Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in the Western world, and safe and effective therapies are needed. Bile acids (BAs) and their receptors (including the nuclear receptor for BAs, FXR) play integral roles in regulating whole body metabolism and hepatic lipid homeostasis. We hypothesized that interruption of the enterohepatic BA circulation using a luminally-restricted Apical Sodium-dependent BA Transporter (ASBT) inhibitor (ASBTi; SC-435) would modify signaling in the gut-liver axis and reduce steatohepatitis in high fat diet (HFD)-fed mice. Administration of this ASBTi increased fecal BA excretion and mRNA expression of BA synthesis genes in liver, and reduced mRNA expression of ileal BA-responsive genes, including the negative feedback regulator of BA synthesis, Fibroblast Growth Factor 15 (FGF15). ASBT inhibition resulted in a marked shift in hepatic BA composition, with a reduction in hydrophilic, FXR antagonistic species and an increase in FXR agonistic BAs. ASBT inhibition restored glucose tolerance, reduced hepatic triglyceride and total cholesterol concentrations, and improved NAFLD Activity Score (NAS) in HFD-fed mice. These changes were associated with reduced hepatic expression of lipid synthesis genes (including LXR target genes), and normalized expression of the central lipogenic transcription factor, Srebp1c. Accumulation of hepatic lipids and SREBP1 protein were markedly reduced in HFD-fed Asbt−/− mice, providing genetic evidence for a protective role mediated by interruption of the enterohepatic BA circulation. Taken together, these studies suggest that blocking ASBT function with a luminally-restricted inhibitor can improve both hepatic and whole body aspects of NAFLD.
BackgroundA lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia.MethodsA large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls.FindingsAccuracies of approximately 85·0% and 81·0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks.InterpretationThe findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the “disconnectivity” model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites.
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