Dr. Alfonso Valencia

Dr. Alfonso Valencia, Group Leader and Director of the Life Sciences Department of the Barcelona Supercomputing Center, will present the lecture "Networks based approaches in biology and biomedicine."

Meet the Speaker

If you want to meet Dr. Alfonso Valencia, please book a time slot at https://doodle.com/poll/eqrwee9qmcst6ebc and send an email to anthony.mathelier@ncmm.uio.no.


The representation of biological objects as networks has become a full filed of research on its own. In this talk, I will present three different areas of research in which my group is developing and applying network based methods.
In the first area, we study comorbidities, defined as the presence of one or more additional diseases co-occurring with a primary disease or disorder. Large direct comorbidity networks have been previously constructed based on the presence of disease identifiers (ICD codes) in HER by the Barabasi4 and Brunak’s5 groups. We also know about disease relations from population studies1 . We have proposed a different approach to the study of comorbidity based on the comparison of gene expression profiles. Using this approach, we have described some of the potential molecular basis of the relations between central nervous system disorders (CNSd) and cancer2,3.
More recently6, we have analyzed the gene expression profiles of thousands of individuals classified in 133 diseases with information available in public databases. The resulting network connects pairs of patients with a significant overlap between genes, positively if they are deregulated in the same direction and negatively if they are oppositely-regulated. This gene expression-based network has a significant similarity with the ones produced by the Barabasi4 and Brunak5 groups. The clustering of the patient based comorbidity network, reveals that patient-subgroups labelled with the same disease tag can have very different - and even opposite- relations with groups of patients with other diseases. These specific relations between groups can be interpreted in terms of direct and inverse comorbidity relations specific of groups of patients, moving the study from the general concept of disease comorbidity to specific relations between groups of patients within each specific disease.
In the second application of network methods7, we processed heterogeneous ChIP-Seq information to build a comprehensive genome co-localization network of Chromatin Related Proteins (CRPs), histone marks and DNA modifications in mouse embryonic stems cells. In this network, co-localization preferences can be translated into specific of “mESC Chromatin States”, such as active regions or enhancers. The study of the properties of the “co-localization” network points to the 5hmC DNA modifications, as the key component in the organization of the mouseESC network. The importance of 5hmC, as organizer of the epigenetic network, was reinforced by the evolutionary analysis of the protein components of the network. There, 5hmC acts as a mediator in the co-evolution of the CRPs protein components of the mESC network.
We then investigated the mESC Epigenetic Properties and Chromatin States, by analysing them in the context of the structure of the chromatin in the cell nucleus8. The results revealed interesting properties of the organization of the mESC epigenetic control system, in line with the emerging models of gene expression control and chromatin organization, and support the  role of 5hmC as afactor present in a significant number of interactions related with active transcription in mouse embryonic stems cells.
Finally, in the third network based approach we analysed the potential consequences at the introduction of chimeric proteins product of chromosomal translocations in protein interaction networks, with particular focus in the implications for the connectivity of oncogenes and tumor suppressors9.

[1] Barnett, K. et al. (2012) Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. The Lancet, 380, p37-43.
[2] Ibañez, K. et al (2014) Molecular Evidence for the Inverse Comorbidity between Central Nervous System Disorders and Cancers Detected by Transcriptomic Meta-analyses. PLoS Genetics. 10:e1004173.
[3] Sánchez-Valle J. et al (2017) A molecular hypothesis to explain direct and inverse co-morbidities between Alzheimer’s Disease, Glioblastoma and Lung cancer. Scientific Reports. 7:4474.
[4] Hidalgo, C. et al. (2009) A dynamic network approach for the study of human phenotypes. PLoS Computational Biology. 5(4):e1000353.
[5] Jensen, A. et al. (2014) Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications. 5:4022
[6] Sanchez-Valle, J. et al. (2018). Unveiling the molecular basis of disease co-occurrence: towards personalized comorbidity profiles bioRxiv 431312 doi:10.1101/431312.
[7] Perner et al., (2016) Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs. Cell Rep. Cell Reports 14, 1246–1257
[8] Pancaldi et al., (2016) Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity. Genome Biol. Genome Biol. 2016; 17: 152.
[9] Frenkel-Morgenstern et al, (2017) ChiPPI: a novel method for mapping chimeric protein–protein interactions uncovers selection principles of protein fusion events in cancer Nucleic acids research 45 (12), 7094-7105

Parts of this work were developed in collaboration with: Vingron's (MPIMG, Berlin), Fraser’s (Babraham Institute), and Baudot’s labs (CNRS, Marseille).



Junior Talk

Dr. Stefania Salvatore, postdoctoral fellow at the Biomedical Informatics Group, UiO, will present her current research with the talk "Beware of the Jaccard: the choice of metric is important and non-trivial in genomic colocalization analysis".

Published Aug. 13, 2019 11:08 AM - Last modified Aug. 13, 2019 11:09 AM