Bioinformatics for metabolism and endocrinology

Discover metabolic and hormonal disease drivers and biomarkers.

Metabolism encompasses the complex biochemical reactions essential for sustaining life. A cell’s metabolic state is shaped by the intercellular exchange of metabolites and signaling molecules, including hormones. The connection between metabolism and the endocrine system—and how dysfunction in one can pathologically impact the other—is particularly evident in diseases like diabetes.

High-throughput measurements, such as metabolomics and single-cell transcriptomics are standard approaches for studying the biology of metabolic and endocrine diseases and for discovering therapeutic targets and diagnostic biomarkers.

Our team has extensive experience in bioinformatics analysis in areas such as diabetes and its complications, metabolic diseases, nutrition, obesity, aging, and hormone-driven cancers.

Scroll down to explore our references in metabolism and endocrinology, including research articles published by our scientists as well as those in which we have supported our customers.

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Diabetes and its complications

Diabetes is a chronic condition affecting over 422 million people globally. It is characterized by the dysfunction and eventual loss of insulin-producing cells in the pancreas, leading to elevated blood sugar and fat levels if left untreated. Diabetes encompasses a range of disorders involving insulin cell failure, with Type 1 and Type 2 diabetes being the most common forms.

Type 1 diabetes is an autoimmune disease, potentially influenced by viral factors, in which the immune system attacks insulin-producing cells. Type 2 diabetes is a polygenic condition, where insulin-producing cells fail, and tissues such as muscle, liver, and adipose become resistant to insulin's effects. Genetic susceptibility to Type 2 diabetes can be triggered by factors such as obesity.

Gene expression studies, particularly single-cell and spatial transcriptomics, enable a detailed view of pathological changes in pancreatic islets, as well as in sites of diabetic complications, such as the kidneys and liver.

The complications of diabetes are chronic and life-threatening. Understanding the biology of insulin cell failure and diabetic complications, as well as discovering biomarkers and therapeutic targets, are active areas of research to which we have contributed.

Metabolism

Disordered metabolism can play a central role in a wide range of diseases including diabetes, obesity, cancer, and cardiovascular disease.

Mass spectrometry-based metabolomics allows for the study of both normal and pathological metabolic functions, with disease-associated metabolite levels in body fluids serving as particularly valuable biomarkers.

Approaches also exist to infer metabolic fluxes—the rates at which metabolites are produced or consumed in biochemical pathways—from bulk and single-cell RNA sequencing data. These approaches rely heavily on prior knowledge, specifically genome-scale metabolic models (GEMs). A GEM defines the metabolic network and its all reactions down to details such as the metabolites and enzymes involved, the cellular compartment in which the reaction takes place, and the reaction's reversibility.

While not a direct measurement of metabolites, transcriptome-based metabolic flux analysis is highly informative for studying the metabolic states of organs, tissues, and even individual cells. Metabolic flux analyses are, however, limited by the quantity and quality of available prior knowledge, as well as by assumptions made, such as on nutrient availability.

Hormone-driven cancers

Some cancers in hormone-sensitive tissues, such as the breast and prostate, rely on hormones to sustain growth. Our team’s extensive experience in cancer research includes studying the oncogenic mechanisms of hormone receptor overexpression and amplification, as well as resistance mechanisms to antihormone therapies.

Learn more

• RNA-seq data analysis
• Single-cell RNA-seq data analysis
• Spatial transcriptomic data analysis
• Proteomic and metabolomic data analysis

All analyses

References and customer cases

• Tailored bioinformatics support for a clinical trial (Amazentis)
• Deciphering progression mechanisms in chronic kidney disease with single-nucleus and spatial transcriptomics (University of Geneva)

Selected publications from our customers

• Liu, S. et al. (2025). Urolithin A provides cardioprotection and mitochondrial quality enhancement preclinically and improves human cardiovascular health biomarkers. iScience, 28(2), 111814. https://doi.org/10.1016/j.isci.2025.111814
• Schmidt-Christensen, A. et al. (2024). Structure-function analysis of time-resolved immunological phases in metabolic dysfunction-associated fatty liver disease (MASH) comparing the NIF mouse model to human MASH. Scientific reports, 14(1), 23014. https://doi.org/10.1038/s41598-024-73150-z
• Zhong, M. et al. (2024). A meta-analysis and polygenic score study identifies novel genetic markers for waist-hip ratio in African populations. Obesity (Silver Spring, Md.), 10.1002/oby.24123. Advance online publication. https://doi.org/10.1002/oby.24123
• Barreiro , K. et al. (2024) Selected miRNAs in urinary extracellular vesicles show promise for non-invasive diagnostics of diabetic kidney disease. medRxiv 2024.09.12.24312889; doi: https://doi.org/10.1101/2024.09.12.24312889
• D’Amico, D. et al. (2023). Topical application of Urolithin A slows intrinsic skin aging and protects from UVB-mediated photodamage: Findings from Randomized Clinical Trials. medRxiv 2023.06.16.23291378. https://doi.org/10.1101/2023.06.16.23291378
• Singh, A. et al. (2022). Urolithin A improves muscle strength, exercise performance, and biomarkers of mitochondrial health in a randomized trial in middle-aged adults. Cell reports. Medicine, 3(5), 100633. https://doi.org/10.1016/j.xcrm.2022.100633
• Pommergaard, H. C. et al. (2022). Aldehyde dehydrogenase expression may be a prognostic biomarker and associated with liver cirrhosis in patients resected for hepatocellular carcinoma. Surgical oncology, 40, 101677. https://doi.org/10.1016/j.suronc.2021.101677
• Chaudhary, P., et al. (2021). An exploratory analysis of comparative plasma metabolomic and lipidomic profiling in salt-sensitive and salt-resistant individuals from The Dietary Approaches to Stop Hypertension Sodium Trial. Journal of hypertension, 39(10), 1972–1981. https://doi.org/10.1097/HJH.0000000000002904
• Oksanen, M. et al. (2020). NF-E2-related factor 2 activation boosts antioxidant defenses and ameliorates inflammatory and amyloid properties in human Presenilin-1 mutated Alzheimer's disease astrocytes. Glia, 68(3), 589–599. https://doi.org/10.1002/glia.23741

Selected publications from our team members

• Eigentler, A. et al. (2024). Glucocorticoid treatment influences prostate cancer cell growth and the tumor microenvironment via altered glucocorticoid receptor signaling in prostate fibroblasts. Oncogene, 43(4), 235–247. https://doi.org/10.1038/s41388-023-02901-5

• Lin, J. et al. (2023). Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection. Nature communications, 14(1), 7630. https://doi.org/10.1038/s41467-023-42763-9
• Nätkin, R. et al. (2023). Adaptive and non-adaptive gene expression responses in prostate cancer during androgen deprivation. PloS one, 18(2), e0281645. https://doi.org/10.1371/journal.pone.0281645
• Colwell, M. L. et al. (2023). Intergenerational arsenic exposure on the mouse epigenome and metabolic physiology. Environmental and molecular mutagenesis, 64(2), 72–87. https://doi.org/10.1002/em.22526
• Thomas, P. et al. (2022). Differential routing and disposition of the long-chain saturated fatty acid palmitate in rodent vs human beta-cells. Nutrition & diabetes, 12(1), 22. https://doi.org/10.1038/s41387-022-00199-y
• Pellegrinelli, V. et al. (2022). Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance. Nature metabolism, 4(4), 476–494. https://doi.org/10.1038/s42255-022-00561-5
• Furse, S. et al. (2022). Dietary PUFAs drive diverse system-level changes in lipid metabolism. Molecular metabolism, 59, 101457. https://doi.org/10.1016/j.molmet.2022.101457
• Smith, C. et al. (2022). A comparative transcriptomic analysis of glucagon-like peptide-1 receptor- and glucose-dependent insulinotropic polypeptide-expressing cells in the hypothalamus. Appetite, 174, 106022. https://doi.org/10.1016/j.appet.2022.106022
• Furse, S. et al. (2022). A mouse model of gestational diabetes shows dysregulated lipid metabolism post-weaning, after return to euglycaemia. Nutrition & diabetes, 12(1), 8. https://doi.org/10.1038/s41387-022-00185-4
• Roos, K. et al. (2022). Single-cell RNA-seq analysis and cell-cluster deconvolution of the human preovulatory follicular fluid cells provide insights into the pathophysiology of ovarian hyporesponse. Frontiers in endocrinology, 13, 945347. https://doi.org/10.3389/fendo.2022.945347
• Kukkonen, K. et al. (2022). Nonmalignant AR-positive prostate epithelial cells and cancer cells respond differently to androgen. Endocrine-related cancer, 29(12), 717–733. https://doi.org/10.1530/ERC-22-0108
• Furse, S. et al. (2022). Paternal nutritional programming of lipid metabolism is propagated through sperm and seminal plasma. Metabolomics : Official journal of the Metabolomic Society, 18(2), 13. https://doi.org/10.1007/s11306-022-01869-9
• Campion, R. et al. (2021). Proteomic analysis of dietary restriction in yeast reveals a role for Hsp26 in replicative lifespan extension. The Biochemical journal, 478(24), 4153–4167. https://doi.org/10.1042/BCJ20210432
• Thomas, P. (2021). Long-chain saturated fatty acid species are not toxic to human pancreatic β-cells and may offer protection against pro-inflammatory cytokine induced β-cell death. Nutrition & metabolism, 18(1), 9. https://doi.org/10.1186/s12986-021-00541-8
• Machado-Lopez, A. et al. (2021). Molecular and Cellular Insights into the Development of Uterine Fibroids. International journal of molecular sciences, 22(16), 8483. https://doi.org/10.3390/ijms22168483
• Carobbio, S. et al. (2021). Unraveling the Developmental Roadmap toward Human Brown Adipose Tissue. Stem cell reports, 16(3), 641–655. https://doi.org/10.1016/j.stemcr.2021.01.013
• Alvarez-Guaita, A. et al. (2021). Phenotypic characterization of Adig null mice suggests roles for adipogenin in the regulation of fat mass accrual and leptin secretion. Cell reports, 34(10), 108810. https://doi.org/10.1016/j.celrep.2021.108810
• Hall, Z. et al. (2021). Lipid Remodeling in Hepatocyte Proliferation and Hepatocellular Carcinoma. Hepatology (Baltimore, Md.), 73(3), 1028–1044. https://doi.org/10.1002/hep.31391
• Furse, S. et al. (2021). Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings' lipid metabolism. Communications biology, 4(1), 163. https://doi.org/10.1038/s42003-021-01686-1
• Furse, S. et al. (2021). Lipid Metabolism Is Dysregulated before, during and after Pregnancy in a Mouse Model of Gestational Diabetes. International journal of molecular sciences, 22(14), 7452. https://doi.org/10.3390/ijms22147452
• Polinski, J. M. et al. (2020). Unique age-related transcriptional signature in the nervous system of the long-lived red sea urchin Mesocentrotus franciscanus. Scientific reports, 10(1), 9182. https://doi.org/10.1038/s41598-020-66052-3
• Kron, N. S. et al. (2020). Changes in Metabolism and Proteostasis Drive Aging Phenotype in Aplysia californica Sensory Neurons. Frontiers in aging neuroscience, 12, 573764. https://doi.org/10.3389/fnagi.2020.573764

• Binenbaum, I. et al. (2020). Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity. BMC genomics, 21(1), 761. https://doi.org/10.1186/s12864-020-07173-x

• Gao, Y. et al. (2020). LKB1 Represses ATOH1 via PDK4 and Energy Metabolism and Regulates Intestinal Stem Cell Fate. Gastroenterology, 158(5), 1389–1401.e10. https://doi.org/10.1053/j.gastro.2019.12.033
• Kohvakka, A. et al. (2020). AR and ERG drive the expression of prostate cancer specific long noncoding RNAs. Oncogene, 39(30), 5241–5251. https://doi.org/10.1038/s41388-020-1365-6
• Adriaenssens, A. E. et al. (2019). Glucose-Dependent Insulinotropic Polypeptide Receptor-Expressing Cells in the Hypothalamus Regulate Food Intake. Cell metabolism, 30(5), 987–996.e6. https://doi.org/10.1016/j.cmet.2019.07.013
• Roberts, G. P. et al. (2019). Comparison of Human and Murine Enteroendocrine Cells by Transcriptomic and Peptidomic Profiling. Diabetes, 68(5), 1062–1072. https://doi.org/10.2337/db18-0883
• Gubina, N. et al. (2019). Essential Physiological Differences Characterize Short- and Long-Lived Strains of Drosophila melanogaster. The journals of gerontology. Series A, Biological sciences and medical sciences, 74(12), 1835–1843. https://doi.org/10.1093/gerona/gly143
• Kallio, H. M. L. et al. (2018). Constitutively active androgen receptor splice variants AR-V3, AR-V7 and AR-V9 are co-expressed in castration-resistant prostate cancer metastases. British journal of cancer, 119(3), 347–356. https://doi.org/10.1038/s41416-018-0172-0
• Holmes, A. P. et al. (2016). Reductions in hypothalamic Gfap expression, glial cells and α-tanycytes in lean and hypermetabolic Gnasxl-deficient mice. Molecular brain, 9, 39. https://doi.org/10.1186/s13041-016-0219-1
• Zajitschek, F. et al. (2016). Evolution under dietary restriction increases male reproductive performance without survival cost. Proceedings. Biological sciences, 283(1825), 20152726. https://doi.org/10.1098/rspb.2015.2726

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