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Systems and Network-based Approaches to Complex Metabolic Diseases

Time: Fri 2021-06-11 13.00

Location:, Stockholm (English)

Subject area: Biotechnology

Doctoral student: Muhammad Arif , Systembiologi, Science for Life Laboratory, SciLifeLab, SysMedicine

Opponent: Professor Thomas Sauter, Department of Life Sciences and Medicine, Faculty of Science, Technology, and Medicine, University of Luxembourg, Luxembourg

Supervisor: Professor Adil Mardinoglu, Science for Life Laboratory, SciLifeLab, Systembiologi

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The future of healthcare is personalized medicine, in which disease treatments are tailored based on the individual characteristics of each patient. To reach that objective, we need to obtain a better understanding of diseases. The main facilitator of personalized medicine is systems and data-driven biology, which makes omics data a top commodity in this era. Coupled with computational and biological expertise, omics data can be a useful asset for obtaining mechanistic insights into the biological conundrum, particularly in disease-related contexts. This thesis describes systems biology approaches and their applications in disease-specific contexts. Systems biology assists us in systematically and comprehensively understanding complex biological systems as a whole interconnected system.

The first part of the thesis describes the generation of more than 100 biological networks based on personalized data originated from several different omics, usually referred to as multiomics data, including clinical data and metabolomics, proteomics, and metagenomics data collected from the same individuals. Moreover, we present a web-based multiomics biological network database and visualization platform called iNetModels.

In the second part of the thesis, we describe systems biology frameworks and their applications to the study of various biological questions in disease contexts using single- and multiomics data. First, we present our findings on the integrative view of metabolic activities from multiple tissues after myocardial infarction using transcriptomics data from the heart and other metabolically active tissues. Second, we used transcriptomics data to describe the mechanistic effect of lifelong training on skeletal muscle in both men and women and the role of short-term training in reversing damage from metabolic-related diseases. Third, we deciphered the molecular mechanism of nonalcoholic fatty liver disease (NAFLD) based on clinical data, plasma metabolomics, plasma inflammatory proteomics, and oral and gut metagenomics data. Finally, we elucidated the mechanism of action of CMA supplementation, a potential treatment for NAFLD, based on proteomics and metabolomics data.

In summary, this thesis presents a novel platform for biological network analysis and proven systems biology frameworks to provide mechanistic and systematic understandings of specific diseases using single- and multiomics data.