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Warre-Cornish et al., Sci. Adv. 2020; 6 : eaay9506 19 August 2020
SCIENCE ADVANCES | RESEARCH ARTICLE
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DEVELOPMENTAL NEUROSCIENCE
Interferon- signaling in human iPSC–derived neurons
recapitulates neurodevelopmental disorder phenotypes
Katherine Warre-Cornish1,2*, Leo Perfect1,2*, Roland Nagy1,2, Rodrigo R. R. Duarte3,4,
Matthew J. Reid1,2, Pooja Raval1,2, Annett Mueller5, Amanda L. Evans5, Amalie Couch1,2,
Cédric Ghevaert5, Grainne McAlonan2,6, Eva Loth2,6, Declan Murphy2,6, Timothy R. Powell3,4,
Anthony C. Vernon1,2, Deepak P. Srivastava1,2†, Jack Price1,2,7†
Maternal immune activation increases the risk of neurodevelopmental disorders. Elevated cytokines, such as
interferon- (IFN-), in offspring’s brains play a central role. IFN- activates an antiviral cellular state, limiting viral
entry and replication. Moreover, IFN- is implicated in brain development. We tested the hypothesis that IFN-
signaling contributes to molecular and cellular phenotypes associated with neurodevelopmental disorders.
Transient IFN- treatment of neural progenitors derived from human induced pluripotent stem cells increased
neurite outgrowth. RNA sequencing analysis revealed that major histocompatibility complex class I (MHCI) genes were
persistently up-regulated through neuronal differentiation—an effect that was mediated by IFN--induced promyelocytic
leukemia protein (PML) nuclear bodies. Critically, IFN--induced neurite outgrowth required both PML and
MHCI. We also found evidence that IFN- disproportionately altered the expression of genes associated with
schizophrenia and autism, suggesting convergence between genetic and environmental risk factors. Together,
these data implicate IFN- signaling in neurodevelopmental disorder etiology.
INTRODUCTION
Multiple lines of evidence point to immune activation during fetal
development as an important risk factor for neurodevelopmental dis-
orders (1). Epidemiological studies consistently find an association
between maternal infection during pregnancy and increased risk
of autism spectrum disorder (ASD) and schizophrenia (SZ) (2–5).
Animal studies have shown that induction of an antiviral immune
response during pregnancy using the double-stranded RNA mimetic
polyinosinic:polycytidylic acid (poly(I:C) leads to behavioral abnor-
malities in offspring that are thought to be relevant to neuropsychiatric
disorders. These include repetitive behavior, decreased social inter-
actions, deficits in prepulse inhibition, and altered cognition (6). In
parallel, transcriptomic studies of both ASD and SZ post-mortem
brain tissue consistently show enrichment for inflammatory and
innate immune genes (7–10). Nonetheless, the pathological mech-
anisms through which transient inflammatory activation increases
susceptibility to neurodevelopmental disorders remain unclear.
A potential mechanistic link between maternal immune activa-
tion and neurodevelopmental disorders is the exposure of the devel-
oping brain to inflammatory cytokines. Among the inflammatory
cytokines up-regulated during maternal immune activation (11),
interferon- (IFN-) is of particular interest. It is an activator of
innate cellular antiviral signaling and transcription programs whose
primary function is to defend the cell against viral infection (12).
Mid-pregnancy maternal serum IFN- is increased during gestation
of offspring with ASD (13), and circulatory IFN- levels are elevated
in neonates subsequently diagnosed with ASD relative to develop-
mental delay controls (14). Intriguingly, within the brain, many an-
tiviral IFN- signaling targets also play important roles in neuronal
development and synaptic activity, independent of microbial infec-
tion (15,16). More recently, IFN- has also been described to play a
role in social behavior in rodents through modulation of inhibitory
neuronal GABAergic tone (17). Thus, it is now emerging that IFN-
has a physiological role beyond its antiviral and immune actions.
Previous studies have shown that antiviral activation establishes
enduring cellular changes that persist beyond the acute inflamma-
tory response. Exposure to IFN- primes cells to induce an enhanced
transcriptional response upon restimulation, allowing cells to mount
a faster and more effective antiviral response (18,19). Examination
of transcriptional priming at the major histocompatibility complex
(MHC) locus revealed a critical role for antiviral promyelocytic
leukemia protein (PML) nuclear bodies (19). MHC class I (MHCI)
gene expression has also been shown to be persistently up-regulated
in developing neurons following gestational poly(I:C) exposure in
rodents (20). In the brain, seemingly independently of their antiviral
functions, both PML and MHCI proteins play important roles in
many aspects of neuronal development and function, including
neurite outgrowth and axon specification (15), synaptic specificity
(16), synaptic plasticity (21), and cortical lamination (22). All of
these processes have been shown to be altered following gestational
poly(I:C) exposure in rodents (6,11,20,23). Thus, PML nuclear
bodies and MHCI proteins may link antiviral inflammatory activa-
tion to neuronal abnormalities. However, the impact of this pathway
on neurodevelopment following inflammatory activation has never
been examined.
In this study, we explore the hypothesis that IFN--induced anti-
viral signaling perturbs neurodevelopmental processes associated
with neurodevelopmental disorders. We used human induced pluri-
potent stem cells (hiPSCs) to investigate how transient IFN- exposure
1Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology
and Neuroscience, King’s College London, London, UK. 2MRC Centre for Neuro-
developmental Disorders, King’s College London, London, UK. 3Social, Genetic and
Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuro-
science, King’s College London, London, UK. 4Division of Infectious Diseases, Weill
Cornell Medicine, Cornell University, New York, NY, USA. 5Wellcome-MRC Cambridge
Stem Cell Institute, University of Cambridge, Cambridge, UK. 6Department of Forensic
and Neurodevelopmental Sciences, King’s College London, London, UK. 7National
Institute for Biological Standards and Control, South Mimms, UK.
*These authors contributed equally to this work.
†Corresponding author. Email: deepak.srivastava@kcl.ac.uk (D.P.S.); jack.price@kcl.
ac.uk (J.P.)
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affects developing human neurons. We demonstrate that exposing
hiPSC-derived neural progenitor cells (NPCs) to IFN- led to increased
neurite outgrowth in hiPSC neurons, similar to a phenotype observed
in hiPSC neurons from individuals with ASD (24–26). RNA sequenc-
ing was used to characterize the acute and persistent transcriptomic
responses to IFN-. We observed that IFN- responding genes were
enriched for those with genetic association to ASD and SZ. Moreover,
these genes overlapped significantly with those differentially ex-
pressed in the brains of individuals with these disorders. Genes of the
MHCI protein complex were acutely and persistently up-regulated.
This was accompanied by an enduring increase in MHCI protein
levels and PML body numbers. Critically, both PML and MHCI pro-
teins were required for IFN--dependent effects on neuronal mor-
phology. Together, these findings highlight a potential mechanism
through which antiviral signaling could contribute to intrinsic neuronal
phenotypes in neurodevelopmental disorders.
RESULTS
IFN- increases neurite outgrowth in a hiPSC model
of neurodevelopment
We and others have previously observed alterations in the morphology
of hiPSC neurons derived from individuals with ASD (24–26).
Since inflammatory mechanisms have been implicated in both neural
development and neurodevelopmental pathology, we hypothesized
that activation of antiviral signaling pathways influences the devel-
opment of neuronal architecture. To investigate this, we used
hiPSC-NPCs from three typically developing male individuals with
no history of psychiatric illness [M1, M2, and M3; table S1 and fig.
S1; (27,28)] and treated these cells with IFN- (25 ng/ml) daily on
days (D) 17 to 21 of differentiation. Subsequently, IFN- was excluded
from cell culture media, and neuronal differentiation was continued,
resulting in post-mitotic hiPSC neurons (Fig.1A and fig. S1). Cells
were fixed on D26, D30, D35, and D40 and stained for III-tubulin
Fig. 1. IFN- treatment of NPCs leads to enhanced neurite outgrowth in post-mitotic neurons. (A) Schematic representation of the experimental timeline of iPSC differentia-
tion and IFN- treatment strategy. NPCs received IFN- (25 ng/ml) daily in cell culture media from D17 to D20 before terminal plating on D21 and examination of neurite
outgrowth. (B) Automated tracing of III-tubulin–stained neurites on D26, D30, D35, and D40 untreated (UNTR) and IFN-gamma treated cells carried out with CellInsight high
content screening operated by HCS Studio Software. (C) Fluorescence images of III-tubulin and Hoechst staining acquired with CellInsight. (D to G) Graphs show the time courses
of neuronal morphological properties including neurite total length per cell (D), neurite average length per cell (E), branch point count per cell (F), and neurite count per cell
(G) in three control male cell lines, M1, M2, and M3. D26 untreated: n = 8 independent biological replicates, 6382 cells analyzed; D26 IFN-: n = 8 independent biological
replicates, 7122 cells analyzed; D30 untreated: n = 9 independent biological replicates, 5651 cells analyzed; D30 IFN-: n = 9 independent biological replicates, 7741 cells analyzed;
D35 untreated: n = 7 independent biological replicates, 4250 cells analyzed; D35 IFN-: n = 7 independent biological replicates, 4733 cells analyzed; D40 untreated: n = 4 inde-
pendent biological replicates, 2792 cells analyzed; D40 IFN-: n = 4 independent biological replicates, 2872 cells analyzed. Data generated with CellInsight high content
screening operated by HCS Studio Software. Results are presented as means ± SEM. Two-way RM ANOVA with Sidak’s multiple comparison adjustment method. *P < 0.05.
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(Tuj1), and high content automated neurite tracing was carried out
(Fig.1,BandC). Total neurite length measurements were averaged
for each biological replicate for all three hiPSC lines. A repeated
measures (RM) two-way analysis of variance (ANOVA) was per-
formed, pairing untreated and IFN--treated samples from each
biological replicate. The main effects of both IFN- treatment and
days in culture were associated with a significant increase in total
neurite length per cell across the time course examined (IFN- treat-
ment: F1,24=17.02, P=0.0004; days in culture: F3,24=12.07, P<0.0001;
Fig.1D). After demonstrating the effect of IFN- on neurite outgrowth
across the entire dataset, we used Sidak’s multiple comparison
adjustment to compare individual time points. This revealed a sig-
nificant increase in total neurite length in IFN--treated lines at D30
(P=0.010). We did not observe a significant interaction between
IFN- treatment and time in culture (interaction of treatment × time
point: F3,24=0.60, P=0.62), indicating that both factors influence
neurite outgrowth independently of each other.
We next examined whether IFN--treated cells had differences in
branching, neurite length, and neurite number. Previous studies of
cells from individuals with ASD have reported increases in these
morphological parameters (24–26). IFN- treatment led to a significant
increase in average neurite length per cell across the time course
(IFN- treatment: F1,24=5.07, P=0.034; days in culture: F3,24=6.25,
P=0.0027; Fig.1E), although Sidak’s multiple comparison adjust-
ment showed no significant difference between IFN- and untreated
cells at individual time points (P>0.05). Branch point count per cell
was also higher in IFN--treated cells across the time course (IFN-
treatment: F1,24=7.22, P=0.013; days in culture: F3,24=2.72, P=0.067;
Fig.1F), although, similarly, no significant difference was observed
with treatment at individual time points. The neurite count per cell
was also higher among IFN--treated neurons but did not change
significantly across the time course (IFN- treatment: F1,24=7.84,
P=0.0099; days in culture: F3,24=2.71, P=0.068; Fig.1G). Again,
we observed no significant difference in neurite count per cell be-
tween IFN- and untreated cells at individual time points. No inter-
action effects were observed between these morphological features
and time in culture, indicating that treatment and days in culture
were affecting neurite morphology independently. Together, these
data indicate that exposing hIPSC-NPCs to IFN- results in an in-
crease in neurite outgrowth due to longer, more numerous neurites
and increased branching in hIPSC-derived neurons compared to
untreated controls. These observations are consistent with previous
findings in autism-derived neuronal cells and indicate a potential dis-
ruption in the development of early neuronal morphology (24–26).
Transcriptomic analysis of IFN- exposure during
neuronal differentiation
After demonstrating the effect of IFN- on early neuronal morphology,
we sought to explore the mechanisms that underlie these effects. We
carried out RNA sequencing analysis of the same three hiPSC lines
(M1, M2, and M3; table S1) to examine the transcriptomic changes
caused by IFN- treatment of human NPCs and neurons. The exper-
iment had six conditions and is schematized in Fig.2A. We sought
to capture (i) the acute response of NPCs to IFN-, (ii) the acute re-
sponse of neurons to IFN-, (iii) the persistent response of neurons
after IFN- exposure at the NPC stage, and (iv) the effect of repeated
IFN- exposure at the NPC and neuronal stages. Principal compo-
nents analysis revealed that the largest source of variation across the
dataset corresponded to cell type, with the first principal component
explaining 72% of variance and separating NPCs and neurons (Fig.2B).
The second principal component segregated the samples by IFN- ex-
posure and described 20% of the observed variance between samples.
Differential gene expression analysis was performed using DESeq2
(29). We found 1834 differentially expressed genes (DEGs) by com-
paring untreated and treated NPCs [18 U versus 18 T, Wald test,
false discovery rate (FDR)<0.05; Fig.2,CandD]. The comparison
of untreated and treated neurons revealed 751 DEGs (30 UU versus
30 UT; Fig.2,CandE). There were 464 DEGs in common between
the two comparisons (fig. S2A). The canonical IFN- signaling path-
way was activated in both NPCs and neurons, resulting in signifi-
cant up-regulation of JAK2, STAT1, STAT2, and downstream
IFN-stimulated genes such as IRF1 (Fig.2,DandE). Up-regulated
genes were highly enriched for the Gene Ontology (GO) term
“IFN--mediated signaling pathway” (9-fold in NPCs and 14-fold in
neurons; FDR=4.47 × 10−21 and 2.68 × 10−23, respectively; Fig.2H).
Genes belonging to the MHCI complex (GO term “MHCI protein
complex”) were overrepresented in the up-regulated genes (11-fold
in NPCs and 21-fold in neurons; FDR=8 × 10−4 and 3.98 × 10−5,
respectively; Fig.2H) and among the highest ranked DEGs in NPC
and neurons treated with IFN- (Fig.2,DandE). We also found that
the genes down-regulated by treated neurons were enriched twofold
for the GO term “plasma membrane” (FDR=0.002). Full differential
expression and GO results are reported in tables S2 and S3, respectively.
The persistent transcriptional response to IFN- was of interest
given the enduring impact of IFN- on neuronal morphology de-
scribed above. To this end, we compared untreated and pretreated
neurons (30 UU versus 30 TU). Neurons exposed to IFN- at the
NPC stage showed enduring transcriptional changes, with 26 genes
significantly up-regulated and 2 genes downregulated in post-mitotic
neurons 9 days after treatment (Fig.2F). Notably, the up-regulated
gene set was highly enriched for MHCI genes, with the GO term
MHCI protein complex enriched 357-fold (FDR=3.33 × 10−8; Fig.2H).
The top five ranked DEGs were all involved in MHCI antigen pre-
sentation. Also of interest, the metabotropic glutamate receptor gene
GRM3 was up-regulated, while the GABAergic transcription factor
gene LHX6 was down-regulated in pretreated neurons (Fig.2F).
We also found evidence of IFN--induced cellular priming, where
repeated exposure at the NPC and neuronal stages (30 TT) induced
considerably more DEGs than a single neuronal treatment (30 UT)
when compared to untreated neurons (30 UU). This double hit
induced 1091 DEGs, 45% more than were detected after a single
neuronal treatment (Fig.2G and fig. S2B). This is consistent with
previous reports of IFN--induced transcriptional priming (18,19).
The genes down-regulated by neurons that received a double hit
were enriched fourfold for the GO term “synapse” (FDR=0.049).
Together, these results demonstrate that IFN- exposure induces
widespread and persistent transcriptional changes during human
neuronal differentiation. Considering their previously proposed role
in neurite outgrowth in mouse (15,30), these data highlight MHCI
proteins as candidates to explain the IFN--induced morphological
phenotype described above.
IFN- disproportionately alters SZ and ASD risk genes
Viral infection has previously been linked with neurodevelopmental
and psychiatric disorders (1). Therefore, we examined whether
SZ- and ASD-associated genes were disproportionately altered in
NPCs and neurons upon exposure to IFN-. We first performed gene
set enrichment analysis using MAGMA (31) to test for overlap with
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genes associated with SZ by a recent genome-wide association study
(GWAS) (32). Variants corresponding to the MHC locus were
excluded due to complex linkage disequilibrium in the region. The
analysis revealed that SZ genes overlapped with genes down-regulated
upon IFN- exposure in NPCs (18 U versus 18T; =0.14, SD=0.024,
P=0.002, FDR=0.007) and neurons (30 UU versus 30 UT; =0.42,
SD=0.026, P=0.002, FDR=0.007). No enrichment was observed
in the up-regulated gene sets. Additional analysis was carried out
Fig. 2. RNA sequencing analysis reveals a widespread and persistent transcriptomic response of human NPCs and neurons to IFN-. (A) Schematic representation
of the experimental conditions. (B) Principal components analysis biplot of all samples. The first principal component segregates conditions by time point, while the
second separates conditions by recent treatment. Cell lines are represented by point shape: M1 = square, M2 = cross, M3 = circle. PC, principal component. (C) Heatmap
of all DEGs clustered by row and column. Replicates are collapsed by condition. (D to G) Volcano plots with selected genes annotated. The dotted red line represents
the threshold for statistical significance of Padj = 0.05. Red dots identify genes of the MHCI protein complex. (H) Cleveland plot of selected enriched GO terms from the
up-regulated gene sets illustrating statistical significance and fold enrichment (FE).
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using an SZ risk gene list compiled by the PsychENCODE Consortium
by linking GWAS loci to potential disease genes using integrated
regulatory networks (33). Consistent with the MAGMA analysis, the
PsychENCODE list was enriched in the gene set down-regulated by
NPCs in response to IFN- [Fisher’s exact test, odds ratio (OR)=1.7,
FDR= 0.023; Fig.3A]. Genes in this group included GRIN2A,
PRKD1, and TSNARE1 (fig. S3A). This list was also enriched in the
gene set up-regulated by neurons in response to IFN- (OR=1.7,
FDR=0.027; Fig.3A). Notable genes in this group included TCF4,
ATXN7, and ZNF804A (fig. S3B).
Similar analyses were carried out to investigate the association
between IFN- signaling and ASD risk genes. MAGMA analysis did
not reveal enrichment of common risk variants identified by the
largest GWAS for ASD carried out to date (P>0.05) (33). Given the
fact that ASD GWAS are relatively underpowered and that this
analysis neglects the contribution of rare variants, we also tested for overlap
with ASD risk genes from the Simons Foundation Autism Research
Initiative (SFARI) database (34). The SFARI database is a manually
curated list of ASD risk genes scored by the strength of evidence.
Category 1 to 4 risk genes were significantly overrepresented in the
gene set down-regulated by NPCs in response to IFN- (OR=1.7,
FDR=0.037; Fig.3A), which included genes commonly associated
with ASD such as NLGN3, SHANK2, UPF3B, and NRXN3 (fig. S4).
After restricting the analysis to a subset of higher confidence,
category 1 and 2 genes, the enrichment was no longer statistically signif-
icant. Neither of the SFARI gene lists overlapped with the up-regulated
gene sets from NPCs or neurons. Collectively, these data suggest that
IFN- exposure during human neuronal differentiation dispropor-
tionately alters genes associated with SZ and ASD.
IFN- response recapitulates the transcriptomic signatures
observed in SZ and ASD brains
Overlap between IFN--responding genes and those found to be dif-
ferentially expressed in the post-mortem brains of individuals with
SZ and ASD would corroborate the role of IFN- signaling in neuro-
developmental disorders. We used data from the PsychENCODE
cross-disorder study, the largest transcriptome-wide analysis of SZ
and ASD brains carried out to date (10). We found a highly signifi-
cant enrichment of genes from both disorders (Fig.3B). More spe-
cifically, there was a significant overlap between genes up-regulated
(OR= 2.5, FDR = 3.62 × 10−15) and down-regulated (OR = 2.1,
FDR=0.012) in NPCs in response to IFN- with those up- and
down-regulated in the post-mortem brains of individuals with SZ.
We also report a significant overlap between genes up-regulated by
neurons in response to IFN- with those up-regulated in the brains
of patients with SZ (OR=2.7, FDR=1.98 × 10−11). Next, we tested
for overlap with genes found to be differentially expressed in the
post-mortem brains of individuals with ASD. Again, there was a sig-
nificant overlap between genes up-regulated (OR=3.7, FDR=2.26 ×
10−21) and down-regulated (OR=2.0, FDR=2.82 × 10−4) in NPCs.
The effect was even more pronounced for genes up-regulated (OR=
5.6, FDR=1.39 × 10−25) and down-regulated (OR=4.6, FDR=3.80 ×
10−4) in neurons. The overlapping up-regulated genes at both time
points in both disorders were enriched for GO terms including
IFN--mediated signaling pathway and “defense response to virus”
(table S4). These results indicate that IFN- signaling modifies gene
expression in a manner consistent with the dysregulation observed
in the brains of individuals with SZ and ASD.
Regulation of MHCI genes by PML nuclear bodies
The RNA sequencing analysis described above revealed an acute
and persistent up-regulation of MHCI genes in hiPSC neurons after
exposure to IFN-. In non-neuronal cells, IFN- has been shown to
induce long-lasting changes in signal-dependent transcription of
MHC proteins through the formation of PML bodies (19). PML
bodies are dynamic, DNA binding protein complexes that mediate
myriad transcriptional functions from viral gene silencing (35) to
neurogenesis (22) and neuronal homeostatic plasticity (21). Because
the MHCI pathway was highly enriched in the pretreated neurons
and PML was similarly up-regulated in acutely treated NPCs and
neurons (Fig.2,DandE), we investigated the putative role of PML
bodies in mediating the effects of IFN-. We hypothesized that PML
bodies could underlie the persistent up-regulation of MHCI follow-
ing IFN- treatment. Cells from the six IFN- treatment conditions
(Fig.2A), using the three hiPSC lines, were stained for PML
(Fig.4,AandC). Acute 24-hour treatment of NPCs at D17 to D18
resulted in a significant increase in PML bodies per cell (P<0.0001;
Fig.4,AandB). The increase in PML bodies persisted through differ-
entiation and was still observed in neurons (P<0.0001; Fig.4,CandD).
Conversely, treatment of post-mitotic neurons had no effect on
numbers of PML bodies, with or without pretreatment at D17 to
D21 (Fig.4D). These results indicate a time window of sensitivity
in neuronal differentiation, during which IFN- exposure leads to a
persistent increase in PML bodies.
PML nuclear bodies are known to be specifically disrupted fol-
lowing binding of arsenic trioxide (As2O3) (36). Therefore, we pre-
treated NPCs with As2O3 from D16 to D17, followed by cotreatment
with As2O3 and IFN- from D17 to D18, and then counted PML
bodies per nucleus [Fig.4,E(i)andF]. As previously observed
Fig. 3. Relevance of IFN--dependent gene expression changes to SZ and ASD.
(A) Enrichment of SZ and ASD risk genes and (B) DEGs detected in the brains of
patients with SZ and ASD among our IFN--responding genes. Log2 odds ratio (OR)
is represented by color and statistical significance is indicated by asterisks
(*FDR < 0.05, ***FDR < 0.001, and ****FDR < 0.0001). Fisher’s exact test BH corrected
for multiple comparisons.
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(Fig.4A), IFN- treatment led to a notable increase in PML bodies per
nucleus (P<0.0001); this was blocked by cotreatment with As2O3.
Alone, As2O3 had no detectable impact on the number of PML bodies.
To test whether As2O3 treatment also prevented the persistent in-
crease in PML bodies, the experiment was repeated, this time allowing
NPCs to differentiate into neurons [Fig.4,E(ii)andG]. Again, we
observed a significant increase in PML bodies with IFN- treatment
alone (P<0.0001), which was entirely prevented by cotreatment with
As2O3. We could thus conclude that As2O3 prevented both acute
and persistent PML body induction by IFN-.
To investigate whether PML bodies were required for IFN--
dependent transcriptional activation of MHCI genes, we performed
Fig. 4. PML bodies persistently increase following IFN- treatment, regulate MHCI gene transcription, and associate spatially with HLA-B transcription. (A and
B) Increased PML nuclear body expression following acute IFN- treatment: D18 U, n = 354 cells; D18T, n = 286 cells. Two-tailed Mann-Whitney test. (C and D) PML body
expression was persistently increased following IFN- treatment: D30 UU, n = 236 cells; D30 TU, n = 264 cells; D30 UT, n = 231 cells; and D30 TT, n = 307 cells. Kruskal-Wallis
test with Dunn’s multiple comparison adjustment. (E) Treatment schematic. (F and G) As2O3 (As) blocked IFN--induced PML body expression: D18 UNTR, n = 57 cells; D18
IFN-, n = 65 cells; D18 As, n = 64 cells; and D18 IFN- + As, n = 54 cells. D30 UNTR, n = 104 cells; D30 IFN-, n = 129 cells; D30 As, n = 119 cells; and D30 As + IFN-, n = 105 cells.
Kruskal-Wallis test with Dunn’s multiple comparison test. (H) IFN--induced HLA-B, HLA-C, and B2M expression is blocked by As. One-way ANOVA with Tukey’s multiple
comparison test. (I to K) Expression of HLA-B pre-mRNA and PML in IFN--treated NPCs (D18): UNTR, n = 57 cells; IFN-, n = 65 cells; As, n = 64 cells; and IFN- + As, n = 54 cells.
Kruskal-Wallis test with Dunn’s multiple comparison adjustment. (L to N) Colocalization of HLA-B pre-mRNA and PML in D18 and 30 IFN--treated cells: D18, n = 67 cells; D30,
n = 58 cells. Kruskal-Wallis test with Dunn’s multiple comparison test. Three control cell lines throughout. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001; ns, not significant.
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quantitative polymerase chain reaction (qPCR) on NPCs from three
hiPSC lines exposed to As2O3, IFN-, or combined treatment [Fig.4E(i)].
We observed that IFN--dependent induction of the MHCI genes
human leukocyte antigen–B (HLA-B) and HLA-C was blocked
by exposure to As2O3 (Fig.4H). Induction of the MHCI receptor
subunit gene B2M was not blocked by As2O3, suggesting that the
PML-dependent effect is specific to MHC genes. Similarly, neither of
the genes encoding IFN- receptor subunits, IFNGR1 and IFNGR2,
were affected by As2O3 cotreatment (fig. S5, A and B). Collectively,
these data support a model in which PML nuclear bodies mediate
IFN--dependent transcription of MHCI genes.
Increased expression of both PML and HLA-B has previously been
shown in post-mortem brain tissue from individuals with ASD
(9,10,37). PML is also up-regulated in post-mortem SZ brains (10).
As these findings are from adult post-mortem brain tissue, it is un-
clear whether this signaling pathway is altered from an early develop-
mental time point in ASD brains. To determine whether PML and
HLA-B are similarly increased in hiPSC-derived neural cells invitro,
we differentiated hiPSCs from three male individuals diagnosed with
ASD into NPCs, alongside the three control cell lines, as previously
described (Fig.1A and fig. S1). The individuals diagnosed with ASD
were recruited from the Longitudinal European Autism Project
(LEAP) (38). All three individuals (ASD_M1_08, 004_ASM_02, and
010_ASM_02) had a primary diagnosis of ASD (table S1). These
ASD cases were considered to be idiopathic as no ASD-associated
copy number variations or single-nucleotide variants were detected
in these individuals. Furthermore, no serological complications were
reported during gestation. We used qPCR to examine relative ex-
pression of PML, HLA-B, and B2M in NPCs from the three ASD and
three control lines. We observed statistically significant increases in
the expression of both PML and HLA-B (P=0.0005 and P=0.0002,
respectively; fig. S6, A and B). No difference in B2M expression was
detected (P=0.93; fig. S6C). We further examined the expression of
PML bodies in NPCs from the same ASD and control cell lines using
immunocytochemistry (fig. S6D). We observed a significant increase
in PML bodies per nucleus in the ASD NPCs relative to the control
NPCs (fig. S6E). While it must be noted that these observations arise
from only three ASD individuals and therefore should not be general-
ized, these findings suggest that increased expression of PML and
HLA-B could be present in early stages of ASD neural development.
IFN- induces HLA-B transcription near PML nuclear bodies
To further interrogate the necessity of PML for IFN--induced
MHCI gene expression, RNA fluorescence in situ hybridization (FISH)
was carried out on NPCs from three hiPSC lines treated with IFN-,
As2O3, or cotreated with IFN- and As2O3 [Fig.4,E(i)andI]. Our
qPCR experiments demonstrated that IFN--dependent up-regulation
of HLA-B and HLA-C was similarly blocked by As2O3. However, as
HLA-B demonstrated the greatest up-regulation in pretreated neu-
rons, we focused on this MHCI gene (Fig.2F). We used a probe
specific to the pre-spliced HLA-B RNA transcript to tag the site of
transcription. IFN- treatment led to a marked increase in HLA-B
pre-mRNA (P< 0.0001; Fig.4,ItoK). This induction was largely
prevented by cotreatment with As2O3 (Fig.4,ItoK), supporting
the requirement for PML in IFN--dependent HLA-B transcription.
Furthermore, costaining for PML and HLA-B pre-mRNA revealed
a positive correlation in the IFN--treated condition. This was ob-
served both as integrated density of PML and HLA-B expression per
nucleus (slope significantly greater than zero, P=0.027; fig. S7A)
and as number of spots per nucleus in each channel (slope signifi-
cantly greater than zero, P=0.0037; fig. S7B). A significant positive
correlation between PML and HLA-B spots per nucleus was also ob-
served in the unstimulated condition (P=0.0014; fig. S7F), although
there was no correlation between the integrated density of PML and
HLA-B (P=0.38; fig. S7E). A relationship between PML bodies and
IFN--induced HLA-B transcription is thus strongly supported.
If PML bodies directly regulate MHCI gene expression, then we
would predict them to be in close proximity to the site of transcription.
To investigate this spatial relationship, we carried out RNA FISH
using the probe described above, specific to pre-spliced HLA-B RNA
transcript. Because splicing is highly localized (39), we reasoned that
the site of the pre-spliced transcript could be used as a proxy for the
location of transcription. Costaining for PML and HLA-B pre-mRNA
revealed that, following IFN- treatment, HLA-B spots were frequently
located immediately adjacent to, or overlapping with, PML bodies
(Fig.4L). To investigate this further, we measured the density of
PML bodies (spots per micrometer) within HLA-B spots or HLA-B
spot perimeters (see Materials and Methods for full definitions) and
compared this to the density of PML bodies across the nucleus as a
whole in IFN--treated NPCs and neurons (Fig.3,MandN, and fig
S7, C and D). In IFN--treated NPCs, the increased density of PML
bodies in HLA-B pre-mRNA spot perimeters did not reach statistical
significance (P=0.09). However, in IFN--treated neurons, a signifi-
cantly higher density of PML bodies was observed in the HLA-B
pre-mRNA spots (P<0.0001) and spot perimeters (P=0.0007) than
the nucleus as a whole, indicating a positive spatial association. By
contrast, in untreated NPCs and neurons, PML spots were never
observed to overlap with HLA-B pre-mRNA spots or spot perimeters
(fig. S7, C and D). To confirm the existence of a nonrandom spatial
relationship, we carried out random shuffle nearest neighbor analysis
on IFN--treated NPCs (40). Actual distances between HLA-B spots
and PML bodies were observed to be significantly shorter than sim-
ulated distances following randomization (P<0.0001), confirming
a positive spatial relationship (fig. S7, G and H). These results confirm
that PML bodies are in closer proximity to the site of HLA-B tran-
scription than would be expected by chance, supporting the hypothesis
that PML is required for IFN--induced MHCI gene transcription.
PML and MHCI mediate IFN--induced neurite outgrowth
Having demonstrated a role for PML bodies in IFN--induced MHCI
gene transcription, we next asked whether PML and MHCI were
required for the IFN--dependent increased neurite outgrowth. MHCI
proteins have previously been reported to be expressed during, and
required for, neurite outgrowth in primary cultured rodent neurons
(15). To examine the role of MHCI proteins in IFN--induced neurite
outgrowth, we first carried out instant (i) Structured Illumination
Microscopy (SIM) super-resolution microscopy to determine the
subcellular localization of MHCI proteins HLA-A, HLA-B, and HLA-C
(Fig.5A). We found MHCI proteins to be present in the neurites, growth
cones, and cell bodies of untreated neurons. We then examined the effects
of IFN-- and As2O3-induced PML disruption on MHCI abundance in
neurites and growth cones in neurons from three control hiPSC lines,
following the experimental outline schematized in Fig.4E(ii). IFN-
treatment induced a significant increase in MHCI intensity within growth
cones and neurite compartments (neurites: P=0.0021; growth cones:
P=0.0012; Fig.5,BandC). This increase was prevented by cotreat-
ment with As2O3 in both compartments (P=1 for both). MHCI was
enriched in growth cones relative to the associated neurite in both
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Fig. 5. MHCI proteins are enriched in neuronal growth cones in a PML-dependent manner, and disruption of PML prevents IFN--dependent neurite outgrowth.
(A and B) iSIM super-resolution images of actin and MHCI in D30 neurons, with MHCI observable in cell bodies, neurites, and growth cones in UNTR, IFN-, As, and As + IFN-
treatment conditions. (C to F) Quantification of MHCI and actin in D30 neurons in neurites and growth cones (GC). UNTR, n = 52 cells; IFN-, n = 82 cells; As, n = 55 cells; and
As + IFN-, n = 59 cells; three control cell lines. (C and E) One-way ANOVA with Tukey’s multiple comparison test. (D and F) RM two-way ANOVA with Sidak’s multiple compar-
ison adjustment. (G) Fluorescence images of III-tubulin and Hoechst staining acquired with CellInsight high content screening system operated by HCS Studio Software.
(H) Automated tracing of III-tubulin–stained neurites in D30 UNTR, IFN-, As, and As + IFN- conditions carried out with CellInsight high content screening system. (I) Graph
showing neurite total length per cell in D30 UNTR, IFN-, As, and As + IFN- conditions. n = 9 independent biological replicates. UNTR, 5038 cells analyzed; IFN-, 5668 cells
analyzed; As, 5169 cells analyzed; and As + IFN-, 3210 cells analyzed; three control cell lines. RM one-way ANOVA with Tukey’s multiple comparison test. Data generated
with CellInsight high content screening operated by HCS Studio Software. Results are presented as means ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.
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untreated (P=0.0034) and IFN--treated neurons (P<0.0001; Fig.5D).
The enrichment of MHCI in growth cones was blocked by As2O3
treatment, both with and without IFN- (As: P=0.40; As=IFN-:
P=0.13; Fig.5D). These results support a requirement for PML-
dependent signaling in the expression and appropriate subcellular local-
ization of MHCI proteins both basally and following IFN- exposure.
Significantly increased actin intensity was also observed in both
neurites and growth cones following IFN- treatment (neurites:
P=0.0020; growth cones: P=0.0021; Fig.5E). Cotreatment with
As2O3 prevented this increase in both compartments (P=1 for both).
Actin staining intensity was also increased in growth cones relative
to adjoining neurites following IFN- treatment (P<0.0001), and this
enrichment was prevented by cotreatment with As2O3 (As: P=0.57;
As + IFN-: P=0.13; Fig.5F). Together, these results indicate that
PML-dependent IFN--activated signaling pathways have a functional
impact on growth cone composition and actin dynamics.
To determine whether intact PML bodies are required for IFN--
dependent increased neurite outgrowth, we treated NPCs with IFN-
and As2O3 and then continued differentiation of these cells into
post-mitotic neurons and assessed neurite outgrowth. As previously
described, we observed an increase in total neurite length per cell in
the IFN--treated relative to the untreated neurons (P=0.04; Fig.5,
GtoI). This increase was prevented by cotreatment with As2O3
(P=0.98), supporting a requirement for PML in IFN--induced
neurite outgrowth. These results support a model whereby IFN- acti-
vates PML body formation, leading to MHCI gene transcription, ex-
pression of MHCI in growth cones, and increased neurite outgrowth.
IFN--mediated increase in MHCI and neurite outgrowth
requires expression of B2M
To rule out nonspecific effects of As2O3 and confirm the requirement
for MHCI in IFN--dependent neurite outgrowth, we investigated the
effect of blocking MHCI cell surface expression on IFN--induced
morphological changes. To achieve this, we took advantage of the
requirement for the B2M protein for cell surface expression of MHCI
(41). First, we sought to demonstrate that IFN--induced expression
of B2M protein was required for MHCI expression in our NPCs.
We observed a significant increase in both B2M and MHCI expres-
sion following exposure to IFN- (P=0.0011; Fig.6,AtoC). We then
knocked down B2M in NPCs using a lentiviral vector containing
B2M-targeting short hairpin RNA (shRNA) in the three control
hiPSC lines. Virus containing nontargeting scrambled shRNA was
used as a control. shRNA-mediated knockdown of B2M blocked
IFN--induced MHCI expression in NPCs (IFN- versus IFN- + shRNA
B2M k/d, P<0.0001; Fig.6,AandC). These data confirm that B2M
is required for IFN--induced up-regulation of MHCI in NPCs. Next,
we used a human embryonic stem cell (hESC) line, MS3 HLA null,
in which the B2M gene has been excised using a CRISPR-Cas9 nickase
approach. This results in cells lacking cell surface MHCI. We used
the MS3 HLA null hESC line to further investigate whether MHCI
was required for IFN--induced neurite outgrowth. Five independently
cultured biological replicates of MS3 HLA null hESCs were differ-
entiated using the same neural induction protocol used with our
neurons and, subsequently, treated with IFN- (25 ng/ml) on D17
to D21 (Fig.1A and fig. S1). Treatment of MS3 HLA null NPCs with
IFN- did not increase MHCI expression (B2M: P=0.68; MHCI:
P=0.34; Fig.6,D toF), consistent with the requirement for B2M
for the expression of MHCI proteins. Critically, when we assessed
neurite outgrowth in MS3 HLA null neurons following IFN- treat-
ment, we no longer observed an effect of treatment on total neurite
length (P=0.74; Fig.6,GandH). Together, these data demonstrate
that IFN--mediated alterations in neurite outgrowth require the
expression of B2M and MHCI proteins (Fig.6I).
DISCUSSION
Epidemiological and animal studies support a role for antiviral
inflammatory activation in the etiology of neurodevelopmental dis-
orders. While the molecular mechanisms that underlie this associa-
tion are unclear, inflammatory cytokines are thought to play a central
role (11). In this study, we used hiPSCs to examine the impact of
IFN- exposure during human neuronal differentiation. Intriguingly,
we observed morphological and transcriptomic changes previously
associated with neurodevelopmental disorders. We propose a model
where IFN- promotes neurite outgrowth by a PML-dependent long-
lasting up-regulation of MHCI proteins at neuronal growth cones
(Fig.6I). To our knowledge, the mechanisms through which transient
developmental immune activation cause lasting alterations in neuronal
phenotypes have not previously been examined in a human system.
IFN- exposure at the neural progenitor stage increased neurite
outgrowth in human neurons. This was measurable as both neurite
length and branch number. Our observation is consistent with studies
of mouse NPCs (42) and a human cancer cell line (43). This study is
the first report of this effect in human neurons. Neurite outgrowth
is a fundamental stage of neuronal maturation, where neural pro-
genitors extend processes, which can later become axons or dendrites.
However, an increase in neurite outgrowth at an early stage of de-
velopment may not result in a more extensive dendritic or axonal
arbor in mature neurons. Nevertheless, alterations in neurite out-
growth in the developing brain would be predicted to have implications
for neuronal connectivity and ultimately brain function. Increased
neurite outgrowth has been found in hiPSC neurons derived from
individuals with ASD (24–26). Abnormalities in neurite outgrowth
have also been observed in hiPSC neurons from patients with SZ
(44). Note that the extent to which cultured hiPSC neurons faithfully
recapitulate neuronal morphogenesis in the developing brain is un-
known. Nonetheless, post-mortem studies of individuals diagnosed
with ASD and SZ have pointed to abnormal cortical neuron organi-
zation, dendritic arborization, and dendritic spine density (45). Fur-
thermore, the macrocephaly present in some ASD cases has been
attributed to increased dendrite number and size (46). Offspring of
pregnant dams exposed to a single gestational dose of poly(I:C) also
display altered cortical development, with perturbed dendritic and
synapse formation (6,11,20,23). Further evidence comes from large-
scale genetic studies of ASD and SZ, which consistently identify genes
encoding synaptic proteins and those involved in neuronal matura-
tion (33). Thus, although a causative role has not been established,
these reports suggest that disturbed neurite outgrowth may be relevant
to the pathophysiology of neurodevelopmental disorders.
A key question we sought to address was how transient immune
activation could have a lasting impact on neuronal phenotype. PML
nuclear bodies are chromatin-associated organelles that play an im-
portant role in viral infection response and transcriptional regula-
tion. Moreover, PML is involved in neuronal development and
function (21,22). We found that IFN- established a long-lasting in-
crease in PML body number during neuronal differentiation. PML
bodies were spatially associated with the site of HLA-B transcription,
and their disruption prevented IFN--induced MHCI transcription.
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Furthermore, PML body disruption blocked IFN--induced neurite
outgrowth. While this phenomenon has not been examined before
in human neurons, IFN-induced transcriptional memory has previ-
ously been observed in human cancer cells (19), mouse embryonic
fibroblasts, and mouse bone marrow–derived macrophages (18). In
the latter study, IFN- pretreated cells were shown to mount an
enhanced antiviral transcriptional response, conferring resistance
to viral infection (18). PML nuclear bodies were shown to mediate
Fig. 6. Loss of B2M prevents MHCI cell surface expression and IFN--dependent neurite outgrowth. (A) Confocal images of B2M-, MHCI-, and Hoechst-stained D18
NPCs, UNTR, IFN-, IFN- + shRNA scrambled, and IFN- + shRNA B2M knockdown treatment conditions. (B and C) Quantification of B2M and MHCI in D18 UNTR, IFN-, IFN- +
shRNA scrambled, and IFN- + shRNA B2M knockdown. n = 3 independent biological replicates; >10,000 cells per condition; three control cell lines. One-way ANOVA with
Tukey’s multiple comparison correction. (D) Confocal images of B2M-, MHCI-, and Hoechst-stained D18 MS3 HLA null UNTR and IFN--treated NPCs. (E and F) Quantifica-
tion of B2M and MHCI in D18, MS3 HLA null UNTR, and IFN--treated NPCs. MS3 HLA null UNTR: n = 3 biological replicates; >10,000 cells analyzed. MS3 HLA null IFN-: n = 3
biological replicates; >10,000 cells analyzed. Paired t test. (G) Fluorescence images of III-tubulin– and Hoechst-stained neurons and automated tracing of III-tubulin–
stained neurites in D30 MS3 HLA null UNTR and IFN- neurons. (H) Neurite total length per cell in D30 MS3 HLA null UNTR and IFN- neurons. n = 5 independent biologi cal
replicates. MS3 HLA null UNTR, >10,000 cells analyzed; MS3 HLA null IFN-, >10,000 cells analyzed. Paired t test. All images and data generated using Opera Phenix high
content screening system. (I) Schematic depicting our proposed model for IFN--induced PML and MHCI-dependent neurite outgrowth. Results are presented as means ±
SEM. **P < 0.01, and ****P < 0.0001.
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IFN--dependent transcriptional memory for the Major Histocom-
patibility Complex, Class II, DR Alpha (MHCII DRA) gene in human
cancer cells (19). Building on these findings, our data indicate that IFN-
exposure in NPCs establishes PML-dependent transcriptional memory in
MHCI genes that persists through human neuronal differentiation. Both
PML and HLA-B genes are up-regulated in the post-mortem ASD brain
(10). Consistent with this, we also observed increased expression of PML
and HLA-B in NPCs from a small collection of ASD-iPSCs. This indicates
that this pathway may be up-regulated during development in ASD brains.
Whether altered neurite outgrowth or neuronal/synaptic morphology in
ASD brains is associated with elevated expression levels of PML or HLA-B
requires further and detailed investigation. Moreover, these findings
will need to be confirmed using a larger cohort of ASD-iPSCs gen-
erated from both syndromic and idiopathic individuals to understand
if these findings could potentially be generalized to a wider population.
Genes of the MHCI complex were among the top differentially
expressed in both neural progenitors and neurons following IFN-
exposure. They exhibited evidence of transcriptional memory, where
expression remained elevated in neurons that received a single IFN-
treatment at the NPC stage. Furthermore, these genes had a heightened
response to neuronal treatment when primed at the progenitor stage.
Using super-resolution microscopy, we found that MHCI proteins
were enriched in neuronal growth cones. Disruption of PML body
formation prevented both MHCI enrichment in growth cones and
IFN--induced neurite outgrowth. Moreover, IFN- did not elicit the
same neurite outgrowth phenotype in cells devoid of MHCI at the
cell surface. These data indicate that MHCI proteins are involved in
IFN--induced neurite outgrowth. The role that MHCI proteins play
in the cellular antiviral response is well established; they present
antigens at the cell surface for detection by T cells. In addition, evi-
dence is mounting for nonimmune functions in the central nervous
system. MHCI proteins have been found in neurons of the develop-
ing mammalian brain, where they have been shown to localize to
neurites and neuronal growth cones (16,30). They have been impli-
cated in neurite outgrowth in mouse primary hippocampal neurons
(15) and synaptic stability in the mouse visual system (41). Intrigu-
ingly, gestational poly(I:C) also leads to an enduring increase in
MHCI protein in mouse cortical neurites (20). Note that the MHC
locus has shown the strongest association with SZ in multiple GWASs
(47,48); however, strong linkage disequilibrium in the region has
made identifying the causal variants a challenge. Combined with the
existing literature, our results indicate that MHCI proteins are
involved in IFN--induced neurite outgrowth. Further research is
required to determine the mechanisms through which MHCI pro-
teins alter growth cone dynamics.
A growing body of evidence indicates that genetic variants asso-
ciated with SZ and ASD manifest their risk during critical periods of
early brain development (8,49,50). Alterations in the expression
and function of risk genes disturb fundamental processes involved
in neuronal differentiation and maturation (33,51). In this study, we
found evidence that IFN- exposure during human neuronal differen-
tiation disproportionately altered the expression of genes associated
with these disorders. More specifically, we found that common
variants associated with SZ by GWAS significantly overlapped with
genes that were down-regulated by both NPCs and neurons in re-
sponse to IFN-. The association between IFN- signaling and genetic
risk for SZ was supported by additional analyses of risk genes identi-
fied by the PsychENCODE consortium, which revealed enrichment
in both genes down-regulated by NPCs and up-regulated by neurons
in response to IFN-. In total, 104 of the PsychENCODE SZ risk genes
responded to IFN- in our study. Many high profile candidates were
among them, including FOXP1, ATXN7, TSNARE1, and ZNF804A.
This convergence of genetic and environmental risk factors may speak
to the association between maternal immune activation and SZ.
The extent to which the IFN- response overlaps with genetic risk
for ASD is less clear. A similar association between IFN--responding
genes and common ASD risk variants was not found. This could be
due to the fact that GWAS studies have uncovered considerably
fewer genome-wide significant loci for ASD to date (51). As common
variants are thought to represent less than 20% of genetic risk for
ASD (52), we also carried out overlap analysis with ASD risk genes
from the SFARI database that includes rare single gene mutations.
We found enrichment of SFARI category 1 to 4 genes in the gene set
down-regulated by NPCs in response to IFN-. However, this result
was not supported by analysis with the shorter high confidence cat-
egory 1 to 2 gene list. This inconsistency could be explained either
by the reduced sample size of the shorter list reducing statistical
power or by the inclusion of false positives in the broader gene list.
Regardless, a total of 66 SFARI-defined ASD risk genes responded to
IFN- in NPCs and neurons. High confidence genes such as PTEN,
TCF4, SHANK2, NLGN3, and NRXN3 were among them. PTEN was
down-regulated in neurons after IFN- exposure in our study, con-
sistent with the reduced activity caused by ASD-associated variants
(53). This example illustrates how IFN- can influence risk genes in
a similar manner to disease-associated genetic variants. It is not clear
whether IFN--responding risk genes are involved in the neurite
phenotype we describe above. However, similar dysregulation in
response to developmental inflammatory stimuli invivo would be
expected to have implications for brain development.
We also observed a significant overlap between genes that were
differentially expressed in response to IFN- in our model with those
found to be dysregulated in post-mortem ASD and SZ brains. The
overlapping genes were enriched for genes of the IFN- signaling
pathway and antiviral response genes. The up-regulation of immune
and inflammatory factors is a consistent finding of transcriptomic
studies of ASD and SZ brains (10). While this signal has typically
been assumed to be driven by microglia, our results suggest that it
may also have a neuronal origin. This observation provides validity to
our model as IFN- alters gene expression in a manner consistent with
the dysregulation observed in the brains of patients with these disorders.
In summary, we find that antiviral immune activation in human
NPCs induces morphological and transcriptomic changes associated
with neurodevelopmental disorders. NPCs are thought to be central
to the etiology of these conditions. Their disruption can have lasting
implications for neuronal migration, maturation, and function (54).
Recently, Schafer and colleagues observed that hiPSC neurons from
individuals with ASD displayed increased neurite outgrowth (26).
They demonstrated that this phenotype was mediated by aberrant
gene expression in NPCs and could be prevented by bypassing the
NPC stage through direct conversion to neurons. In our study, we
find that pathological priming of NPCs with IFN- has a similar effect.
Thus, perturbation of normal gene expression in NPCs, whether
genetic or environmental, leads to altered neuronal maturation. The
degree to which this phenotype contributes to neurodevelopmental
disorders remains an open question. Further investigation is re-
quired to determine whether the IFN--responding SZ and ASD risk
genes contribute to the neurite outgrowth phenotype and whether
PML or MHCI mediates perturbed risk gene expression. Nonetheless,
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our results highlight IFN- signaling as a plausible link between ear-
ly immune activation and neurodevelopmental disorders. This
work provides a framework for future study of immune activation
and gene-environment interaction in human neural development.
MATERIALS AND METHODS
Study design
The initial objective was to determine whether IFN- treatment of
hiPSC-NPCs followed by continued differentiation into post-mitotic
neurons led to recapitulation of morphological characteristics of
hiPSC neurons derived from individuals with ASD. Subsequently,
we carried out RNA sequencing to determine the transcriptional
changes brought about by acute treatment, pretreatment, and double
treatment with IFN- in NPCs and neurons. We carried out gene
enrichment analysis to test the hypotheses that IFN- would dispro-
portionately alter the expression of SZ and ASD risk genes and
those that are differentially expressed in post-mortem brains of in-
dividuals with these disorders. Further hypotheses were generated
to test the requirement for PML nuclear bodies in IFN--dependent
MHCI transcription and the requirement for PML and MHCI in
IFN--dependent increased neurite outgrowth. No data were excluded
from any dataset. To account for variability between cultures, multi-
ple biological replicates were generated where appropriate and as
specified. Cells from a given hiPSC line were considered to be inde-
pendent biological replicates when they were generated from hiPSC
samples with a different passage number. Treatments were carried
out on cells within the same biological replicate, and paired or grouped
statistical analysis was carried out to limit the impact of this vari-
ability on measured outcomes. The numbers of hiPSC lines and
biological replicates, sample sizes, and statistical tests used are spec-
ified in figure legends. Experiment-specific parameters are outlined
in the methodology sections RNA Sequencing; High content cello-
mic screening and Microscopy and image analysis below.
HiPSC generation and neuralization and treatment
Participants were recruited, and methods were carried out in accord-
ance to the “Patient iPSCs for Neurodevelopmental Disorders (PiNDs)
study” (REC no. 13/LO/1218). Informed consent was obtained from
all subjects for participation in the PiNDs study. Ethical approval for
the PiNDs study was provided by the National Health Service (NHS)
Research Ethics Committee at the South London and Maudsley NHS
R&D Office. HiPSCs were generated from human hair follicle kerat-
inocytes, derived from three control males with no known psychiatric
conditions and three males with diagnosed ASD (table S1). Three
control hiPSC lines and one ASD hiPSC line were generated using a
polycistronic lentiviral construct coexpressing the four reprogramming
transcription factors, OCT4, SOX2, KLF4, and c-MYC, as previously
reported (55). Two ASD lines were generated using CytoTune Sendai
reprogramming kits (Thermo Fisher Scientific, A16517). HiPSC re-
programming was validated as previously described (24, 27, 28,56).
Briefly, genome-wide expression profiling using Illumina BeadChip v4
and the bio informatics tool “Pluritest” was used to confirm hiPSC identity.
Pluripotency was established through differentiation into embryoid bodies,
followed by immunocytochemistry for markers from the three germ
layers. Expression of pluripotency markers, NANOG, OCT4, SSEA4,
and TRA1-81, was confirmed by immunocytochemistry. The Illumina
Human CytoSNP-12v2.1 BeadChip array and KaryoStudio analysis
software (Illumina) were used to assess genome integrity (table S1).
HiPSCs were cultured in StemFlex media (Gibco, A3349401) on
six-well plates (Thermo Fisher Scientific, 140675) coated with Geltrex
basement membrane matrix (Thermo Fisher Scientific, A1413302).
Cells were passaged upon reaching 60 to 70% confluency by incubation
with a calcium chelator, EDTA (Thermo Fisher Scientific, 15040-33),
followed by detachment with a cell lifter to maintain intact hiPSC
colonies.
HiPSCs were differentiated as previously reported (24). HiPSCs
were grown until 95 to 100% confluent and then neuralized using a
modified dual SMAD inhibition protocol (24,27,28,56). For neural-
ization, StemFlex was replaced with a 1:1 mixture of N2- and B27-
supplemented medium, made following the manufacturer’s guidelines
(Thermo Fisher Scientific, 17502048 and 17504044) to which SMAD
and Wnt inhibitors (10 M SB431542, 1 M dorsomorphin, and
2 M XAV939) were added (hereafter known as N2:B27+++). This
was replaced daily until D7, when cells were dissociated using
Accutase (Life Technologies, A11105-01) and replated at a 1:1 ratio
into N2:B27+++ supplemented with Rock inhibitor (Merck, Y27632)
to prevent apoptosis. From D8 onward, SMAD and WNT inhibitors
were excluded from medium, and cells received daily N2:B27 medium
alone. Cells were dissociated and replated on D12, D16, D19, and
D21. From D9, a uniform sheet of SOX2/NESTIN-positive cells was
observed (fig. S1). From D17, cells were observed to self-organize
into neuroepithelial rosettes, expressing apical polarity marker PKC
with mitotic, PH3-expressing cells located at the apical pole (fig. S1).
On D21, cells were plated into poly-d-lysine–coated (5 g/ml; Merck,
P6407) and laminin–coated (20 g/ml; Merck, L2020) plates, in B27
media, supplemented with Notch inhibitor, DAPT (N-[N-(3,5-
difluorophenacetyl)-l-alanyl]-S-phenylglycine t-butyl ester), to
induce cell cycle exit. From this point onward, III-tubulin expres-
sion and neurite extension were observed (fig. S1). Cells received
IFN- (Abcam, AB9659) at a concentration of 25 ng/ml on D17 to
D21 and D29 to D30 and As2O3 (Trisenox, Teva) at a concentration
of 1 M on D16 to D21, as specified.
Embryonic stem cell generation and neuralization
and treatment
The hESC line MasterShef 3 HLA null was cultured as previously
described for hiPSCs but using laminin LN-521–coated (5 g/ml;
BioLamina, Sweden) plates. MasterShef 3 HLA null was created
using CRISPR-Cas9 nickase technology. Two plasmids containing
a mutant Cas9D10A nickase plus different reporter genes green
fluorescent protein (GFP) and dTomato [pSpCas9n(BB)_2A_GFP,
pSpcas9n(BB)_2A_dTomato] with different guide RNAs targeting
the -2-microglobulin (B2M) gene were transfected into the parent
line, followed by single cell sorting 4 days later for GFP/dTomato
double-positive cells. Single cells were seeded onto LN-521–coated
96-well plates, and colonies were picked after 7 to 10 days. Colonies
were screened by PCR for B2M expression, by flow cytometry for
MHCI knockout, by T7 mismatch cleavage assay, and, lastly, by
Sanger sequencing of chosen clones. hESCs were expanded and dif-
ferentiated using identical protocols to hiPSC lines described above.
B2M knockdown
B2M was knocked down using lentiviral particles containing B2M-
targeting shRNA (OriGene, TL314543V, Virus A). Nontargeting
scrambled shRNA from the same kit was used as a control. Cells
were exposed to the shRNA on D14 for 18 hours at a multiplicity of
infection of 8. NPCs were treated with IFN- on D17 for 24hours
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and fixed on D18. These experiments were carried out in biological
triplicate for all three control hiPSC lines.
RNA sequencing
RNA samples were collected in TRIzol reagent (Thermo Fisher Sci-
entific, 15596026), and RNA extraction was performed using an
RNEasy kit (Qiagen, 74104). Each experimental condition was
repeated in the three control hiPSC lines (table S1). Libraries were
prepared using the TruSeq RNA Library Preparation Kit (Illumina,
RS-122-2001) by the Wellcome Trust Centre for Human Genetics,
University of Oxford. Libraries were pooled and sequenced on an
Illumina HiSeq 4000, resulting in an average of 38 million 75–base
pair paired-end reads per sample. FASTQ files were processed with
Trimmomatic (57) to quality trim the reads and remove adapters.
The reads were then mapped to the human (GRCh38) reference
genome using STAR (58). Count matrices were prepared using
GenomicAlignments on Bioconductor (59), and differential gene
expression analysis was carried out using the default Wald test in
DESeq2 version 1.12.3 (29). All samples were run together using the
design formula ~ cell line + treatment condition before using the
contrast argument to extract the comparisons of interest. P values
were adjusted using the Benjamini-Hochberg (BH) method, and
the threshold for differential expression was a BH-adjusted P < 0.05.
FASTQ files can be accessed from www.synapse.org/IFNG.
Gene set enrichment analysis
GO analysis was carried out with DAVID (60) to test for enrichment
of biological process, molecular function, and cellular component.
DEG lists were tested for enrichment relative to a background of all
genes awarded an adjusted P value for that comparison by DESeq2.
The threshold for enrichment was a BH-adjusted P < 0.05.
MAGMA 1.07b was used to test whether genes differentially ex-
pressed upon IFN- treatment were enriched for genes implicated in
SZ or ASD according to GWAS (31). GWAS summary statistics were
downloaded from the Walters Group Data Repository (https://walters.
psycm.cf.ac.uk/) and the Psychiatric Genomics Consortium website
(www.med.unc.edu/pgc/download-results/). Variants corresponding
to the extended MHC region (assembly: GRCh37; location: chr6,
25,000,000–34,000,000) were excluded due to the complex linkage
disequilibrium structure of this locus. MAGMA was used to identify
disease-associated genes as part of a gene-level enrichment analysis
step that generates gene-level association statistic adjusted for gene size,
variant density, and linkage disequilibrium using the 1000 Genomes
Phase 3 European reference panel. Subsequently, MAGMA was used
to test whether genes differentially expressed in NPCs or neurons in
association with IFN- treatment (split into up- and down-regulated
sets) were enriched with disease-associated genes identified in the
gene-level enrichment analysis. Gene-set enrichment P values cal-
culated in MAGMA were corrected for eight tests (two time points,
two directions of effect, and two psychiatric disorders tested) using
the BH method.
Additional gene-set enrichment analysis was carried out for SZ
risk genes defined by the PsychENCODE Consortium (33) and ASD
risk genes from the SFARI database (categories 1 to 4 and categories
1 and 2, 15 January 2019 release) (34). None of the risk genes in-
cluded in these analyses fell in the MHC region. Enrichment was
tested using a two tailed Fisher’s exact test in R to test whether the
proportion of risk genes in the differentially expressed set is more or
less than expected by chance. Thus, the two variables analyzed were
differential expression status (differentially expressed versus not
differentially expressed) and risk gene status (risk gene versus not
risk gene). Analysis was limited to genes considered expressed in
our samples according to the internal filtering criteria of DESeq2.
Up- and down-regulated genes were separately tested. P values were
corrected for 12 tests (two time points, two directions of effect, and
three gene lists tested) using the BH method.
Overlap analysis was carried out between IFN--responding genes
detected in this study, and genes found to be DEGs from post-mortem
brains of patients with ASD and SZ. The post-mortem data were from
the PsychENCODE cross-disorder study, which used 559 SZ, 51 ASD,
and 936 control post-mortem frontal and temporal cerebral cortex
samples (10). Enrichment was tested using a two-tailed Fisher’s exact
test in R. Up- and down-regulated genes were separately tested. P values
were corrected for eight tests (two time points, two directions of ef-
fect, and two psychiatric disorders tested) using the BH method.
Immunocytochemistry
Cells were washed in phosphate-buffered saline (PBS), fixed in 4%
paraformaldehyde (20min at room temperature), washed three times
in PBS, and permeabilized and blocked using a solution of 4% donkey
serum and 0.1% Triton in PBS for 1 hour at room temperature
(RT). Primary antibodies (table S5) were diluted in blocking solu-
tion and applied overnight at 4°C. Following this, cells were washed
three times in PBS, and appropriate Alexa Fluor secondary antibodies
(A-21202, A-21203, A-21206, and A-21207, Thermo Fisher Scien-
tific) were applied in blocking solution for 2 hours at RT. Cells were
then washed three times in PBS and counterstained with Hoechst
33342 (Merck, B2261). F-actin was detected using Alexa Fluor 488–
conjugated phalloidin (ActinGreen 488 ReadyProbes, Thermo Fisher
Scientific, R37110) applied in PBS for 30min following secondary
antibody incubation and three washes in PBS.
High content cellomic screening
For the initial neuronal morphology experiments, high content screen-
ing was performed using CellInsight (Thermo Fisher Scientific)
operated by HCS Studio Software for automated measurement of
neurite outgrowth. Cells were plated at a density of 9000 cells/cm2
in poly-d-lysine– and laminin-coated 96 well Nunclon plates (Merck,
P8366), stained for III-tubulin (Tuj1) for detection of both mature
and immature neurites and counterstained with Hoechst 33342
to detect nuclei. The analysis pipeline involved initial detection of
Hoechst-stained nuclei, detection of cell bodies using nuclei as seeds,
and then detection of neurites originating from cell bodies. The fol-
lowing features were assessed for comprehensive examination of
neuronal morphology: neurite total length per cell, neurite average
length per cell, neurite count per cell, and branch point count per
cell. Averages were then taken for each condition within biological
replicates. Identical detection parameters were used between condi-
tions to allow direct comparison and paired statistical analysis.
Analogous analysis was carried out for the MS3 HLA null hESC
experiment with the Opera Phenix high content screening system
(PerkinElmer). Plating and staining were carried out as described
above. The Opera Phenix was also used to quantify B2M and MHCI
protein levels for the B2M shRNAi-mediated knockdown and MS3
HLA null hESC knockout studies. In both cases, cells were fixed and
stained immediately after IFN- treatment at D18. Protein expres-
sion was measured as average cellular intensity normalized to
no-primary controls.
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Quantitative PCR
RNA samples were collected in TRIzol reagent (Thermo Fisher Sci-
entific, 15596026), and RNA extraction was performed using an
RNEasy kit (Qiagen, 74104). DNA digest was performed using a
TURBO DNA-free kit (Thermo Fisher Scientific, AM1907) to remove
residual genomic DNA from samples. Reverse transcription to gen-
erate complementary DNA was performed using SuperScript III
(Invitrogen, 18080-044). qPCR was performed using EvaGreen Hot
FirePol qPCR Mix Plus (Solis Biodyne, 08-24-00001) in a Bio-Rad
Chromo4 PCR detection system. qPCR primer sequences can be
found in table S6. Gene expression fold changes between conditions
were calculated using the comparative cycle threshold (Ct) method.
RNA FISH
RNA FISH was carried out using an RNAScope double Z probe tar-
geting pre-splicing HLA-B RNA (ACD, probe named Hs-HLAB-
intron). Hybridization and signal amplification were carried out
using an RNAScope 2.5 HD Detection Reagent kit (ACD, 322350),
following the manufacturer’s instructions. Briefly, cells were grown
in eight-well chamber slides (Merck, C7057), fixed in 10% neutral-
buffered formalin for 30min at RT, washed in PBS, and incubated
with the probe for 2 hours at 40°C. Slides were washed, and signal
was amplified through a six-step amplification process, followed by
signal detection with a Fast-red chromogen label.
Microscopy and image analysis
Confocal images were taken with a Leica SP5 laser scanning confocal
microscope using 405/488/594-nm lasers and a 63× [numerical
aperture (NA), 1.4] oil immersion lens (Leica, Wetzlar, Germany),
operated through Leica Application Suite Advanced Software (v.2.7.3).
Super-resolution images were taken using a Visitech-iSIM module
coupled to a Nikon Ti-E microscope with a Nikon 100× 1.49 NA total
internal reflection fluorescence oil immersion lens (Nikon, Japan)
using 405/488/561-nm lasers. Super-resolution images were decon-
volved to increase contrast and resolution using a Richardson-Lucy
algorithm specific to the iSIM mode of imaging using the supplied
NIS-Elements Advanced Research software (Nikon, Japan, v.4.6).
Identical laser gain and offset settings were used within each biolog-
ical replicate to enable direct comparison between conditions.
Semi-automated image analysis was performed using ImageJ
software. Gaussian filters were applied to PML and RNAScope
images to reduce noise, and binary images were generated. Nuclei
were used as boundaries within which PML and RNAScope spots
were counted. Identical processing parameters were applied across
conditions to allow paired or grouped statistical analysis. Numbers
of PML bodies per micromolar squared were measured within
RNAScope spots, within RNAScope spot perimeter regions, and
within whole nuclei. RNAScope spot perimeter regions were defined
as detected spot perimeters ±0.3 m, generating a ring-shaped re-
gion of interest.
DiAna, an ImageJ plugin (40), was used to assess the spatial dis-
tribution of objects. Binary images were generated from confocal
images of PML and RNAScope staining. Center-to-center distances
were measured from PML spots to RNAScope spots. Binary images of
nuclei were used as a bounding box (mask), and randomized shuffle
of objects in both channels was carried out 100 times. Center-to-center
distances in shuffled images were computed, and cumulated fre-
quency distributions were calculated for real and simulated distances.
This was repeated for nine images across three cell lines. Real and
simulated distances within a given image were matched by percentile
for paired analysis.
Data collection and statistics
Details of the statistical tests relating to differential expression and
gene set enrichment are described in the “RNA sequencing” section
above. All experiments were carried out using three independent iPSC
lines derived from three unrelated male control or ASD-diagnosed
individuals. Where possible, multiple biological replicates of each
line were used (as specified in figure legends). Paired sample tests
were used for data obtained from matched treated and untreated
pairs of samples from the same cell line and biological replicate.
Biological replicates were cultured together until 1 to 2 days before
IFN-, As2O3, or combined treatment, whereupon they were passaged
into separate wells. All datasets were tested for normal distribution
using the D’Agostino and Pearson test before further statistical anal-
ysis. Statistical tests used for analysis of each dataset and “n” numbers
of repeats are specified in the figure legends. Statistical tests used
included t tests, Mann-Whitney tests, Wilcoxon matched-pairs test,
Kruskal-Wallis test, and one-way and two-way ANOVAs with
Tukey and Sidak’s multiple comparison adjustment methods, re-
spectively. Statistical tests used for analysis of each dataset are spec-
ified in the figure legends. RM ANOVAs were used for comparison
of data from the same cell line and biological replicate. The t test
and Mann-Whitney test were used for comparison of two groups
where data were unpaired and normally or non-normally distributed,
respectively. Paired t tests and Wilcoxon matched-pairs test were
used for comparison of two groups, where data were paired and
normally or non-normally distributed, respectively. Normal or RM
one-way ANOVAs were performed on datasets with more than two
groups, where samples were ungrouped or grouped, respectively.
Normal or RM two-way ANOVAs were performed on datasets with
two input variables, where samples were ungrouped or grouped, re-
spectively. Statistical analysis was carried out using GraphPad Prism 8.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/6/34/eaay9506/DC1
View/request a protocol for this paper from Bio-protocol.
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Acknowledgments: We thank the Wohl Cellular Imaging Centre (WCIC) at the IoPPN, King’s
College, London, for help with microscopy. We would like to thank N. Bray (Cardiff University)
and R. Sorrentino (Sapienza University of Rome) for critical discussions on this manuscript.
Funding: The study was supported by grants from the European Autism Interventions
(EU-AIMS) and AIMS-2-TRIALS: EU-AIMS received support from the IMI Joint Undertaking (JU)
under grant agreement no.115300, resources of which are composed of financial contribution
from the European Union’s Seventh Framework Programme (FP7/2007-2013), from the
European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in kind
contribution, and from Autism Speaks (to J.P., D.P.S., G.M., and G.M.). AIMS-2-TRIALS received
funding from the IMI 2 JU under grant agreement no. 777394. The JU receives support from
the European Union’s Horizon 2020 research and innovation programme and EFPIA, Autism
Speaks, Autistica, and the Simons Foundation Autism Research Initiative (to G.M. and D.M.);
StemBANCC: support from the Innovative Medicines Initiative joint undertaking under grant
115439-2, whose resources are composed of financial contribution from the European Union
(FP7/2007-2013) and EFPIA companies’ in-kind contribution (to J.P. and D.P.S.); and MATRICS:
the European Union’s Seventh Framework Programme (FP7-HEALTH-603016) (to D.P.S. and
J.P.). In addition, funds from the Wellcome Trust ISSF Grant (no. 097819) and the King’s Health
Partners Research and Development Challenge Fund, a fund administered on behalf of King’s
Health Partners by Guy’s and St Thomas’ Charity awarded to D.P.S., and the Brain and Behavior
Foundation [formally National Alliance for Research on Schizophrenia and Depression
(NARSAD); grant no. 25957] awarded to D.P.S. were used to support this study. Author
contributions: K.W.-C., L.P., R.N., R.R.R.D., M.J.R., P.R., and A.C. carried out all experiments,
performed data analy sis, or generated iPSC lines. A.M., A.L.E., and C.G. produced the
gene-edited MS3 HLA null hESC line. G.M. and E.L. identified patients and obtained clinical
data. D.M. supervised the stu dies where iPSC lines were collected. T.R.P., A.C.V., D.P.S., and J.P.
supervised all experiments. K.W.-C., L.P., T.R.P., A.C.V., D.P.S., and J.P. drafted the manuscript.
D.P.S. and J.P. oversaw the project. D.P.S. finalized the manuscript. Competing interests: The
authors declare that they have no competing interests. Data and materials availability: All
data neede d to evaluate the conclusions in the paper are present in the paper and/or the
Supplementary Materials. Additional data related to this paper may be requested from the
authors. RNA sequencing data can be found at www.synapse.org/IFNG.
Submitted 1 October 2019
Accepted 7 July 2020
Published 19 August 2020
10.1126/sciadv.aay9506
Citation: K. Warre-Cornish, L. Perfect, R. Nagy, R. R. Duarte, M. J. Reid, P. Raval, A. Mueller, A. L. Evans,
A. Couch, C. Ghevaert, G. McAlonan, E. Loth, D. Murphy, T. R. Powell, A. C. Vernon, D. P. Srivastava,
J. Price, Interferon- signaling in human iPSC–derived neurons recapitulatesneurodevelopmental
disorder phenotypes. Sci. Adv. 6, eaay9506 (2020).
on August 20, 2020http://advances.sciencemag.org/Downloaded from
disorder phenotypes derived neurons recapitulates neurodevelopmental− signaling in human iPSCγInterferon-
C. Vernon, Deepak P. Srivastava and Jack Price
Amanda L. Evans, Amalie Couch, Cédric Ghevaert, Grainne McAlonan, Eva Loth, Declan Murphy, Timothy R. Powell, Anthony
Katherine Warre-Cornish, Leo Perfect, Roland Nagy, Rodrigo R. R. Duarte, Matthew J. Reid, Pooja Raval, Annett Mueller,
DOI: 10.1126/sciadv.aay9506
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