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Open Source Pharma /Genomics and clinical practice / Prof Hosur

  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 [] 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) [] 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 (  (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