutathione, three important anti-oxidative metabolites, were co-reduced with GABA suggesting that GABAergic activity and anti-oxidative capacity are interconnected and failure of this axis contributes to neuropathic "pain".

neuroscience↗

Temporary deterioration of health and behavior during pexidartinib-mediated microglia depletion and repopulation in progranulin-deficient mice

Progranulin (PGRN) is a neurotrophic and anti-inflammatory factor produced mainly by neurons and microglia in the central nervous system. Progranulin haploinsufficiency causes frontotemporal dementia (FTD). In a previous study we showed that transgenic restoration of progranulin in neurons in progranulin knockout mice (NestinGrn KOBG knockout background) did not prevent the dementia-like phenotype. Here, we assessed if pharmacologic microglia depletion via PLX3397-diet (CSF1R-antagonist) had therapeutic value in these mice. Microglia depletion and spontaneous repopulation was confirmed in immunofluorescence and rtPCR studies. There was no difference in depletion or repopulation efficiency between NesGrn KOBG, PGRN KO and heterozygous (het) PGRN mice, but microglia repopulated faster than in control Grn-flfl mice, and the morphology of primary PGRN deficient microglia during repopulation was closer to homeostatic microglia, and it was accompanied by a remarkable restoration of dendritic spines and synaptic structures. Regardless of these positive effects, NesGrn KOBG and PGRN het mice experienced serious side effects during microglia depletion which peaked around the microglia nadir. Overactivity and excessive grooming escalated and caused serious skin lesions. Bulk transcriptomic and metabolomic studies in the brain taken 8 weeks after the end of PLX-diet clearly revealed differences between genotypes but mostly no lasting impact of PLX-diet, except for a further increase of proinflammatory genes, cathepsins and complement factors in PLX-treated groups. Cell type specific lipidomic studies revealed a time dependent switch not only in microglia but also astrocytes upon PLX3397 treatment. While nadir-microglia were triglyceride-laden, repopulated microglia returned to normal TG levels but were enriched in ether-bound phosphatidylcholines (PC-O) and lysophosphatidylglycerol species which are pro-inflammatory lipids; and astrocytes overtook the TG burden during repopulation. Our data suggest that microglia depletion may cause a deterioration in progranulin-deficiency.

neuroscience↗

LipiDetective - a deep learning model for the identification of molecular lipid species in tandem mass spectra

Lipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a "ready-to-use" application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved.

bioinformatics↗

Ether lipid and sphingolipid expression patterns are G-protein coupled estrogen receptor 1-dependently altered in breast cancer cells

Identifying co-expression of lipid species is challenging, but indispensable to identify novel therapeutic targets for breast cancer treatment. Lipid metabolism is often dysregulated in cancer cells, and changes in lipid metabolism affect cellular processes such as proliferation, autophagy, and tumor development. In addition to mRNA analysis of sphingolipid metabolizing enzymes, we performed liquid chromatography time-of-flight mass spectrometry analysis in three breast cancer cell lines. These breast cancer cell lines differ in estrogen receptor and G-protein coupled estrogen receptor 1 status. Our data show that sphingolipids and non-sphingolipids are strongly increased in SKBr3 cells. SKBr3 cells are estrogen receptor negative and G-protein coupled estrogen receptor 1 positive. Treatment with G15, a G-protein coupled estrogen receptor 1 antagonist, abolishes the effect of increased sphingolipid and non-sphingolipid levels in SKBr3 cells. In particular, ether lipids are expressed at much higher levels in cancer compared to normal cells and are strongly increased in SKBr3 cells. Our analysis reveals that this is accompanied by increased sphingolipid levels such as ceramide, sphingadiene-ceramide and sphingomyelin. This shows the importance of focusing on more than one lipid class when investigating molecular mechanisms in breast cancer cells. Our analysis allows unbiased screening for different lipid classes leading to identification of co-expression patterns of lipids in the context of breast cancer. Co-expression of different lipid classes could influence tumorigenic potential of breast cancer cells. Identification of co-regulated lipid species is important to achieve improved breast cancer treatment outcome. HighlightsO_LILC-HRMS analysis allows identification of co-expression between lipid classes C_LIO_LIPutative co-expression of sphingolipid and non-sphingolipid classes C_LIO_LIEther lipids are strongly upregulated in SKBr3 cells (ER negative, GPER1 positive) C_LI

cancer biology↗