RNA-Seq guided gene therapy for
vision loss
Michael H. Farkas
4
The retina is a complex tissue
• Many cell types
• Neural retina vs. RPE
• Each highly dependent on the other
Graw, Nature Reviews Genetics, 2003
Inherited Retinal Degenerations
• 212+ disease genes identified to date
• Genetically and clinically diverse
(Modified from Berger, et al 2010)
Retinitis Pigmentosa (RP)
• RP is the most common form of inherited blindness
– Affects 1:1000-4000 individuals worldwide
• Patients typically present in early adulthood with progressive night blindness and loss of peripheral vision
– Complete vision loss occurs later in life
• Currently, nearly 50 genes implicated in non-syndromic RP
Many expressed specifically in the retina
mRNA Splicing and RP
•6 spliceosome-associated genes identified as important causes of dominant RP:
-PRPF3, PRPF6, PRPF8, PRPF31, RP9, SNRNP200
• All function in the U4/U6/U5 tri-snRNP
RNA Splicing Factor RP
• How do mutations in spliceosome components cause retina-specific disease?
– Hypotheses:
1. via production of specific altered transcripts that are pathogenic in the retina
2. via global alterations in splicing that affect the retina most
• Goals:
– Test these hypotheses in gene targeted Prpf3, Prpf8 and Prpf31 mice, and human RPE cells
– Develop therapeutic strategies for these common forms of RP
RPE and Retinal Degeneration in Prpf Mutant Mice
(Graziotto, et al 2011)
Defective Function of Prpf Mutant RPE
(Farkas, et al AJP, 2014)
RNA-Seq Library Preparation
• RPE is a single cell layer
– Tissue/RNA is limited in
mice
– Have nanogram levels of
total RNA to start
– Traditional protocols use
microgram levels of total
RNA
• Protocol is optimized for
200 ng of total RNA, but
can be adjusted to use less
RNA-Seq Unified Mapper (RUM)
Grant, et al., Bioinformatics, 2011
• Alignment of RNA-Seq reads was problematic when RNA-Seq was first developed
– Requires accurately mapping reads over splice junctions that can span hundreds of kilobases
– Many algorithms have since been developed
• RUM is one of the most accurate in mapping reads
– Provides data for exons, introns,
splice junctions, and transcripts
– Very well suited for detecting novel
splicing variants • novel isoforms and novel genes
Aberrant Splicing - RPE
• How many aberrant transcripts are detected in the mutant
RPE transcriptomes?
• Prpf3 – 179
• Prpf8 – 44
• Prpf31 – 24
• No aberrant transcripts are shared amongst the models,
further suggesting that disease pathogenesis is caused by
different mechanisms.
• 68 – 211 aberrant transcripts are detected in the retina,
brain, and muscle samples, suggesting aberrant splicing is
widespread.
Altered Splicing - Rgr
Altered Splicing - Rgr
Characterization of the Human and Mouse Retinal Transcriptome
• RNA-Seq libraries prepared from human and mouse retina, and mouse
RPE and ARPE-19 cells.
• Approximately 80% of all annotated exons are expressed in the retina.
• An additional 3.5 Mb of novel features were found to be expressed.
• Large number of novel splicing events detected by RUM:
• Human retina – 79,915
• Mouse retina – 47,078
• Mouse RPE – 34,061
• ARPE-19 cells – 32,178
Farkas, et al., BMC Genomics, 2013
10 40 70 100
130
160
190
220
250
280
0
10000
20000
30000
Reads (Millions)
# o
f No
ve
l Fe
atu
res
Novel Exons
Exon Skipping
Alternate 3'/5' Splice Site
Novel Exons in the Retina
Nov
el >
10x
Nov
el 5
-10x
Nov
el 2
-5x
Nov
el =
Ann
otat
ed
Annot
ated
2-5
x
Ann
otat
ed 5
-10x
Annot
ated
> 1
0x0
5
1060
75
% o
f To
tal J
un
ctio
ns
Ratio of Novel to Annotated Junctions
• Nearly 30,000 novel exons identified
in the human retina.
• ~5% of the novel exons compose the
major isoform.
• 14% are predicted to maintain an
open reading frame (ORF).
Farkas, et al., BMC Genomics, 2013
Novel Exon Skipping
• Exon skipping is the most prevalent
alternative splicing event.
• 82% of the novel exon skipping
events skip one exon
• 17% are predicted to maintain the
ORF
Novel Alternate Splice Sites
• Nearly 19,000 novel alternate splice
sites identified.
• ~25% are predicted to maintain an
open reading frame (ORF).
• Conclusion – current transcriptome
annotations are under-represented
and comprehensive annotation can
be important for finding mutations
• Novel exons have been found to harbor
mutations in IRD genes (ABCA4)
What do transcriptome analyses tell us?
• Large number of novel splicing events in neural retinal transcriptomes – Human retina – 79,915
• 19,637 novel internal exons • Including 206 in 99 of the known IRD disease genes • Data are available via OGI website
– Mouse retina – 47,078
• Large number of novel splicing events in the RPE – Mouse RPE – 34,061 – Human ARPE19 cells – 32,178
• 15,000 novel events were chosen from RNA-Seq data
– Exon skipping, novel exons, alternate 3’/5’ splice sites.
• Read depth as low as 1
• Baits developed using Agilent’s SureSelect Targeted RNA Capture System.
• Sequenced on a HiSeq2000
Large Scale Validation of Novel Transcriptome Features
• Evaluated 15,000 novel splicing events by targeted RNA capture
• 99% of the novel events were validated in retinal samples
• 71%, 61%, and 58% events detected in brain, liver, and muscle
• Nearly 2,000 novel splicing events may be restricted to the retina
• Data are available via OGI website
(Farkas et al BMC Genomics 2013)
PRPF31-associated RP • Mutations in PRPF31 are the second most common cause
of adRP
– 50,000-190,000 affected people worldwide
• Pathogenesis of disease due to haploinsufficiency
– Mutations can be nonpenetrant
(Rivolta et al, Human Mutation, 2006)
• Family with adRP
underwent whole exome
sequencing.
• PRPF31 c.-9+1G>C
variant identified as top
candidate for potential
pathogenicity.
– First base of donor site in
intron 1.
Functional validation of PRPF31 variants
X X
Control 330 6230.0
0.5
1.0
Re
lative
Expre
ssio
n
**
*p < 0.05
Functional validation of PRPF31 variant
330 623
A* B
C D
Functional validation of PRPF31 variant
330 623
A* B
C D
A*
B
C
D
• No splice variants produced premature
stop codons.
• Additional studies, including knock-in
and patient iPS cells, are in progress to
study mRNA stability and affect on
phagocytosis.
Functional validation of PRPF31 variants
CRISPR/Cas9 Knockout of PRPF31
• Guide RNAs (gRNA) designed to exon 6 of PRPF31
• iPS cells and ARPE-19 co-transfected with gRNA and Cas9:GFP
• GFP positive cells were single cell sorted into a 96-well plate
• Sanger validated:
• 25% heterozygous sites undergoing non-homologous end joining, which
included 1-13 bp deletions
• No homozygous knockout clones observed – consistent with
haploinsufficiency mechanism
Decreased PRPF31 Expression Levels
Re
lative
PR
PF
31
Exp
ressio
n
Con
trol
GE31
-1
GE31
-2
GE31
-3
GE31
-6
GE31
-70.0
0.5
1.0
1.5
** ****
**
*P < 0.05**P < 0.01
• qPCR primers designed to 3 locations in PRPF31
• γ-tubulin 1 (TUBG1) used as a control due to its similar
expression level in ARPE-19 cells
• 5 lines chosen for analysis
Functional Characterization - Phagocytosis
• Each line plated in triplicate wells of a 48 well plate.
• Assayed 3 weeks after cells reached confluence to ensure
polarization.
• FITC-labeled photoreceptor outer segments phagocytosed
by ARPE-19 were counted using flow cytometry.
Con
trol
GE31
-1
GE31
-2
GE31
-3
GE31
-6
GE31
-7
0
50
100
Rela
tive P
hagocyto
sis
Functional Characterization - Phagocytosis
Incubation Time
Re
lativ
e P
OS
Up
take
1 Hou
r
2 Hou
r
3 Hou
r0
10
20
30
40Control
GE31-1
GE31-2
GE31-3
GE31-6
GE31-7
• While POS uptake begins to reach a maximum after 3
hours, the knockout lines seem to be increasing in the rate
of uptake.
• Is phagocytosis slow, rather than non-existent in PRPF31
models?
AAV-PRPF31 Gene Augmentation
WT
GE31
0.0
0.5
1.0
Re
lative
PO
S U
pta
ke No AAV-PRPF31
MOI = 10,000
MOI = 15,000*
Massachusetts Eye and Ear Pierce Lab Dr. Libby Au Dr. Kinga Bujakowska Dr. Donna Garland Dr. Chari Fernandez Godino Matt Maher Daniel Navarro Dr. Eric Pierce Emily Place Maria Sousa Dr. Scott Greenwald Dr. Joe White Dr. Qi Zhang
Liu Lab Mihoko Leon Dr. Qin Liu
Comander Lab Dr. Jason Comander Ally Lansdorf
Acknowledgements Harvard Stem Cell Institute Cowan Lab Dr. Chad Cowan Dr. Derek Peters Jen Shay Johns Hopkins University
Zack Lab Dr. Melissa Liu Dr. Tomo Masuda Dr. Don Zack
Qian Lab Dr. Jiang Qian Dr. Jun Wan University of Wisconsin-Madison
Gamm Lab Dr. Beth Capowski Dr. David Gamm Anna Petelinsek Jishnu Saha
Institut de la Vision Dr. Shomi Bhattacharya Dr. Emeline Nandrot UCLA Dr. Xiaowu Gai University of Pennsylvania Dr. Greg Grant Funding NEI NRSA Foundation Fighting Blindness MEEI
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