Genomics is increasingly being used in clinical practice to inform diagnosis and treatment. The document discusses several examples where genomics has identified disease-causing genes and mutations, leading to new treatments. It also describes methods for genome sequencing and variant calling. The author's studies on epilepsy found variants in genes related to brain function in drug-resistant epilepsy patients. A large number of variants were in the 3' UTR, suggesting expression level variations in these genes may cause epilepsy.

1. Genomics and clinical practice
M. V. Hosur
National Institute of Advanced Studies
Bangalore & CDAC-Mumbai.

2. Outline of the Talk
1. Examples of genomics use in medicine.
2. Methodology of sequencing and variant calling.
3. Results of our studies on epilepsy.

3. Examples of Genomics in Medicine

4. Genomics and clinical practice
Genome sequence, a new medical toolset.
Used in two modes:
1. disease-specific genomic medicine
Focus is on known disease-associated genes. The interrogation includes
rigorous evaluation of novel variants that may have little or no prior
exposure in the scientific literature or in available databases.
2. general genomic medicine
It is a screen similar to other screens. (newborn screening for metabolic
disorders) and physical adult screening (breast, cervical, and colorectal
cancer).
general genomic medicine requires higher standards of certainty and
clinical significance to avoid false-positives.

5. Examples of Genomics in
Medicine: Intellectual disability
 The causes of intellectual disability are often
unknown, but a team in The Netherlands has
used diagnostic exome sequencing of 100
affected individuals and their unaffected parents
in order to uncover novel candidate genes and
mutations that cause severe intellectual
disability. (NEJM, 2012).
 Based on this study a ketogenic diet was
recommended for patients with a mutation
in PDHA1.


6. Examples of Genomics in
Medicine: Cancer, Cystic Fibrosis and
Epilepsy.
 Cancer-Colorectal cancer patients with a particular
mutation in the PIK3CA gene are found to benefit from
treatment with aspirin post-diagnosis. (NEJM, 2012).
 CF- approximately four percent of Cystic fibrosis carry
G551D mutation in the CTFR gene. Now a drug called
ivacaftor has been developed that is extraordinarily
effective [nejm.org] for such patients.
 Epilepsy- In a specific antiepileptic treatment, sodium-
channel blockers were replaced when SCN1A mutation
was discovered. This therapy leads to better seizure
control and improvement in cognitive functioning and
quality of life in patients with SCN1A mutations.

7. Examples of Genomics in
Medicine: Early Detection of Cancer
 Cell-free circulating DNA is also being explored
as a biomarker for cancers. As tumor cells die
they release fragments of their mutated DNA
into the bloodstream. Sequencing this DNA can
give insights into the tumor and possible
treatments, and even be used to monitor tumor
progression (as an alternative to invasive
biopsies). Sci Transl Med, 2014.

8. Examples of Genomics in
Medicine: Screening
 More than 3500 monogenic diseases have been
characterized.
 Unbiased diagnostic approaches such as exome
sequencing may also reveal clinically relevant mutations
that are not related to the disease under investigation.
 Currently, every baby born in the United States is tested
at birth for between 29 and 50 severe, inherited, treatable
genetic diseases through a public health program called
new-born screening.
 Rapid whole genome sequencing has been shown to
provide a useful differential diagnosis within 50 hours for
children in the neonatal intensive care unit. (Science,
2012).
 .

9. Examples of Genomics in
Medicine: Correcting mis-diagnoses
Whole genome sequencing or whole exome sequencing has been used to
help doctors diagnose-and in some extraordinary cases to identify
available treatments-in rare disease cases.
For example, Alexis and Noah Beery, a pair of Californian twins, were
misdiagnosed with cerebral palsy, but DNA sequencing pointed to a new
diagnosis, as well as a treatment, to which both children responded well.
Another patient who was misdiagnosed (for 30 years) with cerebral palsy
was also found to have a treatable dopa-responsive dystonia thanks to
whole exome sequencing.
In another case, a young boy in Wisconsin, Nic Volker, was able to be
cured of an extreme form of inflammatory bowel disease after his genome
sequence revealed that a bone marrow transplant would likely be life-
saving.

10. Examples of Genomics in Medicine:
identify pathogen
DNA sequencing is being used to investigate:
infectious disease outbreaks, including Ebola
virus, drug-resistant strains of Staphylococcus
aureas and Klebsiella pneumoniae,
food poisoning following contamination with
Escherichia coli.
infection by bacterial meningoencephalitis.
Knowing the correct pathogen helps in rapidly
identifying the correct therapeutic agent for the
patient.

11. Examples of Genomics in
Medicine: Pharmacogenomics
 Pharmacogenomics involves using an individual's
genome to decide:
 whether or not a particular therapy, or
 effective dose of therapy.
 Currently, more than 100 FDA-approved drugs (in
diverse fields such as analgesics, antivirals,
cardiovascular drugs, and anti-cancer therapeutics)
[fda.gov] have pharmacogenomics information in
their labels.

12. The method
Genomic DNA – string of characters: directionality
5’……ATGCGTAC…….3’
Determining the sequence of these characters is
‘genome sequencing’.
Done either by cleaving one residue at a time
or
By synthesising one residue at a time on the
complementary strand.

13. Methods of sequencing
1.Maxam-Gilbert sequencing, 1970’s (Specific DNA
cleavage, end-labelling and electrophoresis)
2.Sanger sequencing 1970 - 80’s (Chain terminator
nucleotide, Electrophoresis, staining and ladder
readout)
3.Sequencing by Synthesis (SBS technology,
Nextgen sequencing – Chain extension,
Fluorescently labeled nucleotides, colour
reading, Bioinformatics)

14. Next – Gen sequencing: cost reduction
and speed up
Human genome
project started 1990.
Involved hundreds of
researchers around
the world, took 12
years, cost $3 billion.
NGS gives few
human genome
sequences in a week.

15. NGS: Parallel Processing
Because of random hydrolysis of mRNA
there will be multiple reads for any given
nucleotide position – Depth of Read (DP)
Alignment of fragments a very challenging
computer science problem

16. Pipeline for Analysis of
transcriptome
QUAL MQQUAL
Phred Score = -10 log 10 (P)
P being the probability of incorrect
call or mapping.

17. Variant types
Higher DEPTH = Higher confidence in
called variant. Minimum DEPTH should be
prescribed for variant calling.
Structural
variants

18. Application to Epilepsy

19. Epilepsy – a challenging disorder
GENES PHENOTYPES
Challenging mainly because of
multiple mapping between genes
and phenotypes.
Whole Exome and Whole Genome
sequencing using microarray or NGS
technologies is likely to give
important insights.
Large numbers of phenotypes:
Generalised epilepsy
Focal epilepsy
Many sub-categories in each
type. Many lines of evidence indicate genetics – epilepsy linkage.

20. Isolate Tumour patient (C1)
Age 30
biomaterial provider Dr. P. Sarat Chandra
Sex female
Tissue brain tissue, SRR1957110
Isolate MTLE patient (E3)
age 30
biomaterial provider Dr. P. Sarat Chandra
sex male
tissue hippocampus (Brain tissue) SRR1956833
Isolate MTLE patients (E2)
age 25
biomaterial provider Dr. P. Sarat Chandra
sex male
tissue hippocampus (Brain tissue) SRR1956809
RNA-seq Patient details
Patients E2
& E3
resistant to
AED’s
Levi… and
Carba…

21. RNA-Seq Data (Transcriptome or
Gene expression)
 Two patients resistant to anti-epilleptic drugs
 Patient 1 – SRR1956809, Patient 2 –
SRR1956833, Control - SRR1957110
SRA Data Details
Parameter Value
Data volume, Gbases 11
Data volume, Mbytes 6589
Centre of Excellence for Epilepsy, National Brain Research Centre, New Delhi.

22. Epilepsy Research : (NIAS, C-DAC)
 Two aims
 (i) Molecular modelling and genome comparison to
understand Resistance to Anti-Epileptic Drugs, and
 (ii) Identification of Novel Drug Targets using Tools
of Data Science.

23. SNP Variants – Transitions and
Transversions
Transitions out-number Transversions, as expected.
Preferred changes are C to T and G to A.
Here we see the opposite.
But still substantial number of Transversions.
Potential serious consequences.

24. SNP classes – Novel or Observed
 Each class analysed in two different ways:
 1. Position of SNP, regulatory or coding
 2. Predicted effect on gene functionality.





25. Novel SNP's – positional analysis
 1090 – regulatory region
 4 – protein-coding region
 – 3/3 transcripts for gene
 NOTCH2NL: protein length 236 aa,
Interacts with 244 proteins!
• mutation 158 T/I. Also involved in
Ca2+binding.



T158I
At the edge of a
beta ribbon

26. Novel SNP- positioned in ARHGAP21
Rho GTPase activating protein 21
protein length - 1959 amino acids.
Ubiqutous expression in brain.
26/26 exons for gene ARHGAP21:
mutation 1950 S/T, G/C Transversion
Downstream-gene variant in another transcript.
No structural Model available.

27. Novel variants: HIGH Impact-analysis
PKN1 – Splice-donor variant.
PKN1 (Protein Kinase N1). Multifunctional and
has PPIs with about 15 binding partners.
Diseases associated with PKN1 include
Paraneoplastic Cerebellar Degeneration. Have to
investigate the role in this disease.
PKN1 sequencing to confirm?

28. Novel variants: HIGH Impact-genes
RAPGEF4 – Frame Shift Variant.
Is also called Epac2. Big protein (1011 aacids)
and is expressed mainly in brain.
In neurons, Epac is involved in neurotransmitter
release in glutamatergic synapses.
As a binding partner with Rap, it regulates
intracellular Ca2+ dynamics.
In brain, down-regulation of Epac2 mRNA is
observed in patients with Alzheimer’s disease. An
Epac2 rare coding variant is found in patients with
autism.

29. Existing Variants : Gene distribution
 Number of genes – 76
 Number of genes associated with diseases – 34
 Number of mutant genes associated with epilepsy – 10/24
(http://www.sbg.bio.ic.ac.uk/)
 (ALDH7A1, ASAH1, CST3, GABRA1, GRIN2B, KCNG2,
KCTD15, LGI4, NHLRC2, SCN2A)
 Two variants are mis_sense variants (ASAH1, CST3)
 Others are majorly in 3'-UTR region.(microRNA binding
sites are here. MicroRNAs control neurodegenerative
disorders like Alzheimer's disease, Parkinson's disease by
influencing APP production.)

30. Homology model ASAH1
Residue range: 38 to
155
Based on template:
2nvvA
Sequence identity: 28%
(ACETYL-COA
HYDROLASE)
Resolution: 2.70 (X-RAY)
Model creation date: 2016-
10-13
Variant
D124E
124 D – 145 S = 3.0 A. (D and S both on helices).
This hydrogen bond will be lost and there will be steric clash with D124E
substitution.
Conformation of ASAH1 will change affecting protein – protein interactions
drastically.
 Acid ceramidase is a lysosomal enzyme.

31. SNP’s in regulatory region- Mechanism of
Gene expression variation in MTLE
 Expressions of about 56 genes are found
to be significantly altered in drug-resistant
MTLE patients (A.B. Dixit et al. / Genomics 107 (2016) 178–
188). Regulation Number of Genes
Reported
Number of 3'-UTR
variant genes
found in the
present study
Up regulation 34 18
Down regulation 22 12
Are these genes being controlled by miRNAs? The ability of
miRNAs to regulate multiple genes within a molecular pathway
makes them excellent candidates for novel molecular targeting for
treatment.

32. Summary
 Genome sequencing has demonstrated clinical utility in
diagnosis and treatment of certain cancers and rare
diseases. Shows promise for use in infectious disease
outbreaks and fetal diagnosis in prenatal medicine.
 WGS can capture lot of information in a single clinical test for
an individual. These are intended to inform clinicians in
recommending treatments and lifestyle changes.
 In the DRE Epilepsy patients analysed, variants in many genes
related to brain function are identified: e.g. PKN1 and RAPGEF4.
 A large number of variants are located in 3'-UTR suggesting
expression level variations in these genes as causes of epilepsy.

33. Thank you