Recent CIG publications Archive


Nat Metab.: co-auth.: B.Thorens

Nat Metab. 2021 Jun 28. doi: 10.1038/s42255-021-00420-9. Online ahead of print.

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes

Leonore Wigger # 1Marko Barovic # 2 3 4Andreas-David Brunner # 5Flavia Marzetta 1Eyke Schöniger 2 3 4Florence Mehl 1Nicole Kipke 2 3 4Daniela Friedland 2 3 4Frederic Burdet 1Camille Kessler 1Mathias Lesche 6Bernard Thorens 7Ezio Bonifacio 3 4 8Cristina Legido-Quigley 9 10Pierre Barbier Saint Hilaire 11Philippe Delerive 12Andreas Dahl 6Christian Klose 13Mathias J Gerl 13Kai Simons 13Daniela Aust 14 15Jürgen Weitz 16Marius Distler 16Anke M Schulte 17Matthias Mann 18Mark Ibberson 19Michele Solimena 20 21 22


Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.


Cancers (Basel).: co-auth.: W.Wahli

Cancers (Basel). 2021 Jun 30;13(13):3279. doi: 10.3390/cancers13133279.

LRG1 Promotes Metastatic Dissemination of Melanoma through Regulating EGFR/STAT3 Signalling

Yuet Ping Kwan 1 2Melissa Hui Yen Teo 1 2Jonathan Chee Woei Lim 3Michelle Siying Tan 4Graciella Rosellinny 1 2Walter Wahli 5 6 7Xiaomeng Wang 1 2 8


Although less common, melanoma is the deadliest form of skin cancer largely due to its highly metastatic nature. Currently, there are limited treatment options for metastatic melanoma and many of them could cause serious side effects. A better understanding of the molecular mechanisms underlying the complex disease pathophysiology of metastatic melanoma may lead to the identification of novel therapeutic targets and facilitate the development of targeted therapeutics. In this study, we investigated the role of leucine-rich α-2-glycoprotein 1 (LRG1) in melanoma development and progression. We first established the association between LRG1 and melanoma in both human patient biopsies and mouse melanoma cell lines and revealed a significant induction of LRG1 expression in metastatic melanoma cells. We then showed no change in tumour cell growth, proliferation, and angiogenesis in the absence of the host Lrg1. On the other hand, there was reduced melanoma cell metastasis to the lungs in Lrg1-deficient mice. This observation was supported by the promoting effect of LRG1 in melanoma cell migration, invasion, and adhesion. Mechanistically, LRG1 mediates melanoma cell invasiveness in an EGFR/STAT3-dependent manner. Taken together, our studies provided compelling evidence that LRG1 is required for melanoma metastasis but not growth. Targeting LRG1 may offer an alternative strategy to control malignant melanoma.


Proc Natl Acad Sci U S A. auth.: group Franken

Proc Natl Acad Sci U S A. 2021 Jun 22;118(25):e2017364118. doi: 10.1073/pnas.2017364118.

Dissecting and modeling photic and melanopsin effects to predict sleep disturbances induced by irregular light exposure in mice

Jeffrey Hubbard 1 2Mio Kobayashi Frisk 1 2Elisabeth Ruppert 1 2Jessica W Tsai 3Fanny Fuchs 1 2Ludivine Robin-Choteau 1 4Jana Husse 5Laurent Calvel 1 2Gregor Eichele 5Paul Franken 6Patrice Bourgin 7 2


Artificial lighting, day-length changes, shift work, and transmeridian travel all lead to sleep-wake disturbances. The nychthemeral sleep-wake cycle (SWc) is known to be controlled by output from the central circadian clock in the suprachiasmatic nuclei (SCN), which is entrained to the light-dark cycle. Additionally, via intrinsically photosensitive retinal ganglion cells containing the photopigment melanopsin (Opn4), short-term light-dark alternations exert direct and acute influences on sleep and waking. However, the extent to which longer exposures typically experienced across the 24-h day exert such an effect has never been clarified or quantified, as disentangling sustained direct light effects (SDLE) from circadian effects is difficult. Recording sleep in mice lacking a circadian pacemaker, either through transgenesis (Syt10 cre/cre Bmal1 fl/- ) or SCN lesioning and/or melanopsin-based phototransduction (Opn4 -/- ), we uncovered, contrary to prevailing assumptions, that the contribution of SDLE is as important as circadian-driven input in determining SWc amplitude. Specifically, SDLE were primarily mediated (>80%) through melanopsin, of which half were then relayed through the SCN, revealing an ancillary purpose for this structure, independent of its clock function in organizing SWc. Based on these findings, we designed a model to estimate the effect of atypical light-dark cycles on SWc. This model predicted SWc amplitude in mice exposed to simulated transequatorial or transmeridian paradigms. Taken together, we demonstrate this SDLE is a crucial mechanism influencing behavior on par with the circadian system. In a broader context, these findings mandate considering SDLE, in addition to circadian drive, for coping with health consequences of atypical light exposure in our society.

Keywords: circadian and noncircadian; melanopsin; photoperiods; phototransduction; sleep–wake cycle.


Nat Commun.: auth.: group Roignant

Nat Commun. 2021 Jun 18;12(1):3778. doi: 10.1038/s41467-021-23892-5.

Hakai is required for stabilization of core components of the m 6 A mRNA methylation machinery

Praveen Bawankar # 1Tina Lence # 2 3Chiara Paolantoni # 4Irmgard U Haussmann 5 6Migle Kazlauskiene 7Dominik Jacob 1Jan B Heidelberger 2Florian M Richter 1Mohanakarthik P Nallasivan 5Violeta Morin 2Nastasja Kreim 8Petra Beli 2 9Mark Helm 1Martin Jinek 7Matthias Soller 10 11Jean-Yves Roignant 12 13Affiliations expand


N6-methyladenosine (m6A) is the most abundant internal modification on mRNA which influences most steps of mRNA metabolism and is involved in several biological functions. The E3 ubiquitin ligase Hakai was previously found in complex with components of the m6A methylation machinery in plants and mammalian cells but its precise function remained to be investigated. Here we show that Hakai is a conserved component of the methyltransferase complex in Drosophila and human cells. In Drosophila, its depletion results in reduced m6A levels and altered m6A-dependent functions including sex determination. We show that its ubiquitination domain is required for dimerization and interaction with other members of the m6A machinery, while its catalytic activity is dispensable. Finally, we demonstrate that the loss of Hakai destabilizes several subunits of the methyltransferase complex, resulting in impaired m6A deposition. Our work adds functional and molecular insights into the mechanism of the m6A mRNA writer complex.


Commun Biol.: auth.: D.Gatfield

Commun Biol. I(nsp1)ecting SARS-CoV-2-ribosome interactions

I(nsp1)ecting SARS-CoV-2-ribosome interactions

Matthieu Simeoni # 1Théo Cavinato # 1Daniel Rodriguez # 1David Gatfield 2Affiliations expand

Free article


While SARS-CoV-2 is causing modern human history’s most serious health crisis and upending our way of life, clinical and basic research on the virus is advancing rapidly, leading to fascinating discoveries. Two studies have revealed how the viral virulence factor, nonstructural protein 1 (Nsp1), binds human ribosomes to inhibit host cell translation. Here, we examine the main conclusions on the molecular activity of Nsp1 and its role in suppressing innate immune responses. We discuss different scenarios potentially explaining how the viral RNA can bypass its own translation blockage and speculate on the suitability of Nsp1 as a therapeutic target.


Diabetologia.: co-auth.: B.Thorens

Diabetologia. 2021 Jun 10. doi: 10.1007/s00125-021-05490-8. Online ahead of print.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study

Roderick C Slieker 1 2Louise A Donnelly 3Hugo Fitipaldi 4Gerard A Bouland 2Giuseppe N Giordano 4Mikael Åkerlund 4Mathias J Gerl 5Emma Ahlqvist 4Ashfaq Ali 6Iulian Dragan 7Andreas Festa 8 9Michael K Hansen 10Dina Mansour Aly 4Min Kim 6 11Dmitry Kuznetsov 7Florence Mehl 7Christian Klose 5Kai Simons 5Imre Pavo 8Timothy J Pullen 12 13Tommi Suvitaival 6Asger Wretlind 6Peter Rossing 6Valeriya Lyssenko 14 15Cristina Legido-Quigley 6 11Leif Groop 4 16Bernard Thorens 17Paul W Franks 4 18Mark Ibberson 7Guy A Rutter 13 19Joline W J Beulens 1 20Leen M ‘t Hart 21 22 23Ewan R Pearson 24Affiliations expand


Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic.

Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster.

Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression.

Conclusions/interpretation: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.

Keywords: C-peptide; Clusters; Cross-validation; HDL-cholesterol; Type 2 diabetes.