Neo4j GraphDay Munich - Graphs to fight Diabetes
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This document discusses using graphs to connect diverse diabetes-related data and advance research. It describes how different research areas like hospitals, basic research and data analysis view the same "customer" (patient) differently. A graph database called DZDConnect was created to connect heterogeneous data like clinical studies, biobanks, omics data and literature at various levels. Examples show how the graph enables querying across data silos. The graph is being extended with various data sources and tools like natural language processing. The goal is personalized prevention and therapy by identifying diabetes subtypes using machine learning. This will help validate individualized treatment approaches.
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Neo4j GraphDay Munich - Graphs to fight Diabetes
- 1. Graphs to fight diabetes Dr. Alexander Jarasch Head of Data and Knowledge Management The German Center for Diabetes Research (DZD)
- 2. Evolutionary advantage becomes disadvantage energy storage essential for survival upon lack of food energy storage essential for survival upon food abundance
- 3. What is diabetes mellitus? • metabolic disease • insulin production is reduced in pancreas or body poorly responds on insulin (insulin=hormone, the body needs to get glucose out of the blood stream into the cells) • consequences: • less absorbtion of sugar • sugar will not be stored in liver and muscle cells • persistently high levels of sugar in blood (hyperglycemia) • tremendous complications • currently, not curable (only treatable) diabetes T1D diabetes Gestational diabetes special types T2D diabetes
- 4. Diabetes TYPE 1 (T1D) • appr. 5-10 % of diabetes patients have T1D • often starts in childhood • autoimmune reaction • independent from life style • patients need external insulin source throughout their life • appr. 20 genes involved • currently, T1D is not curable
- 5. Diabetes TYPE 2 (T2D) • appr. 90-95 % of diabetes patients have T2D (mostly after age 40) • insulin resistance, pancreas is not able to produce enough insulin • symptoms develop slowly • >150 genes are identified that increase risk • “the cocktail of evil“: predisposition + overweight + physical inactivity
- 6. Some numbers (worldwide) 1 in 11 adults has diabetes (425 million) Since 1980 quadrupled 12% of global health expenditure is spent on diabetes ($727 billion) Over 1 million children and adolescents have type 1 diabetes Two-thirds of people with diabetes are of working age (327 million) 2017 Three quarters of people with diabetes live in low and middle income countries 2017 1 in 2 adults with diabetes is undiagnosed (212 million) International Diabetes Federation (IDF)
- 7. Some numbers (USA and Germany) 30 million have diabetes (9.4 % of adults )1 +1‘500‘000 p.a. 84 mio. prediabetes2 16 billion € costs p.a.1 7 million have diabetes (7.4 % of adults)1 +500‘000 p.a. ~ 7 mio. prediabetes and undiagnozed $327 billion USD costs p.a.1 ($237 bn. medical costs, $90 bn. reduced productivity)2 1 www.statistica.com 2 American Diabetes Association
- 8. Overweight/obesity in the US (1985- 2009) obese adults in the US (BMI* >= 30) *BMI=30: 5”11 = 220,46 lbs (180cm = 100 kg)
- 9. Complications develop after many years kidney Diabetic nephropathy 40 % of kidney failure/dialysis feet 70 % of all foot amputations eyes Diabetic retinopathy 30 % of loss of sight brain 2-4 fold increased risk for stroke acute cardiac death Main reason of death of diabetic patients (33 % of all heart attacks) nerves Diabetic Neuropathy Amputations of extremeties
- 10. Complex emergence / complex disease live style gene epigenetics metabolism cellular processes environment
- 12. Epigenetics – beyond generation weight[g] age [weeks] daughters of obese mice having diabtes daughters of healthy mice Huypens and Beckers, Nat Genet. 2016
- 13. The German Center for Diabetes Research funded by the Federal Ministry for Education and Research and the states 5 Partners, 5 associated partners – 400 researchers (basic research and university hospitals) DZD bundles competencies so that those affected benefit more quickly from research results. academic, non-profit
- 14. The German Center for Diabetes Research hospitals prevention nutrition / diet beta cells genetics therapy clinial studies cohorts basic researchhealthcare diabetes treatment diabetes prevention prevention of complications
- 15. Goal: better diabetes prevention and therapy personalized prevention and therapy identify and cluster diabetes subtypes individualized treatment of subtypes
- 16. How do we fight diabetes with graphs?
- 17. The challenge Easy question -> Complex query Find information within our organisation
- 18. Originally different research areas Hospitals Basic Research Data Analysis
- 19. We all “serve“ the same “customer“ Hospitals Basic Research Data Analysis
- 20. But we all see the “customer“ a little differently “Patient“ “Gene“ “Study“ “Metabolite“ “drug“ “statistics“ 64kg, 178cm, male C6H12O6 Metformin T2D AAGCTTCACATGG cell insulin resistance inactive mice prediabetic pig microscope image complications
- 21. Look at our “customer“ in a new way “Patient“ “Gene“ “Study“ “Metabolite“ “drug“ “statistics“ 64kg, 178cm, male C6H12O6 Metformin T2D AAGCTTCACATGG cell insulin resistance inactive mice prediabetic pig microscope image complications
- 22. Look at our “customer“ from many perspectives simultaneously – connect data Hospitals Basic Research Data Analysis data
- 23. Connect data – one option Hospitals Basic Research Data Analysis “Patient“ 64kg, 178cm, male “drug“ Metformin “Study“ T2D insulin resistance “Gene“ AAGCTTCACATGG “Metabolite“ C6H12O6 cell inactive mice prediabetic pig “statistics“ microscope image complications
- 24. Connect data – better option “Patient“ 64kg, 178cm, male “drug“ Metformin “Study“ T2D “statistics“ “Gene“ AAGCTTCACATGG “Metabolite“ C6H12O6 insulin resistance cell inactive mice prediabetic pig microscope image complications
- 25. DZDConnect – a Neo4j graph database Graph that can help answering bio-medical questions across locations across disciplines across species extendable scalable visualizable
- 26. Homogenous and heterogenous data
- 27. (First) connect data a meta level RAW DATA RAW DATA RAW DATA RAW DATA RAW DATA
- 28. Classify types of data
- 29. Classify types of data clin. study clin. study clin. study statis tics statis tics RNA DNA RNA DNA images chem istry patient patient patient bio sample bio sample bio sample wet lab chem istry drug
- 30. Connect types of data statis tics statis tics RNA DNA images chem istry patient wet lab chem istry drug patient patient bio sample bio sample bio sample clin. study clin. study clin. study RNA DNA
- 31. Build graph model clin. study statis tics RNA DNA images bio sample wet lab chem istry drug patient
- 32. Why graph? • in „biology“ everything is connected anyway • data is connected • human readable – easy-to-understand for non-computer scientists • easy to query: queries are similar to human-like questions • scalable • easy-adoptable and extendable • visualization
- 33. Meta data name: IL-6 unit: mg/ml sample: blood organism: pig amount: 50ml aliquots: 362 location: Freezer68 name: pancreas dissection format: TIFF dimension: 3840x2160 amount: 125 staining: no staining microscope: Zeiss Light sheet Z1 location: Dresden title: „about diabetes and Alzheimer‘s“ PMID: 1255864 doi: http://doi.102r3d year: 2016 journal: Diabetes
- 35. lipid metabolism Diabetes is a metabolic disease
- 37. Extending our graph RNA-seq proteomicsAssociations ~800 mio. nodes ~800 mio. relationships Dr. Martin Preusse Dr. Nikola Müller
- 38. Extending our graph Dr. Jan Krumsiek, Assistant Professor, Weill Cornell Medicine, NYC metabolic pathway data from 15-20 very rich data sources ~900’000 nodes ~1.7 mio. relationships phenotype associations studies
- 39. Summary “Patient“ 64kg, 178cm, male “drug“ Metformin “Study“ T2D “statistics“ “Gene“ AAGCTTCACATGG “Metabolite“ C6H12O6 insulin resistance cell inactive mice prediabetic pig microscope image complications
- 40. Examples
- 41. How many biosamples were aquired in visit 17 of ‘PLIS‘ and which parameters were measured?Goals: 1. Connect data from our clinical studies and biobanks 2. Researches can easily browse through measured parameters and available biosamples 3. Meta data of parameters helps to assess which samples are comparable
- 42. name: HMGU name: AJ position: data mgmt name: PLIS multi-center: true recruiting: closed analysis: on-going no. of patients: 1105 visit: 17 name: blood type: OGTT number of samples: 3436 organism: Human name: laboratory
- 43. Study
- 46. Can human T2D genes be studied in the pre-diabetic pig model? Goals: 1. Connect data from different species (i.e. mice, pig, human) 2. Connect multiomics data 3. Researches can easily find information between human and comparable data from animal models
- 48. Human GWAS cataloge (Diabetes) 103 genes 97 genes 96 genes 16 enzymes 63 compounds 31 compounds 7 compounds 16 metabolites Targeted metabolomics analysis in prediabetic pig ENSEMBL Gennamen (human) KEGG Gen IDs KEGG Enzyme KEGG compounds Biocrates IDs 7/16 metabolites Xxaa C11:0 Xxaa C11:1 Xxaa C11:2 Xxaa C11:3 Xxaa C11:4 Xxaa C11:5 Xxaa C11:6 genomics transcriptomics proteomics metabolomics pathway analysis
- 49. Outlook
- 50. Automatically learn from large literature texts
- 51. Natural language processing (NLP) example Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes, like ABC1, XYZ2, are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome. Metabolic disorders, including obesity and type 2 diabetes mellitus, are major challenges for public health. Rozman and Hrabe de Angelis, Nat Commun. 2018 NLP method by GraphAware
- 53. Machine learning for personalized prevention and therapy identify and cluster diabetes subtypes individualized treatment of subtypes Expert Knowledge validation of personalized treatment Graph Technology
- 54. DDPC – Digital Diabetes Prevention Center • pattern recognition in huge amounts of data • (un)supervised ML methods to identify subtypes of diabetes • developing/validating individulized prevention/therapy transparency to people benefit for people benefit for society
- 55. Next level in diabetes prevention and treatment Hospitals Basic Research Data Analysis
- 56. Acknowledgements The scientists of the DZD at: Funding by:
- 57. Thank you
Notas do Editor
- lebenstil der eltern vor zeugung hat einfluss bereits für risk obisisty für die knder durch epigenetische enflüsse VOR der schwangerschaft
- cover all aspects of diabetes research from molecular studies in cell models. and animal models to clinicial investigations in patients and health care research