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Transcriptome Profiling of Human Cardiac Tissues in Hypoplastic Left Heart Syndrome Karl D. Stamm, MS Donna K. Mahnke, MS; Mary A. Goetsch, MS; D. Woodrow.

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Presentation on theme: "Transcriptome Profiling of Human Cardiac Tissues in Hypoplastic Left Heart Syndrome Karl D. Stamm, MS Donna K. Mahnke, MS; Mary A. Goetsch, MS; D. Woodrow."— Presentation transcript:

1 Transcriptome Profiling of Human Cardiac Tissues in Hypoplastic Left Heart Syndrome Karl D. Stamm, MS Donna K. Mahnke, MS; Mary A. Goetsch, MS; D. Woodrow Benson, MD, PhD; Xing Li, PhD; Aoy Tomita-Mitchell, PhD; Timothy J. Nelson, MD, PhD; James S. Tweddell, MD; Michael E. Mitchell, MD September 2013 Research Update

2 Overview Medical Research Trouble with humans Rare diseases are common in a large enough population Next-Generation Sequencing Tech Illumina HiSeq methodology Differential expression Further Mining Principle components analyses Gene profiles and the self-organizing-map

3 Trouble with Humans Small sample sizes Low statistical power High interpersonal variability Ethnic backgrounds imply metabolic differences Phenocopy Multiple distinct diseases showing identical presentation Confounds clustering or association studies Ruins Case/Control study power PHI – Private/Protected Health Information Data security is paramount Cross-disciplinary collaborations are limited DNA is theoretically but not practically identifiable

4 Congenital Heart Defect Incidence Down Syndrome 1:700 live births 50-60% have some structural heart defect 22qD Syndrome 1:4000 live births 75-90% have some structural heart defect ‘Healthy’ 99:100 live births 0.8% have some structural heart defect Proportion Explained:

5 C.H.D. in particular Hypoplastic Left Heart Syndrome All sequencing costs for this study provided by 1 in 40 CHD cases are HLHS 2.5 : of all births Complex developmental disorder 100% fatal before the invention of the Norwood Procedure 1981 No multigenerational pedigrees Spontaneous mutation: immune to detection by genetic linkage

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7 Generate Reads – Illumina Tech 10 to 500 million short reads are generated in pairs, 2x50 to 2x100 bp each.

8 Align Reads to Reference Which one? NCBI #37.3 has 3.1 billion bases across 190 contiguous scaffolds UCSC hg19 has 3.2 billion bases across 163 contiguous scaffolds Haploid reference contains disease alleles and chimeric sequence like an A+B+O blood type. Image of patches modifying the CHR17 reference from 2011 according to Ensembl

9 Millions of Variants The 1000 Genomes project found 38 million SNPs, 1.4 million short insertions or deletions, and more than 14 thousand larger deletions The NHLBI Exome Sequencing Project targeted 22MBases across 2,440 individuals and found 563,700 variants, 82% of which were novel. They averaged 200 novel, coding mutations per person. We find about thousand SNVs in an exome, 10% of which are nonsynonymous SAMTOOLS is the software of choice for variant calling relative to your reference genome.

10 CCG/Proline -> CTG/Leucine HOPX is a gene known to regulate heart development!

11 Very common mutation

12 RNA-Seq vs. Whole Genome 1.Extract and purify mRNA by polyadenylation 2.Convert spliced mRNA to DNA fragments 3.Run standard genome sequencing on the product 4.Result: Expression level dependent sequence coverage Image found at

13 RNA-Seq Reconstructs Transcripts From the CuffLinks paper, Trapnell et al. /n5/abs/nbt.1621.html Nature Biotechnology Volume: 28, Pages: 511–515 Year published: (2010)

14 IGV – aligned reads viewer

15 CoverageBED BEDTOOLS : a flexible suite of utilities for comparing genomic features. Simple arbitrary feature read depth counting. -Count by gene, exon, whatever

16 Example of bad alignment

17 Variance and mean linked by local regression - for robust parameter estimation.

18 Negative Binomial Models count as ‘binomial successes until a set number of failures’ which better fits the RNA-Seq fragment generation (limited reagent) Allows/captures the ‘overdispersion’ seen in RNA-Seq experiments.

19 Scale the totals for compatible means

20 Mean-Variance Connection

21 Detection in Low Values

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26 Per-gene mean by difference ratio

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28 DESeq Starting from 18,000 Rsids minus 1200 NA 1000 entries p<0.05

29 Theme Big lists Noisy data Complex correlation Heterogeneous background

30 Precious Tissue Samples Collecting tissue during surgery is an extra burden placed on overloaded surgical teams. Samples must be processed carefully to avoid degradation of sensitive molecules. Many steps and costs prior to gene sequencing. Collaborators have provided 35 patients’ atrial septal tissues. Still no ethical source of healthy control.

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32 Hope to see separation between red/notred or solid/notsolid points Lack of discrimination in major variation dimensions Implying uncontrolled heterogeneity dominates Therefore, more difference person to person than between subtypes

33 Top25 Consistent Genes Anyone know what it means when Adducin2 and HomeoboxA4 are overexpressed? Is it significant that a dehydrogenase is under-expressed?

34 Group Profiles at Selected Dimensions

35 Self-Organizing Map Kohonen 1990 Halfway between neural networks and k-means (horrible oversimplification) Enforced grid layout and local neighborhood similarity Data points (here 25-dimensional vectors) lay out in natural organization

36 Stochastic - Iteration

37 Pairwise Similarity Co-clustering frequency determines sample similarity Sub-clusters are identified organically

38 Results Lists of genes differential across conditions Many conditions, uncertain homogeneity List cutoff subjective No healthy control group We can mine these lists for pathways or biological processes Resulting in more lists of more complex results

39 Transcriptome Project Future Work A few more samples are coming… Can we build a classifier? Predict non-measured variables? Signatures of immune response point towards treatment targets. Predict compensatory effects? Samples are taken just days after birth, but 8 months after the heart started beating. How else we could look at this rich, unique dataset?

40 Thanks for listening


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