MBOAT7 is anchored to endomembranes by six transmembrane domains.

Abstract Membrane bound O-acyltransferase domain- containing 7 (MBOAT7, also known as LPIAT1) is a protein involved in the acyl chain remodeling of phospholipids via the Lands' cycle. The MBOAT7 is a susceptibility risk genetic locus for non-alcoholic fatty liver disease (NAFLD) and mental retardation. Although it has been shown that MBOAT7 is associated to membranes, the MBOAT7 topology remains unknown. To solve the topological organization of MBOAT7, we performed: A) solubilization of the total membrane fraction of cells overexpressing the recombinant MBOAT7-V5, which revealed MBOAT7 is an integral protein strongly attached to endomembranes; B) in silico analysis by using 22 computational methods, which predicted the number and localization of transmembrane domains of MBOAT7 with a range between 5 and 12; C) in vitro analysis of living cells transfected with GFP-tagged MBOAT7 full length and truncated forms, using a combination of Western Blotting, co-immunofluorescence and Fluorescence Protease Protection (FPP) assay; D) in vitro analysis of living cells transfected with FLAG-tagged MBOAT7 full length forms, using a combination of Western Blotting, selective membrane permeabilization followed by indirect immunofluorescence. All together, these data revealed that MBOAT7 is a multispanning transmembrane protein with six transmembrane domains. Based on our model, the predicted catalytic dyad of the protein, composed of the conserved asparagine in position 321 (Asn-321) and the preserved histidine in position 356 (His-356), has a lumenal localization. These data are compatible with the role of MBOAT7 in remodeling the acyl chain composition of endomembranes

Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants

AbstractAging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large cross-sectional sample of 37,543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging

Exome-Wide Association Study on Alanine Aminotransferase Identifies Sequence Variants in the GPAM and APOE Associated With Fatty Liver Disease

BACKGROUND & AIMS: Fatty liver disease (FLD) is a growing epidemic that is expected to be the leading cause of end-stage liver disease within the next decade. Both environmental and genetic factors contribute to the susceptibility of FLD. Several genetic variants contributing to FLD have been identified in exome-wide association studies. However, there is still a missing hereditability indicating that other genetic variants are yet to be discovered. METHODS: To find genes involved in FLD, we first examined the association of missense and nonsense variants with alanine amino transferase at an exome-wide level in 425,671 participants from the UK Biobank. We then validated genetic variants with liver fat content in 8930 participants in whom liver fat measurement was available, and replicated 2 genetic variants in 3 independent cohorts comprising 2621 individuals with available liver biopsy. RESULTS: We identified 190 genetic variants independently associated with alanine aminotransferase after correcting for multiple testing with Bonferroni method. The majority of these variants were not previously associated with this trait. Among those associated, there was a striking enrichment of genetic variants influencing lipid metabolism. We identified the variants rs2792751 in GPAM/GPAT1, the gene encoding glycerol-3phosphate acyltransferase, mitochondrial, and rs429358 in APOE, the gene encoding apolipoprotein E, as robustly associated with liver fat content and liver disease after adjusting for multiple testing. Both genes affect lipid metabolism in the liver. CONCLUSIONS: We identified 2 novel genetic variants in GPAM and APOE that are robustly associated with steatosis and liver damage. These findings may help to better elucidate the genetic susceptibility to FLD onset and progression.Peer reviewe

Rare ATG7 genetic variants predispose patients to severe fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver disorders and has a strong heritable component. The aim of this study was to identify new loci that contribute to severe NAFLD by examining rare variants

PSD3 downregulation confers protection against fatty liver disease

Fatty liver disease (FLD) is a growing health issue with burdening unmet clinical needs. FLD has a genetic component but, despite the common variants already identified, there is still a missing heritability component. Using a candidate gene approach, we identify a locus (rs71519934) at the Pleckstrin and Sec7 domain-containing 3 (PSD3) gene resulting in a leucine to threonine substitution at position 186 of the protein (L186T) that reduces susceptibility to the entire spectrum of FLD in individuals at risk. PSD3 downregulation by short interfering RNA reduces intracellular lipid content in primary human hepatocytes cultured in two and three dimensions, and in human and rodent hepatoma cells. Consistent with this, Psd3 downregulation by antisense oligonucleotides in vivo protects against FLD in mice fed a non-alcoholic steatohepatitis-inducing diet. Thus, translating these results to humans, PSD3 downregulation might be a future therapeutic option for treating FLD. Employing a candidate gene approach, Mancina et al. identify a genetic variant of the Pleckstrin and Sec7 domain-containing 3 (PSD3) gene that reduces susceptibility to fatty liver disease. Functional studies in vitro and in vivo demonstrate that targeting PSD3 protects against fatty liver disease.Peer reviewe

Macrophage scavenger receptor 1 mediates lipid-induced inflammation in non-alcoholic fatty liver disease

Background & Aims: Obesity-associated inflammation is a key player in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). However, the role of macrophage scavenger receptor 1 (MSR1, CD204) remains incompletely understood. Methods: A total of 170 NAFLD liver biopsies were processed for transcriptomic analysis and correlated with clinicopathological features. Msr1(-/-) and wild-type mice were subjected to a 16-week high-fat and high-cholesterol diet. Mice and ex vivo human liver slices were treated with a monoclonal antibody against MSR1. Genetic susceptibility was assessed using genome-wide association study data from 1,483 patients with NAFLD and 430,101 participants of the UK Biobank. Results: MSR1 expression was associated with the occurrence of hepatic lipid-laden foamy macrophages and correlated with the degree of steatosis and steatohepatitis in patients with NAFLD. Mice lacking Msr1 were protected against diet-induced metabolic disorder, showing fewer hepatic foamy macrophages, less hepatic inflammation, improved dyslipidaemia and glucose tolerance, and altered hepatic lipid metabolism. Upon induction by saturated fatty acids, MSR1 induced a pro-inflammatory response via the JNK signalling pathway. In vitro blockade of the receptor prevented the accumulation of lipids in primary macrophages which inhibited the switch towards a proinflammatory phenotype and the release of cytokines such as TNF-alpha Targeting MSR1 using monoclonal antibody therapy in an obesity-associated NAFLD mouse model and human liver slices resulted in the prevention of foamy macrophage formation and inflammation. Moreover, we identified that rs41505344, a polymorphism in the upstream transcriptional region of MSR1, was associated with altered serum triglycerides and aspartate aminotransferase levels in a cohort of over 400,000 patients. Conclusions: Taken together, our data suggest that MSR1 plays a critical role in lipid-induced inflammation and could thus be a potential therapeutic target for the treatment of NAFLD. Lay summary: Non-alcoholic fatty liver disease (NAFLD) is a chronic disease primarily caused by excessive consumption of fat and sugar combined with a lack of exercise or a sedentary lifestyle. Herein, we show that the macrophage scavenger receptor MSR1, an innate immune receptor, mediates lipid uptake and accumulation in Kupffer cells, resulting in liver inflammation and thereby promoting the progression of NAFLD in humans and mice. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver.Peer reviewe

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms

Neural network training with highly incomplete medical datasets

Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Since the data to train the neural networks need to be complete, most studies discard the incomplete datapoints, which reduces the size of the training data, or impute the missing features, which can lead to artifacts. Alas, both approaches are inadequate when a large portion of the data is missing. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. First, the dataset is split into subsets of samples containing all values for a certain cluster of features. Then, these subsets are used to train individual neural networks. Finally, this ensemble of neural networks is combined into a single neural network whose training is fine-tuned using all complete datapoints. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer's disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets