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Lohoefer, F.

Publications and source records attributed to Lohoefer, F..

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The gut microbiota promotes liver regeneration through hepatic membrane phospholipid synthesis

Background & AimsHepatocyte growth and proliferation is dependent on the synthesis of membrane phospholipids. Lipid synthesis, in turn, requires short chain fatty acids (SCFA) generated by bacterial fermentation, delivered through the gut- liver axis. We therefore hypothesized that dysbiotic insults like antibiotics treatment not only negatively affect gut microbiota, but also impair hepatic lipid synthesis and liver regeneration. MethodsStable isotope labelling and 70% partial hepatectomy (PHx) was carried out in C57Bl/6J wildtype mice, in mice treated with broad-spectrum antibiotics, in germfree mice and gnotobiotic mice colonized with minimal microbiota. Microbiome was analysed by 16S rRNA gene sequencing and microbial culture. Gut content, liver and blood were tested by lipidomics mass spectrometry, qRT-PCR, immunoblot and immunohistochemistry for expression of proliferative and lipogenic markers. Matched biopsies from hyperplastic and hypoplastic liver tissue of human patients subjected to portal vein embolization were analysed by qRT-PCR for lipogenic enzymes and results were correlated with liver volumetry. ResultsThree days of antibiotics treatment induced persistent dysbiosis with significantly decreased beta-diversity and richness, but massive increase of Proteobacteria, accompanied by decreased colonic SCFA. After PHx, antibiotics- treated mice showed delayed liver regeneration, increased mortality, impaired hepatocyte proliferation and decreased hepatic phospholipid synthesis. Expression of the key lipogenic enzyme SCD1 was upregulated after PHx, but delayed by antibiotics-treatment. Germfree mice essentially recapitulated the phenotype of antibiotics-treatment. Importantly, phospholipid synthesis, hepatocyte proliferation, liver regeneration and survival were rescued in gnotobiotic mice colonized with a minimal SCFA-producing microbial community. SCD1 was required for human hepatoma cell proliferation, and its hepatic expression was associated with liver regeneration and hyperproliferation in human patients. ConclusionGut microbiota are pivotal for hepatic membrane phospholipid synthesis and liver regeneration. Lay SummaryGut microbiota affects the liver lipid metabolism through the gut-liver axis, and microbial metabolites promote liver regeneration. Perturbations of the microbiome, e.g., by antibiotics treatment, impair the production of bacterial metabolites, which serve as building blocks for new membrane lipids in liver cells. As a consequence, hepatocyte growth and proliferation, and ultimately, liver regeneration and survival after liver surgery is impaired. HighlightsO_LIPartial hepatectomy in mice pretreated with antibiotics is associated with impaired hepatocyte proliferation and phospholipid synthesis, delayed liver regeneration and increased mortality C_LIO_LIThe delay in liver regeneration and impaired lipogenesis upon antibiotics treatment is preceded by dysbiosis of gut microbiota, increase of Proteobacteria and decreased short-chain fatty acids in the gut C_LIO_LIPartial hepatectomy in germfree mice essentially phenocopies the detrimental effects of antibiotic treatment C_LIO_LILiver regeneration and mortality, as well as phospholipid synthesis and hepatocyte proliferation in germfree mice are fully rescued upon colonisation with a minimal gut bacterial consortium capable of short-chain fatty acid production C_LIO_LIIn human patients, the intrahepatic expression of lipid synthesis enzymes positively correlates with proliferation and liver regeneration in the clinic C_LIO_LIThus, liver regeneration is affected by composition of gut microbiota C_LIO_LIClinically, pre-operative analysis of the gut microbiome may serve as biomarker to determine the extent of liver resections C_LI

cell biology↗

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗