WCGALP talk, August 18th, 2014.

1. Exploring Marek’s Disease
Resistance with RNAseq
C. Titus Brown
Michigan State University

2. Genetic resistance to Marek’s Disease
• MHC (B) locus has a major influence on MD
resistance
• Several haplotypes of B locus have been found to
correlate with resistance
– B21 most resistance
– B19 susceptibility
• Lines 6 and 7 (ADOL*) are B2 homozygous, but
line 6 is resistant and line 7 is susceptible to MD
• Relatively few non-MHC genes have been
identified
*Avian disease and Oncology Laboratory, East Lansing

3. Research Goal
• Identify non-MHC genes influencing MD
resistance from a genome-wide gene and
isoform expression analysis based on RNA-Seq
data
• Generate hypotheses for studying the
mechanism controlling MD resistance
Collaboration with Hans Cheng (ADOL) and Jerry Dodgson (MSU)
Dr. Likit Preeyanon

4. Research Plan
GCCGCGGTTCCGTGGTT
ACCGCGGTGGTGGTT
ACCGCGTTTGTGGTT
ACCGCGGTGGTGGTT
ACCGCGGTCCGTGGCC
CCCGCGGTGGTGGTT
Differential Gene Expression
Pathway Analysis
A B C D
B CA D
Differential Exon Usage
Lines 6 and 7
Control and infected (4 dpi)
Single-end
and Paired-end
Illumina Sequencing
Dr. Likit Preeyanon

5. RNA-Seq Method
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Fragmented and sequenced
Short reads (<200bp)
Adapted from Shirley et al Nat Methods 2009

6. Gene models and isoforms are woefully incomplete –
e.g. ENSEMBL missing many exon-exon junctions.
De novo reconstruction
Ab initio reconstruction
Dr. Likit Preeyanon

7. GIMME: Software for Merging Gene Models
Assembly-
based
Local
Assembly
GIMME
Reference-
guided
Merged
Models
In-house software
Dr. Likit Preeyanon
Dr. Likit Preeyanon

8. Merged Gene Models
Global Assembly
Local Assembly
Reference-guided
Merged (consensus) Model
Newly predicted isoform

9. Merged models connect fragmented gene models & provide new
isoforms
Merged models can glue fragmented gene models and
include unannotated isoforms.
Gene B
Gene A
Gene A
Reference-guided
Merged model

10. IDH3A Gene – now with both UTRs!
Merged
RefSeq
ENSEMBL
UTR

11. IDH3A– different models, different predicted
expression…
SE : single-end, PE: paired-end
Not signif..
Signif

12. Differentially Expressed Genes from Different Gene Model Sets
…Differ.
DE genes by EBseq FDR < 0.05
Ref-guided
Ref-guided

13. In addition, many of the diff expr genes are not
annotated in KEGG
Ref-guided

14. GOseq FDR 0.05
Chicken + Human
KEGG Pathway
40 pathways
Must merge in
human KEGG
annotations

16. Enriched KEGG Pathways by GOSeq
GOseq FDR < 0.05

17. Biological Processes (BP) categories involved in Adaptive
Immune Responses are Enriched in Line 7 (susceptible)
GO ID Description Adjusted p-value
0009615 Response to virus 0.00023
0050670 Regulation of lymphocyte
proliferation
0.00048
0002252 Immune effector process 0.00068
0051249 Regulation of lymphocyte
activation
0.0027
0042129 Regulation of T cell
proliferation
0.0032
0002250 Adaptive immune response 0.0106
At early stage of infection, elicitation of the adaptive immune responses
appears to be delayed in line 6.

18. Isoform Expression Estimation
Gene Expression = 400x
20%
80%
Gene Expression = 405x
2%
98%
Sample A
Sample B

19. How to Estimate Isoform Expression
Spliced reads

20. Differential Exon Usage of ITGB2 Gene from MISO
Spliced reads
Percent Spliced In (Ψ)
Read coverage

21. Genes with predicted differential splicing can be
categorized into four groups
Cutoff = 0.2
6 Ctrl
6 Inf
7 Ctrl
7 Inf
1
1
1
1
0
0
0
0
Group I
11 Genes
ψ
1
1
1
1
0
0
0
0
Group II
19 Genes
ψ
1
1
1
1
0
0
0
0
Group III
20 Genes
ψ
0 1
0 1
0 1
0 1
Group IV
1 Genes
ψ

22. The main point
• We are completely at the mercy of
annotations to interpret our large-scale data.
• Need more experimental information!
• But also, better methods => better signal

23. Concluding thoughts (I)
• Computational analysis of high-throughput
sequencing data can help refine hypotheses,
but cannot conclusively resolve mechanism.
• Don’t knock “refining hypotheses”, though!
Complex biological phenomena like disease
are refractory to simplifying assumptions.

24. Concluding thoughts (II)
• Much of the -omic data being gathered by all of
you has utility far beyond your specific research
question.
• This is particularly true in “semi-model”
organisms where annotations are generally poor
and not species-specific, and where there may be
significant intra-species variation.
• How can we better share this data, to make faster
and better progress?

25. Where should we spend our –omics
money?
• Improving genomes is still expensive and
requires significant technical expertise.
• mRNAseq is inexpensive, broadly useful and
wonderful for building better gene models.
• Proteomics and metabolomics?
• Better tools, annotation, and data sharing and
exploration portals are critically important to
the future of (agricultural genomics.
Thanks!