Biomarkers deciphering organ dysfunction
In RHAPSODY, we have analysed organs crucial in the onset and progression of type 2 diabetes (pancreas, liver, fat and muscle) from humans and laboratory mice, using various cutting-edge techniques and technologies (multi-omics, genetic data, bioinformatics etc.). Notably, we have identified several gene signatures, as well as lipid species produced by the liver in prediabetes, which impair pancreatic function and insulin secretion.

How many samples and how much data did we process?

We have been able to collect pancreatic and blood samples from several hundred living subjects as well as brain-dead organ donors with prediabetes (impaired glucose tolerance) or type 2 diabetes. Human pancreatic samples from non-diabetic subjects have also been collected and exposed in vitro to experimental conditions mimicking the diabetic milieu driving pancreatic beta cell failure in vivo. We have processed those samples and carried out some of the most extensive and in-depth analyses of gene and protein expression. In brief, we have generated more than 20,000 multi-omics data (learn more here), including quantitative peptidomics, lipidomics and metabolomics, as well as genomics, epigenetics and gene expression data.

What did we learn?

By integrating these omics data with the clinical traits of the human donors, we have been able to identify molecular signatures associated with the progression of hyperglycemia and deterioration of pancreatic beta cell function.

More specifically, we have identified for the first time (i) a set of genes which are already dysregulated in prediabetic subjects and (ii) some lipids circulating in the blood stream which are associated with gene dysregulation and pancreatic beta cell failure in type 2 diabetes. Learn more here.

What are the next steps?

Our studies have resulted in the identification of novel candidate biomarkers specifically related to pancreatic beta cell function and viability in type 2 diabetes. These biomarkers could be valuable tools to better identify subjects with the highest risk of developing diabetes, or to develop more effective diabetes treatments. Learn more here.

Why did we use mouse preclinical models?

In type 2 diabetes, muscle, fat, liver and pancreatic beta cells communicate with each other via circulating molecules (including lipids and peptides) to adapt to metabolic dysregulation.

Mouse preclinical models have been used to further investigate such inter-organ communication. More specifically, we aimed to identify genes deregulated in pancreatic beta cells and insulin target tissues, which could link blood biomarkers of type 2 diabetes susceptibility with alterations of insulin secretion and/or insulin action.

Which experimental design did we develop?

Three strains of mice have been used and fed with two different diets: regular chow or “high-fat high-sucrose” diet, in order to mimic both human genetic heterogeneity and imbalanced dietary intake frequently observed in the human population. We have performed multi-omics analysis (lipidomics and transcriptomics, learn more here) in liver, fat, muscle, pancreas and blood samples.

What did we learn?

After data integration and analysis, we identified a set of genes and a number of lipids associated with the development of prediabetes: read more about the gene Elovl2 and the class of lipids “dihydroceramide” here. Importantly, our findings in mice models have been translated to humans. Mouse preclinical studies are therefore complementary to clinical studies in order to develop better treatment options for type 2 diabetes. Learn more here.

Set of genes and lipids associated with human pancreatic beta cell failure

For the first time, we have identified a set of genes which are already dysregulated in prediabetic subjects. We also found that progressive dysregulation of islet gene expression concomitantly with continuous elevation of blood glucose levels is a broadly divergent disharmonic process which does not resemble a linear trajectory of mature beta cells toward a precursor or trans-differentiation stage. Furthermore, by integrating information on the clinical state of the donors with data on gene expression in the islets and lipids in their blood, we have been able to define the relative importance of molecular variables positively or negatively associated with the continuous elevation of blood glucose levels beyond thresholds for clinical classification of donors and with a direct or indirect causal relationship with pancreatic beta cell failure in type 2 diabetes.

Reversible damages to human pancreatic islets?

The impact of metabolic stress on gene expression in pancreatic islet cells has also been assessed by exposing isolated human islets in vitro to conditions mimicking the diabetogenic milieu in vivo. Interestingly, it was shown that upon removal of the metabolic stress islet beta cells can recover from the damage induced by their prolonged exposure to highly saturated fatty acids or high glucose alone, while their dysfunction was irreversible upon prolonged exposure to both fatty acids and high glucose.

Epigenetics bridges type 2 diabetes with pancreatic cancer

Studies in human islets from organ donors further suggested that hypermethylation of the gene PNLIPRP1 might have a role in the development of pancreatic cancer – a severe condition frequently associated with hyperglycemia and type 2 diabetes.

C-peptide based-assay for diabetes monitoring

The C-peptide is a portion of the proinsulin molecule whose plasma levels can be used to reliably measure insulin secretion. We have developed a novel method to quantify the C-peptide, which may enable better monitoring of the progression to diabetes and/or testing of drugs that can prevent it.

From mice to humans: focus on dihydroceramides and Elovl2

By integrating multi-omics data from mice tissue samples, we obtained a map of gene modules in islets and insulin target tissues, and their interaction in the development of prediabetes (correlation with plasma lipids and phenotypic traits). Based on that, we identified a set of genes and lipids able to explain a phenotypic trait. For instance, dihydroceramides are now considered as potential biomarkers of type 2 diabetes susceptibility both in mice and humans. Also, the role of the Elovl2 gene, which is required for the production of the fatty acid DHA, is further clarified in relation to glucose-stimulated insulin secretion and susceptibility to glucolipotoxicity-induced apoptosis, in both mouse and human pancreatic beta cells.

Related publications
1.
Pancreatic Steatosis Associates With Impaired Insulin Secretion in Genetically Predisposed Individuals.
The Journal of Clinical Endocrinology & Metabolism 105, dgaa435 (2020). doi: 10.1210/clinem/dgaa435
2.
Transcription factors that shape the mammalian pancreas.
Diabetologia 63, 1974–1980 (2020). doi: 10.1007/s00125-020-05161-0
3.
The making of insulin in health and disease.
Diabetologia 63, 1981–1989 (2020). doi: 10.1007/s00125-020-05192-7
4.
A surrogate of Roux-en-Y gastric bypass (the enterogastro anastomosis surgery) regulates multiple beta-cell pathways during resolution of diabetes in ob/ob mice.
EBioMedicine 58, 102895 (2020). doi: 10.1016/j.ebiom.2020.102895
5.
The Constitutive Lack of α7 Nicotinic Receptor Leads to Metabolic Disorders in Mouse.
Biomolecules 10, 1057 (2020). doi: 10.3390/biom10071057
6.
Synthesis and in vivo behaviour of an exendin-4-based MRI probe capable of β-cell-dependent contrast enhancement in the pancreas.
Dalton Transactions 49, 4732–4740 (2020). doi: 10.1039/D0DT00332H
7.
Dysfunction of Persisting β Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis.
Cell Reports 31, 107469 (2020). doi: 10.1016/j.celrep.2020.03.033
8.
Klf6 protects β-cells against insulin resistance-induced dedifferentiation.
Molecular Metabolism (2020). doi: 10.1016/j.molmet.2020.02.001
9.
Stearoyl CoA desaturase is a gatekeeper that protects human beta cells against lipotoxicity and maintains their identity.
Diabetologia 63, 395–409 (2020). doi: 10.1007/s00125-019-05046-x
10.
The supply chain of human pancreatic β cell lines.
The Journal of Clinical Investigation 129, 3511–3520 (2019). doi: 10.1172/JCI129484
11.
Metabolically phenotyped pancreatectomized patients as living donors for the study of islets in health and diabetes.
Molecular Metabolism 27, S1-S6 (2019). doi: 10.1016/j.molmet.2019.06.006
12.
Use of preclinical models to identify markers of type 2 diabetes susceptibility and novel regulators of insulin secretion – A step towards precision medicine.
Molecular Metabolism 27, S147-S154 (2019). doi: 10.1016/j.molmet.2019.06.008
13.
The tRNA Epitranscriptome and Diabetes: Emergence of tRNA Hypomodifications as a Cause of Pancreatic β-Cell Failure.
Endocrinology 160, 1262–1274 (2019). doi: 10.1210/en.2019-00098
14.
Laser capture microdissection of human pancreatic islets reveals novel eQTLs associated with type 2 diabetes.
Molecular Metabolism (2019). doi: 10.1016/j.molmet.2019.03.004
15.
ICA512 RESP18 homology domain is a protein condensing factor and insulin fibrillation inhibitor.
bioRxiv 521351 (2019). doi: 10.1101/521351
16.
The Expression of Aldolase B in Islets Is Negatively Associated With Insulin Secretion in Humans.
The Journal of Clinical Endocrinology & Metabolism 103, 4373–4383 (2018). doi: 10.1210/jc.2018-00791
17.
Inflammatory stress in islet β-cells: therapeutic implications for type 2 diabetes?.
Current Opinion in Pharmacology 43, 40–45 (2018). doi: 10.1016/j.coph.2018.08.002
18.
Pancreatic β-cell tRNA hypomethylation and fragmentation link TRMT10A deficiency with diabetes.
Nucleic Acids Research 46, 10302–10318 (2018). doi: 10.1093/nar/gky839
19.
Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test.
Frontiers in Endocrinology 9, (2018). doi: 10.3389/fendo.2018.00082
20.
Modeling human pancreatic beta cell dedifferentiation.
Molecular Metabolism 10, 74–86 (2018). doi: 10.1016/j.molmet.2018.02.002
21.
MondoA Is an Essential Glucose-Responsive Transcription Factor in Human Pancreatic β-Cells.
Diabetes 67, 461–472 (2018). doi: 10.2337/db17-0595
22.
Painting a new picture of personalised medicine for diabetes.
Diabetologia 60, 793–799 (2017). doi: 10.1007/s00125-017-4210-x

A biomarker can be any naturally occurring molecule (protein, lipids, DNA, RNA etc.) found in human tissues or blood stream, which is a measurable characteristic that indicates a normal biologic process, a pathogenic process, and/or a response to therapeutic interventions (Source: ICH)

Clinical Data Interchange Standards Consortium. A set of widely used standards for clinical data.

A research study in which one or more human subjects are prospectively assigned to one or more interventions (which may include placebo or other control) to evaluate the effects of those interventions on health-related biomedical or behavioral outcomes (source: https://grants.nih.gov/).

Patient cohort: A group of individuals affected by common diseases, environmental or temporal influences, treatments, or other traits whose progress is assessed in a research study (source: https://medical-dictionary.thefreedictionary.com/)

Computer simulation models are a common tool to predict how different treatments may affect disease progression and outcomes. They are used to extrapolate the findings of clinical trials into lifetime costs and health benefits.

Diabetes is a chronic disease characterised by chronically elevated levels of blood sugar (hyperglycemia), due to alterations in the production or the use of insulin by the body. Insulin action is vital to ensure that glucose (sugar, basic source of energy) from our food is properly utilised by cells of the human body.

Insulin is a hormone produced by the pancreas (more precisely by pancreatic beta cells), which is crucial to control blood glucose (glycemia). Once secreted into the blood stream, insulin orchestrates a coordinated response to lower blood glucose by acting on several tissues (namely insulin-target tissues). Notably, insulin stimulates glucose uptake by muscle and fat (adipose tissue) and prevents glucose production by the liver.

For a long time, diabetes has been labelled either ‘type 1’ or ‘type 2’ diabetes. Type 2 diabetes is the result of failures across several complex biological systems. Compared to those with type 1 diabetes, patients with type 2 diabetes can still produce insulin, at least during the first stages of the disease (prediabetes and early type 2 diabetes). However, their insulin-target tissues stop responding to the normal amounts of insulin produced after eating: this so-called “insulin resistance” process prevents glucose uptake by cells, resulting in rise of blood glucose. Chronic hyperglycemia tricks the pancreas into over-producing insulin. If untreated, this vicious circle of events can spiral out of control leading to an exhaustion of the pancreas (“pancreatic beta cell failure”) to produce insulin (insulin deficiency).

Diabetes is a major public health problem since over time, it can damage heart and blood vessels (heart attacks and strokes), nerves (neuropathy resulting in foot ulcers and possibly limb amputation), eyes (retinopathy leading to blindness) and kidneys (nephropathy).


Epigenetics is the study of how behaviors and environment can cause changes that affect the way genes work. Unlike genetic changes, epigenetic changes are reversible and do not change DNA sequence, but they can change how the body reads a DNA sequence (source: https://www.cdc.gov/genomics/disease/epigenetics.htm).

Elovl2: this gene encodes for the protein called "Elongation of very long chain fatty acids protein 2" which catalyzes the first and rate-limiting reaction of the four reactions that constitute the long-chain fatty acids elongation cycle. This endoplasmic reticulum-bound enzymatic process allows the addition of 2 carbons to the chain of long- and very long-chain fatty acids (VLCFAs) per cycle (source: https://www.uniprot.org/uniprot/Q9JLJ4)

A server containing a clinical database which can be connected to from a central computer using web services (HTTPS)

A surgical bypass operation that typically involves reducing the size of the stomach and reconnecting the smaller stomach to bypass the first portion of the small intestine so as to restrict food intake and reduce caloric absorption in cases of severe obesity (source: https://merriam-webster.com/).

Organs where insulin exerts its blood glucose lowering action. Liver: insulin decreases glucose production; muscles and adipose tissue: insulin stimulates glucose uptake.

Multi-omics refer to biological information in the form of any molecules in our bodies which are part of our everyday functioning and can change during diseases.

Genomics: study of the full genetics components of an organism (the genome) which includes the entire DNA sequence. It also considers the inter-relationships between the genes and their interaction with environmental factors.


Transcriptomics: transcriptomics is used to identify the qualitative and quantitative RNA levels in the whole genome. It can tell us which transcripts are present together with the levels of their expression. Almost 80% of the genome is transcribed to RNA.


Proteomics: study of the structure, function and physiological role of the entire proteins in a cell or tissue of an organism.


Metabolomics:study of the metabolites present in a cell or tissue/fluids of an organism. This includes small molecules, carbohydrates, peptides, lipids, nucleosides and metabolism products.


A set of concepts and categories in a subject area or domain that shows their properties and the relations between them.

A phenotypic trait is an obvious, observable, and measurable trait; it is the expression of genes in an observable way. An example of a phenotypic trait is a specific hair color. Underlying genes, which make up the genotype, determine the hair color, but the hair color observed is the phenotype. The phenotype is dependent on the genetic make-up of the organism, and also influenced by the environmental conditions (source: https://en.wikipedia.org/wiki/Phenotypic_trait)

PNLIPRP1: this gene encodes for the protein called "pancreatic lipase-related protein 1" which may function as inhibitor of dietary triglyceride digestion (source: https://www.uniprot.org/uniprot/P54315)

Precision medicine refers to the tailoring of medical treatment to the individual characteristics of each patient, […] the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology or prognosis of those diseases they may develop, or in their response to a specific treatment (definition from the US National Research Council, https://en.wikipedia.org/wiki/Precision_medicine).

When fasting blood glucose is raised beyond normal levels, but is not high enough to warrant a diabetes diagnosis (source: https://diabetes.co.uk/)

Health benefits are often expressed as quality-adjusted life years (QALYs). QALYs combine longevity and quality of life in a single metric and allow comparisons across different treatments and diseases. As a result, the impact on QALYs are required by several European agencies to make decisions about whether to adopt and reimburse medicines at a national level.

A programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis (source: https://en.wikipedia.org/wiki/R_(programming_language)).

An integrated development environment for R that runs on Windows, Mac or Linux operating systems.

Sensitivity is the proportion of true-positives which actually test positive, and how well a test is able to detect positive individuals in a population (source: https://www.fws.gov/aah/PDF/SandS.pdf).

Specificity is the proportion of true-negatives which actually test negative, and reflects how well an assay performs in a group of disease negative individuals (source: https://www.fws.gov/aah/PDF/SandS.pdf).

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This project receives funding from the Innovative Medicines Initiative 2 Joint Undertaking (www.imi.europa.eu) under grant agreement No 115881. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097-2.

The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies.