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Genetics and the environment: evaluating the role of noncoding RNA in autism spectrum disorder
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Genetics and the environment: evaluating the role of noncoding RNA in autism spectrum disorder
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Content
i
GENETICS
AND
THE
ENVIRONMENT:
EVALUATING
THE
ROLE
OF
NONCODING
RNA
IN
AUTISM
SPECTRUM
DISORDER
By
Jessica
Jolynn
DeWitt
______________________________________________________________________________
A
Dissertation
Presented
to
the
FACULTY
OF
THE
USC
GRADUATE
SCHOOL
UNIVERSITY
OF
SOUTHERN
CALIFORNIA
In
Partial
Fulfillment
of
the
Requirement
for
the
Degree
DOCTOR
OF
PHILOSOPHY
(DEVELOPMENT,
STEM
CELL,
AND
REGENERATIVE
MEDICINE)
August
2017
ii
DEDICATION
For
my
sister,
who
was
always
so
proud
of
me
and
my
work
and
whose
impact
on
my
life
sparked
my
interest
in
complex
neurodevelopmental
disorders,
and
her
son,
whose
generation
this
work
will
hopefully
effect.
iii
ACKNOWLEDGEMENTS
First
and
foremost,
I
have
to
thank
my
mentor,
Dr.
Dan
Campbell,
for
developing
my
scientific
writing,
pushing
me
to
think
harder,
and
honing
my
presentation
skills.
I
was
Dan’s
first
grad
student,
and
as
I
grew
as
a
scientist,
he
grew
as
a
mentor.
In
the
beginning,
he
provided
a
lot
of
hand-‐holding
as
I
got
my
bearings
in
the
new
world
of
graduate
school.
He
was
always
gentle
and
kind
with
all
the
mistakes
I
made.
It
seemed
very
little
could
ruffle
his
feathers.
As
the
years
progressed,
my
mistakes
were
fewer
and
he
gave
me
more
independence
as
I
figured
things
out
faster
and
tried
new
approaches.
He
never
looked
over
my
shoulder,
but
trusted
me
to
do
the
work
and
come
to
him
if
I
needed
advice.
He
showed
me
what
a
mentor
should
be
and
I’m
very
thankful
for
that
mentorship.
I
also
want
to
thank
my
qualifying
and
dissertation
committees:
Dr.
Gerry
Coatzee,
Dr.
Karen
Chang,
Dr.
Ruchi
Bajpai,
Dr.
Ruth
Wood,
and
Dr.
Justin
Ichida.
Dr.
Coatzee
provided
tough
feedback
during
my
qualifying
exams
and
pushed
me
to
really
think
hard
about
what
I
was
doing
and
why
I
was
doing
it.
More
importantly,
he
forced
me
to
justify
my
research
methods
as
opposed
to
other
methods.
I
learned
that,
as
scientists,
we
must
understand
why
we
do
what
we
do,
as
well
as
why
we
are
not
doing
something
else.
I
thank
Dr.
Chang
for
serving
as
my
qualifying
committee
chair.
Dr.
Bajpai
served
as
my
dissertation
committee
chair
and
inspired
me
multiple
times
when
I
had
existential
crises.
Every
conversation
I
had
with
her
left
me
encouraged
and
inspired
to
dig
deeper
and
make
scientific
discoveries.
She
provided
quality
feedback
and
always
believed
that
I
could
and
would
be
successful.
Dr.
Wood,
not
only
was
the
outside
member
of
my
committee,
but
also
allowed
me
to
rotate
in
her
lab
at
the
beginning
of
my
time
in
graduate
school.
Her
false
modesty
to
hide
her
brilliance
taught
me
to
think
iv
carefully
about
how
I
explained
my
research
to
someone
not
in
my
field,
even
though
I’m
sure
she
knew
exactly
what
I
was
talking
about.
Lastly,
I
thank
Dr.
Ichida
for
his
helpful
insights.
I
would
like
to
thank
Nicole
Campbell,
for
teaching
me
laboratory
techniques
and
helping
me
with
experiments.
I
especially
appreciate
her
patience
and
mentoring
guidance
in
non-‐science
topics.
I
also
need
to
thank
the
various
undergraduates
who
came
through
our
lab,
for
helping
with
experiments
and
making
grad
school
more
entertaining.
I
especially
owe
gratitude
to
Sarah
Danehower,
who
really
got
me
through
second
year
when
I
had
familial
crises.
I
have
to
thank
my
undergraduate
mentors,
Dr.
Joshua
Morris,
who
sparked
my
original
interest
in
genetics,
Dr.
Kevin
Huang,
Ryon
Maland,
and
all
the
faculty
and
students
at
my
alma
mater,
Azusa
Pacific
University,
who
helped
shape
who
I
am
as
a
scientist,
a
researcher,
and
a
person.
I
want
to
thank
my
friends
and
family
members
for
their
support
throughout
my
graduate
career.
Rafael
Martinez
held
me
when
I
cried.
My
grandmas,
Rosie
and
Glenda,
constantly
expressed
enthusiasm
and
interest
in
what
I
was
doing
and
kept
me
from
losing
sight
of
how
lucky
I
have
been
to
pursue
something
unknown
and
completely
new.
Dr.
Matthew
Barnes
helped
me
integrate
my
scientific
work
with
my
faith.
Kerrie
Hawkes
and
Dr.
Jennifer
Martin
remind
me
to
breathe
and
provide
female
mentorship.
Thank
you
to
all
my
friends
who
kept
asking
me
when
I
was
going
to
be
done
and
telling
me
to
hurry
up
and
finish.
Lastly,
I
want
to
thank
my
graduate
school
friends
for
giving
each
other
reality
checks
and
reminding
each
other
that
we
are
not
imposters:
we
deserve
to
be
here.
v
Kimberly
Babos,
Stephanie
Kuwahara,
Dr.
Trisha
Staab,
Dr.
Jillian
Shaw,
Matthew
Wong,
Aaron
Wolfe,
Julia
Taylor,
Marisa
Jones,
Dominic
Shayler,
Dr.
Coco
Dong,
Mary
Donhoffner,
Dr.
Nick
Goeden,
Dr.
Tara
Kerin,
Dr.
Mallory
Indi
Bradley,
Lisa
Nguyen,
Dr.
Patrick
Hecht,
Brent
Wilkinson,
and
Maria
Soria.
vi
TABLE
OF
CONTENTS
DEDICATION
ii
ACKNOWLEDGEMENTS
iii
LISTS
OF
TABLES
xi
LISTS
OF
FIGURES
xiii
ABSTRACT
15
CHAPTER
1:
Introduction
16
1.1
Autism:
Diagnosis,
prevalence,
impact,
and
etiology
16
1.2
Evidence
that
lncRNAs
Contribute
to
ASD
19
1.3
What
is
a
ncRNA?
23
1.3.1
Small
ncRNA
24
1.3.2
Long
ncRNA
(lncRNA)
25
1.4
NcRNA
in
Clinical
Trials
26
25
1.5
MSNP1AS
as
a
candidate
gene
for
autism
spectrum
disorder
28
1.6
Future
Directions
and
Conclusions
29
CHAPTER
2:
Impact
of
the
autism-‐associated
noncoding
RNA
MSNP1AS
on
neuronal
30
architecture
and
gene
expression
in
human
neural
progenitor
cells.
2.1
Abstract
30
2.2
Introduction
31
2.3
Materials
and
Methods
33
2.3.1
Cell
Culture
33
32
vii
2.3.2
Transfection
of
Over-‐Expression
Constructs
33
2.3.3
Imaging
34
33
2.3.4
Neural
Progenitor
Cell
Harvest
for
RNA
Purification
34
2.3.5
RNA
Purification
35
34
2.3.6
Quantitative
RT-‐PCR
(qRT-‐PCR)
35
2.3.7
Construction
of
Strand-‐Specific,
Ribosomal
RNA
Depleted
RNA
Sequencing
35
Libraries
2.3.8
Data
Analysis
37
2.4
Results
38
2.4.1
Overexpression
of
MSNP1AS
Decreased
Neurite
Number
and
Length
in
SK-‐N
38
SH
and
ReNcell
CX
Human
Neural
Progenitor
Cells
2.4.2
Genome-‐Wide
Changes
in
Gene
Expression
Following
MSNP1AS
41
Overexpression
in
Human
Neural
Progenitor
Cells
2.4.3
Transcriptional
Consequences
of
MSNP1AS
Overexpression
Are
Enriched
46
in
Protein
Synthesis
and
Chromatin
Regulation
2.5
Discussion
47
2.5.1
Summary
of
Results
47
51
2.6
Conclusion
55
2.7
Acknowledgements
56
CHAPTER
3:
Transcriptional
gene
silencing
of
the
autism-‐associated
long
noncoding
RNA
56
MSNP1AS
in
human
neural
progenitor
cells.
3.1
Abstract
56
viii
3.2
Introduction
57
3.3
Materials
and
Methods
59
3.3.1
Cell
culture
59
3.3.2
Design
of
antisense
RNA
to
the
MSNP1AS
proximal
gene
promoters
60
3.3.3
Transfection
of
small
interfering
RNAs
(sasRNAs)
60
3.3.4
Neural
progenitor
cell
harvest
61
3.3.5
RNA
purification
62
3.3.6
Quantitative
RT-‐PCR
(qPCR)
to
confirm
MSNP1AS
knockdown
62
3.3.7
Construction
of
strand-‐specific,
ribosomal
RNA
depleted
RNA
sequencing
62
libraries
3.3.8
Data
analysis
63
3.3.9
qPCR
to
confirm
altered
gene
expression
observed
in
RNA-‐seq
63
3.3.10
Analysis
of
Brainspan
data
64
3.4
Results
65
3.4.1
MSNP1AS
knockdown
results
in
genome-‐wide
transcriptional
changes
65
3.4.2
Transcriptional
consequences
of
MSNP1AS
knockdown
66
3.4.3
Expression
of
MSNP1AS
in
developing
human
brain
66
3.5
Discussion
71
CHAPTER
4:
Distinct
gene
expression
patterns
resulting
from
prenatal
pesticide
exposure
76
in
autism
spectrum
disorder.
4.1
Abstract
76
4.2
Introduction
77
ix
4.3
Materials
and
Methods
78
4.3.1
RNA
sequencing
analysis
78
4.3.2
Functional
enrichment
79
4.3.3
Weighted
Gene
Co-‐Expression
Network
Analysis
(WGCNA)
79
4.3.4
Module
Preservation
80
4.4
Results
81
4.4.1
All
differentially
expressed
genes:
exposed
vs
nonexposed
81
4.4.2
TD
Exposed
vs
Nonexposed
81
4.4.3
DD
Exposed
vs
Nonexposed
83
4.4.4
ASD
Exposed
vs
Nonexposed
83
4.4.5
All
ASD
vs
DD
86
4.4.6
All
ASD
vs
TD
86
4.4.7
Differentially
Expressed
Genes
Separated
by
Age
86
4.4.7.1
TD
Exposed
vs
Nonexposed
87
4.4.7.2
DD
Exposed
vs
Nonexposed
87
4.4.7.3
ASD
Exposed
vs
Nonexposed
88
4.4.7.4
Exposed
ASD
vs
Exposed
TD
89
4.4.7.5
Exposed
ASD
vs
Exposed
DD
90
4.4.8
Weighted
gene
co-‐expression
network
analysis
90
4.5
Discussion
91
CHAPTER
5:
Summary
and
Future
Directions
93
5.1
Summary
93
x
5.2
Overexpression
of
MSNP1AS
leads
to
changes
in
MSN
protein
expression,
94
protein
synthesis
and
chromatin
organization.
5.3
Knockdown
of
MSNP1AS
reveals
changes
in
genes
involved
in
chromatin
95
organization
and
immune
response
5.4
Immune
response
is
observed
in
vivo.
96
5.5
Future
Directions
97
REFERENCES
99
xi
LIST
OF
TABLES
Table
2.1.
Read
mapping
of
RNA
Sequencing
libraries.
36
Table
2.2.
Relative
expression
of
MSNP1AS
after
transfection
with
overexpression
37
construct
in
SK-‐N-‐SH
cells.
Table
2.3.
Relative
expression
of
MSNP1AS
after
transfection
with
overexpression
39
construct
in
ReNcell
CX
cells.
Table
2.4.
Top
thirty
differentially
expressed
genes
in
SK-‐N-‐SH
cells
24
hrs
after
MSNP1AS
42
overexpression.
Table
2.5.
Top
thirty
differentially
expressed
genes
in
SK-‐N-‐SH
cells
72
hrs
after
MSNP1AS
43
overexpression.
Table
2.6.
Top
thirty
differentially
expressed
genes
in
ReNcell
CX
cells
24
hrs
after
44
MSNP1AS
overexpression.
Table
2.7.
Top
thirty
differentially
expressed
genes
in
ReNcell
CX
cells
72
hrs
after
45
MSNP1AS
overexpression.
Table
2.8.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
SK-‐N-‐SH
48
cells
24
hrs
post-‐transfection.
Table
2.9.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
SK-‐N-‐SH
49
cells
72
hrs
post-‐transfection.
Table
2.10.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
ReNcell
CX
50
cells
24
hrs
post-‐transfection.
Table
2.11.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
ReNcell
CX
51
xii
cells
72
hrs
post-‐transfection.
Table
3.1.
MSNP1AS
Fold
Change
After
Transfection.
61
Table
3.2.
MSNP1AS
Stranded
Cuffdiff
Tophat
Read
Count.
63
Table
3.3.
Top
thirty
differentially
expressed
genes
following
knockdown
of
MSNP1AS.
67
Table
3.4.
Top
Ten
Differentially
Expressed
Genes
Following
Knockdown
of
MSNP1AS.
73
Table
3.5.
Relative
Fold
Change
of
MSN
After
MSNP1AS
Knockdown.
75
Table
4.1.
Differentially
Expressed
Genes
(Exposed
vs.
Not
Exposed).
82
Table
4.2.
Differentially
Expressed
Genes
(TD
Exposed
vs.
Not
Exposed).
82
Table
4.3.
Differentially
Expressed
Genes
(ASD
Exposed
vs.
Not
Exposed).
84
xiii
LIST
OF
FIGURES
Chapter
2
2.1.
Representative
ReNcell
CX
cells
at
24
h
post-‐transfection.
40
2.2.
Overexpression
of
MSNP1AS
decreases
neurite
number
and
length.
40
2.3.
GO
enrichment
analysis
on
all
differentially
expressed
genes
47
due
to
overexpression
of
MSNP1AS.
2.4.
Potential
mechanisms
by
which
MSNP1AS
alters
neuronal
architecture.
52
Chapter
3
3.1.
Small
noncoding
antisense
RNAs
(sasRNAs)
were
designed
to
silence
60
transcription
by
targeting
the
MSNP1AS
gene
promoter.
3.2.
Quantitation
of
MSNP1AS
knockdown.
68
3.3.
Volcano
plot
generated
to
show
the
differentially
expressed
genes
resulting
69
from
the
MSNP1AS
knockdown.
3.4.
Confirmation
of
altered
expression
of
genes
identified
by
RNA
seq.
70
3.5.
Gene
Ontology
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
71
in
SK-‐N-‐SH
cells
after
24
hours
of
MSNP1AS
knockdown
revealed
changes
in
chromatin
assembly.
3.6.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
in
SK-‐N-‐SH
72
cells
after
24
hours
of
MSNP1AS.
3.7.
Boxplot
representing
the
expression
of
MSNP1AS
through
human
cerebral
73
cortex
development.
xiv
Chapter
4
4.1.
Volcano
plot
generated
to
show
the
differentially
expressed
genes
between
84
the
exposed
to
pesticides
group
and
the
unexposed.
4.2.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
of
the
85
exposed
to
pesticides
versus
not
exposed
ASD
children.
4.3.
Venn
diagrams
of
differentially
expressed
genes
across
age
groups.
87
4.4.
GO
enrichment
analysis
of
differentially
expressed
genes
(p<0.05)
of
the
TD
88
children
exposed
versus
not
exposed
to
pesticides
in
the
younger
(less
than
3.5
years)
children.
4.5.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
of
the
ASD
91
children
exposed
to
pesticides
versus
TD
children
exposed
to
pesticides
in
the
younger
(less
than
3.5
years)
children.
4.6.
Weighted
Gene
Co-‐Expression
Network
Analysis.
91
15
ABSTRACT
From
genome-‐wide
association
studies
(GWAS),
our
laboratory
discovered
a
novel
gene
that
has
a
highly
significant
association
with
ASD.
This
gene
is
a
long
noncoding
RNA
(lncRNA)
called
MSNP1AS
(moesin
pseudogene
1,
antisense).
MSNP1AS
has
increased
expression
in
the
cerebral
cortex
of
people
with
ASD
and
people
with
the
ASD-‐associated
genetic
marker
revealed
by
the
GWAS.
I
hypothesized
that
MSNP1AS
overexpression
changes
the
expression
of
other
genes
that
contribute
to
neuronal
processes.
I
collaborated
with
experts
in
psychiatry,
HIV
research,
and
prostate
cancer
research
to
uncover
the
effect
of
ASD-‐associated
noncoding
RNAs
(ncRNAs)
on
human
neuronal
cells.
First,
I
overexpressed
MSNP1AS
in
human
neuronal
cell
lines
to
provide
a
working
model
of
the
ASD
phenotype.
I
used
RNA-‐Seq
to
find
transcripts
with
altered
expression
in
the
cells
after
overexpression
and
recorded
the
impact
of
the
increased
MSNP1AS
expression
on
cell
morphology.
MSNP1AS
is
94%
identical
and
antisense
to
moesin,
a
protein-‐coding
gene.
Moesin
is
an
X
chromosome
gene
that
plays
a
role
in
neuronal
processes
and
stability
as
well
as
immune
response.
While
MSNP1AS
overexpression
significantly
decreased
moesin
protein
expression,
moesin
transcript
levels
remained
unchanged.
RNA-‐sequencing
after
MSNP1AS
overexpression
revealed
in
changes
in
gene
expression
of
genes
related
to
chromatin
organization
and
protein
synthesis,
pointing
to
a
pathway
independent
of
the
MSNP1AS/MSN
interaction.
Moesin
contributes
to
neuron
growth,
and
ASD
patients
have
decreased
long-‐
distance
brain
connectivity,
which
makes
moesin
a
good
candidate
for
study
in
this
disorder.
Cell
growth
was
stunted
by
MSNP1AS
overexpression.
Neurite
length
and
number
were
decreased
after
overexpression,
hinting
at
an
effect
on
connectivity.
16
Second,
I
determined
the
best
way
to
effectively
decrease
MSNP1AS
in
human
neural
progenitor
cells
to
reveal
transcripts
with
altered
expression
as
potential
therapeutic
targets.
These
results
validated
MSNP1AS’s
effect
on
chromatin
organization
and
uncovered
an
immune
response.
Within
ASD
is
a
subset
of
patients
who
experience
immune
dysfunction,
such
as
autoimmune
disorders
and
gastrointestinal
upset.
More
work
needs
to
be
done
to
evaluate
whether
or
not
MSNP1AS
dysregulation
is
specific
to
these
patients.
Through
a
collaboration
with
UC
Davis,
I
was
able
to
validate
these
changes
in
immune
response
genes’
expression
in
vivo
and
point
to
a
potential
environmental
contribution.
The
Childhood
Autism
Risks
from
Genetics
and
the
Environment
(CHARGE)
study
took
blood
samples
from
children
with
and
without
autism
who
had
either
been
exposed
to
pesticides
in
their
environment
or
not.
The
RNA
from
the
blood
samples
was
sequenced
and
the
genes
changed
showed
pathways
involved
in
the
immune
system.
Taken
together,
these
experiments
suggest
molecular
mechanisms
that
are
working
together
to
alter
brain
development,
contributing
to
ASD
pathogenesis.
Chapter
1:
Introduction
1.1 Autism:
Diagnosis,
prevalence,
impact,
and
etiology
Autism
spectrum
disorder
is
a
neurodevelopmental
disorder
marked
by
two
core
symptoms:
impairments
in
social
communication
and
behavioral
flexibility.
Deficits
in
social
communication
include
avoidance
of
social
interaction
and
eye
contact
and
an
inability
to
use
verbal
language
to
communicate.
Impairments
in
behavioral
flexibility
include
repetitive
behaviors
such
as
hand
flapping
and
restricted
interests
such
as
demonstrating
an
all-‐
encompassing
interest
in
specific
objects
(1).
The
behavioral
diagnosis
is
not
possible
until
the
17
child
has
reached
an
age
at
which
they
begin
to
develop
language,
usually
between
two
and
three
years
old
(2).
The
prevalence
of
autism
spectrum
disorder
has
increased
in
recent
years,
due
in
part
to
redefining
the
diagnostic
criteria.
A
meta-‐analysis
of
epidemiological
studies
spanning
the
United
States,
United
Kingdom,
Japan
and
Sweden
from
1996
to
2001
estimated
the
prevalence
to
be
between
1
in
167-‐334
(3).
A
more
recent
study
from
2010
estimated
autism
prevalence
to
be
1
in
68
(4).
This
increase
may
be
due
to
increased
awareness,
surveys,
public
interest,
and
changes
in
service
ability
at
both
the
public
and
professional
level
(5).
Quantifying
prevalence
is
difficult
and
pinpointing
specific
environmental
effects
is
challenging.
Further,
data
from
low-‐
income
countries
is
difficult
to
obtain,
making
regional
comparisons
another
hurdle
(6).
One
motivating
factor
towards
studying
autism
spectrum
disorder
is
the
impact
it
has
on
the
caregivers.
Parents
of
autistic
children
score
higher
on
stress
tests
than
any
other
parental
groups.
These
parents
also
have
a
higher
incidence
of
mental
health
problems
(7).
The
current
treatments
for
autism
spectrum
disorder
are
limited
(8).
They
start
with
intensive
behavioral
interventions
called
applied
behavioral
analysis,
and
can
include
counseling,
speech
therapy,
social
skills
training,
occupational
therapy,
and
physical
therapy
(9).
The
type
and
extent
of
therapy
needed
is
determined
by
where
the
child
falls
on
the
spectrum;
accessibility
of
therapies
depends
on
the
location
and
living
situation
of
the
family.
Many
families
find
therapy
inaccessible
(10).
Families
living
in
rural
areas
are
more
likely
to
use
alternative,
untested
treatments
due
to
reduced
access
to
care
(11).
Cost
can
also
be
a
prohibitive
factor
(12,
13),
and
costs
increase
as
patients
get
older
(14).
Often,
the
treatments
available
are
not
18
particularly
effective
(15)
because
they
only
apply
to
a
subset
of
autism
patients
(16-‐19),
further
reducing
the
likelihood
of
effective
treatment.
Subtyping
ASD
is
also
an
area
of
contention.
Redefining
diagnostic
criteria
has
aided
somewhat
in
unifying
incongruous
aspects
of
the
disorder,
but
heterogeneity
in
the
ASD
phenotype
contributes
to
the
complexity
of
etiology.
While
an
individual
must
have
social
communication
deficits
and
repetitive,
restricted-‐interest
behaviors
(RRB),
the
types
of
deficits
in
those
categories
differ
greatly.
For
example,
a
subset
of
individuals
with
ASD
might
have
varying
levels
of
language
development.
One
group
might
have
trouble
with
grammar
and
syntax,
while
another
subset
might
not
speak
at
all
(20).
The
variation
observed
in
the
phenotype
is
reflected
in
the
genetic
variation.
The
estimated
heritability
of
autism
spectrum
disorder
is
more
than
70%
(21-‐23).
Chromosomal
regions
and
linkage
regions
with
genome-‐wide
significant
association
with
ASD
have
been
identified
using
whole
genome
methods
(24-‐28).
Rare
mutations
of
protein
coding
genes
(29-‐34)
and
rare
copy
number
variations
(CNVs)
(35-‐37)
have
also
been
associated,
but
these
powerful
genome-‐wide
approaches
have
not
produced
reliable
candidate
genes.
Most
genes
that
have
been
identified
do
not
overlap
with
parallel
studies.
Of
the
70%
of
phenotypic
variability
that
can
be
contributed
to
genetics,
half
of
heritability
is
due
to
common
variants
(38),
so
it
makes
sense
to
focus
on
the
common
variants
responsible.
In
tandem
with
heritability,
it
is
suggested
that
ASD
etiology
can
be
attributed
to
environmental
factors.
Seasonal
factors,
nutrition,
birth
order,
maternal
stress,
migration,
air
pollution,
and
organic
toxicants
have
all
been
implicated
in
ASD
risk
(39).
19
1.2
Evidence
that
lncRNAs
Contribute
to
ASD
As
for
all
common
complex
genetic
disorders,
there
is
an
on
going
debate
about
the
relative
contributions
of
rare
and
common
genetic
variants
to
ASD.
The
identification
of
rare
de
novo
copy
number
variants
(CNVs)
on
chromosomes
2p,
7q,
15q,
and
16p
provide
support
for
rare
variants
and
important
biological
clues
to
the
etiology
of
ASD
(35-‐37,
40-‐42).
However,
the
contribution
of
these
CNVs
remains
at
<6%
of
cases
(with
each
CNV
observed
in
<1%)
despite
extensive
cohort
collection
and
technological
advances
that
increased
CNV
detection
resolution
by
10-‐fold
(42).
It
is
now
clear
that
CNVs
do
not
account
for
a
large
fraction
of
ASD,
either
individually
or
collectively
(43).
Further,
the
implicated
CNVs
are
typically
large
(~250
kb)
and
encompass
multiple
protein-‐coding
genes,
making
it
difficult
to
determine
which
gene(s)
contribute
to
the
disorder.
Genome-‐wide
association
studies
(GWASs)
have
proven
effective
in
identifying
associated
genetic
variants
in
other
complex
genetic
disorders,
such
as
type
2
diabetes,
Crohn’s
disease,
and
prostate
cancer
(44-‐50).
However,
application
of
GWAS
to
ASD
yielded
mixed
results
(25-‐28).
Because
GWAS
is
the
definitive
technique
for
identifying
associated
common
variants
of
large
effect,
the
lack
of
obvious
protein-‐coding
genes
implicated
by
GWAS
pushed
the
ASD
genetics
fields
toward
searching
for
rare
causal
variants
(29-‐34,
40,
42,
51-‐53).
Whole
exome
sequencing
(next-‐generation
sequencing
restricted
to
the
exons
of
protein-‐coding
genes)
has
proven
effective
in
identifying
causal
genetic
variants
in
Mendelian
disorders,
such
as
Miller
syndrome,
high
myopia,
and
malformations
of
cortical
development
(54-‐56).
However,
application
of
whole
exome
sequencing
to
ASD
also
yielded
mixed
results
(29-‐34).
Whole
exome
sequencing
approaches
have
identified
de
novo
mutations
that
may
be
specific
to
individuals
with
ASD,
but
the
identified
de
novo
mutations
do
not
account
for
a
large
20
fraction
of
cases,
either
individually
or
collectively
(29-‐34).
Thus,
the
definitive
experiments
have
not
yet
resolved
the
common
variant
versus
rare
variant
debate,
and
suggest
that
both
common
variants
and
rare
variants
contribute
to
ASD
risk
(1).
Based
on
a
variety
of
evidence,
we
consider
common
variants
likely
to
play
a
large
role
in
ASD
risk.
First,
the
high
heritability
of
ASD
suggests
that
the
de
novo
mutations
implicated
by
exome
sequencing
and
CNV
detection
play
a
small
role
in
the
disorders.
Second,
sub-‐clinical
ASD
traits
are
present
in
>50%
of
family
members
of
affected
individuals
(57-‐60),
suggesting
that
common
heritable
factors
contribute
to
a
large
fraction
of
cases.
Third,
the
heritability
of
ASD
traits
is
high
and
indistinguishable
between
extreme
cases
and
the
general
population,
indicating
that
these
traits
are
the
quantitative
extreme
of
a
neurodevelopmental
continuum,
not
a
mutation-‐induced
abnormality
(61,
62).
Fourth,
the
conclusions
from
the
exome
sequencing
studies
that
no
single
gene
is
causal
and
that
hundreds
of
genes
will
increase
risk
(29-‐34)
indicate
that
evidence
for
association
of
each
genetic
variant
with
the
disorder
will
continue
to
be
of
utmost
importance.
While
genome-‐wide
significant
association
of
common
markers
may
drift
with
additional
genotyping,
the
association
is
likely
to
remain
significant.
In
contrast,
the
nominally
significant
association
of
a
rare
variant
can
disappear
with
the
positive
identification
of
a
mutation
in
a
single
unaffected
individual.
Although
the
genetic
effect
sizes
of
common
variants
for
ASD
risk
may
be
small,
similar
effect
sizes
have
identified
therapeutic
targets
for
Crohn’s
disease
and
type
2
diabetes
(1).
The
exome
sequencing
studies
(29-‐34)
all
concluded
that
no
single
gene
is
causal
for
ASD
and
that
400–1000
different
genes
may
increase
ASD
risk.
An
alternative
interpretation
of
these
data
is
that
de
novo
mutation
of
protein-‐coding
genes
may
contribute
little
to
ASD.
21
Instead,
genetic
variation
affecting
long
noncoding
RNA
(lncRNA),
which
are
by
definition
excluded
from
exome
sequencing
studies,
may
contribute
substantially.
Three
lines
of
evidence
support
this
interpretation.
First,
while
individual
common
genetic
variants
contribute
small
risk
to
ASD,
collectively
common
genetic
variants
explain
more
than
40%
of
genetic
risk
for
ASD
(63).
Second,
large-‐scale
whole
genome
sequencing
for
high-‐density
lipoprotein
(HDL)
indicates
that
noncoding
regions
contribute
as
much
as
protein-‐coding
genes
to
this
complex
genetic
condition
(64).
Third,
nearly
two-‐thirds
(64%)
of
all
long
transcripts
in
the
human
brain
are
lncRNAs
(65).
It
is
also
becoming
increasingly
clear
that
the
number
of
ncRNAs
encoded
in
a
genome
increases
with
developmental
complexity
(66,
67),
and
that
genetic
variation
within
human
lncRNAs
contributes
to
neurodevelopmental
disorders
(1,
67-‐69).
Concurrent
with
these
genetic
observations,
the
contribution
of
lncRNAs
became
more
apparent
in
other
complex
genetic
disorders,
including
cancer
(70,
71)
and
schizophrenia
(72).
Emerging
evidence
from
multiple
approaches
indicate
a
contribution
of
lncRNA
to
ASD
(1).
The
first
evidence
that
lncRNAs
may
contribute
to
ASD
was
derived
from
a
study
of
X-‐
linked
protein-‐coding
genes.
Noor
et
al.
found
that
0.5%
of
individuals
have
mutation
in
the
protein-‐coding
PTCHD1
gene.
An
additional
0.5%
of
ASD
cases
also
had
deletions
of
a
complex
noncoding
RNA
(ncNRA)
locus
(PTCHD1AS1/PTCHD1AS2)
5ʹ′
to
the
PTCHD1
locus
that
are
associated
with
ASD
(55).
Although
the
deletions
are
rare
and
the
functional
mechanisms
of
the
PTCHD1AS1
and
PTCHD1AS2
ncRNAs
have
not
been
explored
extensively,
they
are
presumed
to
regulate
expression
of
the
protein-‐coding
gene
on
the
opposite
strand,
PTCHD1
(1,
55).
The
second
report
indicating
evidence
for
lncRNA
contribution
to
ASD
was
based
on
a
genome-‐wide
significant
association
in
a
protein-‐coding
gene-‐poor
region
of
chromosome
22
5p14.1.
A
GWAS
of
ASD
indicated
the
most
significant
association
to
date
(P
=
10
−10
)
for
rs4307059
on
chromosome
5p14.1.7
The
same
rs4307059
allele
was
also
associated
with
social
communication
phenotypes
in
a
general
population
sample
(73).
However,
rs4307059
genotype
was
not
correlated
with
expression
of
either
of
the
flanking
protein-‐coding
genes,
CDH9
and
CDH10
(26).
We
identified
a
3.9
kb
ncRNA
that
is
transcribed
directly
at
the
site
of
the
chromosome
5p14.1
ASD
GWAS
peak
(74).
The
ncRNA
is
encoded
by
the
opposite
(anti-‐sense)
strand
of
moesin
pseudogene
1
(MSNP1),
and
is
thus
designated
MSNP1AS
(moesin
pseudogene
1,
anti-‐sense).
MSNP1AS
is
94%
identical
and
anti-‐sense
to
the
X
chromosome
transcript
MSN,
which
encodes
a
protein
(moesin)
that
regulates
neuronal
architecture
and
immune
response.
Expression
of
MSNP1AS
in
postmortem
temporal
cortex
is
increased
12.7-‐
fold
in
individuals
with
ASD
and
increased
22-‐fold
in
individuals
with
the
rs4307059
risk
allele
(74).
The
MSNP1AS
ncRNA
binds
MSN
and
its
over-‐expression
in
cultured
neurons
causes
significant
decreases
in
moesin
protein,
neurite
number
and
neurite
length.
Thus,
our
discovery
reveals
a
functional
lncRNA
which,
based
on
the
GWAS
findings,
contributes
to
ASD
risk
(1).
To
evaluate
whether
or
not
changes
in
lncRNA
expression
are
part
of
ASD
pathogenesis,
33,000
lncRNAs
and
30,000
mRNA
transcripts
from
ASD
and
control
prefrontal
cortex
and
cerebellum
postmortem
brain
tissue
were
profiled
using
microarray.
Two
hundred
lncRNAs
were
differentially
expressed
in
ASD.
Previously,
it
had
been
reported
that
mRNA
transcription
within
ASD
brains
was
homogenous
(75,
76).
Another
study
also
found
increased
transcriptional
homogeneity
in
lncRNAs
in
ASD
brains
(77).
Although
the
conclusions
of
this
study
implicate
lncRNA,
the
sample
size
was
small
(2
ASD
cases
and
2
matched
controls).
More
studies
with
larger
sample
sizes
will
be
necessary
to
determine
specific
lncRNAs
with
consistently
altered
23
expression
in
ASD
brains
(1).
Therefore,
accumulating
evidence
from
multiple
experimental
approaches
suggest
that
lncRNAs
contribute
to
ASD.
The
promise
of
a
new
pharmacology
to
target
lncRNA
suggests
possible
treatment
options
(1).
1.3
What
is
a
ncRNA?
The
eukaryotic
genome
is
much
more
complex
than
previously
thought.
Protein-‐coding
gene
exons
comprise
<2%
of
the
total
genomic
material.
However,
analyses
of
the
transcriptome
revealed
that
the
human
genome
is
differentially
and
dynamically
transcribed
to
produce
a
spectrum
of
ncRNA
of
all
shapes
and
sizes
(78).
NcRNAs
have
been
arbitrarily
sorted
into
two
categories
based
on
their
size:
long
ncRNA
(greater
than
200
nucleotides
in
length)
and
short
ncRNAs
(less
than
200
nucleotides
in
length)
(79).
These
categories
can
be
further
broken
down
into
subcategories
based
on
other
properties.
Few
ncRNAs
have
been
characterized
functionally.
However,
the
functions
that
have
been
discovered
are
obviously
an
important
part
of
development
and
organismal
maintenance.
As
key
parts
of
cellular
processes,
these
ncRNAs
leave
the
organism
vulnerable
to
disease
when
they
become
dysregulated.
Several
ncRNAs
have
been
shown
to
play
a
role
in
the
pathogenesis
of
diseases
such
as
cancer,
HIV,
heart
disease,
and
neurological
disorders.
Characterizing
these
cellular
components
is
important
to
our
understanding
of
these
diseases
and
may
provide
insight
into
their
pathogenesis
and
treatment
(1).
24
1.3.1
Small
ncRNA
Small
ncRNA,
as
a
category,
include
RNA
such
as
microRNAs
(miRNAs),
short
interfering
RNAs
(siRNAs),
small
nucleolar
RNAs
(snoRNAs),
and
PIWI-‐interacting
RNAs
(piRNAs).
The
genes
encoding
these
RNA
can
be
found
throughout
the
genome
(80).
Small
ncRNA
can
also
be
derived
from
processing
lncRNA
(81,
82).
The
main
method
used
by
small
ncRNA
to
regulate
the
genome
is
by
binding
to
their
specific
target
transcripts
via
complementary
nucleotide
sequences,
altering
downstream
events
to
influence
the
translation
of
the
target.
To
read
more
about
small
ncRNA
subclasses,
biogenesis,
and
forms
of
genetic
regulation,
see
the
following
reviews
(1,
80,
83-‐87).
MiRNA
is
one
of
the
best-‐studied
classes
of
small
ncRNAs
and
was
first
discovered
in
1993
in
C.
elegans
(88).
Since
its
discovery,
miRNA
is
believed
to
be
relatively
conserved
among
vertebrates
(89-‐91).
MiRNAs
can
regulate
genes
post
translation
by
binding
to
either
the
3’-‐(92)
or
the
5’-‐UTR
(93)
of
a
target
transcript
which
typically
inhibits
translation.
One
particular
miRNA
can
target
multiple
transcripts
and
one
transcipt
can
be
regulated
by
multiple
miRNA
(94).
More
than
60%
of
human
protein-‐coding
genes
are
predicted
targets
of
miRNA,
lending
credence
to
the
idea
that
miRNAs
are
prevalent
and
important
in
the
human
genome
(95).
Regulation
by
miRNA
is
vital
to
mammalian
developmental
processes
and,
like
many
other
ncRNAs,
miRNA
expression
can
be
specifically
and
differentially
expressed
throughout
tissue
types
and
the
developmental
process
as
reviewed
(1,
96-‐98).
Like
miRNA,
siRNA
also
binds
to
complementary
sequences
on
target
transcripts
and
regulates
gene
expression.
The
difference
is
that
siRNA
complementary
sequences
have
stricter
requirements
in
that
they
must
have
close
to
perfect
sequence
complementation
(99).
Due
to
25
this
difference,
siRNA
and
miRNA
have
two
separate
mechanisms
for
repression.
SiRNA
targets
with
very
close
complementation
undergo
direct
cleavage,
and
miRNA
targets
are
destabilized
or
transcriptionally
repressed
(100-‐102).
Endogenous
siRNAs
(endo-‐siRNAs)
are
purported
to
protect
against
transposons
and
dsDNA
viruses
in
somatic
tissues
in
Drosophila
(103-‐105).
Endo-‐siRNAs
have
also
been
identified
in
mice
(106-‐109)
and
in
human
cells
(110-‐112).
SiRNA
has
been
extensively
researched
for
use
in
gene
therapy
and
may
be
able
to
correct
ncRNA
dysregulation
in
a
number
of
different
diseases
(1,
113).
1.3.2
Long
ncRNA
(lncRNA)
LncRNA
is
an
expansive
category
that
includes
many
different
structural
features
and
mechanisms
of
action.
LncRNA
include
sense
and
antisense
strands
in
pseudogenes,
in
intergenic
regions
(lincRNA)
and
in
protein-‐coding
genes.
After
transcription,
they
may
be
further
processed
through
splicing
and/or
the
addition
of
a
5’-‐methyl-‐guanosine
cap
and
3’
poly
(A)
tail.
They
also
can
contain
localization
signals
that
direct
them
to
the
cytoplasm
or
the
nucleus
(114).
LncRNAs
have
many
diverse
functional
roles
as
well,
such
as
employing
enhancer-‐like
functions,
participating
in
the
recruitment
of
chromatin-‐modifying
complexes,
modulating
alternative
splicing,
providing
a
scaffold
for
the
assembly
of
protein
complexes,
and
acting
as
competing
endogenous
RNAs
(ceRNAs)
(115-‐117).
XIST,
as
well
as
its
interaction
with
the
polycomb
repressive
complex,
PRC2,
is
the
most
thoroughly
studied
lncRNA
(118).
XIST’s
antisense
gene,
TSIX,
was
discovered
in
1999
(119).
TSIX
serves
as
a
regulatory
element
of
XIST.
It
is
part
of
a
repression
pathway
along
with
other
pluripotency
factors
(120).
There
is
much
to
be
discovered
about
lncRNA
before
it
can
be
used
to
treat
disease
(1).
26
1.4
NcRNA
in
Clinical
Trials
NcRNAs
are
part
of
many
gene
regulatory
pathways.
When
lncRNAs
are
dysregulated
by
genetic,
epigenetic
or
environmental
factors,
they
can
cause
dysregulation
in
other
genes
of
gene
networks
that
contribute
to
altered
development.
It
would
be
difficult
to
alter
these
dysregulated
translated
genes
that
contribute
to
clinical
diagnoses.
However,
they
can
be
manipulated
through
the
use
of
their
regulatory
ncRNAs.
The
ncRNAs
complementary
to
these
translated
disease-‐causing
genes
can
target
these
genes
that
are
over
or
under-‐expressed.
The
primary
challenge
is
to
transport
the
complementary
ncRNA
to
a
position
in
which
they
can
be
effective.
Using
ncRNA
as
therapeutic
targets,
especially
lncRNAs,
is
a
relatively
new
field
of
research,
but
next-‐generation
RNA
sequencing
is
quickly
being
developed
that
will
help
identify
the
relevant
transcripts
(1,
121).
Several
types
of
ncRNAs
have
been
utilized
as
drugs
to
suppress
or
upregulate
genes
implicated
in
disease.
SiRNA
and
RNAi
are
the
main
candidates
for
gene
suppression
and
have
successfully
repressed
their
target
genes
in
several
diseases
including
cancer
as
mentioned
previously.
In
addition
to
siRNA
and
RNAi,
antisense
RNAs
and
ribozymes
(RNA
that
is
catalytically
active)
are
also
candidates
for
use
in
clinical
trials
(1).
The
first
example
of
RNA
therapeutics
used
in
clinical
trials
is
antisense
RNA.
Antisense
RNA
usually
targets
mRNA,
but
they
have
also
been
used
to
target
miRNA
as
well.
These
RNA
are
chemically
synthesized
oligonucleotides
(ON)
that
are
antisense
to
miRNA
(antagomirs)
and
can
inhibit
miRNA
involved
in
disease.
A
locked
nucleic
acid
(LNA)-‐modified
ON,
called
miravirsen,
inhibits
miR-‐122,
a
liver
miRNA
involved
in
Hepatitis
C
viral
(HCV)
infection.
Miravirsen
has
completed
two
Phase
1
clinical
trials
and
is
safe.
It
is
currently
in
Phase
IIa
27
clinical
trials
to
test
tolerability
and
efficacy
in
HCV
patients
(122).
Twenty-‐two
different
siRNA
therapies
have
now
reached
clinical
testing
for
the
treatment
of
16
disorders
(1,
123).
SiRNA
has
enjoyed
moderate
success
with
both
systemic
and
local
delivery
as
a
drug.
SiRNA
can
specifically
knockdown
any
mRNA
and
is
easily
designed
and
screened.
Its
silencing
effects
also
last
a
long
time
and
can
retain
its
catalytic
ability
in
RISC
for
long
periods
as
well
(123).
Many
siRNA
drugs
treating
vision
loss
using
local
delivery
have
initiated
Phase
I
clinical
trials
(124-‐127).
There
are
several
siRNA
and
shRNA
drugs
delivered
locally
to
treat
diseases
from
vision
loss
to
respiratory
viruses
that
are
currently
in
Phase
0
and
1
clinical
trials
(1,
128,
129).
RNA
aptamers
are
single-‐stranded
nucleic
acids
with
stable
three-‐dimensional
shapes
that
allow
them
to
bind
to
molecular
targets
with
high
affinity
and
specificity
(129,
130).
RNA
aptamers
are
similar
to
antibodies
in
affinity
and
specificity
for
targets,
but
they
also
have
certain
advantages
over
antibodies.
Since
aptamers
are
formed
in
vitro
with
SELEX,
they
are
more
efficiently,
accurately,
and
economically
produced,
which
makes
them
a
better
choice
for
clinical
application.
Aptamers
are
chemically
modified,
therefore
they
can
target
ligands
without
producing
the
immune
response
present
with
antibodies.
The
ability
of
aptamers
to
be
modified
is
another
advantage
they
hold
over
antibodies.
Aptamers
can
be
joined
to
ribozymes
to
make
riboswitches
or
siRNA
to
make
aptamer-‐siRNA
chimeras.
They
are
also
smaller
than
antibodies,
which
improves
tissue
penetration
and
transport.
At
least
six
RNA
aptamers
have
been
clinically
tested.
One,
a
VEGF-‐specific
modified
RNA
aptamer,
treats
AMD
and
is
now
an
FDA
approved
drug
(1,
131-‐133).
Ribozymes
are
RNA
molecules
that
can
perform
biochemical
actions
similar
to
those
of
28
an
enzyme.
Ribozymes
have
gone
under
clinical
testing
as
gene
therapy-‐based
approaches
to
treating
HIV
using
CD4+
T
cells
or
CD34+
hematopoietic
stem
cells
(HSCs).
CD34+
HSCs
differentiate
into
various
hematopoietic
lineages
including
CD4+
T
cells
(134-‐137).
ShRNA
can
work
in
conjunction
with
ribozymes.
RNA-‐based
treatment
was
developed
for
HIV-‐1
using
gene-‐
modified
autogolous
CD34+
hematopoietic
progenitor
cells
(HPCs)
and
is
now
in
a
Phase
0
clinical
study
at
the
City
of
Hope
(138).
The
RNA-‐based
treatment
involved
three
RNA
components,
an
shRNA,
a
Trans-‐activation-‐responsive
(TAR)
decoy,
and
a
hammerhead
ribozyme,
all
encoded
in
the
lentiviral
gene
vector.
The
hammerhead
ribozyme
cleaves
the
mRNA
of
the
chemokine
receptor
5
(CCR5)
protein
(139).
The
CCR5
receptor
also
is
a
coreceptor
for
HIV-‐1
infection
and
a
subset
of
CD4+
T
cells
express
it.
This
receptor
is
not
necessary
for
normal
T
cell
function
and
is
not
prone
to
mutational
escape,
therefore
it
could
be
a
good
target
for
anti-‐HIV
therapy
(1).
1.5
MSNP1AS
as
a
candidate
gene
for
autism
spectrum
disorder
Wang
et
al
(26)
found
several
common
variants
after
performing
a
GWAS
on
ASD
patients
(26).
In
2012,
the
Campbell
lab
published
a
paper
on
a
ncRNA
under
this
GWAS
peak
(74).
The
transcript
was
a
pseudogene
called
MSNP1
(moesin
pseudogene
1).
MSNP1
is
not
transcribed
and
is
94%
identical
to
the
protein-‐coding
gene
moesin,
which
is
on
the
X
chromosome.
Northern
blots
revealed
a
transcript
expressed
at
the
MSNP1
locus.
The
opposite
strand
of
MSNP1
is
transcribed
to
produce
MSNP1AS
(moesin
pseudogene
1
antisense).
A
gene
94%
identical
to
a
gene
on
a
different
chromosome,
going
in
the
opposite
direction,
is
under
the
GWAS
peak.
There
is
a
significant
12.7-‐fold
increase
in
expression
of
MSNP1AS
in
autism
29
brains
compared
to
control.
I
evaluated
the
effect
of
this
increase
and
developed
a
way
to
knockdown
the
gene
in
the
hopes
that
the
effects
would
be
negated
(67,
69).
There
is
also
a
significant,
but
smaller,
2.4-‐fold
increase
of
MSN
expression
in
autism
brains.
CDH9
and
CDH10
were
also
tested,
but
there
was
not
a
significant
difference
between
the
control
brains
and
the
autism
brains.
Preliminary
data
suggested
that
one
effect
of
this
increase
in
expression
of
MSNP1AS
is
altered
neuronal
function
in
mouse
primary
cortical
neurons.
When
transfected
with
MSNP1AS
overexpression
constructs,
the
neurite
length
and
number
was
decreased.
1.6
Future
Directions
and
Conclusions
Based
on
recent
genetics
findings,
I
anticipate
that
ncRNAs
will
account
for
a
large
proportion
of
ASD
risk.
My
own
published
and
unpublished
data
indicate
that
the
most
significant
genetic
associations
with
ASD
implicate
lncRNAs.
The
contributions
of
these
lncRNAs
to
ASD
etiology
need
to
be
established
mechanistically
before
antisense
therapies
for
ASD
can
be
developed.
Antisense
therapy
has
proven
effective
for
other
common
disorders,
including
cancer
and
HIV.
Antisense
siRNA
treatments
for
HIV
and
cancer
are
effective
at
inhibiting
the
progression
of
the
diseases
in
adults.
Application
of
antisense
siRNA
to
treat
developmental
brain
disorders
poses
unique
challenges,
both
medical
and
societal.
The
medical
challenges
include
timing
and
delivery
of
antisense
therapy
to
specific
brain
regions
at
the
correct
developmental
stages.
The
societal
challenges
include
the
parental
decision
to
alter
brain
development.
However,
given
the
burden
of
ASD
on
both
families
and
society,
and
the
lack
of
effective
treatment
options,
we
propose
that
experiments
designed
to
understand
potential
biological
interventions
are
worthwhile
(1).
30
Chapter
2:
Impact
of
the
Autism-‐Associated
Long
Noncoding
RNA
MSNP1AS
on
Neuronal
Architecture
and
Gene
Expression
in
Human
Neural
Progenitor
Cells
This
paper
was
published
in
Genes
(67).
2.1
Abstract
We
previously
identified
the
lncRNA,
MSNP1AS,
(moesin
pseudogene
1,
antisense)
as
a
functional
element
revealed
by
genome
wide
significant
association
with
ASD.
MSNP1AS
expression
was
increased
in
the
postmortem
cerebral
cortex
of
individuals
with
ASD
and
particularly
in
individuals
with
the
ASD-‐associated
genetic
markers
on
chromosome
5p14.1.
Here,
we
mimicked
the
overexpression
of
MSNP1AS
observed
in
postmortem
ASD
cerebral
cortex
in
human
neural
progenitor
cell
lines
to
determine
the
impact
on
neurite
complexity
and
gene
expression.
ReNcell
CX
and
SK-‐N-‐SH
were
transfected
with
an
overexpression
vector
containing
full-‐length
MSNP1AS.
Neuronal
complexity
was
determined
by
the
number
and
length
of
neuronal
processes.
Gene
expression
was
determined
by
strand-‐specific
RNA
sequencing.
MSNP1AS
overexpression
decreased
neurite
number
and
neurite
length
in
both
human
neural
progenitor
cell
lines.
RNA
sequencing
revealed
changes
in
gene
expression
in
proteins
involved
in
two
biological
processes:
protein
synthesis
and
chromatin
remodeling.
These
data
indicate
that
overexpression
of
the
ASD-‐associated
lncRNA
MSNP1AS
alters
the
number
and
length
of
neuronal
processes.
The
mechanisms
by
which
MSNP1AS
overexpression
impacts
neuronal
differentiation
may
involve
protein
synthesis
and
chromatin
structure.
These
31
same
biological
processes
are
also
implicated
by
rare
mutations
associated
with
ASD,
suggesting
convergent
molecular
mechanisms.
2.2
Introduction
Autism
spectrum
disorder
(ASD)
is
a
neurodevelopmental
disorder
characterized
by
deficits
in
social
communication
and
repetitive
behaviors
with
restricted
interests
(1,
140).
Highly
penetrant,
rare,
de
novo
loss
of
function
mutations
have
been
associated
with
ASD,
and
many
of
the
associated
genes
converge
upon
the
biological
processes
of
protein
synthesis
and
chromatin
structure
(141,
142).
However,
40%
of
the
heritability
of
ASD
resides
in
common
genetic
variants
(143,
144).
The
first
reported
common
genetic
variants
with
genome-‐wide
significant
association
with
ASD
mapped
to
a
small
cluster
of
chromosome
5p14.1,
including
the
common
genetic
variant
rs4307059
with
ASD
association
of
P=10
−10
(26).
The
same
rs4307059
allele
was
also
identified
as
a
predictor
of
stereotyped
conversation
and
poorer
communication
skills
in
a
population-‐based
sample
of
>7,000
individuals
(73),
suggesting
that
rs4307059
may
be
a
quantitative
trait
locus
for
social
communication
phenotypes.
We
identified
a
3.9
kb
lncRNA
that
is
transcribed
directly
at
the
site
of
the
chromosome
5p14.1
ASD
association
signal
(74).
The
lncRNA
is
encoded
by
the
opposite
(anti-‐sense)
strand
of
moesin
pseudogene
1
(MSNP1),
and
is
thus
designated
MSNP1AS
(moesin
pseudogene
1,
anti-‐sense).
Expression
of
MSNP1AS
in
the
postmortem
temporal
cortex
is
increased
12.7-‐fold
in
individuals
with
ASD
and
increased
22-‐fold
in
individuals
with
the
rs4307059
risk
allele
(74).
Thus,
our
discovery
revealed
a
lncRNA,
which
based
on
the
highly
significant
genetic
association
findings
(26),
contributes
to
ASD
risk
(74).
32
MSNP1AS
is
94%
identical
and
anti-‐sense
to
the
X
chromosome
transcript
MSN,
which
encodes
a
protein
(moesin)
that
regulates
neuronal
architecture
(145-‐149).
The
lncRNA
MSNP1AS
binds
MSN
and
its
over-‐expression
in
cell
lines
caused
significant
decreases
in
moesin
protein
(74),
which
influences
neuronal
process
stability
(147).
Based
on
these
observations,
a
direct
hypothesis
is
that
overexpression
of
MSNP1AS
in
progenitors
of
cortical
projection
neurons
will
decrease
neuronal
complexity,
consistent
with
observations
of
decreased
long
distance
connectivity
observed
in
the
cerebral
cortex
of
individuals
with
ASD
(150-‐155).
We
tested
this
hypothesis
by
transfecting
differentiating
human
neural
progenitor
cells
with
an
overexpression
vector
that
drives
full-‐length
MSNP1AS
and
directly
measuring
the
number
and
length
of
the
neurites.
We
demonstrated
previously
that
one
mechanism
by
which
MSNP1AS
may
alter
neuronal
complexity
is
by
decreasing
the
expression
of
moesin
protein
(74).
However,
alternative
hypotheses
about
the
mechanism(s)
of
the
ASD-‐associated
lncRNA
MSNP1AS
had
not
been
tested.
While
our
previous
experiments
revealed
that
MSNP1AS
over-‐expression
decreased
expression
of
moesin
protein
(74),
it
was
not
yet
clear
if
MSNP1AS
regulated
the
expression
of
the
MSN
transcript
or
if
MSNP1AS
over-‐expression
altered
the
expression
of
other
genes.
Therefore,
we
performed
unbiased
RNA
sequencing
on
differentiating
human
neural
progenitor
cells
to
determine
the
impact
of
increased
MSNP1AS
on
gene
expression.
These
experiments
mimic
the
increased
expression
of
MSNP1AS
observed
in
postmortem
brains
of
individuals
with
ASD
and
reveal
multiple
mechanisms
by
which
the
lncRNA
MSNP1AS
may
contribute
to
altered
neuronal
architecture.
33
2.3
Materials
and
Methods
2.3.1
Cell
Culture
The
human
neural
progenitor
cell
lines
SK-‐N-‐SH
cells
(American
Type
Culture
Collection,
Manassas,
VA,
USA)
and
ReNcell
CX
cells
(Millipore,
Billerica,
MA,
USA)
were
cultured
according
to
the
manufacturer’s
protocols
and
maintained
in
a
75
cm
2
flask
at
37
°C
and
5%
CO
2
.
When
cells
were
75%
confluent,
the
human
neural
progenitor
cells
were
subcultured
to
a
density
of
1
×
10
6
cells
per
75
cm
2
flask
for
harvests
24
h
post-‐transfection,
and
5
×
10
5
cells
per
75
cm
2
flask
for
harvests
72
h
post-‐transfection.
2.3.2
Transfection
of
Over-‐Expression
Constructs
Full-‐length
MSNP1AS
was
inserted
into
pIRES2-‐AcGFP
(Clontech)
(74),
a
mammalian
over-‐expression
construct.
Each
experiment
was
made
up
of
one
pIRES2-‐AcGFP
negative
control
transfection
and
one
pIRES2-‐AcGFP-‐MSNP1AS
transfection.
Cells
were
transfected
using
Amaxa
Nucleofector
(Lonza,
Walkersville,
MD,
USA)
technology,
using
2
µg
of
vector
per
well,
and
subcultured
into
6-‐well
plates.
One
milliliter
of
fresh
prewarmed
media
was
added
to
each
well
and
the
cells
were
centrifuged
twice
at
130×
g
for
10
min
for
the
SK-‐N-‐SH
cells
and
300×
g
for
5
min
for
the
ReNcell
CX
cells
with
PBS
washes
of
7
mL
and
4
mL,
respectively.
The
cell
pellet
was
resuspended
in
Nucleofector
solution
containing
a
supplement
using
1
×
10
6
cells
per
well.
One
hundred
microliters
of
cell/Nucleofector
solution
was
added
to
a
new
cuvette
and
placed
in
the
Amaxa
Nucleofector
using
the
T-‐16
program.
Five
hundred
microliters
of
fresh
prewarmed
media
was
added
to
the
cuvette,
mixed,
and
transferred
to
the
well
with
a
disposable
pipet.
A
two
mL
culture
medium
was
added
to
the
transfected
cells
in
the
6-‐well
34
plate.
The
cells
were
incubated
at
37
°C
in
5%
CO
2
until
harvest.
Each
experiment
was
repeated
four
times.
2.3.3
Imaging
At
24
and
72
h
post-‐transfection,
human
neural
progenitors
were
viewed
using
an
Olympus
CKX41
inverted
microscope
and
an
attached
Q
Imaging
QICAM
Fast
1394
Digital
Camera
captured
digital
images.
Randomly
selected
fields
of
the
6-‐well
plate
were
imaged.
Three
randomly
selected
fields
were
imaged
in
each
experiment.
Neurite
length
and
number
of
each
GFP-‐positive
cell
within
the
field
were
quantified
using
Autoneuron
software.
Only
neurons
unobstructed
by
other
neurons
or
debris
were
analyzed.
Statistical
significance
was
calculated
using
the
Mann-‐Whitney
U
test.
2.3.4
Neural
Progenitor
Cell
Harvest
for
RNA
Purification
After
24
h
and
72
h,
the
culture
medium
was
removed
and
discarded.
The
cells
were
washed
with
one
mL
of
PBS
wash
and
the
PBS
wash
was
aspirated.
One
mL
of
Trypsin/EDTA
solution
for
SK-‐N-‐SH
cells
and
Accutase
for
ReNcell
CX
cells
was
added
to
each
well
and
the
cells
were
incubated
for
4
min.
Five
mL
of
cell
type-‐specific
medium
was
added
and
cells
were
triturated
and
transferred
to
15
mL
conical
tubes.
SK-‐N-‐SH
cells
were
centrifuged
at
130×
g
for
10
min
and
ReNcell
CX
cells
were
centrifuged
at
300×
g
for
5
min.
The
supernatant
was
aspirated
and
5
mL
of
PBS
added.
The
cell
pellet
was
triturated
and
centrifuged
at
130×
g
for
10
min
and
300×
g
for
5
min
respectively.
The
PBS
wash
was
aspirated
and
1
mL
of
fresh
PBS
was
added.
Half
of
the
solution
was
transferred
to
each
of
two
1.5
mL
eppendorf
tubes.
The
tubes
were
centrifuged
at
4
°C
at
130×
g
for
10
min
for
SK-‐N-‐SH
cells
and
300×
g
for
5
min
for
ReNcell
CX
cells.
The
supernatant
was
decanted
and
the
cell
pellets
were
frozen
at
−80
°C.
35
2.3.5
RNA
Purification
The
Qiagen
RNEasy
kit
was
used
to
isolate
total
RNA
using
vacuum
technology
according
to
the
manufacturer’s
protocol
(Qiagen,
Valencia,
CA,
USA).
The
RNA
was
eluted
with
35
microliters
of
RNAse-‐free
water
and
quantified
using
the
NanoDrop
ND-‐1000
Spectrophotometer
(v3.1.2;
Thermo
Fisher
Scientific,
Waltham,
MA,
USA).
The
RNA
was
stored
at
−20
°C.
2.3.6
Quantitative
RT-‐PCR
(qRT-‐PCR)
To
confirm
overexpression
of
MSNP1AS,
consistent
with
the
~12-‐fold
increase
observed
in
postmortem
brains
of
individuals
with
ASD,
cDNA
was
synthesized
using
the
SuperScript
III
First-‐Strand
Synthesis
System
for
qRT-‐PCR
protocol
(Invitrogen/Thermo
Fisher
Scientific,
Waltham,
MA,
USA).
Five
hundred
nanograms
of
RNA
were
used
to
make
one
and
a
half
reaction
volumes
(35
μL)
of
cDNA.
The
cDNA
was
stored
at
−20
°C.
The
qRT-‐PCR
protocol
described
in
Kerin
et
al.
(74)
was
used
to
validate
over-‐expression
of
MSNP1AS.
2.3.7
Construction
of
Strand-‐Specific,
Ribosomal
RNA
Depleted
RNA
Sequencing
Libraries
Directional
RNA-‐Seq
libraries
were
prepared
for
Illumina
HiSeq
2000
sequencing
using
the
Stranded
Total
TruSeq
RNA
Sample
Preparation
kit
with
Ribo-‐Zero
Gold
(Illumina)
using
the
manufacturer’s
protocol
using
the
Hamilton
Starlet
Liquid
Handling
robot.
One
nanogram
of
RNA
was
used
for
each
sample.
In
brief,
Ribo-‐Zero
was
used
to
deplete
cytoplasmic
and
mitochondrial
rRNA
from
total
RNA.
The
depleted
RNA
was
fragmented
and
primed
with
random
hexamers
to
synthesize
first
strand
cDNA
using
Superscript
II
(Life
Technologies,
Carlsbad,
CA,
USA).
Next,
the
second
strand
was
synthesized,
incorporating
dUTP
in
place
of
dTTP.
A
single
“A”
base
was
added
to
the
3ʹ′
ends
of
the
fragments
and
the
indexed
adaptors
36
were
ligated
to
the
ends
of
the
ds
cDNA
to
prepare
them
for
hybridization
onto
the
flow
cell.
PCR
was
used
to
selectively
enrich
the
fragments
with
ligated
adapters
and
to
amplify
the
amount
of
DNA
in
the
library.
The
libraries
were
produced
in
a
96-‐well
format
and
quality
controlled
using
the
Agilent
Technologies
2200
TapeStation
Instrument.
Libraries
were
pooled
(four
samples
per
lane)
and
sequenced
on
Illumina
HiSeq
2000
to
a
targeted
depth,
generating
an
average
of
20
million
paired-‐end
50-‐cycle
reads
for
each
sample
(Table
2.1
and
2.2).
Table
2.1.
Read
mapping
of
RNA
Sequencing
Libraries
from
SK-‐N-‐SH
cells
Number
of
reads
Number
of
reads
mapped
%
mapped
SK-‐N-‐SH
24
hr
control
biological
replicate
1
11,099,318
10,746,390
96.8
control
biological
replicate
2
20,496,459
19,422,292
94.8
control
biological
replicate
3
10,824,013
10,458,919
96.6
control
biological
replicate
4
31,400,592
29,709,967
94.6
Average
18,455,096
17,584,392
95.7
MSNP1AS
OE
biological
replicate
1
19,971,956
19,337,134
96.8
MSNP1AS
OE
biological
replicate
2
13,807,707
12,881,017
93.3
MSNP1AS
OE
biological
replicate
3
18,326,170
17,660,733
96.4
MSNP1AS
OE
biological
replicate
4
10,900,335
10,366,157
95.1
Average
15,751,542
15,061,260
95.4
SK-‐N-‐SH
72
hr
control
biological
replicate
1
46,378,868
44,126,735
95.1
control
biological
replicate
2
22,219,398
20,950,969
94.3
control
biological
replicate
3
25,049,815
24,123,433
96.3
control
biological
replicate
4
24,021,135
23,272,061
96.9
Average
29,417,304
28,118,300
95.7
MSNP1AS
OE
biological
replicate
1
17,844,865
17,128,866
96.0
MSNP1AS
OE
biological
replicate
2
10,176,928
9,651,721
94.8
MSNP1AS
OE
biological
replicate
3
14,363,510
13,878,004
96.6
MSNP1AS
OE
biological
replicate
4
15,219,358
14,512,024
95.4
Average
14,401,165
13,792,654
95.7
37
Table
2.2.
Read
mapping
of
RNA
Sequencing
Libraries
from
ReNcell
CX
cells
ReNcell
CX
24
hr
control
biological
replicate
1
27,003,948
26,267,471
97.3
control
biological
replicate
2
20,860,192
19,778,456
94.8
control
biological
replicate
3
15,548,803
14,713,657
94.6
control
biological
replicate
4
26,490,502
25,268,925
95.4
Average
22,475,861
21,507,127
95.5
MSNP1AS
OE
biological
replicate
1
22,807,209
22,258,018
97.6
MSNP1AS
OE
biological
replicate
2
17,922,225
17,200,036
96.0
MSNP1AS
OE
biological
replicate
3
13,290,230
12,659,451
95.3
MSNP1AS
OE
biological
replicate
4
16,948,688
16,375,844
96.6
Average
17,742,088
17,123,337
96.4
ReNcell
CX
72
hr
control
biological
replicate
1
20,666,642
20,014,343
96.8
control
biological
replicate
2
14,360,503
13,888,459
96.7
control
biological
replicate
3
33,262,563
31,348,111
94.2
control
biological
replicate
4
35,502,944
34,507,246
97.2
Average
25,948,163
24,939,540
96.2
MSNP1AS
OE
biological
replicate
1
11,987,370
11,670,193
97.4
MSNP1AS
OE
biological
replicate
2
12,533,511
11,987,081
95.6
MSNP1AS
OE
biological
replicate
3
25,523,571
24,097,726
94.4
MSNP1AS
OE
biological
replicate
4
22,607,983
21,685,434
95.9
Average
18,163,109
17,360,109
95.8
2.3.8
Data
Analysis
Data
analysis
was
performed
using
TopHat
(version
2.0.10;
https://ccb.jhu.edu/software/tophat/index.shtml)
(156)
to
align
the
Illumina
short
reads
against
the
reference
human
genome
ENSEMBL
GrCH38
version
81.
Sequence
alignments
were
generated
as
BAM
files
(157),
and
then
Cuffdiff
(version
2.2.1;
http://cole-‐trapnell-‐
lab.github.io/cufflinks/)
(158)
was
used
to
summarize
the
gene
expression
values
as
FPKM
measures.
The
gene
expression
of
samples
with
MSNP1AS
over-‐expression
were
compared
to
the
gene
expression
of
the
negative
control
experiment
samples
to
find
other
differentially-‐
38
expressed
genes.
Cuffdiff
was
also
used
to
calculate
the
expression
fold
change,
p-‐values
and
FDR
values.
Genes
with
p
<
0.05
were
used
as
the
input
for
the
Database
for
Annotation,
Visualization
and
Integrated
Discovery
(DAVID)
(version
6.7;
Leidos
Biomedical
Research,
Inc.,
Frederick,
MD,
USA)
functional
annotation
(159,
160).
2.4
Results
2.4.1
Overexpression
of
MSNP1AS
Decreased
Neurite
Number
and
Length
in
SK-‐N-‐SH
and
ReNcell
CX
Human
Neural
Progenitor
Cells
To
mimic
the
overexpression
of
MSNP1AS
observed
in
postmortem
brains
of
individuals
with
ASD,
MSNP1AS
was
overexpressed
~50-‐fold
(Table
2.3)
in
human
neural
progenitor
cells
using
a
human
overexpression
vector
containing
the
full
length
MSNP1AS
transcript.
Human
neural
progenitor
cells
that
overexpressed
MSNP1AS
had
decreased
neurite
length
compared
to
human
neural
progenitor
cells
transfected
with
the
empty
vector
control
(Figure
2.1
and
Figure
2.2).
In
SK-‐N-‐SH
cells,
neurite
length
was
reduced
6-‐fold
(p
=
1.1
×
10
−2
)
at
24
h
post-‐
transfection
and
5-‐fold
(p
=
6.0
×
10
−4
)
at
72
h
post-‐transfection
in
neural
progenitor
cells
transfected
with
the
MSNP1AS
over-‐expression
vector
compared
to
controls
(Figure
2.2A,B).
Similarly,
MSNP1AS
overexpression
in
ReNcell
CX
human
neural
progenitor
cells
caused
a
significant
decrease
in
neurite
length
at
72
h
post-‐transfection
by
12.5-‐fold
(p
=
2.5
×
10
−2
),
but
the
decreased
neurite
length
observed
at
24
h
post-‐transfection
was
not
significant
(2.3-‐fold;
p
=
2.9
×
10
−1
)
(Figure
2.2C,D).
39
Table
2.3.
Relative
expression
of
MSNP1AS
after
transfection
with
overexpression
construct.
MSNP1AS
Relative
Expression
ReN24
-‐
1
30.84
ReN24
-‐
2
25.58
ReN24
-‐
3
5.17
ReN24
-‐
4
145.82
average
51.85
ReN72
-‐
1
7.29
ReN72
-‐
2
29.08
ReN72
-‐
3
37.17
ReN72
-‐
4
39.34
average
28.22
SK24
-‐
1
4855.15
SK24
-‐
2
1586.70
SK24
-‐
3
87.60
SK24
-‐
4
1596.98
average
2031.61
SK72
-‐
1
816.43
SK72
-‐
2
1170.18
SK72
-‐
3
6254.52
SK72
-‐
4
142.42
average
2095.89
total
average
1051.89
40
Figure
2.1.
Representative
ReNcell
CX
cells
at
24
h
post-‐transfection.
The
human
neural
progenitor
cells
were
transfected
with
(A)
an
empty
vector
control
or
(B)
the
MSNP1AS
overexpression
vector.
For
each
experiment,
the
transfection
of
MSNP1AS
over-‐expression
or
control
vector
was
repeated
four
times.
Three
randomly
selected
fields
from
each
of
the
four
replicates
were
imaged
and
all
isolated
GFP-‐positive
cells
within
the
imaged
fields
were
examined
for
neurite
length
and
neurite
number
using
Autoneuron
software.
Figure
2.2.
Overexpression
of
MSNP1AS
decreases
neurite
number
and
length.
SK-‐N-‐SH
cells
that
overexpressed
MSNP1AS
had
decreased
neurite
length
after
(A)
24
h
and
(B)
72
h.
ReNcell
CX
cells
that
overexpressed
MSNP1AS
also
had
decreased
neurite
length
after
(D)
72
h
but
the
decreased
neurite
length
after
(C)
24
h
was
not
significant.
SK-‐N-‐SH
cells
that
overexpressed
MSNP1AS
had
fewer
neurites
per
cell
after
(E)
24
h
and
a
trend
toward
reduced
neurites
per
cell
after
(F)
72
h.
ReNcell
CX
cells
that
overexpressed
MSNP1AS
also
had
trend
toward
fewer
neurites
per
cell
after
(H)
72
h,
but
the
neurite
number
decrease
after
(G)
24
h
was
not
significant
(*
p
<
0.05,
Mann-‐Whitney
U
test).
MSNP1AS
overexpression
also
caused
a
reduction
in
the
number
of
neurites
in
human
neural
progenitor
cells.
In
SK-‐N-‐SH
cells,
MSNP1AS
overexpression
caused
an
8.3-‐fold
reduction
in
neurite
number
at
24
h
post-‐transfection
(p
=
2.1
×
10
−2
)
and
a
trend
toward
reduced
neurite
number
at
72
h
post-‐transfection
(4.4-‐fold;
p
=
6.2
×
10
−2
)
(Figure
2.2E,F).
Similarly,
MSNP1AS
overexpression
in
ReNcell
CX
cells
caused
a
trend
toward
fewer
neurite
number
at
72
h
post-‐
transfection
(4.6-‐fold;
p
=
1.0
×
10
−1
),
but
the
neurite
number
decrease
at
24
h
post-‐
transfection
was
not
significant
(1.3-‐fold;
p
=
7.9
×
10
−1
)
(Figure
2.2G,H).
41
2.4.2
Genome-‐Wide
Changes
in
Gene
Expression
Following
MSNP1AS
Overexpression
in
Human
Neural
Progenitor
Cells
Changes
in
the
transcriptome
caused
by
MSNP1AS
overexpression
may
provide
insight
into
the
mechanisms
by
which
an
increase
of
MSNP1AS
influences
neuronal
architecture.
Therefore,
RNA
sequencing
was
used
to
perform
genome-‐wide
transcriptome
profiling
on
the
two
human
neural
progenitor
cells
lines
SK-‐N-‐SH
and
ReNcell
CX
cells
with
and
without
MSNP1AS
overexpression.
For
all
experiments,
the
average
number
of
reads
ranged
from
14–29
million
with
96%
of
the
reads
mapping
to
the
genome
(Table
2.1
and
2.2).
No
change
in
gene
expression
survived
Bonferroni
correction
for
multiple
comparisons
in
any
of
the
experiments
(Table
2.4,
2.5,
2.6,
and
2.7).
However,
each
of
the
experiments
revealed
100–400
genes
with
nominally
significant
changes
in
gene
expression
(p
<
0.05).
All
downstream
analyses
were
performed
with
the
sets
of
genes
that
had
significant
(p
<
0.05)
changes
in
expression
following
MSNP1AS
over-‐expression.
Differential
gene
expression
analysis
in
SK-‐N-‐SH
cells
revealed
157
differentially
expressed
genes
(p
<
0.05)
at
24
h
post-‐transfection
and
351
genes
differentially
expressed
(p
<
0.05)
at
72
h
post-‐transfection
(Table
2.4
and
2.5).
In
ReNcell
CX
cells,
MSNP1AS
overexpression
revealed
267
genes
differentially
expressed
at
24
h
post-‐transfection
and
164
genes
differentially
expressed
at
72
h
post-‐transfection
(Table
2.6
and
2.7).
No
single
gene
was
significantly
(p
<
0.05)
altered
in
each
of
the
four
experimental
conditions
(Table
2.4,
2.5,
2.6,
and
2.7).
MSNP1AS
overexpression
did
not
alter
expression
of
MSN,
the
transcript
that
encodes
moesin
protein
and
is
bound
by
the
MSNP1AS
lncRNA.
In
SK-‐N-‐SH
cells,
MSN
transcript
was
slightly
increased
at
24
h
post-‐transfection
(1.17-‐fold;
p
=
3.0
×
10
−2
)
and
at
72
h
post-‐
42
Table
2.4.
Top
thirty
differentially
expressed
genes
in
SK-‐N-‐SH
cells
24
hrs
after
MSNP1AS
overexpression.
gene
locus
log2
(fold_change)
p_value
q_value
CXCL8
4:73740505-‐73743716
-‐1.85
5.00E-‐05
1.07E-‐01
ADGRG1
16:57610651-‐57665580
1.08
5.00E-‐05
1.07E-‐01
TXNIP
1:145992434-‐145996600
1.27
5.00E-‐05
1.07E-‐01
BIRC3
11:102317449-‐102339403
1.33
5.00E-‐05
1.07E-‐01
INSM2
14:35534041-‐35537054
1.41
5.00E-‐05
1.07E-‐01
MIR320A
8:22244961-‐22245043
4.40
5.00E-‐05
1.07E-‐01
SPP1
4:87975649-‐87983426
-‐2.48
1.00E-‐04
1.83E-‐01
CHGA
14:92923079-‐92935293
1.06
1.50E-‐04
2.40E-‐01
FST
5:53480408-‐53487134
-‐1.37
3.00E-‐04
4.26E-‐01
PPP1R15A
19:48872391-‐48876057
0.96
4.50E-‐04
5.76E-‐01
CBR3-‐AS1
21:36131766-‐36294274
-‐1.74
5.00E-‐04
5.81E-‐01
TFPI2
7:93591572-‐93911265
-‐0.93
5.50E-‐04
5.86E-‐01
NTS
12:85874294-‐85882992
-‐0.92
6.50E-‐04
6.40E-‐01
NPY
7:24284162-‐24291865
-‐0.79
8.00E-‐04
7.15E-‐01
GADD45B
19:2476121-‐2478259
1.42
8.50E-‐04
7.15E-‐01
TMEM158
3:45224465-‐45226278
-‐0.79
9.00E-‐04
7.15E-‐01
SLC5A7
2:107986522-‐108013994
1.13
9.50E-‐04
7.15E-‐01
TRAF1
9:120902392-‐120929173
1.34
1.30E-‐03
8.75E-‐01
ATF3
1:212565333-‐212620777
1.39
1.30E-‐03
8.75E-‐01
KLF10
8:102648778-‐102655902
0.70
1.55E-‐03
9.91E-‐01
OPRD1
1:28812141-‐28877336
0.98
2.25E-‐03
1.00E+00
STC1
8:23841914-‐23854807
-‐1.48
2.40E-‐03
1.00E+00
ARRDC4
15:97960697-‐97973838
0.92
2.40E-‐03
1.00E+00
EGR1
5:138465489-‐138469315
0.84
2.85E-‐03
1.00E+00
AXL
19:41219202-‐41261766
0.68
2.90E-‐03
1.00E+00
RP11-‐69E11.4
1:39491645-‐39558707
2.57
2.95E-‐03
1.00E+00
ASS1
9:130444928-‐130501274
-‐1.52
3.15E-‐03
1.00E+00
SULT1E1
4:69791871-‐69860152
1.18
3.30E-‐03
1.00E+00
GADD45A
1:67685060-‐67688338
0.70
3.95E-‐03
1.00E+00
DPP4
2:161992240-‐162075169
-‐0.90
4.70E-‐03
1.00E+00
PI15
8:74599774-‐74866939
-‐0.75
5.05E-‐03
1.00E+00
43
Table
2.5.
Top
thirty
differentially
expressed
genes
in
SK-‐N-‐SH
cells
72
hrs
after
MSNP1AS
overexpression.
gene
locus
log2
(fold_change)
p_value
q_value
Metazoa_SRP
14:49853615-‐49853914
-‐2.13
5.00E-‐05
1.56E-‐01
RMRP
9:35657750-‐35658018
-‐1.83
5.00E-‐05
1.56E-‐01
KIFC3
16:57758216-‐57863053
-‐1.60
5.00E-‐05
1.56E-‐01
PUF60
8:143816343-‐143829859
-‐1.31
5.00E-‐05
1.56E-‐01
GPR137
11:64269829-‐64289500
-‐1.72
1.50E-‐04
3.37E-‐01
TMEM134
11:67461709-‐67469272
-‐1.96
2.50E-‐04
3.37E-‐01
FAM195B
17:81822360-‐81833302
-‐1.87
2.50E-‐04
3.37E-‐01
YIF1B
19:38305103-‐38317273
-‐1.67
2.50E-‐04
3.37E-‐01
RPS15
19:1438357-‐1440494
-‐1.37
2.50E-‐04
3.37E-‐01
PKD1
16:2039814-‐2135898
-‐1.46
3.00E-‐04
3.37E-‐01
RPL8
8:144789764-‐144792587
-‐1.08
3.00E-‐04
3.37E-‐01
DCXR
17:82035135-‐82037732
-‐1.74
3.50E-‐04
3.37E-‐01
RPL36
19:5674946-‐5720572
-‐1.42
3.50E-‐04
3.37E-‐01
RPS9
19:54200741-‐54281184
-‐1.31
4.00E-‐04
3.57E-‐01
FKBP2
11:64241002-‐64244132
-‐1.51
5.00E-‐04
3.91E-‐01
RPPH1
14:20343047-‐20357905
-‐1.22
5.00E-‐04
3.91E-‐01
RAC3
17:82031623-‐82034204
-‐2.02
6.00E-‐04
4.17E-‐01
PLA2G4C
19:48047842-‐48110817
-‐1.51
6.00E-‐04
4.17E-‐01
EEF1A2
20:63488012-‐63499315
-‐1.38
6.50E-‐04
4.28E-‐01
ACTL6B
7:100643096-‐100656461
-‐1.85
7.00E-‐04
4.38E-‐01
VGF
7:101162508-‐101165593
-‐1.38
7.50E-‐04
4.47E-‐01
BCAR1
16:75226073-‐75268053
-‐1.82
8.00E-‐04
4.55E-‐01
Metazoa_SRP
14:49862546-‐49862849
-‐1.05
1.00E-‐03
5.44E-‐01
RHBDF1
16:58058-‐76355
-‐1.79
1.50E-‐03
7.51E-‐01
MRPL55
1:228106678-‐228109312
-‐1.02
1.50E-‐03
7.51E-‐01
PLP1
X:103773717-‐103792619
-‐1.20
1.60E-‐03
7.60E-‐01
CCL2
17:34255217-‐34257203
-‐1.01
1.65E-‐03
7.60E-‐01
HIST1H1E
6:26156390-‐26157050
-‐1.39
1.70E-‐03
7.60E-‐01
NR4A1
12:52022831-‐52059507
-‐1.58
1.80E-‐03
7.77E-‐01
TNK2
3:195863363-‐195911945
-‐1.62
1.90E-‐03
7.92E-‐01
44
Table
2.6.
Top
thirty
differentially
expressed
genes
in
ReNcell
CX
cells
24
hrs
after
MSNP1AS
overexpression.
gene
locus
log2
(fold_change)
p_value
q_value
MIR663AHG
20:26186919-‐26251526
1.44
5.00E-‐05
6.75E-‐01
CTGF
6:131948175-‐132077393
0.67
1.50E-‐04
8.44E-‐01
TEX101
19:43401495-‐43418597
1.39
2.00E-‐04
8.44E-‐01
SIRT6
19:4174108-‐4182604
1.70
2.50E-‐04
8.44E-‐01
IFI6
1:27666060-‐27703063
-‐0.65
3.50E-‐04
9.46E-‐01
MX1
21:41420303-‐41459214
-‐0.86
5.00E-‐04
1.00E+00
SLC7A11
4:138027421-‐138242349
0.61
5.50E-‐04
1.00E+00
FOSL1
11:65892048-‐65900573
0.99
6.50E-‐04
1.00E+00
SNORD3A
17:19188015-‐19188714
-‐0.54
8.00E-‐04
1.00E+00
OLFM1
9:135075242-‐135121179
1.78
8.00E-‐04
1.00E+00
NTRK2
9:84668550-‐85027070
0.94
9.00E-‐04
1.00E+00
PDE2A
11:72576140-‐72674591
1.28
1.25E-‐03
1.00E+00
NFATC2
20:51386956-‐51562831
0.75
1.80E-‐03
1.00E+00
EGR1
5:138465489-‐138469315
0.61
2.15E-‐03
1.00E+00
MNT
17:2384059-‐2401118
1.04
2.20E-‐03
1.00E+00
LGALS3
14:55124109-‐55145413
-‐0.50
2.35E-‐03
1.00E+00
ANKRD52
12:56230048-‐56258391
0.63
2.35E-‐03
1.00E+00
EP300
22:41091785-‐41197456
0.53
2.65E-‐03
1.00E+00
CLDN1
3:190305700-‐190322475
0.78
2.90E-‐03
1.00E+00
C9orf116
9:135495180-‐135506447
0.72
3.65E-‐03
1.00E+00
NEXN
1:77881347-‐77943895
0.84
3.75E-‐03
1.00E+00
AC010970.2
Y:10197255-‐10199103
0.73
3.90E-‐03
1.00E+00
UBR4
1:19072109-‐19210276
0.60
4.05E-‐03
1.00E+00
CMIP
16:81445169-‐81711762
0.60
4.40E-‐03
1.00E+00
STC2
5:173314712-‐173329503
0.59
4.45E-‐03
1.00E+00
CYR61
1:85580760-‐85583962
0.52
4.75E-‐03
1.00E+00
IFI44
1:78649795-‐78664078
-‐0.84
4.80E-‐03
1.00E+00
RFX7
15:56087279-‐56243266
0.59
4.90E-‐03
1.00E+00
MYC
8:127735433-‐127741434
0.46
4.95E-‐03
1.00E+00
TTYH3
7:2631950-‐2664802
0.57
5.05E-‐03
1.00E+00
45
Table
2.7.
Top
thirty
differentially
expressed
genes
in
ReNcell
CX
cells
72
hrs
after
MSNP1AS
overexpression.
gene
locus
log2
(fold_change)
p_value
q_value
SNAP91
6:83552879-‐83709691
-‐1.80
3.50E-‐04
1.00E+00
SCG5
15:32641675-‐32697098
-‐2.01
7.00E-‐04
1.00E+00
DPP7
9:137110541-‐137115177
1.09
9.50E-‐04
1.00E+00
NPTX1
17:80467147-‐80477843
-‐0.92
1.20E-‐03
1.00E+00
LIMK1
7:74082932-‐74122525
0.61
1.20E-‐03
1.00E+00
EIF4EBP1
8:38030340-‐38060365
0.61
1.30E-‐03
1.00E+00
CDR1-‐AS
X:140782404-‐140784871
-‐2.25
1.50E-‐03
1.00E+00
MTRNR2L8
11:10507886-‐10509189
-‐0.95
2.05E-‐03
1.00E+00
ANKRD1
10:90912095-‐90921276
0.59
2.30E-‐03
1.00E+00
ZRANB2-‐AS2
1:71081323-‐72282734
-‐1.02
3.20E-‐03
1.00E+00
LTBP2
14:74498169-‐74612378
0.46
3.20E-‐03
1.00E+00
JUP
17:41754603-‐41786931
0.62
3.65E-‐03
1.00E+00
MTRNR2L1
17:22523110-‐22525696
-‐0.91
3.90E-‐03
1.00E+00
CHL1
3:195757-‐409417
-‐1.87
4.20E-‐03
1.00E+00
PTP4A1
6:63521760-‐63583587
-‐0.53
4.35E-‐03
1.00E+00
SHISA4
1:201888679-‐201892306
0.68
5.75E-‐03
1.00E+00
PQLC1
18:79902419-‐79951664
0.91
5.95E-‐03
1.00E+00
C16orf13
16:634426-‐636366
0.64
6.05E-‐03
1.00E+00
PLP1
X:103773717-‐103792619
0.57
6.35E-‐03
1.00E+00
NRXN1
2:49918504-‐51032561
-‐1.26
8.00E-‐03
1.00E+00
MYBL2
20:43667018-‐43716496
0.62
8.00E-‐03
1.00E+00
ELN
7:74027788-‐74069907
0.55
8.05E-‐03
1.00E+00
LRRTM3
10:65912517-‐67696169
-‐1.85
8.25E-‐03
1.00E+00
ZBTB20
3:114338093-‐115147271
0.74
9.00E-‐03
1.00E+00
SLC47A2
17:19678287-‐19718979
-‐0.99
1.06E-‐02
1.00E+00
PDCD5
19:32581067-‐32587452
-‐0.41
1.08E-‐02
1.00E+00
BIN1
2:127048026-‐127107355
0.58
1.18E-‐02
1.00E+00
UBTD1
10:97498867-‐97571209
0.69
1.25E-‐02
1.00E+00
CRIP2
14:105472961-‐105480170
0.80
1.32E-‐02
1.00E+00
GIPC1
19:14477759-‐14496149
0.59
1.39E-‐02
1.00E+00
46
transfection
(1.16-‐fold;
p
=
3.0
×
10
−1
).
In
ReNcell
CX
cells,
a
significant
change
in
MSN
transcript
expression
was
not
observed
at
24
h
post-‐transfection
(0.97-‐fold;
p
=
8.0
×
10
−1
)
and
at
72
h
post-‐transfection
(0.92-‐fold;
p
=
5.0
×
10
−1
).
These
results
suggest
that,
although
MSNP1AS
regulates
expression
of
moesin
protein
(74),
MSNP1AS
does
not
directly
regulate
the
expression
of
the
MSN
transcript.
2.4.3
Transcriptional
Consequences
of
MSNP1AS
Overexpression
Are
Enriched
in
Protein
Synthesis
and
Chromatin
Regulation
Gene
ontology
(GO)
analysis
using
the
DAVID
web
server
for
differentially
expressed
(p
<
0.05)
genes
revealed
an
enrichment
of
genes
involved
in
protein
synthesis
and
chromatin
regulation
(Figure
2.3A;
Table
2.8,
2.9,
2.10,
and
2.11).
In
SK-‐N-‐SH
cells
at
72
h
post-‐
transfection
of
the
MSNP1AS
overexpression
construct,
the
351
genes
with
altered
expression
(p
<
0.05)
were
enriched
for
genes
involved
in
translational
elongation
(Bonferroni
corrected
p
=
4.7
×
10
−11
),
structural
constituent
of
ribosome
(Bonferroni
corrected
p
=
1.2
×
10
−7
),
and
nucleosome
organization
(Bonferroni
corrected
p
=
2.8
×
10
−6
).
No
enrichment
that
survived
Bonferroni
correction
was
observed
among
the
157
differentially
expressed
genes
in
SK-‐N-‐SH
cells
at
24
h
post-‐transfection.
However,
a
trend
toward
enrichment
of
chromatin
related
genes
was
observed
in
SK-‐N-‐SH
cells
at
24
h
(uncorrected
p
=
5.8
×
10
−2
)
with
some
of
the
same
genes
differentially
expressed
as
SK-‐N-‐SH
at
72
h
post-‐transfection
(e.g.,
HIST1H2AJ,
(Table
2.8
and
2.9).
Similarly,
in
ReNcell
CX
human
neural
progenitor
cells
at
24
h
post-‐transfection,
differentially
expressed
(p
<
0.05)
genes
were
enriched
for
translational
elongation
(Bonferroni
corrected
p
=
3.3
×
10
−23
),
structural
constituent
of
ribosome
(Bonferroni
corrected
p
=
7.3
×
10
−20
),
and
translation
(Bonferroni
corrected
p
=
1.7
×
10
−13
)
(Figure
2.3B;
Table
2.10
and
2.11).
47
No
enrichment
that
survived
Bonferroni
correction
was
observed
among
the
164
genes
differentially
expressed
in
ReNcell
CX
cells
at
72
h
post-‐transfection.
However,
an
enrichment
of
genes
involved
in
translation
(uncorrected
p
=
4.0
×
10
−2
)
was
among
the
top
results
in
ReNcell
CX
cells
at
72
h
post-‐transfection.
These
data
suggest
that
genes
involved
in
translation
are
altered
by
MSNP1AS
overexpression.
Figure
2.3.
GO
enrichment
analysis
on
all
differentially
expressed
genes
due
to
overexpression
of
MSNP1AS.
GO
enrichment
analysis
was
performed
on
(A)
SK-‐N-‐SH
cells
after
72
h,
and
(B)
ReNcell
CX
cells
after
24
h.
2.5
Discussion
2.5.1
Summary
of
Results
The
data
presented
here
indicate
that
overexpression
of
the
ASD-‐associated
lncRNA
MSNP1AS
alters
neuronal
architecture
in
human
neural
progenitor
cells
by
three
potential
mechanisms
(Figure
2.4).
As
we
previously
described
(74),
MSNP1AS
overexpression
decreases
expression
of
moesin
protein,
which
is
known
to
decrease
neuronal
complexity
(147).
Here,
we
demonstrate
that
MSNP1AS
overexpression
also
changes
the
expression
of
genes
involved
in
protein
synthesis
and
chromatin
organization.
Therefore,
MSNP1AS
overexpression
has
multiple
48
Table
2.8.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
SK-‐N-‐SH
cells
24
hrs
post-‐transfection.
Term
Pvalue
Fold
Enrichment
Bonferroni
Benjamini
GO:0010033~response
to
organic
substance
9.27E-‐05
3.10
0.10
0.10
GO:0006915~apoptosis
1.64E-‐04
3.27
0.17
0.09
GO:0012501~programmed
cell
death
1.91E-‐04
3.22
0.20
0.07
GO:0007167~enzyme
linked
receptor
protein
signaling
pathway
2.53E-‐04
4.22
0.26
0.07
GO:0008219~cell
death
3.07E-‐04
2.92
0.30
0.07
GO:0016265~death
3.30E-‐04
2.90
0.32
0.06
GO:0044421~extracellular
region
part
4.99E-‐04
2.55
0.09
0.09
GO:0001568~blood
vessel
development
5.23E-‐04
4.82
0.46
0.08
GO:0042060~wound
healing
5.98E-‐04
5.50
0.50
0.08
GO:0001944~vasculature
development
6.14E-‐04
4.71
0.51
0.08
GO:0050878~regulation
of
body
fluid
levels
6.83E-‐04
6.52
0.55
0.08
GO:0005576~extracellular
region
8.23E-‐04
1.89
0.15
0.08
GO:0009719~response
to
endogenous
stimulus
9.49E-‐04
3.57
0.67
0.10
GO:0007596~blood
coagulation
1.03E-‐03
7.73
0.70
0.10
GO:0050817~coagulation
1.03E-‐03
7.73
0.70
0.10
GO:0007179~transforming
growth
factor
beta
receptor
signaling
pathway
1.07E-‐03
10.94
0.71
0.09
GO:0007178~transmembrane
receptor
protein
serine/threonine
kinase
signaling
pathway
1.08E-‐03
7.65
0.71
0.09
GO:0007599~hemostasis
1.33E-‐03
7.30
0.79
0.10
GO:0009991~response
to
extracellular
stimulus
1.37E-‐03
4.78
0.80
0.09
GO:0009725~response
to
hormone
stimulus
1.80E-‐03
3.58
0.88
0.12
GO:0009611~response
to
wounding
2.14E-‐03
2.97
0.92
0.13
GO:0005578~proteinaceous
extracellular
matrix
2.27E-‐03
3.82
0.35
0.14
GO:0042127~regulation
of
cell
proliferation
2.28E-‐03
2.50
0.93
0.13
GO:0042981~regulation
of
apoptosis
2.78E-‐03
2.45
0.96
0.15
GO:0043067~regulation
of
programmed
cell
death
3.04E-‐03
2.43
0.97
0.15
49
Table
2.9.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
SK-‐N-‐SH
cells
72
hrs
post-‐transfection.
Term
PValue
Fold
Enrichment
Bonferroni
Benjamini
GO:0006414~translational
elongation
2.68E-‐14
10.63
4.72E-‐11
4.72E-‐11
GO:0022626~cytosolic
ribosome
1.29E-‐13
11.69
3.87E-‐11
3.87E-‐11
GO:0033279~ribosomal
subunit
3.09E-‐11
7.81
9.31E-‐09
4.66E-‐09
GO:0003735~structural
constituent
of
ribosome
2.36E-‐10
6.47
1.15E-‐07
1.15E-‐07
GO:0044445~cytosolic
part
5.60E-‐10
6.58
1.69E-‐07
5.62E-‐08
GO:0034728~nucleosome
organization
1.61E-‐09
8.66
2.83E-‐06
1.42E-‐06
GO:0000786~nucleosome
1.84E-‐09
10.85
5.52E-‐07
1.38E-‐07
GO:0005840~ribosome
4.45E-‐09
5.14
1.34E-‐06
2.68E-‐07
GO:0032993~protein-‐DNA
complex
7.44E-‐09
8.56
2.24E-‐06
3.73E-‐07
GO:0006323~DNA
packaging
3.35E-‐08
6.88
5.91E-‐05
1.97E-‐05
GO:0022627~cytosolic
small
ribosomal
subunit
4.42E-‐08
13.15
1.33E-‐05
1.90E-‐06
GO:0006334~nucleosome
assembly
4.43E-‐08
8.31
7.81E-‐05
1.95E-‐05
GO:0006412~translation
5.51E-‐08
3.89
9.72E-‐05
1.94E-‐05
GO:0031497~chromatin
assembly
6.62E-‐08
8.02
1.17E-‐04
1.95E-‐05
GO:0065004~protein-‐DNA
complex
assembly
1.10E-‐07
7.67
1.94E-‐04
2.78E-‐05
GO:0006333~chromatin
assembly
or
disassembly
6.59E-‐07
5.92
1.16E-‐03
1.45E-‐04
GO:0015935~small
ribosomal
subunit
2.62E-‐06
8.35
7.87E-‐04
9.85E-‐05
GO:0005829~cytosol
3.45E-‐06
1.98
1.04E-‐03
1.15E-‐04
GO:0006325~chromatin
organization
7.78E-‐06
3.12
1.36E-‐02
1.52E-‐03
GO:0051276~chromosome
organization
1.21E-‐05
2.77
2.12E-‐02
2.14E-‐03
GO:0015934~large
ribosomal
subunit
3.66E-‐05
7.07
1.10E-‐02
1.10E-‐03
GO:0034621~cellular
macromolecular
complex
subunit
organization
3.83E-‐05
3.01
6.54E-‐02
6.13E-‐03
GO:0022625~cytosolic
large
ribosomal
subunit
7.19E-‐05
9.69
2.14E-‐02
1.97E-‐03
GO:0034622~cellular
macromolecular
complex
assembly
9.33E-‐05
3.04
1.52E-‐01
1.36E-‐02
GO:0005198~structural
molecule
activity
1.02E-‐04
2.31
4.89E-‐02
2.47E-‐02
50
Table
2.10.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
ReNcell
CX
cells
24
hrs
post-‐transfection.
Term
PValue
Fold
Enrichment
Bonferroni
Benjamini
GO:0006414~translational
elongation
2.14E-‐26
19.44
3.33E-‐23
3.33E-‐23
GO:0003735~structural
constituent
of
ribosome
1.97E-‐22
12.18
7.27E-‐20
7.27E-‐20
GO:0022626~cytosolic
ribosome
3.47E-‐22
21.04
9.29E-‐20
9.29E-‐20
GO:0005840~ribosome
1.35E-‐20
10.45
3.62E-‐18
1.81E-‐18
GO:0033279~ribosomal
subunit
2.67E-‐20
14.53
7.15E-‐18
2.38E-‐18
GO:0006412~translation
1.17E-‐16
6.81
1.72E-‐13
8.62E-‐14
GO:0044445~cytosolic
part
3.44E-‐16
11.21
8.93E-‐14
2.23E-‐14
GO:0022627~cytosolic
small
ribosomal
subunit
5.44E-‐14
25.18
1.46E-‐11
2.92E-‐12
GO:0030529~ribonucleoprotein
complex
2.53E-‐12
4.66
6.79E-‐10
1.13E-‐10
GO:0005198~structural
molecule
activity
2.54E-‐12
4.01
9.39E-‐10
4.70E-‐10
GO:0005829~cytosol
3.73E-‐12
2.91
9.99E-‐10
1.43E-‐10
GO:0015935~small
ribosomal
subunit
2.01E-‐11
15.99
5.39E-‐09
6.74E-‐10
GO:0015934~large
ribosomal
subunit
1.21E-‐08
12.72
3.25E-‐06
3.62E-‐07
GO:0022625~cytosolic
large
ribosomal
subunit
2.18E-‐08
18.35
5.85E-‐06
5.85E-‐07
GO:0003723~RNA
binding
2.63E-‐07
2.95
9.72E-‐05
3.24E-‐05
GO:0043228~non-‐membrane-‐
bounded
organelle
9.08E-‐07
1.82
2.43E-‐04
2.21E-‐05
GO:0043232~intracellular
non-‐
membrane-‐bounded
organelle
9.08E-‐07
1.82
2.43E-‐04
2.21E-‐05
GO:0042981~regulation
of
apoptosis
9.89E-‐05
2.35
1.42E-‐01
4.99E-‐02
GO:0043067~regulation
of
programmed
cell
death
1.16E-‐04
2.33
1.64E-‐01
4.39E-‐02
GO:0010941~regulation
of
cell
death
1.23E-‐04
2.32
1.73E-‐01
3.73E-‐02
GO:0016563~transcription
activator
activity
2.27E-‐04
2.93
8.04E-‐02
2.07E-‐02
GO:0000123~histone
acetyltransferase
complex
4.35E-‐04
9.30
1.10E-‐01
9.67E-‐03
GO:0003712~transcription
cofactor
activity
6.31E-‐04
2.92
2.08E-‐01
4.55E-‐02
GO:0051726~regulation
of
cell
cycle
6.37E-‐04
3.08
6.28E-‐01
1.52E-‐01
GO:0010604~positive
regulation
of
macromolecule
metabolic
process
6.40E-‐04
2.12
6.30E-‐01
1.32E-‐01
51
Table
2.11.
Top
twenty-‐five
significant
p-‐values
of
gene
ontology
categories
for
ReNcell
CX
cells
72
hrs
post-‐transfection.
Term
PValue
Fold
Enrichment
Bonferroni
Benjamini
GO:0043232~intracellular
non-‐
membrane-‐bounded
organelle
2.05E-‐03
1.63
3.46E-‐01
3.46E-‐01
GO:0043228~non-‐membrane-‐bounded
organelle
2.05E-‐03
1.63
3.46E-‐01
3.46E-‐01
GO:0005856~cytoskeleton
2.43E-‐03
1.95
3.96E-‐01
2.23E-‐01
GO:0032268~regulation
of
cellular
protein
metabolic
process
4.58E-‐03
2.88
9.84E-‐01
9.84E-‐01
GO:0044430~cytoskeletal
part
1.38E-‐02
1.97
9.44E-‐01
6.18E-‐01
GO:0044456~synapse
part
1.80E-‐02
3.34
9.76E-‐01
6.08E-‐01
GO:0050840~extracellular
matrix
binding
1.89E-‐02
14.01
9.97E-‐01
9.97E-‐01
GO:0005911~cell-‐cell
junction
2.24E-‐02
3.70
9.91E-‐01
6.09E-‐01
GO:0042734~presynaptic
membrane
2.48E-‐02
12.13
9.94E-‐01
5.79E-‐01
GO:0005198~structural
molecule
activity
2.71E-‐02
2.19
1.00E+00
9.84E-‐01
GO:0005829~cytosol
2.95E-‐02
1.68
9.98E-‐01
5.88E-‐01
GO:0045202~synapse
3.08E-‐02
2.64
9.98E-‐01
5.54E-‐01
GO:0005913~cell-‐cell
adherens
junction
3.51E-‐02
10.05
9.99E-‐01
5.61E-‐01
GO:0005912~adherens
junction
4.23E-‐02
3.78
1.00E+00
5.91E-‐01
GO:0006446~regulation
of
translational
initiation
4.23E-‐02
9.08
1.00E+00
1.00E+00
GO:0016023~cytoplasmic
membrane-‐
bounded
vesicle
4.30E-‐02
2.13
1.00E+00
5.63E-‐01
GO:0031988~membrane-‐bounded
vesicle
5.09E-‐02
2.06
1.00E+00
5.94E-‐01
GO:0005925~focal
adhesion
5.52E-‐02
4.60
1.00E+00
5.95E-‐01
GO:0070161~anchoring
junction
5.79E-‐02
3.41
1.00E+00
5.86E-‐01
GO:0005924~cell-‐substrate
adherens
junction
6.06E-‐02
4.43
1.00E+00
5.78E-‐01
GO:0019226~transmission
of
nerve
impulse
6.13E-‐02
2.48
1.00E+00
1.00E+00
GO:0030055~cell-‐substrate
junction
6.91E-‐02
4.19
1.00E+00
6.04E-‐01
GO:0044449~contractile
fiber
part
7.06E-‐02
4.15
1.00E+00
5.90E-‐01
GO:0030054~cell
junction
7.18E-‐02
2.04
1.00E+00
5.75E-‐01
GO:0015629~actin
cytoskeleton
7.77E-‐02
2.62
1.00E+00
5.86E-‐01
52
Figure
2.4.
Potential
mechanisms
by
which
MSNP1AS
alters
neuronal
architecture.
MSNP1AS
overexpression
decreases
the
expression
of
moesin
protein,
as
well
as
the
expression
of
genes
involved
in
protein
synthesis
and
chromatin
organization,
leading
to
a
decrease
in
neurite
complexity.
molecular
functions:
MSNP1AS
alters
expression
of
genes
that
contribute
to
chromatin
organization;
MSNP1AS
binds
MSN
and
alters
the
translation
of
moesin
protein;
and
MSNP1AS
alters
the
expression
of
genes
that
regulate
translation
more
globally
(Figure
2.4).
MSNP1AS
was
discovered
as
the
functional
element
revealed
by
an
ASD
GWAS
(74).
The
allele
frequencies
of
the
chromosome
5p14.1
genetic
variants
with
genome-‐wide
significant
association
are
high:
greater
than
half
the
population
carries
at
least
one
copy
of
the
risk
allele
(26).
It
is
unclear
whether
individuals
who
are
homozygous
for
the
rs4307059
risk
allele
are
at
higher
risk
for
ASD
than
those
heterozygous
at
rs4307059.
However,
the
expression
of
MSNP1AS
is
increased
in
the
postmortem
temporal
cortex
of
individuals
who
are
homozygous
for
rs4307059
compared
to
individuals
who
are
heterozygous
for
rs4307059
(74).
It
is
striking
that
the
biological
functions
of
this
ASD-‐associated
lncRNA—protein
synthesis
and
chromatin
organization—are
matched
closely
to
the
biological
functions
revealed
by
genes
with
rare
de
novo
mutations
associated
with
ASD
(161,
162).
These
results
suggest
that
both
common
and
53
rare
ASD-‐associated
variants
converge
upon
the
common
molecular
pathways.
Further,
the
chromosome
5p14.1
genetic
marker
with
the
most
significant
association
(rs4307059
with
ASD
association
p
=
10
−10
)
also
contributes
to
altered
social
communication
in
a
general
population
sample
(73).
Together,
these
data
suggest
common
molecular
pathways
that
contribute
to
social
communication
that
involve
protein
translation
and
chromatin
organization.
MSNP1AS
is
antisense
to
moesin
(membrane-‐organizing
extension
spike
protein).
Moesin
is
a
member
of
the
ERM
(exrin/radixin/moesin)
family
of
proteins
that
link
the
actin
filaments
to
the
cellular
membrane.
Along
with
radixin,
moesin
protein
localizes
to
growth
cones
and
filopodia
that
emanate
from
neurite
shafts,
both
regions
of
high
motility
and
growth.
These
proteins
are
especially
important
during
development,
as
shown
in
rat
cerebral
cortex.
In
rats,
ERM
protein
expression
reaches
a
maximum
near
birth
and
gradually
declines
through
postnatal
development
(147),
which
is
during
synaptic
maturation.
In
the
intermediate
zone
of
developing
rat
cerebral
cortex,
ERM
proteins
are
significantly
expressed
in
neurite
extensions
at
embryonic
day
17
(146),
a
time
of
substantial
synapse
formation.
They
regulate
adhesion
receptors,
signaling
molecules
that
provide
spatial
information
to
the
cell,
and
growth
cone
actin
to
mediate
attractive
growth
cone
guidance
(163).
When
antisense
RNA
is
used
to
knockdown
moesin
in
cultured
hippocampal
and
cortical
neurons,
specific
phenotypes
are
observed,
including
a
dramatic
reduction
in
the
rate
of
neurite
advancement
(147),
suppression
of
neurite
formation
(145),
growth
cone
collapse
(147),
suppression
of
an
increase
in
dendritic
spine
formation
induced
by
estrogen
(149),
and
suppression
of
an
increase
in
active
presynaptic
boutons
induced
by
glutamate
(148).
In
addition,
ERM
proteins
have
been
shown
to
mediate
neuritogenesis
(164).
These
data
denote
that
the
function
of
moesin
involves
both
regulating
54
axonal
growth
cone
development
presynaptically
and
initiating
dendritic
spine
development
postsynaptically.
Moesin
knockout
mice
have
not
been
evaluated
for
behavioral
phenotypes
or
brain
development.
While
MSN
transcripts
did
not
undergo
a
significant
change
in
gene
expression,
significant
alterations
are
observed
in
neurite
development.
As
we
previously
described,
overexpressed
MSNP1AS
binds
MSN
transcript
and
prevents
translation
of
moesin
protein.
The
current
study
expands
our
knowledge
of
MSNP1AS
overexpression
in
two
ways.
First,
the
observation
that
MSNP1AS
overexpression
does
not
alter
expression
of
MSN
transcript
indicates
that
MSNP1AS
does
not
directly
contribute
to
the
regulation
of
the
MSN
transcription.
Instead,
in
relation
to
the
regulation
of
moesin,
it
appears
that
MSNP1AS
acts
only
to
inhibit
the
translation
of
moesin
protein.
Second,
MSNP1AS
acts
more
globally
than
just
inhibiting
the
translation
of
moesin.
The
overexpression
of
MSNP1AS
caused
changes
in
the
expression
of
genes
involved
in
the
protein
synthesis
and
chromatin
regulation.
These
transcriptional
changes
suggest
that
MSNP1AS
participates
in
global
biological
processes
that
impact
neuronal
differentiation
beyond
its
impact
on
moesin.
Additional
experiments
will
be
necessary
to
determine
the
relative
contributions
of
MSNP1AS
to
the
biological
processes
of
chromatin
regulation,
protein
synthesis,
and
the
regulation
of
moesin.
It
will
be
important
to
determine
the
expression
of
moesin
protein
and
the
MSNP1AS
ncRNA
in
neurons
derived
from
patients
with
the
ASD-‐associated
rs4307059
allele,
as
well
as
from
patients
with
Cri-‐du-‐chat
syndrome
with
deletion
of
chromosome
5p14.1,
as
these
experiments
may
provide
further
evidence
of
a
contribution
of
MSNP1AS
to
altered
brain
development.
It
will
also
be
important
to
determine
if
individuals
with
ASD
and
the
ASD-‐associated
rs4307059
allele
exhibit
common
comorbid
55
features
of
ASD,
such
as
gastrointestinal
disorders
or
epilepsy.
A
network
analysis
of
microarray
data
from
postmortem
ASD
brain
placed
MSN
as
a
central
node
(165).
However,
there
was
no
enrichment
of
the
genes
in
the
MSN
node
and
the
genes
with
altered
expression
following
MSNP1AS
over-‐expression
identified
here.
In
this
study,
overexpressed
MSNP1AS
results
in
shortened
neurites
and
fewer
neurites
per
cell,
indicating
that
neuritogenesis
and
extension
are
both
impacted.
The
decreased
neurite
number
and
growth
upon
overexpression
of
MSNP1AS
reinforces
studies
that
suggest
that
altered
short-‐
and
long-‐range
connectivity
in
the
brains
of
autism
patients
may
be
contributing
to
the
pathogenesis
of
the
disorder
(166).
This
is
in
line
with
global
observations
of
decreased
connectivity
in
the
brains
of
individuals
with
ASD
(151-‐155,
167),
although
there
is
evidence
for
increased
connectivity
between
some
brain
regions
in
ASD
(154,
155).
NcRNA
comprise
90%
of
the
human
genome
and
are
believed
to
contribute
to
regulatory
function
as
enhancers
and
promoters
(168,
169).
These
elements
differentiate
across
space
and
time
in
a
significant
way,
but
the
exact
functional
roles
of
most
lncRNAs
have
not
been
quantified
(170-‐172).
The
contributions
of
lncRNAs
to
psychiatric
disorders
are
a
topic
of
ongoing
research
(173).
The
data
from
this
study
provide
further
evidence
for
pathways
reported
to
influence
ASD,
as
well
as
giving
additional
insight
into
the
molecular
function
of
the
ASD-‐associated
lncRNA,
MSNP1AS.
2.6
Conclusion
We
previously
identified
the
lncRNA
MSNP1AS
as
a
functional
element
revealed
by
genome
wide
significant
association
with
ASD.
Using
a
candidate
gene
approach,
we
showed
56
that
one
function
of
MSNP1AS
is
to
regulate
the
expression
of
moesin
protein.
The
data
presented
here
indicate
that
MSNP1AS
over-‐expression
does
not
significantly
alter
the
expression
of
the
MSN
transcript,
suggesting
that
MSNP1AS
functions
specifically
at
regulating
the
translation
of
the
MSN
transcript
to
moesin
protein.
Further,
the
unbiased
RNA
sequencing
data
revealed
that
over-‐expression
of
MSNP1AS
alters
the
expression
of
genes
involved
in
protein
synthesis
and
chromatin
organization.
These
biological
processes
are
also
implicated
by
rare
mutations
associated
with
ASD,
suggesting
convergent
molecular
mechanisms
that
contribute
to
the
altered
brain
development
of
ASD.
2.7
Acknowledgements
This
work
was
supported
by
National
Institute
of
Mental
Health
grants
R01MH100172
(to
Daniel
B.
Campbell)
and
R21MH099504
(to
Daniel
B.
Campbell).
Chapter
3:
Transcriptional
Gene
Silencing
of
the
Autism-‐Associated
Long
Noncoding
RNA
MSNP1AS
in
Human
Neural
Progenitor
Cells
These
data
were
published
in
Developmental
Neuroscience
(69).
3.1
Abstract
The
lncRNA,
MSNP1AS
(moesin
pseudogene
1,
antisense),
is
a
functional
element
that
was
previously
associated
to
autism
spectrum
disorder
(ASD)
with
genome
wide
significance.
Expression
of
MSNP1AS
was
increased
12-‐fold
in
the
cerebral
cortex
of
individuals
with
ASD
and
22-‐fold
in
individuals
with
a
genome-‐wide
significantly
associated
ASD
genetic
marker
on
57
chromosome
5p14.1.
Overexpression
of
MSNP1AS
in
human
neuronal
cells
caused
decreased
expression
of
moesin
protein,
which
is
involved
in
neuronal
process
stability.
In
this
study,
we
hypothesize
that
MSNP1AS
knockdown
impacts
global
transcriptome
levels.
We
transfected
the
human
neural
progenitor
cell
line,
SK-‐N-‐SH,
with
constructs
that
caused
a
50%
suppression
of
MSNP1AS
expression.
After
24
hours,
cells
were
harvested
for
total
RNA
isolation.
Strand-‐
specific
RNA-‐Seq
analysis
indicated
altered
expression
of
1,352
genes,
including
altered
expression
of
318
genes
following
correction
for
multiple
comparisons.
Expression
of
the
OAS2
gene
was
increased
>150-‐fold,
a
result
that
was
validated
by
quantitative
PCR.
Gene
ontology
analysis
of
the
318
genes
with
altered
expression
following
correction
for
multiple
comparisons
indicated
that
upregulated
genes
were
significantly
enriched
for
genes
involved
in
immune
response
and
downregulated
genes
were
significantly
enriched
for
genes
involved
in
chromatin
remodeling.
These
data
indicate
multiple
transcriptional
and
translational
functions
of
MSNP1AS
that
impact
ASD-‐relevant
biological
processes.
Chromatin
remodeling
and
immune
response
are
biological
process
implicated
by
genes
with
rare
mutations
associated
with
ASD.
Our
data
suggest
that
the
functional
elements
implicated
by
association
of
common
genetic
variants
impact
the
same
biological
processes,
suggesting
a
possible
shared
common
molecular
pathway
of
ASD.
3.2
Introduction
Autism
spectrum
disorder
(ASD)
is
a
neurodevelopmental
condition
characterized
by
deficits
in
social
communication
and
behavioral
flexibility
(140,
174).
Recent
advances
in
genomics
have
begun
to
identify
the
genetic
variants
that
contribute
to
ASD
risk
(26,
175,
176).
58
The
important
steps
are
to
translate
these
genetic
findings
to
a
biological
understanding
of
ASD
pathogenesis
and
to
development
biologically
based
treatments
(1).
Recently,
we
identified
a
lncRNA
that
contributes
to
ASD
risk
(74).
A
GWAS
identified
a
single
peak
on
chromosome
5p14.1
for
ASD,
indicating
a
significant
association
(P
=
10
-‐10
)
for
rs4307059
(26).
The
same
rs4307059
allele
was
also
associated
with
social
communication
phenotypes
in
a
general
population
sample
(73).
Directly
under
the
chromosome
5p14.1
ASD
GWAS
peak,
we
identified
a
lncRNA
that
was
almost
4
kb
(74).
This
transcript
is
94%
identical
and
anti-‐sense
to
the
X
chromosome
transcript
MSN,
which
encodes
a
neuronal
architecture
protein,
and
is
on
the
opposite
(antisense)
strand
of
moesin
pseudogene
1
(MSNP1)
(74).
Therefore,
we
named
the
newly
discovered
gene
MSNP1AS
(moesin
pseudogene
1,
anti-‐sense)
(74).
Expression
of
MSNP1AS
was
detected
in
5
brain
regions
(frontal
cerebral
cortex,
temporal
cerebral
cortex,
occipital
cerebral
cortex,
cerebellum
and
spinal
cord)
of
the
adult
and
in
fetal
frontal
cerebral
cortex
(74).
In
peripheral
tissues,
MSNP1AS
was
expressed
at
high
levels
in
the
adult
peripheral
blood
and
fetal
heart
(74).
These
data
indicate
that
MSNP1AS
is
expressed
at
high
levels
in
both
the
nervous
and
circulatory
systems.
Here
we
use
publicly
available
databases
of
gene
expression
to
provide
additional
details
of
MSNP1AS
expression.
MSNP1AS
expression
is
increased
12.7
times
in
the
postmortem
temporal
cerebral
cortex
of
individuals
with
ASD
compared
to
controls
and
increased
22
times
in
individuals
with
the
ASD-‐associated
rs4307059
allele
(74).
Therefore,
the
highly
significant
GWAS
findings
led
to
identification
of
a
functional
lncRNA
that
contributes
to
ASD
risk
(74).
Jiang
et
al
(177)
recently
reported
rescue
of
cellular
phenotypes
of
Down’s
syndrome
by
transgenic
expression
of
another
lncRNA,
XIST
(X
inactive
specific
transcript).
When
XIST,
which
59
normally
targets
the
“extra”
X
chromosome
of
females
for
inactivation,
was
expressed
on
chromosome
21
in
cells
derived
from
Down’s
syndrome
patients,
the
extra
copy
of
chromosome
21
was
inactivated
(177).
These
studies
provide
a
clear
path
toward
translation
of
manipulating
lncRNA
expression
to
rescue
disorder
phenotypes.
Because
MSNP1AS
is
over-‐
expressed
in
postmortem
cerebral
cortex
of
individuals
with
ASD
(74),
we
used
transcriptional
gene
silencing
in
human
neuronal
progenitor
cells
to
examine
the
potential
for
rescue.
By
targeting
the
MSNP1AS
gene
promoter,
we
were
able
to
reduce
the
expression
of
MSNP1AS
transcript
to
half
of
its
normal
expression
and
mimic
a
potential
therapeutic
for
ASD
and
social
communication
deficits.
RNA
sequencing
was
used
as
an
unbiased
approach
to
identify
genes
with
altered
expression
following
knockdown
of
MSNP1AS.
3.3
Materials
and
Methods
3.3.1
Cell
culture
SK-‐N-‐SH
neuroblastoma
neural
progenitor
cells
were
demonstrated
previously
to
express
levels
of
MSNP1AS,
MSN,
and
moesin
protein
consistent
with
those
of
developing
human
brain
(74).
The
human
neural
progenitor
cell
line
SK-‐N-‐SH
(American
Type
Culture
Collection;
Manassas,
VA,
USA)
was
cultured
in
minimum
essential
medium
supplemented
with
10%
heat-‐inactivated
fetal
bovine
serum,
1%
penicillin/streptomycin,
nonessential
amino
acids,
and
1.5
g/L
sodium
bicarbonate
in
a
75
cm
2
flask
at
37
̊C
and
5%
CO
2
.
When
cells
were
75%
confluent,
the
human
neuronal
cells
were
subcultured
to
a
density
of
1
x
10
6
cells
per
well
in
a
6
well
plate
for
harvests
24
hours
post-‐transfection.
60
3.3.2
Design
of
antisense
RNA
to
the
MSNP1AS
proximal
gene
promoters
Small
noncoding
antisense
RNAs
(sasRNA)
to
silence
gene
transcription
were
designed
with
proprietary
software
(178-‐181).
The
program
was
used
to
identify
the
best
sasRNA
to
silence
the
lncRNA
MSNP1AS
gene
transcript
(Figure
3.1).
We
cloned
sasRNAs
directed
to
the
putative
gene
promoter
into
the
pCDNA3-‐U6M2
expression
vector
with
the
following
target
sequence
for
MSNP1AS
knockdown
(KD):
AATTCTAGAAATGTGCCAACA
(Figure
3.1).
Figure
3.1.
Small
noncoding
antisense
RNAs
(sasRNAs)
were
designed
to
silence
transcription
by
targeting
the
MSNP1AS
gene
promoter.
3.3.3
Transfection
of
small
interfering
RNAs
(sasRNAs)
Targets
for
transcriptional
gene
silencing
(TGS)
of
MSNP1AS
were
defined
in
human
neuronal
cell
lines.
Eight
sasRNAs
were
designed
directed
to
MSNP1AS
TGS
targets.
The
TGS
were
screened
for
effectiveness
in
U87
or
Lan
6
cells.
The
TGS
sasRNAs
were
screened
for
efficacy
and
three
were
chosen
for
use
in
SK-‐N-‐SH
cells.
Each
transfection
set
was
considered
an
independent
experiment.
The
three
sasRNA
were
tested
at
5
different
vector
concentrations
(Table
3.1).
Cells
harvest
occurred
24
hours
after
transfection.
Based
on
optimization
experiments,
we
used
1
ug
of
sasRNA
per
million
cells
to
achieve
~50%
suppression
of
MSNP1AS
in
further
studies.
61
Table
3.1.
MSNP1AS
Fold
Change
After
Transfection.
0.5
ug
1
ug
2
ug
3
ug
4
ug
AS-‐1
0.96
0.17
0.19
0.41
0.78
AS-‐2
3.04
0.15
0.49
0.52
AS-‐3
0.55
0.19
0.77
0.35
Cells
were
transfected
using
Amaxa
Nucleofector
(Lonza;
Walkersville,
MD,
USA)
technology
and
subcultured
into
6
well
plates.
One
ml
of
fresh
prewarmed
media
was
added
to
each
well
and
the
cells
were
centrifuged
twice
at
130xg
for
10
min
for
the
SK-‐N-‐SH
cells
with
PBS
washes
of
7
ml
and
4
ml,
respectively.
The
cell
pellet
was
resuspended
in
Nucleofector
solution
containing
supplement
using
1
x
10
6
cells
per
well.
One
hundred
microliters
of
cell/nucleofector
solution
was
added
to
a
new
cuvette
and
placed
in
the
Amaxa
Nucleofector
using
the
T-‐16
program.
Five
hundred
microliters
of
fresh
prewarmed
media
was
added
to
the
cuvette,
mixed,
and
transferred
to
the
well
with
a
disposable
pipet.
Two
ml
culture
medium
was
added
to
the
transfected
cells
in
the
6
well
plate.
The
cells
were
incubated
at
37
̊C
in
5%
CO
2
until
harvest.
Each
experiment
was
repeated
4
times.
3.3.4
Neural
progenitor
cell
harvest
After
24
hours,
the
culture
medium
was
removed
and
discarded.
The
cells
were
washed
with
1
ml
of
PBS
wash
and
the
PBS
wash
was
aspirated.
One
ml
of
Trypsin/EDTA
solution
was
added
to
each
well
and
the
cells
were
incubated
for
4
min.
Five
ml
of
cell
type-‐specific
medium
was
added
and
cells
were
triturated
and
transferred
to
15
ml
conical
tubes.
SK-‐N-‐SH
cells
were
centrifuged
at
130xg
for
10
min.
The
supernatant
was
aspirated
and
5
ml
of
PBS
added.
The
cell
pellet
was
triturated
and
centrifuged
at
130xg
for
10
min.
The
PBS
wash
was
aspirated
and
1
ml
of
fresh
PBS
was
added.
Half
of
the
solution
was
transferred
to
each
of
two
1.5
ml
eppendorf
tubes.
The
tubes
were
centrifuged
at
4
̊C
at
130xg
for
10
min.
The
supernatant
was
decanted
62
and
the
cell
pellets
were
frozen
at
-‐80
̊C.
3.3.5
RNA
purification
The
Qiagen
RNEasy
kit
was
used
to
isolate
total
RNA
using
vacuum
technology
according
to
the
manufacturer’s
protocol
(Qiagen;
Valencia,
CA,
USA).
The
RNA
was
eluted
with
35
microliters
of
RNAse-‐free
water
and
quantified
using
the
NanoDrop
ND-‐1000
Spectrophotometer
(v3.1.2)
(Thermo
Fisher
Scientific;
Waltham,
MA,
USA).
The
RNA
was
stored
at
-‐80
̊C.
3.3.6
Quantitative
RT-‐PCR
(qPCR)
to
confirm
MSNP1AS
knockdown
To
confirm
knockdown
of
MSNP1AS,
cDNA
was
synthesized
using
the
SuperScript
III
First-‐Strand
Synthesis
System
for
qRT-‐PCR
protocol
(Invitrogen/Thermo
Fisher
Scientific;
Waltham,
MA,
USA).
Five
hundred
nanograms
of
RNA
were
used
to
make
one
and
a
half
reaction
volumes
(35
ml)
of
cDNA.
The
cDNA
was
stored
at
-‐20
̊C.
The
qRT-‐PCR
protocol
described
in
Kerin
et
al
(74)
was
used
to
validate
knockdown
of
MSNP1AS.
3.3.7
Construction
of
strand-‐specific,
ribosomal
RNA
depleted
RNA
sequencing
libraries
Directional
RNA-‐Seq
libraries
were
prepared
for
Illumina
HiSeq
2000
sequencing
using
the
Stranded
Total
TruSeq
RNA
Sample
Preparation
kit
with
Ribo-‐Zero
Gold
(Illumina)
using
manufacturer’s
protocol
using
the
Hamilton
Starlet
Liquid
Handling
robot.
One
nanogram
of
RNA
was
used
for
each
sample.
Ribo-‐Zero
was
used
to
deplete
cytoplasmic
and
mitochondrial
rRNA
from
total
RNA.
The
depleted
RNA
was
fragmented
and
primed
with
random
hexamers
to
synthesize
first
strand
cDNA
using
Superscript
II
(Life
Technologies).
Next,
the
second
strand
was
synthesized,
incorporating
dUTP
in
place
of
dTTP.
A
single
‘A’
base
was
added
to
the
3’
ends
of
the
fragments
and
the
indexed
adaptors
were
ligated
to
the
ends
of
the
ds
cDNA
to
63
prepare
them
for
hybridization
onto
the
flow
cell.
PCR
was
used
to
selectively
enrich
the
fragments
with
ligated
adapters
and
to
amplify
the
amount
of
DNA
in
the
library.
The
libraries
were
produced
in
a
96-‐well
format
and
quality
controlled
using
the
Agilent
Technologies
2200
TapeStation
Instrument.
Libraries
were
pooled
(four
samples
per
lane)
and
sequenced
on
Illumina
HiSeq
2000
to
a
targeted
depth
generating
an
average
of
20
million
paired-‐end
50-‐
cycle
reads
for
each
sample
(Table
3.2).
Table
3.2.
MSNP1AS
Stranded
Cuffdiff
Tophat
Read
Count.
Sample
input
mapped
%
mapped
MSNP1AS
KD
Biological
Replicate
1
30
943
117
29
969
022
96.9
MSNP1AS
KD
Biological
Replicate
2
16
186
531
15
361
602
94.9
MSNP1AS
KD
Biological
Replicate
3
17
890
746
16
932
628
94.6
MSNP1AS
KD
Biological
Replicate
4
28
801
262
27
527
759
95.6
Control
Biological
Replicate
1
11
099
318
10
746
390
96.8
Control
Biological
Replicate
2
20
496
459
19
422
292
94.8
Control
Biological
Replicate
3
10
824
013
10
458
919
96.6
Control
Biological
Replicate
4
31
400
592
29
709
967
94.6
3.3.8
Data
analysis
Data
analysis
was
performed
using
TopHat
(version
2.0.10)
(156)
to
align
the
Illumina
short
reads
against
the
reference
human
genome
ENSEMBL
GrCH38
version
81.
Sequence
alignments
were
generated
as
BAM
files
(157),
and
then
Cuffdiff
(version
2.2.1)
(158)
was
used
to
summarize
the
gene
expression
values
as
FPKM
measures.
The
gene
expression
of
samples
with
MSNP1AS
knocked
down
was
compared
to
the
gene
expression
of
the
negative
control
experiment
samples
to
find
other
differentially-‐expressed
genes.
Cuffdiff
was
also
used
to
calculate
the
expression
fold
change,
P-‐values
and
FDR
values.
Genes
with
FDR
q<0.05
were
used
as
the
input
for
DAVID
(version
6.7)
functional
annotation
(159,
160).
3.3.9
qPCR
to
confirm
altered
gene
expression
observed
in
RNA-‐seq
Ten
RNA
with
q<0.05
in
the
RNA-‐seq
results
were
selected
to
validate
by
qPCR
using
64
Taqman
Gene
Expression
assays
from
Life
Technologies:
ABCA1
(Assay
ID
Hs01059118_m1),
ANGPTL4
(Assay
ID
Hs01101127_m1),
ATF3
(Assay
ID
Hs00231069_m1),
CXCL8
(Assay
ID
H
s00174103_m1),
GADD45B
(Assay
ID
Hs04188837_g1),
ID1
(Assay
ID
Hs03676575_s1),
INSM2
(Assay
ID
Hs00261625_s1),
MIR320A
(Assay
ID
Hs04233529_s1),
OAS2
(Assay
ID
Hs00942643_m1),
and
TMEM215
(Assay
ID
Hs01942498_s1).
3.3.10
Analysis
of
Brainspan
data
Normalized
expression
data
representing
reads
per
kilobase
per
million
mapped
reads
(RPKM)
were
downloaded
from
the
BrainSpan
Atlas
of
the
Developing
Human
Brain
database.
Details
on
the
tissues,
materials,
and
sequencing
protocols
are
available
on
the
BrainSpan
website
(www.brainspan.org).
Data
used
in
this
analysis
were
restricted
to
neocortical
brain
tissues
given
the
importance
of
these
regions
in
ASD
pathology
as
well
as
the
altered
expression
of
MSNP1AS
in
these
regions
of
the
brain
(primary
auditory
cortex,
dorsolateral
prefrontal
cortex,
inferior
parietal
cortex,
inferolateral
termporal
cortex,
primary
motor
cortex,
medial
prefrontal
cortex,
orbital
frontal
cortex,
primary
somatosensory
cortex,
superior
temporal
cortex,
primary
visual
cortex,
ventrolateral
prefrontal
cortex).
The
data
was
also
partitioned
into
10
age
groups
to
identify
the
expression
pattern
of
MSNP1AS
across
age.
Only
genes
showing
a
RPKM
>
1
in
at
least
one
sample
were
used
in
this
analysis
to
reduce
the
amount
of
noise
generated
from
genes
showing
extremely
low
expression
values
in
all
samples.
All
RPKM
values
were
then
transformed
to
the
log2+1
as
suggested
when
constructing
a
weighted
gene
co-‐expression
network.
65
3.4
Results
Expression
of
the
ASD-‐associated
lncRNA
MSNP1AS
is
increased
12
times
in
the
cerebral
cortex
of
individuals
with
ASD
compared
to
controls
(74).
We
used
transcriptional
gene
silencing
to
knock
down
MSNP1AS
and
thus
mimic
a
potential
therapeutic
for
ASD.
To
quantitatively
model
the
consequences
of
MSNP1AS
knockdown,
sasRNA-‐mediated
knockdown
of
MSNP1AS
was
performed
in
the
human
neural
progenitor
cell
line,
SK-‐N-‐SH.
The
knockdown
efficiency
of
MSNP1AS
transcript
levels
was
confirmed
via
qPCR
(Figure
3.2).
3.4.1
MSNP1AS
knockdown
results
in
genome-‐wide
transcriptional
changes
Changes
in
the
transcriptome
caused
by
MSNP1AS
knockdown
may
be
able
to
reveal
the
impact
of
biologically-‐based
therapeutic
approaches
on
human
neural
progenitor
cell
differentiation.
The
transcriptional
changes
resulting
from
the
knockdown
were
evaluated
using
genome-‐wide
transcriptome
profiling
of
human
neuronal
progenitor
cells,
SK-‐N-‐SH,
via
RNA-‐
seq.
Differential
gene
expression
analysis
using
the
Ensembl
GrCH38
genome
annotation
revealed
1,352
differentially
expressed
genes
(p<0.05)
(Table
3.3).
When
a
more
stringent
test
for
multiple
comparisons
was
applied,
a
significance
level
of
q<0.05
showed
318
differentially
expressed
genes
(Table
3.4,
Figure
3.3),
with
65%
of
the
genes
down-‐regulated.
Subsequent
qPCR
validation
was
performed
for
genes
with
large
changes
in
expression
following
MSNP1AS
knockdown.
Comparison
of
gene
expression
levels
measured
by
RNA-‐Seq
and
qPCR
indicated
a
highly
significant
Pearson
correlation
coefficient
(Figure
3.4),
validating
the
magnitude
of
gene
expression
changes
identified
by
RNA-‐seq.
66
3.4.2
Transcriptional
consequences
of
MSNP1AS
knockdown
GO
analysis
using
the
DAVID
web
server
of
the
MSNP1AS
knockdown
revealed
functions
important
in
chromatin
assembly
and
nucleosome
organizaton
(Figure
3.5).
We
also
performed
GO
analysis
grouping
the
differentially
expressed
genes
(q<0.05)
by
their
direction
of
differential
expression.
Genes
that
are
up-‐regulated
by
MSNP1AS
knockdown
were
enriched
for
immune
response
(Figure
3.6A).
Genes
that
are
down-‐regulated
by
MSNP1AS
knockdown
were
enriched
for
nuclesosome
assembly
and
chromatin
assembly
(Figure
3.6B).
GO
analysis
indicates
that
genes
encoding
proteins
involved
in
protein
synthesis
and
chromatin
regulation
were
altered
in
expression
following
MSNP1AS
transcriptional
gene
silencing.
There
is
no
evidence
of
enrichment
of
genes
in
the
moesin
pathway,
suggesting
that
inhibition
of
moesin
protein
translation
is
independent
of
transcriptional
regulation.
Comparison
of
genes
with
altered
expression
following
MSNP1AS
transcriptional
gene
silencing
did
not
reveal
enrichment
of
genes
previously
implicated
in
ASD
(182).
3.4.3
Expression
of
MSNP1AS
in
developing
human
brain
Analysis
of
Brainspan
data
indicate
high
levels
of
MSNP1AS
in
the
cerebral
cortex
during
early
embryogenesis
and
again
immediately
after
birth
(Figure
3.7).
GTex
indicates
levels
of
expression
of
MSNP1AS
at
low
to
undetectable
levels
in
12
regions
of
adult
brain.
However,
GTex
indicates
high
levels
of
MSNP1AS
expression
in
peripheral
blood,
consistent
with
our
previous
report
(74).
67
Table
3.3.
Top
thirty
differentially
expressed
genes
in
SK-‐N-‐SH
cells
24
hrs
after
MSNP1AS
overexpression.
gene
locus
log2(fold_change)
p_value
q_value
OAS2
12:112906776-‐113017751
-‐7.37
5.00E-‐05
4.12E-‐03
TMEM215
9:32783498-‐32787399
-‐4.41
5.00E-‐05
4.12E-‐03
IFI44L
1:78619921-‐78646145
-‐3.87
5.00E-‐05
4.12E-‐03
LRRC55
11:57181746-‐57191717
-‐3.77
5.00E-‐05
4.12E-‐03
CCL2
17:34255217-‐34257203
-‐3.49
5.00E-‐05
4.12E-‐03
IFI6
1:27666060-‐27703063
-‐3.29
5.00E-‐05
4.12E-‐03
CTD-‐2369P2.8
19:10251900-‐10289019
-‐3.25
5.00E-‐05
4.12E-‐03
LCP1
13:46125919-‐46211871
-‐3.08
5.00E-‐05
4.12E-‐03
SOD2
6:159669056-‐159789749
-‐2.94
5.00E-‐05
4.12E-‐03
MX1
21:41420303-‐41459214
-‐2.93
5.00E-‐05
4.12E-‐03
C3
19:6677703-‐6737603
-‐2.92
5.00E-‐05
4.12E-‐03
C1S
12:6970892-‐7071032
-‐2.79
5.00E-‐05
4.12E-‐03
TLR2
4:153701499-‐153705699
-‐2.63
5.00E-‐05
4.12E-‐03
ICAM1
19:10251900-‐10289019
-‐2.60
5.00E-‐05
4.12E-‐03
IL32
16:3065296-‐3087100
-‐2.52
5.00E-‐05
4.12E-‐03
VCAM1
1:100719741-‐100739045
-‐2.48
5.00E-‐05
4.12E-‐03
IFI27
14:94104835-‐94116698
-‐2.45
5.00E-‐05
4.12E-‐03
CMPK2
2:6840569-‐6898239
-‐2.41
5.00E-‐05
4.12E-‐03
CFB
6:31897784-‐31952084
-‐2.40
5.00E-‐05
4.12E-‐03
TNFSF18
1:172775904-‐173064015
-‐2.39
5.00E-‐05
4.12E-‐03
DDX60
4:168216292-‐168318807
-‐2.36
5.00E-‐05
4.12E-‐03
CTSS
1:150730195-‐150765957
-‐2.32
5.00E-‐05
4.12E-‐03
IFIH1
2:162267078-‐162318703
-‐2.32
5.00E-‐05
4.12E-‐03
HERC5
4:88457116-‐88506163
-‐2.29
5.00E-‐05
4.12E-‐03
MMP12
11:102862735-‐102875034
-‐2.21
5.00E-‐05
4.12E-‐03
CBLN4
20:55997439-‐56005472
-‐2.18
5.00E-‐05
4.12E-‐03
PTGS2
1:186671790-‐186680427
-‐2.12
5.00E-‐05
4.12E-‐03
DDX58
9:32455704-‐32526324
-‐2.05
5.00E-‐05
4.12E-‐03
CFH
1:196651877-‐196747504
-‐2.02
5.00E-‐05
4.12E-‐03
IFI44
1:78649795-‐78664078
-‐2.02
5.00E-‐05
4.12E-‐03
68
Figure
3.2.
Quantitation
of
MSNP1AS
knockdown.
Quantitative
PCR
showed
that
a
1
ug
transfection
of
MSNP1AS
sasRNA
resulted
in
an
~45%
reduction
of
MSNP1AS
transcript
(*p<0.05
by
Student
t-‐test).
0
0.2
0.4
0.6
0.8
1
1.2
Control MSNP1AS
Relative expression
*
69
Figure
3.3.
Volcano
plot
generated
to
show
the
differentially
expressed
genes
resulting
from
the
MSP1AS
knockdown.
Each
dot
represents
a
change
in
gene
expression,
with
genes
above
the
FDR-‐corrected
significance
threshold
shown
in
red.
70
Figure
3.4.
Confirmation
of
altered
expression
of
genes
identified
by
RNA
seq.
Ten
genes
with
large
changes
in
gene
expression
following
MSNP1AS
knockdown
were
assayed
via
qPCR
to
establish
the
validity
of
the
RNA-‐seq
differential
expression
analysis.
Knockdown
of
MSNP1AS
was
also
validated.
qPCR
and
RNA-‐seq
values
showed
a
statistically
significant
correlation
with
both
Pearson
(P=0.0002)
and
Spearman
(P=0.009)
methods.
OAS2
GADD45B
CXCL8
ABCA1
INSM2
ANGPTL4
ATF3
ID1
MIR320A
TMEM215
MSNP1AS
R² = 0.79821
-‐6
-‐4
-‐2
0
2
4
6
8
-‐3
-‐2
-‐1
0
1
2
3
4
5
6
RNA Seq -log2 fold change
qPCR -DDCt
71
3.5
Discussion
Our
findings
indicate
that
MSNP1AS
knockdown
disrupted
the
expression
of
318
genes
(q<0.05),
many
of
which
are
involved
in
chromatin
organization
and
immune
response.
MSNP1AS
is
the
functional
element
revealed
by
genome-‐wide
significant
association
with
ASD
(26,
74),
and
is
increased
12.7-‐fold
in
the
postmortem
cerebral
cortex
of
individuals
with
ASD
(74).
We
test
the
effect
of
a
rescue
of
this
phenotype
by
transcriptional
gene
silencing
of
MSNP1AS
in
human
neurons.
After
knocking
down
the
lncRNA
in
SK-‐N-‐SH
cells,
we
evaluated
changes
in
the
transcriptome
using
RNA-‐Seq
to
elucidate
the
consequences
of
suppressing
MSNP1AS
expression.
These
experiments
seek
to
define
the
role
of
MSNP1AS
in
the
pathways
Figure
3.5.
Gene
Ontology
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
in
SK-‐
N-‐SH
cells
after
24
hours
of
MSNP1AS
knockdown
revealed
changes
in
chromatin
assembly.
0 1 2 3 4 5 6 7
structural constituent of ribosome
macromolecular complex assembly
structural molecule activity
ribosome
cytosolic ribosome
ribonucleoprotein complex
ribosomal subunit
cellular macromolecular complex assembly
cellular macromolecular complex subunit
chromatin assembly or disassembly
chromatin
DNA packaging
cytosol
translational elongation
nucleosome organization
protein-DNA complex assembly
chromatin assembly
nucleosome assembly
non-membrane-bounded organelle
intracellular non-membrane-bounded
protein-DNA complex
nucleosome
-Log (Bonferroni)
72
Figure
3.6.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
in
SK-‐N-‐SH
cells
after
24
hours
of
MSNP1AS.
A)
Analysis
of
upregulated
genes
revealed
enrichment
of
genes
involved
in
immune
response
following
knockdown
of
MSNP1AS.
B)
Analysis
of
downregulated
genes
revealed
changes
in
chromatin
and
nucleosome
assembly
following
knockdown
of
MSNP1AS.
0 2 4 6 8 10 12
positive regulation of immune response
activation of immune response
innate immune response
response to molecule of bacterial origin
humoral immune response
inflammatory response
extracellular region
positive regulation of immune system process
response to wounding
response to virus
defense response
immune response
-Log (Bonferroni)
A
0 1 2 3 4 5 6 7
structural constituent of ribosome
macromolecular complex assembly
structural molecule activity
ribosome
cytosolic ribosome
ribonucleoprotein complex
ribosomal subunit
cellular macromolecular complex assembly
cellular macromolecular complex subunit
chromatin assembly or disassembly
chromatin
DNA packaging
cytosol
translational elongation
nucleosome organization
protein-DNA complex assembly
chromatin assembly
nucleosome assembly
non-membrane-bounded organelle
intracellular non-membrane-bounded organelle
protein-DNA complex
nucleosome
-Log (Bonferroni)
B
73
Figure
3.7.
Boxplot
representing
the
expression
of
MSNP1AS
through
human
cerebral
cortex
development.
MSNP1AS
exhibits
a
dynamic
expression
pattern
across
development
with
the
highest
expression
being
evident
during
early
and
late
prenatal
development.
Table
3.4.
Top
Ten
Differentially
Expressed
Genes
Following
Knockdown
of
MSNP1AS.
Gene
a
Locus
a
Fold
Change
Protein-‐
or
noncoding
P-‐value
Q-‐value
OAS2
12:112906776-‐113017751
164.9
protein-‐coding
5.00E-‐05
4.12E-‐03
TMEM215
9:32783498-‐32787399
21.24
protein-‐coding
5.00E-‐05
4.12E-‐03
IFI44L
1:78619921-‐78646145
14.59
protein-‐coding
5.00E-‐05
4.12E-‐03
LRRC55
11:57181746-‐57191717
13.67
protein-‐coding
5.00E-‐05
4.12E-‐03
LAMP3
3:183122212-‐183163839
11.53
protein-‐coding
1.00E-‐04
7.33E-‐03
ITIH4
3:52812974-‐52897596
-‐8.11
protein-‐coding
2.00E-‐04
1.28E-‐02
CDC42EP1
22:37560446-‐37569405
-‐10.38
protein-‐coding
2.00E-‐04
1.28E-‐02
ANGPTL4
19:7958578-‐8489114
-‐13.1
protein-‐coding
1.00E-‐04
7.33E-‐03
HIST1H4J
6:27824107-‐27824480
-‐17.32
protein-‐coding
5.00E-‐05
4.12E-‐03
MIR320A
8:22244961-‐22245043
-‐19.42
noncoding
5.00E-‐05
4.12E-‐03
a
Refers
to
ENSEMBL
human
genome
GrCH38
build
81
74
that
lead
to
ASD
pathogenesis
and
suggest
that
MSNP1AS
acts
at
the
transcriptional
level
to
affect
immune
response
and
chromatin
organization.
The
biological
function
of
the
MSNP1AS
lncRNA
revealed
by
these
data,
chromatin
organization,
is
similar
to
that
of
genes
with
rare
de
novo
mutations
associated
with
ASD
(162,
183).
The
etiology
of
ASD
seems
to
be
effected
by
both
rare
and
common
variants
in
similar
ways.
LncRNA
are
regulated
through
spliceosome
machinery
and
post-‐translational
modifications,
much
like
protein-‐coding
transcripts
(184-‐187).
However,
lncRNA
are
more
loosely
regulated,
are
produced
in
relatively
lower
abundance,
and
have
fewer
exons
than
protein-‐coding
genes.
In
spite
of
this,
lncRNA
expression
is
extremely
specific
to
cell
type
and
tissues
(184,
186,
188).
LncRNAs
can
regulate
proteins
in
a
variety
of
ways,
including
as
a
framework
or
scaffold
to
facilitate
interaction
with
other
proteins
or
DNA,
as
enhancers
or
silencers
to
regulate
transcription,
and
as
decoys
to
bind
proteins
or
other
RNA
(185,
189-‐197).
In
addition,
lncRNAs
are
being
evaluated
for
epigenetic
modifications
and
chromatin
accessibility
to
reveal
their
contribution
to
psychiatric
disease
etiology
(173).
Accumulating
data
supports
the
role
of
the
immune
system
in
the
pathogenesis
of
ASD
(198,
199).
Our
previously
published
data
showed
that
MSNP1AS
binds
moesin
protein,
which
is
involved
in
neuronal
process
stability
and
immune
response
(74).
Our
data
show
that
after
MSNP1AS
is
knocked
down,
MSN
transcript
levels
remain
unchanged
(Table
3.5).
This
lack
of
expression
change
suggests
that,
while
MSNP1AS
regulates
moesin
protein,
suppression
of
MSNP1AS
does
not
alter
MSN
transcription.
The
lack
of
MSN
suppression
combined
with
the
decrease
in
moesin
protein
expression
shown
(74)
suggest
that
MSNP1AS
binds
to
the
MSN
transcript
to
prevent
translation.
Further
experiments
will
be
necessary
to
determine
the
75
mechanisms
of
moesin
protein
regulation.
The
biological
function
revealed
by
genes
upregulated
after
MSNP1AS
knockdown
points
to
the
immune
response
as
well.
Since
MSN
transcript
levels
are
not
perturbed
by
MSNP1AS
knockdown,
these
immune
response
genes
may
be
acting
independently
of
MSN.
Our
results
indicate
a
150-‐fold
increase
in
OAS2
expression
in
SK-‐N-‐SH
human
neural
progenitor
cells
following
MSNP1AS
transcriptional
gene
silencing.
OAS2
is
one
of
three
genes
that
make
up
the
OAS
gene
cluster.
These
genes
regulate
viral
infection
resistance
and
are
involved
in
cell
growth,
differentiation
and
death.
The
role
of
the
OAS
gene
cluster
as
an
ASD
susceptibility
locus
is
currently
under
investigation.
Genetic
variants
in
OAS2
are
associated
with
another
neurodevelopmental
disorder,
ADHD
(200).
Table
3.5.
Relative
Fold
Change
of
MSN
After
MSNP1AS
Knockdown
MSN
MSNP1AS
MSNP1AS
KD
Biological
Replicate
1
1.14
0.62
MSNP1AS
KD
Biological
Replicate
2
0.5
0.71
MSNP1AS
KD
Biological
Replicate
3
1.39
0.68
MSNP1AS
KD
Biological
Replicate
4
1.08
0.17
Because
the
ASD
phenotype
is
highly
heterogeneous,
a
network
of
many
genes
is
likely
contributing
to
abnormal
regulation.
A
variety
of
genes
may
contribute
to
several
different
ASD
phenotypes;
however,
strong
evidence
suggests
that
many
of
the
pathways
involved
may
overlap
to
form
an
interconnected
network.
The
results
presented
here
give
some
insight
into
the
interconnected
network
and
possible
molecular
pathology
of
ASD.
Translation
of
these
findings
to
patients
will
be
a
challenge
due
to
technical
delivery
concerns.
However,
these
first
steps
are
important
to
identify
potential
biomarkers
and
treatment
targets.
76
Chapter
4:
Distinct
gene
expression
patterns
resulting
from
prenatal
pesticide
exposure
in
autism
spectrum
disorder.
In
preparation.
4.1.
Abstract
ASD
Is
a
complex
neurodevelopmental
disorder
characterized
by
deficits
in
social
communication
and
behavioral
flexibility.
The
degree
to
which
these
traits
are
affected
is
influenced
by
both
genetics
and
the
environment.
In
this
study,
the
effect
of
pesticide
exposure
in
relation
to
ASD
diagnosis
on
the
transcriptome
was
evaluated
in
the
population-‐based
case-‐
control
Childhood
Autism
Risks
from
Genetics
and
the
Environment
(CHARGE)
Study.
Blood
was
taken
from
children
diagnosed
as
typically
developing
(TD),
developmentally
delayed
(DD),
or
ASD.
Pesticide
exposure
was
determined
based
on
mapping
addresses
from
the
pregnancy
and
early
childhood.
This
sample
consisted
of
145
cases
of
children
with
autism,
69
cases
with
developmental
delay,
and
75
controls
with
typical
development.
Covariate-‐controlled
mixed-‐
effect
linear
models
were
used
to
identify
gene
transcripts
that
were
significantly
associated
with
diagnostic
or
exposure
status.
Strand-‐specific
RNA
sequencing
analysis
indicated
altered
expression
of
many
genes
between
diagnostic
groups
and
following
comparison
of
exposed
to
non-‐exposed
samples.
Children
with
ASD
that
were
exposed
to
pesticides
had
differential
expression
of
genes
related
to
immune
response,
a
result
that
is
supported
by
previous
studies.
TD
children
who
were
exposed
to
pesticides
had
differentially
expressed
genes
related
to
protein
synthesis.
Protein
synthesis
and
immune
response
are
biological
processes
implicated
by
genes
with
rare
mutations
associated
with
ASD.
Our
data
suggest
that
the
functional
77
elements
implicated
by
association
of
common
genetic
variants
impact
the
same
biological
processes
that
are
influenced
by
environmental
factors.
This
study
reinforces
the
need
to
evaluate
both
environmental
and
genetic
factors
when
considering
the
mechanisms
that
could
be
converging
to
contribute
to
ASD
risk.
4.2.
Introduction
Autism
is
a
complex
neurodevelopmental
disorder
that
effects
social
communication
and
behavior.
As
recently
as
the
1990s,
cases
of
autism
increased
6
fold
in
California
in
births
counting
children
under
5
years
old.
The
reason
for
the
recent
rise
in
autism
is
to
some
degrees
unknown.
Two
of
the
causes
are
clear:
a
younger
age
of
diagnosis
accounts
for
a
12%
increase
and
revision
of
diagnostic
criteria
accounts
for
a
56%
increase
(201).
As
it
stands,
autism
affects
1
in
56
children
(202),
who
are
usually
diagnosed
between
the
ages
of
2
and
3
years
old
(203).
The
behavioral
diagnosis
is
stringent,
but
a
medical
diagnosis
is
desirable
due
to
the
wide
variance
in
phenotype.
While
the
estimated
heritability
of
ASD
is
quite
high,
around
90%
(21-‐23),
the
contributions
of
the
environment
are
not
negated.
Evidence
is
accumulating
that
puts
the
importance
of
environmental
impact
just
as
high
as
the
genetic
effect
(204).
Hundreds
of
genetic
factors
may
be
contributing
to
ASD,
with
any
one
specific
variant
only
imparting
a
tiny
percentage
of
absolute
risk
(205).
Because
the
ASD
phenotype
is
highly
heterogeneous,
a
network
of
many
genetic
variants
(both
common
and
rare)
is
likely
contributing
to
abnormal
regulation,
combining
with
various
environmental
risk
factors
in
a
multitude
of
complex
ways
to
effect
specific
individuals.
78
The
goal
of
this
study
is
to
use
RNA
sequencing
on
peripheral
blood
to
identify
differentially
expressed
genes
in
children
exposed
prenatally
to
pesticides
compared
to
children
not
exposed,
regularly
exposed
(6+
months)
and
or
exposed
to
indoor
pesticides
(including
flea
collar).
Three
groups
of
children
were
evaluated:
children
with
autism
spectrum
disorder
(ASD),
children
with
developmental
delay
(DD),
and
children
drawn
from
the
general
population
of
births,
frequency
matched
to
cases,
by
age,
gender
&
geography
(TD).
DESeq2
is
a
more
conservative
analysis
approach,
but
is
able
to
reduce
the
likelihood
of
false
positives.
It
calculates
a
Cook’s
distance
statistic
in
order
to
identify
genes
that
show
differential
expression
that
is
driven
by
an
outlier.
We
created
weighted
gene
co-‐expression
networks
to
examine
the
more
subtle,
yet
significant,
changes
in
the
transcriptome
in
response
to
prenatal
pesticide
exposure.
This
study
sought
to
examine
the
combined
effect
of
genetic
susceptibility
for
ASD,
shown
through
transcriptomic
changes,
and
pesticide
exposure
on
risk
of
ASD
in
the
Childhood
Autism
Risks
from
Genetics
and
Environment
(CHARGE)
study.
4.3.
Materials
and
Methods
4.3.1
RNA
sequencing
analysis
Quality
control
analyses
of
raw
reads
from
RNA-‐Seq
was
carried
out
using
fastqc.
The
files
that
passed
quality
control
were
processed
using
Cutdapt
(version
1.3)
to
remove
the
adapters
(206).
The
8
replicates
were
then
merged
to
create
one
"merged
fastq"
file.
Reads
were
then
aligned
to
the
ENSEMBL
GrCH38
version
81
transcriptome
using
TopHat
(version
2.0.10)
with
default
settings
(156).
One
sample
with
read
counts
2
standard
deviations
below
the
mean
was
removed.
Accepted_hits.bam
files
from
tophat2
were
then
used
in
htseq-‐count
79
to
count
the
gene
alignments.
Htseq-‐count
output
files
were
used
for
differential
expression
analysis
using
the
R
program,
DESeq2,
using
default
options.
One
outlier
(CHG0550)
was
removed.
Samples
CHG0863,
CHG0879,
CHG0885,
CHG0907,
CHG0915,
CHG0921
were
removed
due
to
apparent
labeling
as
the
incorrect
sex.
The
data
was
adjusted
for
age
and
sex.
Normalized
expression
data
for
every
gene
and
every
subject
were
then
extracted
from
DESeq2.
The
log2+1
was
extracted
for
use
in
gene
co-‐expression
network
analyses.
Gene
co-‐
expression
networks
were
then
formed
for
all
three
diagnostic
groups
using
WGCNA
(version
1.51).
4.3.2
Functional
enrichment
DAVID
(version
6.7)
(207)
was
used
to
functionally
annotate
differentially
expressed
genes
and
gene
co-‐expression
modules
of
interest.
This
enables
identification
of
enriched
gene
ontology
terms.
Redundant
terms
or
terms
with
a
Bonferroni-‐corrected
p-‐value
>
0.05
were
removed.
The
analyses
were
carried
out
using
default
options.
4.3.3
Weighted
Gene
Co-‐Expression
Network
Analysis
(WGCNA)
Weighted
gene
co-‐expression
network
analysis
(WGCNA)
uses
correlation
networks
to
describe
networks
of
co-‐expressed
genes
that
are
related
to
particular
phenotypic
traits.
This
system’s
biological
technique
reveals
patterns
across
microarray
or
RNA-‐Seq
samples
(208).
The
approach
finds
groups
of
genes
(clusters),
summarizes
them
using
a
hub
gene
or
eigengene,
and
calculates
measurements
of
the
module
including
connection
strengths.
The
connectivity
level
calculations
determine
the
organization
of
the
modules
based
on
gene
clustering.
The
hub
gene
has
to
be
related
to
the
phenotypic
data
for
the
modules
and
the
trait
to
be
considered
related.
80
Normalized
gene
expression
values
were
collected
for
each
sample
using
Cuffdiff.
The
genes
used
in
the
coexpression
network
were
restricted
to
those
showing
a
minimum
of
0.1
FPKM
to
reduce
the
amount
of
noise
in
the
network.
This
resulted
in
27,984
expressed
genes.
The
WGCNA
method
creates
an
adjacency
matrix
from
the
gene
correlation
values
using
a
power
function.
The
power
was
selected
from
a
scale
free
topology
fit
and
a
power
of
20
was
chosen
for
the
network.
The
‘blockwiseModules’
function
was
utilized
for
network
construction.
To
facilitate
the
formation
of
large
and
distinct
modules,
the
minimum
module
size
was
set
to
100
with
the
minimum
module
membership
connectivity
(kME)
set
at
0.7.
Modules
showing
a
correlation
value
of
0.75
were
merged.
The
eigengene
of
the
formed
modules
was
then
extracted
and
a
Pearson’s
correlation
was
calculated
with
the
cell
collection
time.
These
calculations
were
used
to
identify
biologically
interesting
modules
for
further
analysis.
Modules
were
selected
for
additional
analysis
if
the
correlations
with
cell
collection
time
had
a
p-‐value
<0.1.
4.3.4
Module
Preservation
The
differences
between
the
three
diagnostic
groups
are
examined
by
testing
how
well
the
modules
are
preserved
in
the
three
networks.
The
DD
and
ASD
modules
were
tested
to
see
how
well
they
are
preserved
with
the
TD
modules
by
calculating
a
Z
statistic.
This
helps
answer
the
question
“Do
the
genes
in
the
test
module
match
the
control
module
significantly
better
than
a
random
sample
of
genes?”
The
individual
Z
scores
are
then
summarized
into
a
composite
measure
called
a
Zsummary.
A
Zsummary
>10
is
considered
to
be
highly
preserved.
81
4.4
Results
4.4.1
All
differentially
expressed
genes:
exposed
vs
nonexposed
When
controlling
for
age,
sex,
educational
level
of
the
mother,
and
the
number
of
hours
since
the
last
meal
before
the
blood
draw,
twenty
genes
are
differentially
expressed
regardless
of
diagnosis:
twelve
were
protein-‐coding
and
eight
were
ncRNA
(q<0.05).
Three
genes
had
an
increase
in
expression
and
17
had
a
decrease
in
expression
(Table
4.1).
When
analyzed
by
DAVID,
9
GO
terms
achieved
significance.
The
category
with
the
lowest
p-‐value
was
“defense
response
to
virus”
(p=
1.95E-‐03).
When
a
less
stringent
statistical
test
was
applied,
the
number
of
differentially
expressed
genes
was
2,829
genes
(p<0.05).
When
analyzed
by
DAVID,
10
GO
terms
achieved
significance.
The
category
with
the
lowest
Bonferroni-‐corrected
p-‐value
was
“extracellular
exosome”
(p=
7.80E-‐09).
4.4.2
TD
Exposed
vs
Nonexposed
In
the
TD
exposed
vs
TD
not
exposed
group,
15
genes
were
differentially
expressed
(Table
4.2,
Figure
4.1)(q
<
0.05).
Two
genes
had
an
increase
in
expression
in
exposed
children:
both
noncoding.
Thirteen
genes
decreased
in
expression:
5
noncoding
and
8
protein
coding
(Table
4.2).
When
analyzed
by
DAVID,
one
GO
term
achieved
significance.
“Anchored
component
of
membrane”
had
a
p-‐value
of
p=
3.66E-‐02.
When
a
less
stringent
statistical
test
was
applied,
the
number
of
differentially
expressed
genes
was
2,006
genes
(p<0.05).
When
analyzed
by
the
DAVID,
10
GO
terms
achieved
significance.
The
category
with
the
lowest
Bonferroni-‐corrected
p-‐value
was
“focal
adhesion”
(p=
2.63E-‐05).
82
Table
4.1.
Differentially
Expressed
Genes
(Exposed
vs.
Not
Exposed)
Gene
Gene
Type
Log
2
Fold
Change
q
value
OR7E115P
unprocessed
pseudogene
-‐2.16
4.23E-‐02
MTATP8P2
processed
pseudogene
-‐2.14
1.95E-‐02
MTCO2P12
unprocessed
pseudogene
-‐1.79
5.59E-‐11
LDLRAD1
protein
coding
-‐1.53
9.82E-‐09
CCL8
protein
coding
-‐1.33
1.68E-‐02
CXCL10
protein
coding
-‐1.31
2.03E-‐05
RP11-‐5K23.5
antisense
-‐1.14
8.98E-‐03
RSAD2
protein
coding
-‐1.08
4.79E-‐02
ISG15
protein
coding
-‐0.92
1.22E-‐02
CPNE4
protein
coding
-‐0.90
1.76E-‐02
MTRNR2L12
protein
coding
-‐0.89
2.62E-‐05
SEPT4
protein
coding
-‐0.83
8.98E-‐03
LAMP3
protein
coding
-‐0.81
1.76E-‐02
IGHV3-‐73
protein
coding
-‐0.71
3.32E-‐04
RP11-‐493L12.6
processed
pseudogene
-‐0.67
4.23E-‐02
RPL39L
protein
coding
-‐0.58
6.13E-‐04
NT5C3A
protein
coding
-‐0.33
4.97E-‐02
ACTG1P20
processed
pseudogene
0.73
8.98E-‐03
MTND6P4
processed
pseudogene
1.11
2.90E-‐02
SEPT7P3
unprocessed
pseudogene
1.97
2.50E-‐02
Table
4.2.
Differentially
Expressed
Genes
(TD
Exposed
vs.
Not
Exposed)
Gene
Gene
Type
Log
2
Fold
Change
q
value
CD177
protein
coding
-‐3.62
4.10E-‐02
RP11-‐166N6.2
antisense
-‐2.43
5.75E-‐03
C1orf226
protein
coding
-‐2.40
2.11E-‐03
MSLN
protein
coding
-‐2.39
1.27E-‐04
MTCO1P40
processed
pseudogene
-‐2.09
1.19E-‐02
CPNE4
protein
coding
-‐1.82
1.31E-‐02
EIF5AP2
processed
pseudogene
-‐1.77
1.37E-‐02
FSTL4
protein
coding
-‐1.69
3.94E-‐02
MTRNR2L12
protein
coding
-‐1.64
3.94E-‐02
RP11-‐169F17.1
processed
transcript
-‐1.53
3.94E-‐02
RPL39L
protein
coding
-‐1.47
8.81E-‐03
TDRD9
protein
coding
-‐1.21
4.10E-‐02
XIST
lincRNA
1.23
1.37E-‐02
MTCO3P12
unprocessed
pseudogene
3.08
3.94E-‐02
83
4.4.3
DD
Exposed
vs
Nonexposed
In
the
DD
exposed
vs
DD
not
exposed
group,
the
number
of
differentially
expressed
genes
with
a
p-‐value
of
less
than
0.05
was
2,171
genes
(Figure
4.1).
When
analyzed
by
the
DAVID,
16
GO
terms
achieved
Bonferroni-‐corrected
significance.
The
category
with
the
lowest
Bonferroni-‐corrected
p-‐value
was
“SRP-‐dependent
cotranslational
protein
targeting
to
membrane”
(p=
4.54E-‐60).
4.4.4
ASD
Exposed
vs
Nonexposed
In
the
ASD
exposed
vs
ASD
not
exposed
group,
13
genes
were
differentially
expressed
(Table
4.3,
Figure
4.1).
Two
genes
had
an
increase
in
expression
in
exposed
children:
both
noncoding.
Eleven
decreased
in
expression:
two
were
noncoding
and
nine
were
protein
coding.
When
analyzed
by
DAVID,
two
GO
terms
achieved
significance
(Figure
4.2).
“Type
I
interferon
signaling
pathway”
had
a
p-‐value
of
p=
1.52E-‐02.
When
a
less
stringent
statistical
test
was
applied,
the
number
of
differentially
expressed
genes
was
2,132
genes
(p<0.05).
When
analyzed
by
DAVID,
14
GO
terms
achieved
significance.
The
category
with
the
lowest
Bonferroni-‐
corrected
p-‐value
was
“defense
response
to
virus”
(p=
7.59E-‐13).
84
Table
4.3.
Differentially
Expressed
Genes
(ASD
Exposed
vs.
Not
Exposed)
Gene
Gene
Type
Log
2
Fold
Change
q
value
MTCO1P42
processed
pseudogene
-‐3.84
2.79E-‐03
MTCO2P12
unprocessed
pseudogene
-‐2.63
2.50E-‐17
IFI27
protein
coding
-‐1.88
9.52E-‐03
CXCL10
protein
coding
-‐1.45
2.79E-‐03
ISG15
protein
coding
-‐1.10
3.54E-‐02
IGHV3-‐73
protein
coding
-‐1.10
4.47E-‐05
SEPT4
protein
coding
-‐1.02
3.33E-‐02
IGLV4-‐69
protein
coding
-‐0.93
4.69E-‐02
IGHV3-‐49
protein
coding
-‐0.83
3.53E-‐02
IGHV3-‐33
protein
coding
-‐0.77
3.55E-‐02
ACOXL
protein
coding
-‐0.54
4.69E-‐02
MTCO3P12
unprocessed
pseudogene
2.14
1.71E-‐04
SEPT7P3
unprocessed
pseudogene
3.18
7.93E-‐04
Figure
4.1.
Volcano
plot
generated
to
show
the
differentially
expressed
genes
between
the
exposed
to
pesticides
group
and
the
unexposed.
A)
Typically
developing.
B)
Developmentally
Delayed.
C)
Autism
Spectrum
Disorder.
Each
dot
represents
a
change
in
gene
expression,
with
genes
above
the
FDR-‐corrected
significance
threshold
shown
in
red.
85
Figure
4.2.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
of
the
exposed
to
pesticides
versus
not
exposed
ASD
children.
Analysis
revealed
enrichment
of
genes
involved
in
defense
response
to
virus.
0
5
10
defense
response
to
virus
type
I
interferon
signaling
pathway
response
to
virus
negative
regulation
of
viral
genome
extracellular
region
inDlammatory
response
chemokine-‐mediated
signaling
pathway
extracellular
space
innate
immune
response
immune
response
endoplasmic
reticulum
chaperone
complex
chemokine
activity
heparin
binding
endoplasmic
reticulum
lumen
-‐Log
(Bonferroni-‐corrected
p-‐value)
ASD
Non-‐exposed
vs
Exposed
86
4.4.5
All
ASD
vs
DD
All
ASD
genes
were
compared
to
all
DD
genes
regardless
of
exposure
and
revealed
35
differentially
expressed
genes.
Ten
genes
had
an
increase
in
expression
in
exposed
children.
Twenty-‐five
genes
decreased
in
expression.
When
analyzed
by
the
DAVID,
6
GO
terms
achieved
significance
(p<0.05).
“Extracellular
space”
had
a
p
value
of
4.80E-‐3.
When
a
less
stringent
statistical
test
was
applied,
the
number
of
differentially
expressed
genes
was
2,989
genes
(p<0.05).
When
analyzed
by
DAVID,
32
GO
terms
achieved
significance.
The
category
with
the
lowest
Bonferroni-‐corrected
p-‐value
was
“SRP-‐dependent
cotranslational
protein
targeting
to
membrane”
(p=
1.26E-‐56).
4.4.6
All
ASD
vs
TD
All
ASD
genes
were
compared
to
all
TD
genes
regardless
of
exposure
and
revealed
14
differentially
expressed
genes
with
a
q-‐value
of
less
than
0.05.
Three
genes
had
an
increase
in
expression
in
exposed
children.
Eleven
decreased
in
expression.
When
a
less
stringent
statistical
test
was
applied,
the
number
of
differentially
expressed
genes
was
3,171
genes
(p<0.05).
When
analyzed
by
DAVID,
5
GO
terms
achieved
significance.
The
category
with
the
lowest
Bonferroni-‐
corrected
p-‐value
was
“poly(A)
RNA
binding”
(p=
4.34E-‐12).
4.4.7
Differentially
Expressed
Genes
Separated
by
Age
There
was
a
significant
difference
in
response
to
exposure
between
age
groups.
The
children
were
split
into
two
groups:
3.5
years
and
younger
and
3.6
years
and
older.
The
exposed
and
not
exposed
children
were
then
compared
in
each
diagnostic
group.
87
Figure
4.3.
Venn
diagrams
of
differentially
expressed
genes
across
age
groups.
TD
children
under
3.5
years
had
33
differentially
expressed
gene
in
common
with
the
children
over
3.5
years.
DD
children
under
3.5
years
had
34
differentially
expressed
gene
in
common
with
the
children
over
3.5
years.
ASD
children
under
and
the
children
over
3.5
years
had
59
differentially
expressed
genes
in
common
(q<0.05).
4.4.7.1
TD
Exposed
vs
Nonexposed
In
the
TD
exposed
vs
TD
not
exposed
group,
the
younger
children
had
56
differentially
expressed
genes.
In
older
children,
100
genes
were
differentially
expressed.
Thirty-‐three
of
these
genes
were
the
same
between
age
groups
(Figure
4.3).
The
genes
with
a
q
value
of
less
than
0.05
were
analyzed
by
DAVID.
Eleven
GO
terms
resulting
from
the
older
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
“histone
demethylase
activity”
with
a
Bonferroni-‐corrected
p-‐value
of
9.60E-‐03.
Thirteen
GO
terms
resulting
from
the
younger
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
“translational
initiation”
with
a
Bonferroni-‐corrected
p-‐value
of
4.02E-‐04.
4.4.7.2
DD
Exposed
vs
Nonexposed
In
the
DD
exposed
vs
DD
not
exposed
group,
the
younger
children
had
149
differentially
expressed
genes.
In
the
older
children,
86
genes
were
differentially
expressed.
Thirty-‐four
of
these
genes
were
the
same
between
age
groups
(Figure
4.3).
The
genes
with
a
q
value
of
less
than
0.05
were
analyzed
by
DAVID.
Nine
GO
terms
resulting
from
the
older
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
“histone
demethylase
88
activity”
with
a
Bonferroni-‐corrected
p-‐value
of
1.26
E-‐02.
Eleven
GO
terms
resulting
from
the
younger
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
“translational
initiation”
with
a
Bonferroni-‐corrected
p-‐value
of
2.95E-‐04
(Figure
4.4).
Figure
4.4.
GO
enrichment
analysis
of
differentially
expressed
genes
(p<0.05)
of
the
TD
children
exposed
versus
not
exposed
to
pesticides
in
the
younger
(less
than
3.5
years)
children.
Analysis
revealed
enrichment
of
genes
involved
in
translational
initiation,
rRNA
binding
and
cytosolic
small
ribosomal
subunit.
4.4.7.3
ASD
Exposed
vs
Nonexposed
In
the
ASD
exposed
vs
ASD
not
exposed
group,
the
younger
children
had
152
differentially
expressed
genes.
In
the
older
children,
152
genes
were
differentially
expressed.
Fifty-‐nine
of
these
genes
were
the
same
between
age
groups
(Figure
4.3).
The
genes
with
a
q
value
of
less
than
0.05
were
analyzed
by
DAVID.
Seventeen
GO
terms
resulting
from
the
older
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
0
2
4
6
translational
initiation
histone
demethylase
activity
rRNA
binding
cytosolic
small
ribosomal
subunit
translation
initiation
factor
activity
histone
H3-‐K27
demethylation
histone
demethylase
activity
(H3-‐K27
histone
demethylase
activity
(H3-‐K4
speciDic)
histone
H3-‐K4
demethylation
oxidoreductase
activity,
acting
on
paired
dioxygenase
activity
cytoplasmic
ribonucleoprotein
granule
-‐Log
(Bonferroni-‐corrected
p-‐value)
TD
Younger
Non-‐exposed
vs
Exposed
89
“histone
demethylase
activity”
with
a
Bonferroni-‐corrected
p-‐value
of
4.39
E-‐03.
Fifteen
GO
terms
resulting
from
the
younger
children
achieved
a
significance
of
p<0.05.
The
category
with
the
highest
significance
was
also
“histone
demethylase
activity”
with
a
Bonferroni-‐corrected
p-‐
value
of
2.74E-‐03.
4.4.7.4
Exposed
ASD
vs
Exposed
TD
The
genes
of
children
with
ASD
who
were
exposed
to
pesticides
were
compared
to
the
genes
of
DD
children
exposed
to
pesticides
separated
by
age
and
revealed
5
differentially
expressed
genes
(q<0.05).
When
less
stringent
significance
requirements
were
applied,
3,399
genes
were
differentially
expressed
(p<0.05).
When
the
genes
with
a
significant
p-‐value
were
analyzed
by
DAVID,
thirty
GO
terms
achieved
Bonferroni-‐corrected
significance
in
the
older
children.
The
category
with
the
highest
Bonferroni-‐corrected
significance
was
“nucleosome“
(q=2.71E-‐31).
When
the
younger
children
were
compared,
3
genes
with
a
significant
q-‐value
were
differentially
expressed.
When
less
stringent
significance
requirements
were
applied,
770
genes
were
differentially
expressed
(p<0.05).
When
the
genes
with
a
significant
p-‐value
were
analyzed
by
DAVID,
eight
GO
terms
achieved
Bonferroni-‐corrected
significance
in
the
younger
children.
The
category
with
the
highest
Bonferroni-‐corrected
significance
was
“type
I
interferon
signaling
pathway”
(q=
1.77E-‐13)
(Figure
4.5).
4.4.7.5
Exposed
ASD
vs
Exposed
DD
The
genes
of
children
with
ASD
who
were
exposed
to
pesticides
were
compared
to
the
genes
of
DD
children
exposed
to
pesticides
separated
by
age
and
revealed
differentially
expressed
1,242
genes
(p<0.05).
When
the
genes
with
a
significant
p-‐value
were
analyzed
by
DAVID,
82
GO
terms
achieved
significance
in
the
older
children
(p<0.05).
The
category
with
the
90
highest
Bonferroni-‐corrected
significance
was
“respiratory
chain
“
(q=
0.012763523).
When
the
younger
children
were
compared,
2
genes
with
a
significant
q-‐value
were
differentially
expressed.
When
less
stringent
significance
requirements
were
applied,
1,635
genes
were
differentially
expressed
(p<0.05).
When
the
genes
with
a
significant
p-‐value
were
analyzed
by
DAVID,
no
GO
terms
achieved
Bonferroni-‐corrected
significance
in
the
younger
children,
but
52
GO
terms
had
a
significant
p-‐value
(p<0.05).
The
category
with
the
highest
Bonferroni-‐
corrected
significance
was
“calcium
ion
binding”
(q=
0.085).
4.4.8
Weighted
gene
co-‐expression
network
analysis
Sorting
and
identifying
transcripts
involved
in
the
biological
regulatory
pathways
is
a
daunting
task
due
to
the
volume
of
RNAs
that
remain
uncharacterized.
Protein-‐coding
and
noncoding
RNAs
activated
as
a
response
to
pesticide
exposure
were
sorted
based
on
developmental
category
and
a
weighted
gene
co-‐expression
network
was
constructed.
The
WGCNA
formed
modules
of
transcripts
that
have
highly
correlated
expression
patterns.
This
narrowed
the
list
of
RNAs
possibly
regulating
autism
spectrum
disorder
and
allowed
for
the
identification
of
transcriptional
networks
involved
(Figure
4.6).
91
Figure
4.5.
GO
enrichment
analysis
of
differentially
expressed
genes
(q<0.05)
of
the
ASD
children
exposed
to
pesticides
versus
TD
children
exposed
to
pesticides
in
the
younger
(less
than
3.5
years)
children.
Analysis
revealed
enrichment
of
genes
involved
in
type
1
interferon
signaling
pathway,
a
pathway
involved
in
transcription,
response
to
virus,
and
the
immune
response.
Figure
4.6.
Weighted
Gene
Co-‐Expression
Network
Analysis.
(A)
Cluster
gene
dendrogram
of
co-‐expressed
genes
revealed
25
modules
of
co-‐expressed
genes
not
including
the
gray
module.
4.5
Discussion
In
ASD
children,
prenatal
exposure
to
regular
use
of
household
pesticides
resulted
in
about
3
times
as
many
differentially
expressed
genes
compared
to
exposed
TD
and
DD
children.
Genes
that
were
identified
as
differentially
expressed
in
exposed
children
with
ASD
were
enriched
in
gene
ontology
terms
related
to
the
immune
response.
Prenatal
pesticide
exposure
significantly
changes
the
expression
of
gene
modules
important
in
immunity
response.
Children
with
ASD
do
not
show
the
same
expression
pattern
0.00
5.00
10.00
15.00
20.00
type
I
interferon
signaling
pathway
defense
response
to
virus
response
to
virus
negative
regulation
of
viral
genome
interferon-‐gamma-‐mediated
signaling
immune
response
2'-‐5'-‐oligoadenylate
synthetase
activity
double-‐stranded
RNA
binding
ASD
vs
TD
Exposed
Younger
92
in
these
modules
as
seen
in
exposed
TD
and
DD
children.
The
GO
category,
immune
response,
is
a
top
category
in
3
comparisons:
All
Exposed
vs
Unexposed
ASD,
Younger
ASD
exposed
vs
DD
exposed,
and
Younger
ASD
exposed
vs
TD
exposed.
Multiple
studies
have
pointed
to
the
role
of
the
immune
system
in
the
etiology
of
ASD
(198,
199).
Our
previously
published
data
showed
that
MSNP1AS,
a
long
non-‐coding
RNA
associated
with
ASD,
binds
moesin
protein,
which
is
involved
in
neuronal
process
stability
and
immune
response
(67,
74).
When
MSNP1AS
is
dysregulated,
the
expression
of
gene
transcripts
involved
in
the
immune
response
is
changed
(69).
Immune
response
pathways
seem
to
be
activated
in
ASD
children
in
response
to
pesticide
exposure.
The
biological
pathways
impacted
by
pesticide
exposure
in
TD
children
are
associated
with
protein
translation,
While
these
pathways
are
similar
to
that
of
processes
associated
with
ASD
(209,
210),
they
were
activated
in
TD
children
without
resulting
in
ASD.
It
is
possible
that
the
appropriate
response
to
pesticide
exposure
is
a
change
in
protein
translation
pathways,
while
an
inappropriate
reaction
is
an
immune
response,
which
potentially
triggers
ASD.
Another
interpretation
would
be
that
children
with
ASD
activate
immune
response
genes
instead
of
protein
translation
genes.
It
is
unclear
whether
the
immune
response
precedes
or
comes
after
the
development
of
ASD
or
is
caused
by
the
ASD.
While
the
disruption
of
protein
translation
processes
as
a
result
of
pesticide
exposure
may
not
be
enough
on
its
own
to
be
causative,
the
changes
in
these
processes
reflect
suspected
etiology
of
ASD.
The
gene
networks
affected
by
pesticide
exposure
and
contributing
to
genetic
risk
are
likely
working
together.
Given
that
the
ASD
phenotype
is
highly
heterogeneous,
a
plethora
of
genes
and
environmental
risk
factors
may
contribute
to
several
different
ASD
93
phenotypes;
however,
strong
evidence
suggests
that
many
of
the
pathways,
including
immune
response
and
protein
translation,
may
be
overlapping
to
form
an
interconnected
network.
These
pathways
may
be
working
together
to
contribute
to
the
disease
pathway
for
a
subset
of
ASD
patients.
Chapter
5:
Summary
and
Future
Directions
5.1
Summary
Examining
ncRNAs
is
a
novel
approach
to
understanding
the
molecular
mechanisms
involved
in
autism.
Even
more
novel
is
the
idea
that
an
antisense
RNA
could
be
having
an
impact
beyond
gene
silencing.
Here,
I
have
begun
this
work
by
focusing
on
an
antisense
lncRNA
associated
with
ASD.
Through
the
use
of
RNA
sequencing,
I
have
revealed
transcriptomic
changes
brought
about
by
dysregulation
of
MSNP1AS
that
provide
insight
into
the
etiology
of
ASD.
I
have
described
the
consequences
of
both
upregulation
of
MSNP1AS
(67),
which
is
seen
in
brains
of
autism
patients,
as
well
as
the
downregulation
of
the
gene
(69),
which
could
be
the
answer
to
amelioration
of
the
disorder.
Overexpression
of
MSNP1AS
revealed
changes
in
gene
expression
in
areas
related
to
chromatin
organization
and
protein
synthesis,
in
addition
to
changes
in
cell
morphology.
Stunted
cell
growth
was
demonstrated
by
an
observed
decrease
in
neurite
length
and
number.
Knockdown
of
MSNP1AS
corroborated
and
expanded
the
story
by
showing
changes
in
expression
of
genes
not
only
involved
in
chromatin
organization,
but
also
immune
response.
94
These
changes
are
all
observed
in
immortal
neuronal
cell
lines,
which,
while
relevant
to
human
health,
cannot
be
said
to
exactly
mirror
what
is
observed
in
vivo.
The
CHARGE
study
takes
directly
from
the
source,
by
using
RNA
derived
from
the
blood
samples
of
children.
With
these
data,
I
showed
that
pathways
impacting
chromatin
disorganization,
protein
translation,
and
immune
response
are
also
observed
in
ASD
in
vivo.
While
a
specific
pathway
was
not
revealed
by
these
studies,
I
have
begun
to
narrow
it
down
by
showing
that
the
in
vitro
data
accurately
reflects
the
changes
also
observed
in
vivo.
Interestingly,
changes
in
MSNP1AS
are
not
seen
in
the
CHARGE
study
data.
This
is
not
entirely
unexpected,
as
it
is
unknown
at
what
point
MSNP1AS
becomes
relevant
in
the
development
of
the
autism
brain.
However,
the
gene
ontology
categories
impacted
by
MSNP1AS
dysregulation
are
similar
to
the
ones
changed
in
the
CHARGE
study,
giving
a
more
solid
direction
to
ASD
transcriptomic
studies.
Secondly,
as
was
discussed
previously
in
this
dissertation,
immune
response,
chromatin
organization,
and
protein
translation
cannot
be
solely
responsible
for
the
onset
of
ASD.
It
is
possible
that
the
mechanism
linking
these
pathways
to
sub-‐phenotypes
of
ASD
is
further
removed
from
the
initial
dysregulation
of
MSNP1AS.
It
is
also
possible
that
the
subset
of
children
with
ASD
that
were
a
part
of
the
CHARGE
study
are
not
in
the
same
subset
of
people
with
ASD
that
have
the
rs4307059
SNP
or
dysregulated
MSNP1AS.
95
5.2
Overexpression
of
MSNP1AS
leads
to
changes
in
MSN
protein
expression,
protein
synthesis
and
chromatin
organization.
MSNP1AS
is
an
antisense
RNA.
Its
most
anticipated
function
would
be
to
silence
the
element
to
which
it
is
antisense.
MSNP1
is
not
transcribed,
therefore,
it
makes
sense
to
look
to
MSN
for
evidence
of
MSNP1AS’s
dysregulation.
While
an
effect
on
MSN
protein
is
observed,
changes
in
MSN
transcript
are
not
seen,
indicating
that
MSNP1AS
prevents
MSN
translation,
but
not
transcription.
MSN
protein
is
a
part
of
the
ezrin/radixin/moesin
family,
which
is
a
family
of
scaffolding
proteins.
The
story
could
end
there,
but
I
found
that
there
was
more
to
it.
MNSP1AS
does
not
affect
MSN
transcript
levels,
but
it
does
bring
about
changes
in
expression
of
many
other
transcripts
in
a
much
more
dramatic
way.
These
transcripts
are
involved
in
a
variety
of
processes,
but
most
notably,
I
saw
changes
in
chromatin
organization
and
protein
synthesis.
These
processes,
while
not
pointing
to
a
highly
specific
pathway,
have
been
implicated
in
the
pathogenesis
of
ASD.
In
addition,
the
expression
of
many
ncRNAs
is
affected
by
the
overexpression
of
MSNP1AS.
This
sets
the
stage
for
the
establishment
of
ncRNA
involvement
in
ASD
as
well
as
a
more
broad
evaluation
of
noncoding
RNA
as
regulatory
elements
in
general.
From
a
more
basic
science
view,
we
can
learn
how
ncRNA
are
contributing
to
cell
growth,
as
well
as
how
small
changes
in
those
processes
influence
the
progression
of
neurodevelopmental
disorders
like
ASD.
Through
this,
we
can
move
away
from
searching
for
specific
genes
that
have
a
large
impact,
to
regulatory
elements
that
can
have
an
additive
effect
to
stimulate
disorder
development.
96
5.3
Knockdown
of
MSNP1AS
reveals
changes
in
genes
involved
in
chromatin
organization
and
immune
response
The
overexpression
of
MSNP1AS
in
my
first
paper
mimicked
what
is
seen
in
autism
brains.
To
amend
this
response,
I
knocked
down
MSNP1AS
in
neuronal
cell
lines.
Through
the
development
of
an
sasRNA
developed
by
Dr.
Kevin
Morris,
I
was
able
to
knockdown
the
antisense
RNA,
MSNP1AS.
This
alone
was
a
challenge
as
the
conventional
method
of
knocking
down
a
gene
involves
using
the
antisense
of
that
gene.
Since
MSNP1AS
is
94%
identical
to
the
protein-‐coding
gene,
MSN,
I
had
to
use
a
different
approach.
The
sasRNAs
designed
by
Dr.
Morris’s
lab
targeted
the
promoter
sequence
of
MSNP1AS.
Using
this
method,
I
was
able
to
successfully
knockdown
MSNP1AS
by
about
45%.
Upon
knockdown
and
subsequent
RNA
sequencing,
many
changes
in
gene
expression
were
observed.
These
changes
validated
the
results
seen
after
the
overexpression.
Genes
changed
upon
overexpression
of
MSNP1AS
were
expected
to
have
an
inverse
response,
meaning
that
genes
that
were
upregulated
upon
overexpression
of
MSNP1AS
would
be
downregulated
upon
MSNP1AS
knockdown.
Many
genes
confirmed
this
inverse
response.
The
genes
changed
in
expression
also
had
ontology
related
to
chromatin
organization.
In
addition
to
affirming
the
overexpression
results,
the
knockdown
revealed
another
pathway
affected
by
dysregulation.
This
lncRNA
appears
to
regulate
the
immune
response.
These
gene
ontology
categories
are
in
line
with
what
is
observed
in
ASD.
Reports
of
chromatin
organization
at
the
molecular
level
in
ASD
are
a
continuing
point
of
interest
(211).
A
subset
of
ASD
patients
97
experiences
dysregulation
of
immune
response
such
as
gastrointestinal
upset
and
autoimmune
disorders.
These
pathways
may
be
working
together
to
contribute
to
the
disease
pathway
for
a
subset
of
ASD
patients.
5.4
Immune
response
is
observed
in
vivo.
While
it
is
impossible
to
completely
recapitulate
complex
and
heterogeneous
disorders
like
ASD
in
cell
models,
these
models
give
us
insight
into
what
might
be
occurring
in
humans.
When
possible,
it
is
always
better
to
confirm
these
insights
in
the
subject
of
interest,
rather
than
a
model.
The
CHARGE
study
gave
me
the
opportunity
to
make
these
confirmations.
One
alternative
explanation
of
immune
response
pathways
being
present
in
cell
models
is
that
transfection
can
sometimes
trigger
an
immune
response.
When
data
is
derived
from
the
blood
samples
of
children,
such
explanations
are
no
longer
valid.
The
CHARGE
study
data
suggests
that
an
immune
response
is
truly
present
in
autism
patients
compared
to
controls,
in
which
it
is
not
found.
These
results
validate
the
cellular
model
studies.
The
CHARGE
study
also
points
to
an
environmental
trigger.
TD
children
exposed
to
pesticides
also
experience
changes
in
immune
system
gene
pathways.
While
MSNP1AS
did
not
appear
to
be
a
key
player
in
the
CHARGE
data,
both
studies
overlap
in
ways
that
contribute
to
defining
a
more
concrete
pathway
for
autism
etiology.
98
5.5
Future
Directions
These
data
linked
MSNP1AS
dysregulation
to
chromatin
regulation,
protein
synthesis,
immune
response,
and
the
regulation
of
moesin.
The
role
of
these
pathways
in
ASD
etiology
is
supported
by
the
data
resulting
from
the
CHARGE
study,
as
does
accumulating
evidence
points
to
those
pathways
(198,
199).
While
the
evidence
points
to
MSNP1AS
involvement,
the
specific
molecular
mechanism,
beyond
binding
of
moesin
protein,
has
yet
to
be
elucidated.
The
transcript
levels
of
MSN
are
not
effected
by
MSNP1AS
dyregulation
and
do
not
appear
to
be
changed
in
the
blood
samples
from
children,
leading
to
the
conclusion
that
these
immune
system
genes
are
operating
in
a
pathway
separate
from
the
MSN/MSNP1AS
pathway.
Additional
experiments
will
be
needed
to
evaluate
the
levels
of
MSNP1AS
and
moesin
protein
in
neurons
derived
from
patients
with
the
ASD-‐associated
rs4307059
allele.
Another
direction
would
be
to
determine
MSNP1AS
contribution
to
altered
brain
development
using
neurons
derived
from
Cri-‐du-‐chat
patients
with
deletion
of
chromosome
5p14.1.
Patients
with
ASD-‐associated
rs4307059
have
not
been
phenotyped.
It
will
be
important
to
determine
if
these
patients
have
features
in
common
with
ASD
patients
such
as
gastrointestinal
distress
or
autoimmune
disorders.
99
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Asset Metadata
Creator
DeWitt, Jessica Jolynn
(author)
Core Title
Genetics and the environment: evaluating the role of noncoding RNA in autism spectrum disorder
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Development, Stem Cells and Regenerative Medicine
Publication Date
07/21/2017
Defense Date
06/06/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
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Tag
antisense RNA,autism spectrum disorder,epigenetics,genes,genetics,lncRNA,long noncoding RNA,Molecular Biology,ncRNA,neurodevelopment,neurodevelopmental disorder,OAI-PMH Harvest,psychiatric disorder,psychiatry,siRNA, genomics
Language
English
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Bajpai, Ruchi (
committee chair
), Campbell, Daniel B. (
committee member
), Ichida, Justin (
committee member
), Wood, Ruth I. (
committee member
)
Creator Email
jdewitt@apu.edu,jjdewitt@usc.edu
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DeWitt, Jessica Jolynn
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Tags
antisense RNA
autism spectrum disorder
epigenetics
genes
genetics
lncRNA
long noncoding RNA
ncRNA
neurodevelopment
neurodevelopmental disorder
psychiatric disorder
psychiatry
siRNA, genomics