Best Poster Awards – Target Validation Using Genomics and Informatics

Meet Giovanni Spirito and Borja Gomez Ramos – the two poster prize winners at the recent EMBL – Wellcome Genome Campus Conference: Target Validation Using Genomics and Informatics (8 – 10 Dec 2019).

Identification and prioritization of candidate causal genomic variations from individuals affected by ASD

PHOTO: Giovanni Spirito

Authors: Giovanni Spirito (1), Diego Vozzi (2), Martina Servetti (3), Margherita Lerone (3), Maria Teresa Divizia (3), Giulia Rosti (3), Livia Pisciotta (4), Lino Nobili (4), Irene Serio (4), Stefano Gustincich (2), Remo Sanges (1)

Next generation sequencing (NGS) technologies enabled the extensive study of the genomics underlying human diseases. Namely whole exome sequencing (WES) represents a cost-efficient method which can lead to the detection of multiple classes of genomic variants and the discovery of novel disease-associated genes. One of the drawbacks of this approach however, is the large number of genomic variants detected in each analysis. Automated variant prioritization strategies are therefore required. This is particularly important in the case of complex disease such as ASD, whose genetic etiology is still poorly understood. To this aim we built a custom computational framework capable, from raw WES data, to automatically detect four classes of genomic variants (SNPs, indels, copy number variants and short tandem repeat variants) and prioritize them in regards to their relevance to a specific phenotype. We tested this framework on a selection of 29 trios including probands affected by severe and undiagnosed rare phenotypes and a small cohort of 10 trios all featuring healthy parents and one offspring affected by autism spectrum disorder (ASD). We were able to successfully detect rare and de novo high penetrance variants which have been validated and confirmed as causative among the undiagnosed probands. In the specific case of the ASD cohort we could highlight several genes which are not implicated in autism susceptibility, but nevertheless whose connections to genes relevant for ASD could suggest a possible involvement in the phenotype. Furthermore, our approach enabled us to detect several instances characterized by the presence of multiple candidate variants within genes belonging to the same canonical pathway in one proband. Our workflow allows to detect and prioritize multiple classes of genomic variants in order to both highlight rare high penetrance disease-causative mutation, and possibly reconstruct the genomics at the basis of complex ASD phenotypes.

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(1) SISSA, Italy, (2) IIT, Italy, (3) Gaslini Institute, Italy, (4) University of Genova, Italy


Omics data integration for the identification of cell-type-specific gene regulatory networks and regulatory variants in Parkinson’s disease

PHOTO: Borja Gomez Ramos

Authors: Borja Gomez Ramos (1,2), Jochen Ohnmacht (1,2), Nikola de Lange (2), Aurélien Ginolhac (1), Aleksandar Rakovic (5), Christine Klein (5), Roland Krause (2) , Marcel H. Schulz (6), Thomas Sauter (1), Rejko Krüger (2,3,4) and Lasse Sinkkonen (1)

Genome-Wide Association Studies (GWAS) have identified many variants associated with different diseases. However, it is still a challenge to make sense of this data as the majority of genetic variants are located in non-coding regions, complicating the understanding of their functionality. In the last few years, it has been found that non-coding genetic variants concentrate in regulatory regions in the genome, which are cell type and cell-stage specific. In this project, we seek to identify functional Parkinson’s disease GWAS non-coding genetic variants that could make carriers more prone to developing PD. To do so, we are using induced pluripotent stem cell (iPSC) technology to differentiate somatic cells into midbrain dopaminergic (mDA) neurons, astrocytes and microglia. Assessing their chromatin accessibility, active chromatin regions and transcriptome, we can identify crucial regulatory regions in the genome, key transcription factors and derive the gene regulatory networks for the three different cell types. Then, we will map the non-coding genetic variants to the different regulatory regions and predict their effect in silico for the subsequent validation in vitro. This innovative approach will also identify novel factors controlling cell fate and cell identity.

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(1) Life Sciences Research Unit, University of Luxembourg, Luxembourg, (2) Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, (3) Centre Hospitalier de Luxembourg (CHL), Luxembourg, (4) Luxembourg Institute of Health (LIH), Luxembourg, (5) Institute of Neurogenetics, University of Lübeck, Germany, (6) Institute for Cardiovascular Regeneration, Uniklinikum and Goethe University Frankfurt, Germany


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Best Poster Awards – Precision Health

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 from TiFN Wageningen and MaCSBio, Maastricht University, The Netherlands, PHOTO: Balazs Erdos

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.

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(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

*E-mail: balazs.erdos@maastrichtuniversity.nl


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 from EMBL Heidelberg, Germany, PHOTO: Petra Jakob

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.

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(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).

Poster currently not available

(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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