140 researchers came together recently at the EMBL Advanced Training Centre in Heidelberg, Germany, for the EMBO Workshop: Precision Health: Molecular Basis, Technology and Digital Health (13 – 16 November 2019) to present and discuss the promises and challenges of precision health and the molecular insights necessary to enable a maintenance of wellness and prevention of disease.
Out of the posters presented, 4 were awarded a poster prize based on popular vote. Here we present the poster abstracts of four of the winners.
A computational modelling approach to characterizing postprandial glucose responses in individuals
Balazs Erdos (1), (2)*, Bart van Sloun (1), (2), Shauna O’Donovan (2), Michiel Adriaens (2), Natal van Riel (3), Ellen Blaak (4), Ilja Arts (2)
The large variability in the dynamic properties of the postprandial glucose response curves in individuals suggest that it is not sufficient to use average values or single time point measures of postprandial glycemia in order to characterize individuals’ glycemic control. Instead, approaches that are capable of capturing the dynamic events are necessary. In this study, we develop personalized computational models based on ordinary differential equations, to describe the glucose and insulin dynamics of individuals in response to an oral glucose tolerance test. We observed that these personalized models are capable of capturing a wide range of glucose and insulin dynamics including normal, prediabetic and type 2 diabetic responses as well as responses from intermediate states.
(1) TiFN, Wageningen, The Netherlands, (2) Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands, (3) Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands, (4) Dept. of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
Predict nephrotoxicity associated with cisplatin-based chemotherapy in testicular cancer patients
Sara Garcia (1), Jakob Lauritsen (2), Zeyu Zhang (3), Mikkel Bandak (2), Marlene Danner Dalgaard (1), Rikke Linnemann Nielsen (1), Gedske Daugaard (2), Ramneek Gupta (1)
In industrialized countries, testicular cancer (TC) is the most common solid tumor in men between 20 and 40 years old and besides being one of the most treatable types of cancer, the long-term side-effects of chemotherapy are worrisome, since they are largely irreversible. Their severity is normally related to the total amount of chemotherapy received, which makes that an important factor to a successful treatment. The standard treatment for TC is 3 cycles of cisplatin, etoposide and bleomycin (BEP), being that the number of cycles can vary between 4-5 or more if the prognosis of the patient is intermediate or poor. Some of the late side-effects include nephrotoxicity, which can be measured by the drop in glomerular filtration rate after the patient follows chemotherapy. Materials and Methods: Integrative machine learning models were built using a dataset of 400 Danish individuals in order to identify clinical and/or genomics features and classify patients at higher risk of developing nephrotoxicity given a treatment of BEP-cycles. Results: First, only clinical features, such as age at the time of treatment, dose of cisplatin, patient’s prognosis, and number of cycles, were considered, and relevant features were selected to use in the classifier (AUC 0.66, SD 0.02). The classifier was then optimized by adding genomics markers, which helped improving the prediction (AUC 0.75, SD 0.02). Conclusions: Therefore, it is proposed a machine learning algorithm which, by helping predicting nephrotoxicity in advance, can benefit to improve chemotherapy efficacy in TC patients. These data driven models can also be applicable to other cancers, such as ovarian, bladder, and lung cancer where more elderly patients are at risk of nephrotoxicity and identification upfront will have direct clinical implications.
Poster currently not available
(1) Technical University of Denmark, Denmark, (2) Copenhagen University Hospital, Denmark, (3) University of Chinese Academy of Sciences, China
Loss of N-glycanase 1 alters transcriptional and translational regulation
Petra Jakob (1), William Mueller (1), Sandra Clauder-Münster (1), Han Sun (2), Sonja Ghidelli-Disse (3), Diana Ordonez (1), Markus Boesche (3), Markus Bantscheff (3), Paul Collier (1), Bettina Haase (1), Vladimir Benes (1), Malte Paulsen (1), Peter Sehr (1), Joe Lewis (1), Gerard Drewes (3), Lars Steinmetz (1)
N-Glycanase 1 (NGLY1) deficiency is an ultra-rare, complex and devastating neuromuscular disease. Patients display multi-organ symptoms including developmental delays, movement disorders, seizures, constipation and lack of tear production. NGLY1 is a deglycosylating protein involved in the degradation of misfolded proteins retrotranslocated from the endoplasmic reticulum (ER). NGLY1-deficient cells have been reported to exhibit decreased deglycosylation activity and an increased sensitivity to proteasome inhibitors. We show that the loss of NGLY1 causes substantial changes in the RNA and protein landscape of K562 cells and results in downregulation of proteasomal subunits, consistent with its processing of the transcription factor NFE2L1. We employed the CMap database to predict compounds that can modulate NGLY1 activity. Utilizing our robust K562 screening system, we demonstrate that the compound NVP-BEZ235 (Dactosilib) promotes degradation of NGLY1-dependent substrates, concurrent with increased autophagic flux, suggesting that stimulating autophagy may assist in clearing aberrant substrates during NGLY1 deficiency.
(1) EMBL Heidelberg, Germany, (2) Stanford University, United States of America, (3) Cellzome, Germany
Data integration for prediction of weight loss in clinically controlled dietary trials
Rikke Linnemann Nielsen (1), Marianne Helenius (1), Sara Garcia (1), Henrik Munch Roager (2), Derya Aytan (3), Lea Benedicte Skov Hansen (1), Mads Vendelbo Lind (2), Josef Vogt (1), Marlene Danner Dalgaard (1), Martin I Bahl (3), Cecilia Bang Jensen (1), Rasa Muktupavela (1), Christina Warinner (4), Vincent Appel (5), Rikke Gøbel (5), Mette B Kristensen (2), Hanne Frøkjær (6), Morten H Sparholt (7), Anders F Christensen (7), Henrik Vestergaard (5), Torben Hansen (5), Karsten Kristiansen (6), Susanne Brix Pedersen (1), Thomas Nordahl Petersen (3), Lotte Lauritzen (2), Tine Rask Licht (3), Oluf Pedersen (5), Ramneek Gupta (1)
Diet is a key strategy in weight loss management. Advances in omics technologies research allow analyses of determinants of clinical interventions outcomes. We have previously reported diet-induced weight loss in non-diabetic middle-aged Danes in two clinically controlled dietary trials where the content of whole grain or gluten was changed. However, it remains elusive how predictable weight loss is at the individual level. We here classify weight loss responders and non-responders from the whole grain and gluten trials by integrating multi-omics data (host genetics, gut microbiome, urine metabolome) together with physiology and anthropometrics into random forest models. The most predictive models for weight loss included features of diet, gut microbial species and urine metabolites (ROC-AUC:0.84-0.88, model only with diet type ROC-AUC:0.62). Furthermore, we demonstrate that a model ensemble is robust to missing information of microbiome and metabolome profiles given features of physiology (including postprandial response), host genetics and transit-time (ROC-AUC:0.72).
(1) Technical University of Denmark, Denmark, (2) University of Copenhagen, National Food Institute, Technical University of Denmark, Denmark, (3) National Food Institute, Technical University of Denmark, Denmark, (4) Harvard University, United States of America, (5) The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark, (6) University of Copenhagen, Denmark, (7) Bispebjerg University Hospital, Denmark
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