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Systems and Network-based Approaches to Complex Metabolic Diseases

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Muhammad Arif's PhD Defense
https://muharif.net
11 June 2021
KTH Royal Institute of Technology, Sweden
Science For Life Laboratory

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Systems and Network-based Approaches to Complex Metabolic Diseases

  1. 1. Systems and Network-based Approaches to Complex Metabolic Diseases Muhammad Arif Science for Life Laboratory, KTH Royal Institute of Technology Supervisors: Prof. Dr. Adil Mardinoglu; Prof. Dr. Mathias Uhlén Stockholm, 11 June 2021
  2. 2. • Need energy to be able to perform activities • Chemical à Kinetic • Complex System • Interconnected Human Body == Car 2
  3. 3. Metabolites Metabolomics DNA Genomics RNA Transcriptomics Proteins Proteomics Microbiome Metagenomics 3
  4. 4. Metabolites Metabolomics DNA Genomics RNA Transcriptomics Proteins Proteomics Microbiome Metagenomics 4 Systems Biology Paradigm (Definition taken from Institute of Systems Biology) • Holistic approach to answer complex and important biological questions • Collaborative effort from multiple disciplines • Biology • Computer Science • Physics, etc • Predictive analysis to understand the condition changes
  5. 5. Approaches in Systems Biology Statistical Inference Network Analysis Machine Learning Omics Data Altered analytes Functional Analysis Classification Regression Clustering Relationships Centrality Community Patient characterization Disease mechanism Novel biomarkers Novel Therapy Drug Repositioning 5
  6. 6. Present Investigation I II Generation of Biological Networks III Systems Biology of Heart IV Systems Biology of Muscle V VI Systems Biology of Liver 6
  7. 7. Paper II iNetModels 2.0: an interactive visualization and database of multi-omics data Arif and Zhang et. al. (2021) Nucleic Acid Research doi: 10.1093/nar/gkab254 7
  8. 8. Study Introduction • More and more personalized multi-omics data were collected • Integration of multi-omics data has been proven to offer novel insights and comprehensive understanding of human body • Problem: Limited studies in collecting and exhibiting data association in a single database • We generated integrated multi-omics networks from multiple studies and conditions • Goal: A database and interactive platform to visualize multi- omics data interactions 8
  9. 9. Platform Description Tissue-specific (GTEx) Cancer-specific (TCGA) Personalized Multi-Omics Profiling (6 sources) Data Sources Co-Expression Network (Spearman Correlation) Low Expression Filters Age and Sex Correction Network Generation Database and Visualization Cross and Delta Networks Tissue; Cancer; Sex; Diseases Statistical & Omics Filtering Integration with other tools Programmatic Access Features https://inetmodels.com 9
  10. 10. Use Case: NAFLD CMA Supplementation Hypothesis Testing Relationship between the supplement with TG and liver enzymes Exploratory Analysis Relationship between the supplement with gut microbiomes Results Validation The effect of the supplement to BCAA metabolism and glucose level New Insights CMA supplementation affects several cholesterol-related variables and inflammation markers Source: P100 Study SCAPIS-SciLifeLab networks 10
  11. 11. Summary Personalized Wellness Profiling Studies Multi-Omics iNetModels 2.0 The Cancer Genome Atlas (TCGA) Genotype-Tissue Expression (GTEx) Data Sources https://inetmodels.com 11 • >100 Networks • Flexible Customization
  12. 12. Paper III Integrative transcriptomic analysis of tissue- specific metabolic crosstalk after myocardial infarction Arif and Klevstig et. al. (2021) eLife doi: 10.7554/eLife.66921 12
  13. 13. Study Introduction • Multiple studies have been performed and provided new insights into MI • Limitation: Single Tissue analysis • Cross-talk between different tissues and their dysregulation has not been examined • In this study, we performed integrated analysis between heart and metabolically active tissues • Goal: More complete picture of metabolic alteration during MI 13
  14. 14. Study Flow 14
  15. 15. Time Series Analysis Gene Ontology Reporter Metabolites 15
  16. 16. Co-expression Network Analysis 16
  17. 17. Co-expression Network Analysis Autophagy Endocytosys (FoxO, Inslin, mTOR, AMPK) Signalling Cell Cycle Circadian Rhythm Fatty acid metabolism Amino Acid Transport TCA Cycle Tight Junction m/RNA metabolism Endosomal Transport (Wnt, NFK-Beta) Signaling (Retinol, Cholesterol,- Fructose and Mannose,- Fatty Acid, Steroid) Metabolism Heart-Specific Functions Oxytocin signalling (Glycogen, Inositol phosphate,- Purnine) Metabolism Central Clusters 17
  18. 18. Final Results Fatty Acid Fatty Acid Retinol Lipid Metabolism (Up) Inflamatory Response (Up) Fatty Acid Metabolism (Down) Lipid Metabolism (Up) Inflamatory Response (Up) Fatty Acid Beta-Oxidation (Up) Glutathione Metabolism (Down) Inflamatory Response (Down) Fatty Acid Metabolism (Down) Response to Lipid (Up) Inflamatory Response (Up) Retinoid metabolic process (Up) Mitochondrial Dysfunction • We identified several targets/biomarkers: • Flnc • Lgals3 • Prkaca • Pprc1 Hypothesized Metabolic Cross-talk 18
  19. 19. Paper V Multi-omics analysis reveals the influence of the oral and gut microbiome on host metabolism in non-alcoholic fatty liver disease Zeybel and Arif et. al. (2021) Manuscript 19
  20. 20. Study Introduction • NAFLD has been labelled as “the silent pandemic” • One of the most prevalent diseases in the world (25% of population) • No approved treatment for this disease • Dysbiosis of microbiomes have been suspected to influence NAFLD • Goal: systematic analysis to study the dysbiosis of microbiomes and their relationships with other omics 20
  21. 21. Study Design No steatosis Mild steatosis Moderate steatosis Severe steatosis Measure Group HS< 5.5% 5.5%≤HS<8% 8%≤HS<16.5% HS≥16.5% MRI-PDFF n=10 n=14 n=20 n=12 Blood Feces Saliva 21
  22. 22. Multi-Omics Data Integration The network was retrieved from iNetModels 22
  23. 23. Multi-Omics Data Integration • Glutathione-related metabolites associated with GGT 23
  24. 24. Multi-Omics Data Integration • Glutathione-related metabolites associated with GGT • Known NAFLD-marker proteins were positively correlated with liver fat and enzymes 24
  25. 25. Multi-Omics Data Integration • Glutathione-related metabolites associated with GGT • Known NAFLD-marker proteins were positively correlated with liver fat and enzymes • Negative correlation of important microbes to liver fat 25
  26. 26. Multi-Omics Data Integration • Glutathione-related metabolites associated with GGT • Known NAFLD-marker proteins were positively correlated with liver fat and enzymes • Negative correlation of important microbes to liver fat • Protagonist and NAFLD- associated gut microbes associated to ALT, AST, and uric acid 26
  27. 27. Summary • Multi-omics data from well-characterized NAFLD patients with different hepatosteatosis severity levels • Implementation of a wide range of systems biology approaches • Single-omics analysis: Finding molecular signatures from each omics type • Multi-omics integration: functional relationships between analytes from different omics types • Elucidating the dysbiosis of microbiomes caused by NAFLD • Identification of candidate novel biomarkers for NAFLD 27
  28. 28. Summary and Concluding Remarks • Systems biology is a great tool to get a holistic and systematic view of human body • One of the main enabler and driver of personalized medicine • Development and application of systems biology tools in complex diseases using multi-tissue and multi-omics data 28
  29. 29. Future Perspectives • More personalized multi-omics studies • Account for individual variation in healthy and disease state • Lead towards better patient characterizations and biomarkers discovery • Incorporation of prior knowledge to the networks • To be able to derive causality from the network • To shorten the analysis cycle • General (and open) framework for data collection and analysis • More robust disease model à Data, Data, and Data! 29
  30. 30. Open Science Open Data Open Access Open Source 30 Adapted from: DOI: 10.3233/ISU-170846
  31. 31. Acknowledgements Adil Mardinoglu Cheng Zhang Woonghee Kim Ozlem Altay Xiangyu Li Mengnan Shi Hong Yang Meng Yuan London: Stephen Doran Simon Lam Abdulahad B. Ali Kaynar Ex-Members: Sunjae Lee Rui Benfeitas Alen Lovric Natasa Sikanic Dorines Rosario Beste Turanli Mohammed A. Feride Eren Mathias Uhlén Linn Fagerberg Max Karlsson Abdelah Tebani Wen Zhong Jan Borén Martina Klevstig Malin Levin Elias Björnson Bash Biotech Saeed Shoaie And many others! 31 Jens Nielsen
  32. 32. 32

Muhammad Arif's PhD Defense https://muharif.net 11 June 2021 KTH Royal Institute of Technology, Sweden Science For Life Laboratory

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