Meet the team

Dobril Ivanov

"My lab aims to understand the molecular mechanisms that underlie Alzheimer's disease, using a computational approach. For this purpose, I am developing a novel methodology to link gene-expression profiles to phenotypes observed in mice and IPSCs, and apply this knowledge to understand the disease mechanisms in humans." Dobril Ivanov
UK DRI Collaborating Fellow

With a background in molecular biology, genetics and bioinformatics, Dr Dobril Ivanov joined the UK DRI at Cardiff as a Group Leader with a Sêr Cymru Rising Star fellowship by the ERDF through the Welsh Government. Dobril did an MSc in molecular biology followed by a wet-lab scholarship to study neurodevelopmental disorders at the MRC Centre in Cardiff. This project was one of the earliest to accurately estimate the frequency of VCFS deletions in patients with psychosis. Dr Ivanov obtained his PhD in bioinformatics from Cardiff University in 2010, with one of the first studies to fully explore the similarities and differences of germline/somatic mutations in human tumour suppressor genes in a variety of cancers. During his first postdoc (MRC CNGG Cardiff University) he developed a novel computational approach that led to the observation that rare CNVs substantially increase one’s susceptibility to developing schizophrenia. Dr Ivanov completed his postdoctoral training at the European Bioinformatics Institute in Cambridge in Prof. Janet Thornton’s group investigating neurodegenerative disorders and signalling mechanisms and biochemical processes by which insulin/IGF-1 signalling modulates lifespan in model organisms. At the UK DRI Dobril leads research to link gene-expression profiles to a wide-range of Alzheimer-related phenotypes in humans, animal and iPSC models.

1. At a glance

Using computers to unravel the complexity of dementia

Through: Sêr Cymru Rising Star Fellowship

Dementia describes a set of symptoms that may include memory loss and difficulties with thinking, problem-solving or language. Although Alzheimer’s disease is the most common cause, there are several other diseases that can lead to the condition.

Traditionally, doctors diagnose and treat diseases based on their symptoms – but this approach is too simplistic for complex conditions, such as dementia. Symptoms can overlap between different diseases and can also vary considerably between people with the same disease – and many different factors may contribute to disease development.

Dr Dobril Ivanov is using sophisticated data analytics and computational approaches to analyse huge datasets generated from experimental models and people living with Alzheimer’s disease and other forms of dementia. He is aiming to improve our understanding of the complex molecular networks that contribute to disease development.

He hopes this will help prioritise key targets for drug development and enable doctors to tailor treatments based on a more accurate diagnosis. It may also lead to new tests that can identify subtle changes that indicate someone is at an increased risk of dementia – at an early stage when treatments may be more effective at preventing or slowing down disease progression.

2. Scientific goals

The idea of trying to deconvolute a complex phenotype, such as dementia, into subphenotypes using animal models and combining multiple human gene expression datasets is an innovative, original and an integrative approach. There have been only a few attempts so far, despite the plethora of data available in the public space. These attempts have been only limited to cellular processes, such as predicting the cell-cycle stage from transcriptome data.

One of the direct uses of these gene-expression profiles is to predict not yet tested subphenotypes within animal and induced pluripotent stem cell (iPSC) models of Alzheimer's disease (AD). Different gene mutants with shared phenotypes could point to overlapping biological mechanisms and pave the way to understanding AD and dementia. Similar methods have been successfully used to discriminate between cell-cycle stage, cell types and even inferring physiological age using transcriptional changes.

The possibility to split dementia phenotype into subphenotypes using gene expression data paves the way for not only asserting the relationship of AD with already known subphenotypes, but also for discovering not yet tested subphenotypes and associated biological pathways. Utilising a quantitative measure, it can also quantify the relationship between different subphenotypes and AD mutant models. Thus, it can potentially be used to distinguish between subphenotypes that have a strong effect in a few model organisms (different human gene orthologs) and those that have a small, but consistent effect.

Main objectives and research goals:

The aim of this project, led by Dr Dobril Ivanov, is to understand the molecular mechanisms that underlie AD and other forms of dementia, using functional computational approaches. The team is developing a novel methodology to link gene-expression profiles to phenotypes observed in animal models and human iPSCs, and apply this knowledge to understand the disease mechanisms in humans.

1. To collect data on specific subphenotypes in mice.

2. To update the methodology and develop prediction from the molecular signatures. 

3. To create specific molecular signatures and impute specific subphenotypes of interest. 

4. To collect gene expression data from in mouse models of AD and dissect disease subphenotypes.

5. To generate molecular signatures in iPSC. 

6. To dissect multi-omics data in humans and build quasi-longitudinal profiles of individuals loving with AD.

The project is also part-funded by the European Regional Development Fund through the Welsh Government

3. Team members

Dr Benoit Lan-Leung (Postdoctoral Researcher)

4. Collaborations

Within UK DRI:

  • Prof Philip Taylor, UK DRI at Cardiff

Beyond UK DRI:

  • Prof Nick Allen, UK DRI at Cardiff
  • OpenTargets - a public-private partnership between the EBI, the Sanger Institute, GSK, Biogen and Takeda

5. Topics

Computational biology, bioinformatics, functional genomics, molecular, cellular and physiological subphenotypes, gene expression

6. Techniques

Machine-learning approaches (Support Vector Machines, Artificial Neuronal Networks); statistical modelling, functional analysis (based on Gene Ontology enrichment)

7. Key publications

Baker E, Sims R, Leonenko G, Frizzati A, Harwood J, Grozeva D, Genetic and Environmental Risk in Alzheimer's Disease (GERAD) Consortium, PERADES consortium, IGAP consortia, Morgan K, Passmore P, Holmes C, Powell J, Brayne C, Gill M, Mead S, Heun R, Bossu P, Spalletta G, Goate A, Cruchaga C, van Duijn C, Maier W, Ramirez A, Jones L, Hardy J, Ivanov D, Hill M, Holmans P, Allen N, Morgan P, Williams J, Escott-Price V. Gene-based Analysis in HRC Imputed Genome-Wide Association Data Identifies Three Novel Genes for Alzheimer's Disease. BioRxiv 208; Preprint doi: https://doi.org/10.1101/374876

Jones, L., Holmans, P.A., Hamshere, M.L., Harold, D., Moskvina, V., Ivanov, D., Pocklington, A., Abraham, R., Hollingworth, P., Sims, R. and Gerrish, A., 2010. Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease. PloS one5(11), p.e13950.

Ziehm, M., Kaur, S., Ivanov, D.K., Ballester, P.J., Marcus, D., Partridge, L. and Thornton, J.M., 2017. Drug repurposing for aging research using model organisms. Aging cell16(5), pp.1006-1015.

Kerr, F., Sofola-Adesakin, O., Ivanov, D.K., Gatliff, J., Perez-Nievas, B.G., Bertrand, H.C., Martinez, P., Callard, R., Snoeren, I., Cocheme, H.M. and Adcott, J., 2017. Direct Keap1-Nrf2 disruption as a potential therapeutic target for Alzheimer’s disease. PLoS genetics13(3), p.e1006593.

Ivanov, D.K., Escott-Price, V., Ziehm, M., Magwire, M.M., Mackay, T.F., Partridge, L. and Thornton, J.M., 2015. Longevity GWAS using the Drosophila genetic reference panel. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences70(12), pp.1470-1478. 

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