Neo4j GraphTalk Basel 2019 Dr Alexander Jarasch, DZD

1. Using graph technology to
drive diabetes research
Dr. Alexander Jarasch
Head of data and knowledge management
German Center for Diabetes Research (DZD)

2. What is diabetes

3. diabetes
T1D diabetes
Gestational
diabetes
special types
T2D diabetes

4. 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 diabetesTwo-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
1 in 2 adults with diabetes is
undiagnosed (212 million)
International Diabetes Federation (IDF)
2017

5. Some numbers (USA and Germany)
30 million have diabetes (9.4 % of adults )1
+1‘500‘000 p.a.
84 mio. prediabetes2
$327 billion USD costs p.a.1
($237 bn. medical costs,
$90 bn. reduced productivity)2
16 billion
€ costs p.a.1
7 million have diabetes (7.4 % of adults)1
+500‘000 p.a.
~ 7 mio. prediabetes and undiagnozed

6. Overweight/obesity in the US
obese adults in the US (BMI* >= 30)
*BMI=30: 5”11 = 220,46 lbs (180cm = 100 kg)

7. Complications
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

8. Who we are

9. German Center for Diabetes Research
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

10. German Center for Diabetes Research
diabetes
treatment
diabetes
prevention
prevention of
complications
hospitals
prevention
nutrition / diet
beta cells
genetics
therapy
clinial studies
cohorts
basic researchhealthcare

11. Goal: Better Prevention and therapy
Precision prevention and therapy
identify and cluster diabetes subtypes
Precision treatment of subtypes

12. How do we use graph technology?

13. lipid metabolism
This is how data in biology actually looks like…

14. A zoom…

15. 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
Why (the heck) is everything stored
in relational data silos?

16. DZDconnect - a Neo4j graphDB
“Patient“
64kg, 178cm, male
“drug“
Metformin
“Study“
T2D
“statistics“
“Gene“
AAGCTTCACATGG
“Metabolite“
C6H12O6
insulin resistance
cell
inactive
mice
prediabetic pig
microscope
image
complications

17. For the public

18. What questions can we answer?

19. 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
How many biosamples were aquired in visit 17 of ‘PLIS‘ and which
parameters were measured?
match (s:Study{name:’PLIS’})->[ ]->(v:Visit {no:17})-[:AQUIRED_BIOSAMPLE]->(b:BioSample)-[:MEASURED_PARAMETER]->(p:Parameter)
return count(b), p

20. Study
Person
Visit
BioSample
Experiment
Parameter
Query clinical parameters and biosamples

21. Mapping of molecules between species through metabolic pathways

22. genomics
transcriptomics
metabolomics
proteomics
Extend in-house knowledge
~800 mio. nodes
~800 mio. relationships

23. Mapping between human and prediabetic pig
SNPs
targeted
metabolomics
n=104
annotated: diabetes
genomics
transcriptomics
metabolomics
pathway analysis
DZD
experiments
GWAS cataloge
metabolite n=16
Biocrates
experim
ent
ENSEMBLE
gene IDs
KEGG
gene IDs
KEGG
protein IDs
KEGG metabolite IDs
metabolite
n=7 (of 16)
Xxaa C11:0
Xxaa C11:1
Xxaa C11:2
Xxaa C11:3
Xxaa C11:4
Xxaa C11:5
Xxaa C11:6
ENSEMBL
gene
n=97
m
apping
KEGG gene
n=96
m
apping
KEGG
enzyme
n=16
translated
in
KEGG
metabolite
n=63
connected to
KEGG
metabolite
n=31
mapping

24. Incorporating external knowledge to our in-house data

25. Natural language processing
Abstract
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
Keywords Abstracts

26. Find connections to
other diseases
Alzheimer‘s
cancer
cardio
vascular
diseases
diabetes
Lung
diseases
infectious
diseases

27. Take home message
From 2D data representation to graphs!
Across locations, disciplines and species (diseases)
Enabling a new level of data analysis