Peroxisomal β-oxidation acts as a sensor for intracellular fatty acids and regulates lipolysis

To liberate fatty acids (FAs) from intracellular stores, lipolysis is regulated by the activity of the lipases adipose triglyceride lipase (ATGL), hormone-sensitive lipase and monoacylglycerol lipase. Excessive FA release as a result of uncontrolled lipolysis results in lipotoxicity, which can in turn promote the progression of metabolic disorders. However, whether cells can directly sense FAs to maintain cellular lipid homeostasis is unknown. Here we report a sensing mechanism for cellular FAs based on peroxisomal degradation of FAs and coupled with reactive oxygen species (ROS) production, which in turn regulates FA release by modulating lipolysis. Changes in ROS levels are sensed by PEX2, which modulates ATGL levels through post-translational ubiquitination. We demonstrate the importance of this pathway for non-alcoholic fatty liver disease progression using genetic and pharmacological approaches to alter ROS levels in vivo, which can be utilized to increase hepatic ATGL levels and ameliorate hepatic steatosis. The discovery of this peroxisomal β-oxidation-mediated feedback mechanism, which is conserved in multiple organs, couples the functions of peroxisomes and lipid droplets and might serve as a new way to manipulate lipolysis to treat metabolic disorders

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

Loss-of-function mutations in SIM1 contribute to obesity and Prader-Willi-like features

Sim1 haploinsufficiency in mice induces hyperphagic obesity and developmental abnormalities of the brain. In humans, abnormalities in chromosome 6q16, a region that includes SIM1, were reported in obese children with a Prader-Willi–like syndrome; however, SIM1 involvement in obesity has never been conclusively demonstrated. Here, SIM1 was sequenced in 44 children with Prader-Willi–like syndrome features, 198 children with severe early-onset obesity, 568 morbidly obese adults, and 383 controls. We identified 4 rare variants (p.I128T, p.Q152E, p.R581G, and p.T714A) in 4 children with Prader-Willi–like syndrome features (including severe obesity) and 4 other rare variants (p.T46R, p.E62K, p.H323Y, and p.D740H) in 7 morbidly obese adults. By assessing the carriers’ relatives, we found a significant contribution of SIM1 rare variants to intra-family risk for obesity. We then assessed functional effects of the 8 substitutions on SIM1 transcriptional activities in stable cell lines using luciferase gene reporter assays. Three mutations showed strong loss-of-function effects (p.T46R, p.H323Y, and p.T714A) and were associated with high intra-family risk for obesity, while the variants with mild or no effects on SIM1 activity were not associated with obesity within families. Our genetic and functional studies demonstrate a firm link between SIM1 loss of function and severe obesity associated with, or independent of, Prader-Willi–like features.Amélie Bonnefond, Anne Raimondo, Fanny Stutzmann, Maya Ghoussaini, Shwetha Ramachandrappa, David C. Bersten, Emmanuelle Durand, Vincent Vatin, Beverley Balkau, Olivier Lantieri, Violeta Raverdy, François Pattou, Wim Van Hul, Luc Van Gaal, Daniel J. Peet, Jacques Weill, Jennifer L. Miller, Fritz Horber, Anthony P. Goldstone, Daniel J. Driscoll, John B. Bruning, David Meyre, Murray L. Whitelaw and Philippe Frogue

Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study

Background Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Methods In this multinational retrospective observational study we enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30 602 patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was 4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery. InterpretationWe developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.Comment: The Lancet Digital Health, 202

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

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

A Federated Database for Obesity Research:An IMI-SOPHIA Study

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).