1. Computational Systems Biology
Array CGH for constitutional disorders: from diagnosis to disease gene discovery
Computational Systems Biology

2. Array CGH: from diagnosis to gene discovery
Patients with congenital & acquired disorders
Location of chromosomal imbalances
CGH microarrays Molecular karyotyping
Statistical analysis
Map chromosomal abnormalities
Improved diagnosis
Discover new disease causing genes and explain their function
Prioritized candidate genes
Validation
Databasing
"The Center of Human Genetics is involved in the development of array-CGH and among the first to clinically implement this novel technology which enables the identification of chromosomal imbalances. This technology has sped up the discovery of chromosomal anomalies causal for human developmental and acquired disorders. The identification of chromosomal
imbalances pinpoints the location of disease causing genes. To stay at the
forefront of unraveling the molecular causes of these disorders and stay at the forefront of the gene discovery process it is essential (1) to improve further the array CGH data analysis proces, (2) enable in silico assignment of gene function and (3) to develop novel bioinformatic data extraction tools to unravel functional interactions among genes involved in human development. By doing so SymBioSys will prioritize candidate genes to fuel and direct future biological research "

3. Part I: Array Comparative Genomic Hybridization (array CGH)

4. Array CGH Child with e.g. heart defect and learning disabilities
Sample is collected and sent to genetic center

5. Cytogenetic diagnostic
2-3% of live birth with major congenital anomaly
15-25% recognized genetic causes
8-12% environmental factors
20-25% multifactorial
40-60% unknown
15-20% of those resolved by array CGH
Importance of diagnosis
Usually limited therapeutic impact BUT
Reduce family distress
End of “diagnostic odyssey”
Estimate risk of recurrence
De novo aberration vs. familial mutation
Knowledge of disorder evolution (life planning)
Prevent complications
Future therapies (e.g., fragile-X, Rett + gene therapy)

6. Deletion del(22)(q12.2) Patient Pulmonary valve stenosis Cleft uvula
Mild dysmorphism
Mild learning difficulties
High myopia

7. Deletion del(22)(q12.2) Deletion on Chromosome 22
~0.8Mb
Deletion contains NF2
NF2  acoustic neurinomas
Benign tumor, BUT
Hard to diagnose
Severe complications

8. The challenge: identifying recurrent imbalances and disease genes

9. The imbalances are scattered across the genome

10. Genotype-phenotype correlation

11. Array CGH: from diagnosis to gene discovery
Processing of array CGH data
Databasing and mining of patient descriptions
Genotype-phenotype correlation
Candidate gene prioritization
Experimental validation of candidate genes

12. Part II: Candidate gene prioritization

13. Candidate gene prioritization
High-throughput genomics
Data analysis
Candidate
genes ?
Information sources
Candidate prioritization
Validation
Identify key genes and their function
Integration of multiple types of information

14. Prioritization by text mining
ENSG
ENSG
...
ENSG
ENSG
ENSG
If we then count the actual number of co-occurences of WHSC1 and Microcephaly, we might find a higher number than expected by chance. This overrepresentation implies a putative link between gene and concept.
Microcephaly
overrepresented in document set for WHSC1 gene

15. Prioritization by example
Several cardiac abnormalities mapped to 3p22-25
Atrioventricular septal defect
Dilated cardiomyopathy
Brugada syndrome
Candidate genes (“test set”)
3p22-25, 210 genes
Known genes (“training set”)
10-15 genes: NKX2.5, GATA4, TBX5, TBX1, JAG1, THRAP, CFC1, ZFPM2, PTPN11, SEMA3E
Congenital heart defects (CHD)
High scoring genes
ACVR2, SHOX2 - linked to heterotaxy and Turner syndrome (often associated with CHD)
Plexin-A1 - reported as essential for chick cardiac morphogenesis
Wnt5A, Wnt7A – neural crest guidance

16. Prioritization by virtual pulldown

17. Endeavour http://www.esat.kuleuven.ac.be/endeavour
Aerts et al. Nature Biotechnology

18. Prioritization by text mining in DECIPHER
A screenshot of gene prioritisation in decipher. This is a patient carrying an aberration in the Prader Willi/Angelman region.

19. Novel DiGeorge candidate
D. Lambrechts, P. Carmeliet, KUL Cardiovascular Biol.
TBX1 critical gene in typical 3Mb aberration
Atypical 2Mb deletion (58 candidates)

20. YPEL1
YPEL1 is expressed in the pharyngeal arches during arch development
YPEL1KD zebrafish embryos exhibit typical DGS-like features
DiGeorge syndrome is characterized by pharyngeal arch, cardiovascular, thymus and parathyroid defects and craniofacial anomalies
Figure 2 Expression of ypel1 in the pharyngeal arches in zebrafish embryos.
All panels show whole-mount in situ hybridization analysis of ypel1 or tbx1 expression in zebrafish embryos; the head of the embryo (anterior) is always facing left. (a) Dorsal view of the anterior half of a 3-somite stage (11 hpf) zebrafish embryo, showing ypel1 expression in the lateral plate mesoderm (arrows). (b) Lateral view of the head and trunk region of a 28 hpf zebrafish embryo, revealing ypel1 expression in the pharyngeal arch primordia (black arrow). (c-f) Dorsal view at 28 hpf, showing expression of ypel1 (c,d) and tbx1 (e,f) in the pharyngeal arches. (d) Black arrows delineate the pharyngeal arch primordium. ypel1 appears to mark neural crest cells, since these cells normally occupy the region between the arch epithelium and mesodermal core. ypel1 is also expressed in the head, ear (white arrow), and fin bud (red arrow). (f) White arrow and asterisk denote the second pharyngeal (hyoid) arch, where expression of tbx1 is restricted to the epithelium and mesodermal core. (g,h) Lateral view at 3 dpf showing ypel1 expression in the pharyngeal arches 3 to 6 (also termed aortic arches or AA3-AA6). Panels d,f,h depict an enlarged view. Scale bar: 10 µm
Figure 3 Ypel1KD zebrafish embryos exhibit typical DGS-like features.
Panels a,c,e,g,i show control embryos, while panels b,d,f,h,j show ypel1KD embryos. In all panels, the head of the embryo is facing to the left. (a,b) Lateral view of the head in live embryos at 4 dpf. The lower jaw is clearly visible in the control, whereas ypel1KD embryos show an underdeveloped lower jaw (mandibular arch). Note: lower jaw is not visible due to masking by flared gill arch tissue in ypel1KD embryos. fig. will be replaced with frontal view Pericardial edema is also apparent, most likely as a result of malformed aortic arch arteries. (c,d) Lateral view of the aortic arch arteries (AAA3-AAA6), stained by tie1 whole mount in situ hybridization, in 3 dpf zebrafish embryos: 18 of 60 ypel1KD embryos but only 1 of 41 control embryos exhibited AAA defects (P<0.05), ranging from isolated vessel narrowing to complete disorganization, individual missing AAAs as in the case of the embryo shown (missing AAA3), or underdevelopment of all AAAs. Occasionally, all AAs were missing. fig. will be replaced with more severe AAA defect (e,f) Ventral view of the pharyngeal arch cartilage using alcian blue stain at 3 dpf. In ypel1KD embryos, the jaw arches were severely malformed with the mandibular arch often reduced in size. The pharyngeal arch cartilage also showed reduced or absent staining. Of the 67 ypel1KD embryos scored, 52 exhibited craniofacial defects (P<0.05) while all 55 controls appeared normal. (g,h) Whole mount rag2 (ref ) in situ staining, revealing that the thymus was hypoplastic or even absent in 35 of the 74 ypel1KD embryos, while all 57 control embryos possessed normal rag2 thymic expression at 3 dpf (P<0.05). (i,j) Whole mount gcm2 (ref ) in situ staining of 3 dpf embryos, revealing that development of the parathyroid (gill arches - see Supplementary Note 4) was impaired in ypel1KD embryos, as evidenced by strongly reduced gcm2 staining in 25 out of 66 ypel1KD embryos, while parathyroid development was normal in all of the 41 control-injected embryos (P<0.05). Scale bar: 20 µm in panels a,b,g,h; 10 µm in panels e,f; 5 µm in panels c,d,I,j. Statistical calculations were performed using the Pearson Chi-squared test. For details on gene-specific and control morpholino oligonucleotides used, see Supplementary Figure 3 online.

21. Congenital heart disease genes
B. Thienpont, K. Devriendt, J. Vermeesch, KUL CME
60 patients without diagnosis
Congenital heart defect
& Chromosomal phenotype
2nd major congenital anomaly
Or mental retardation/special education
Or > 3 minor anomalies
Array Comparative Genomic Hybridization
1 Mb resolution
11 anomalies detected
5 deletions
2 duplications
3 complex rearrangements
1 mosaic monosomy 7

22. Candidate regions
4 regions with known critical genes, 6 new regions, 80 candidate genes
aberration
gene
del(5)(q23) ?
del(5)(q35.1)
NKX2.5
del(5)(q35.2qter)
NSD1
del(14)(q22.1q23.1)
del(22)(q12.2)
dup(22)(q11)
TBX1
dup(19)(p13.12p13.11)
del(9)(q34.3qter),dup(20)(q13.33qter)
NOTCH1, EHMT1
del(13)(q31.1q31.3),dup(13)(q31.3q33.2),inv(13)
del(4)(q34.3q35.1),dup(4)(q34),inv(4)
In the remaining regions no genes are known that cause CHD. Now, how can we go from these questionmarks to pinpointing the gene causing CHDs? Since these regions contain in between 20 and 200 genes, going over all of them individually is not very efficient. Therefore we chose to do an automatic gene prioritisation, using a tool called “endeavour”. How does this work? I shall illustrate this for the del14q.

23. Cis-regulatory module
del(14)(q22.1q23.1) ?
Gene prioritization
Expression data
KEGG pathways
Protein domains
Cis-regulatory module
BLAST
Protein interactions
Pubmed textmining
BMP4
1.CNIH
DACT1
BMP4
RTN1
KIAA1344
EXOC5 2.
DAAM1
PTGER2
DLG7
OTX2
3. KIAA1344
PTGDR
ARID4A
WDHD1
4. CGRRF1
SOCS4
KIAA0586
CDKN3
TIMM9
5. DDHD1
STYX
PSMA3
SAMD4
ERO1L
KTN1
6. ACTR10
PSMC6
7. CDKN3
FBXO34
8. RTN1
GNPNAT1
9. FBXO34
TBPL2
10.
CNIH
11. PLEKHC1
GCH1
12.
DDHD1
PLEKHC1
13.
14. BMP4
15. GCH1
GMFB
16. KTN1
GPR135 …
ACTR10
80.
Haploinsufficiency of some

24. Biological validation
Candidates currently being validated in zebrafish
Screen about 50 candidates for heart expression at different developmental stages
Morpholino knockdowns of candidates expressed in hearts
Screen for heart phenotypes

25. Array CGH: from diagnosis to gene discovery
Patients with congenital & acquired disorders
Location of chromosomal imbalances
CGH microarrays Molecular karyotyping
Statistical analysis
Map chromosomal abnormalities
Improved diagnosis
Discover new disease causing genes and explain their function
Prioritized candidate genes
Validation
Databasing
"The Center of Human Genetics is involved in the development of array-CGH and among the first to clinically implement this novel technology which enables the identification of chromosomal imbalances. This technology has sped up the discovery of chromosomal anomalies causal for human developmental and acquired disorders. The identification of chromosomal
imbalances pinpoints the location of disease causing genes. To stay at the
forefront of unraveling the molecular causes of these disorders and stay at the forefront of the gene discovery process it is essential (1) to improve further the array CGH data analysis proces, (2) enable in silico assignment of gene function and (3) to develop novel bioinformatic data extraction tools to unravel functional interactions among genes involved in human development. By doing so SymBioSys will prioritize candidate genes to fuel and direct future biological research "

26. Some achievements Publications Guidelines for array CGH
Aerts S et al. Gene prioritization through genomic data fusion. Nat Biotechnol May;24(5):
Balikova I et al., Autosomal dominant microtia linked to five tandem copies of copy number variable region at Chromosome 4p16. Am J Hum Genet in press.
Lage K et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol Mar;25(3):
Guidelines for array CGH
Vermeesch J et al. Guidelines for molecular karyotyping in constitutional genetic diagnosis. Eur J Med Genet Nov;15(11):
Strategic Basic Research (SBO) project
Molecular karyotyping
K.U.Leuven, U.Gent, VUB
€2,800,000 (4 years)
Development of new applications of array CGH technology
FP7 proposal on bioinformatics for congenital heart defects
Visibility
European Cytogenetics association – molecular karyotyping workgroup
INSERM workshop array CGH (La Londe les Maures, FR, Sep 07)
Numerous keynote lectures
Contacts with all major array CGH companies

27. Salmonella sys. biology
Partners involved
Joris Vermeesch
array CGH technology
Koen Devriendt
congenital heart defects
Hilde Van Esch
mental retardation
Thierry Voet
- array CGH technology
Femke Hannes
genotype-phenotype correlation
Bernard Thienpont
CHD disease genes
Jeroen Breckpot
Irina Balikova
eye defects
Liesbeth Backx
mental retardation genes
Boyan Dimitrov
skeletal disorders
An Crepel
microcephaly & autism
Caroline Robberechts
fertility
Evelyne Vanneste
- single cell array CGH
Yves Moreau
gene prioritization
Roland Barriot
knowledge mining
Francesca Martella
array CGH statistics
Sonia Leach
gene networks
Steven Van Vooren
text mining
Bert Coessens
- array CGH data mgt.
Leo Tranchevent
Endeavour
Yu Shi
prioritization algorithms
Daniela Nitsch
- prioritization algorithms
Peter Konings
statistical genetics CNVs
Gene prioritization
Module discovery
Network inference
Human genetics
Endocrinology
Salmonella sys. biology
Probabilistic models
CME-UZ
ESAT-SCD
BioStat
Legendo
ESAT-SCD

28. Challenges ahead From genes to networks The $1000 genome
Data big bang
Phenotypic genome annotation by data fusion
Sanger sequencing
Next-gen sequencing