Regenerating islet-derived protein 3α : A promising therapy for diabetes. Preliminary data in rodents and in humans

Publisher Copyright: © 2022The aim of our study was to test the hypothesis that administration of Regenerating islet-derived protein 3α (Reg3α), a protein described as having protective effects against oxidative stress and anti-inflammatory activity, could participate in the control of glucose homeostasis and potentially be a new target of interest in the treatment of type 2 diabetes. To that end the recombinant human Reg3α protein was administered for one month in insulin-resistant mice fed high fat diet. We performed glucose and insulin tolerance tests, assayed circulating chemokines in plasma and measured glucose uptake in insulin sensitive tissues. We evidenced an increase in insulin sensitivity during an oral glucose tolerance test in ALF-5755 treated mice vs controls and decreased the pro-inflammatory cytokine C-X-C Motif Chemokine Ligand 5 (CXCL5). We also demonstrated an increase in glucose uptake in skeletal muscle. Finally, correlation studies using human and mouse muscle biopsies showed negative correlation between intramuscular Reg3α mRNA expression (or its murine isoform Reg3γ) and insulin resistance. Thus, we have established the proof of concept that Reg3α could be a novel molecule of interest in the treatment of T2D by increasing insulin sensitivity via a skeletal muscle effect.Peer reviewe

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Distinct molecular signatures of clinical clusters in people with type 2 diabetes:an IMI-RHAPSODY study

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity a previous study clustered people with diabetes into five diabetes subtypes. The aim of the current study is to investigate the aetiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic- (N=12828), metabolomic- (N=2945), lipidomic- (N=2593) and proteomic (N=1170) data were obtained in plasma. In each datatype each cluster was compared with the other four clusters as the reference. The insulin resistant cluster showed the most distinct molecular signature, with higher BCAAs, DAG and TAG levels and aberrant protein levels in plasma enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher cytokines. A subset of the mild diabetes cluster with high HDL showed the most beneficial molecular profile with opposite effects to those seen in the insulin resistant cluster. This study showed that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous diseas

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

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

Synthèse de g-lactones à partir d esters g,[delta]-insaturés dérivés de terpénols (applications à la synthèse de molécules à propriétés organoleptiques et à la valorisation d huiles essentielles)

Les lactones constituent une classe importante de molécules largement répandues dans la nature. Elles représentent des composés stratégiques pour les industries agroalimentaires, cosmétiques et pharmaceutiques de part leurs nombreuses propriétés biologiques et/ou aromatiques. L hémisynthèse de -hydroxy-g-lactones a été réalisée sur des monoterpénols allyliques en deux étapes. Le réarrangement de Johnson-Claisen assisté par micro-onde a conduit à des esters g, -insaturés qui sont ensuite hydroxylés grâce à la dihydroxylation asymétrique de Sharpless. Cette voie de synthèse a été appliquée à 5 substrats (alcool périllylique, nérol, phytol, linalol et carvéol) et a conduit à la caractérisation de nouveaux produits (2 esters g, -insaturés, 2 produits trihydroxylés, 10 esters g, -insaturés dihydroxylés, 8 esters g, -insaturés tétrahydroxylés, 3 lactones hydroxylées et 8 lactones trihydroxylées). Ainsi, au cours de ce travail, 41 produits ont été isolés et caractérisés dont 33 sont nouveaux à notre connaissance. Les esters g, -insaturés ayant montré d intéressantes propriétés aromatiques, la réaction de Johnson-Claisen a été appliquée à la modification chimique d huiles essentielles (menthe verte, palmarosa, lavandin grosso et coriandre) contenant chacune un monoterpénol allylique comme composant majoritaire. Les analyses sensorielles réalisées par un panel d aromaticiens professionnels ont permis de mettre en évidence l intérêt des produits obtenus pour l industrie agroalimentaire.AIX-MARSEILLE3-BU Sc.St Jérô (130552102) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Parures et artifices : le corps exposé dans l'antiquité

Textes issus de trois journées d'études tenues à l'Université de Rennes 2 et à l'Institut national d'histoire de l'art de Paris entre 2008 et 2009International audienceLoin d'être des futilités, les parures et les artifices corporels, autant que les mots ou les gestes, exposent une société (ici grecque, romaine ou encore étrusque) dans le jeu des apparences. Apposés sur les corps, ils définissent des identités ou des statuts sociaux, marquent la condition humaine dans ses hiérarchies et ses cloisonnements. Codes relatifs aux manières d'être, ils doivent être respectés et peuvent alors servir à intégrer ou à exclure.Ces contributions montrent comment le corps, paré de ses ornements, de ses objets et de ses artifices, dit la condition humaine dans ses hiérarchies et ses cloisonnements. Il séduit, attire et fascine comme il dérange. Les parures et artifices sont ainsi autant de manières d'exposer, par le corps et sur le corps, l'être et le paraître des Anciens