2024.02.09.579580v1.full (1)-html.html

Selective Translation Orchestrates Key Signaling Pathways in Primed Pluripotency

Authors

Chikako Okubo

*, Michiko Nakamura

, Masae Sato

, Yuichi Shichino

2,3

, Mari Mito

2,3

, Yasuhiro

Takashima

, Shintaro Iwasaki

2,3

, Kazutoshi Takahashi

Affiliations

Department of Life Science Frontiers, Center for iPS Cell Research and Application, Kyoto

University; Kyoto, 606-8507, Japan

RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research; Saitama, 351-

0198, Japan

Department of Computational Biology and Medical Sciences, Graduate School of Frontier

Sciences, The University of Tokyo; Chiba, 277-8561, Japan
*Correspondence: chikako.okubo@cira.kyoto-u.ac.jp (C.O.); kazu@cira.kyoto-u.ac.jp (K.T.)

Abstract

Pluripotent stem cell (PSC) identities, such as differentiation and infinite proliferation,

have long been understood within the frameworks of transcription factor networks, epigenomes,
and signal transduction, yet remain unclear and fragmented. Directing attention toward
translational regulation, as a bridge between these events, promises to yield new insights into
previously unexplained mechanisms. Our functional CRISPR interference screening-based
approach revealed that EIF3D maintains primed pluripotency through selective translational
regulation. The loss of EIF3D disrupts the balance of pluripotency-associated signaling pathways,
impairing primed pluripotency. Moreover, we discovered that EIF3D ensures robust proliferation
by controlling the translation of various p53 regulators, which maintain low p53 activity in the
undifferentiated state. In this way, selective translation by EIF3D tunes the homeostasis of the
primed pluripotency networks, ensuring the maintenance of an undifferentiated state with high
proliferative potential. Therefore, this study establishes a paradigm for selective translational
regulation as a defining feature of primed PSC identity.

Introduction

Pluripotent stem cells (PSCs) have the capacity for self-renewal under appropriate

conditions while maintaining their distinct attributes, including differentiation potential and
unlimited proliferation

1-5

. Pluripotency is categorized into two types: naïve, resembling the pre-

implantation inner cell mass, and primed, akin to the post-implantation epiblast

. These categories

differ in their specific requirements for self-renewal, differentiation capacity, and epigenetic status.
Previous studies have shown that inhibiting multiple kinases helps sustain naïve pluripotency in
both rodents and humans, suggesting a conserved, kinase-independent strategy across species

Conversely, shifting from kinase inhibition to specific growth factor stimuli enables naïve

PSCs to transition into primed pluripotency, a state poised for differentiation into various somatic
lineages

7,11

. Unlike the naïve state, the fate of primed pluripotency depends on a range of

signaling inputs, including FGF, IGF, and TGFβ

12-14

. Thus, kinase signaling dynamics are crucial

for the transition between these states and their ongoing maintenance. Paradoxically, the same
growth factors that support primed pluripotency also initiate lineage-specific differentiation
programs

15,16

. Maintaining a delicate balance between strong and weak kinase signaling is key to

preserving the equilibrium between self-renewal and differentiation induction

17,18

. Although primed

pluripotency, maintained by finely tuned signaling, is a significant research area in stem cell and
developmental biology, the complex mechanisms governing this balance remain elusive.

The translation process, converting RNA into proteins, emerges as a critical element in

cellular homeostasis and the study of primed pluripotency. While overall translation remains low
during stem cell maintenance, differentiation cues actively enhance protein synthesis

. This shift

in translation dynamics highlights the importance of translational control in dictating pluripotency
and differentiation. Analysis of genes showing discrepancies between mRNA and protein levels
has unveiled the critical role of context-dependent post-transcriptional regulation in maintaining
primed pluripotency

. This indicates that translational modulation significantly influences primed

pluripotent states, independent of transcriptional regulation. While several translational factors
associated with primed pluripotency have been identified

21-23

, a comprehensive understanding of

the role of translational regulation in stem cell homeostasis and fate determination remains elusive.

Results

EIF3D Essential for Human Primed Pluripotency.

To investigate the complex mechanisms of primed pluripotency, we conducted genome-

wide functional screening using the CRISPR interference (CRISPRi) platform

24,25

. This approach

identified 1,686 genes that positively influence the self-renewal of primed human PSCs (

Fig. 1a

and Supplementary Table 1

). Gene ontology (GO) analysis of these genes, critical for maintaining

primed PSC identity, highlighted translation-related terms as significantly enriched (

Fig. 1b

We focused on one of the top-ranked translation regulators from the screening, the

eukaryotic translation initiation factor subunit D (EIF3D), a cap-binding protein

. EIF3D is

abundantly expressed in undifferentiated induced PSCs (iPSCs) compared to dermal fibroblasts
(HDFs), and its levels sharply decrease upon differentiation (

Fig. 1c

). This expression pattern is

similar to pluripotency factors like OCT3/4, SOX2, and NANOG, suggesting EIF3D's potential role
in primed PSCs.

To explore its function, we created doxycycline (Dox)-inducible EIF3D knockdown (KD)

iPSC lines (

Supplementary Fig. 1a

). Following EIF3D KD, we observed morphological changes

characterized by flattened colonies with indistinct edges and notably enlarged nuclei (

Fig. 1d and

Supplementary Fig. 1b, 1c

). Additionally, EIF3D KD resulted in a marked reduction in cell numbers

between days three and five post-induction (

Fig. 1e

). Investigating this phenotype, we measured

DNA synthesis via 5-ethynyl-2’-deoxyuridine (EdU) incorporation, revealing a significant decrease
in the S phase cell population and a corresponding increase in cells in the G1 and G2/M phases
following EIF3D KD (

Fig. 1f and Supplementary Fig. 1d-f

). These results collectively suggest that

EIF3D KD imposes growth arrest on primed human PSCs.

Moreover, EIF3D KD reduced the expression of transcripts encoding core pluripotency

transcription factors, which are indicative markers of PSCs (

Fig. 1g

). The decrease in NANOG

expression was more rapid and pronounced than that of POU5F1 (encoding OCT3/4) and SOX2.
Protein expression analyses exhibited a similar trend (

Fig. 1h and Supplementary Fig. 1g

Notably, transcriptional targets of p53, such as CDKN1A and MDM2 mRNAs, and their translation
products, were significantly upregulated in EIF3D KD cells, akin to the changes seen in
differentiated iPSCs induced by FGF withdrawal (

Fig. 1c, g, h

). Other hallmarks of senescence

(INK4A and ARF) and cell death (FAS) markers were also elevated (

Fig. 1g

). Similar to iPSC

differentiation, the absence of EIF3D led to increased p53 protein expression, while TP53 mRNA
showed only modest changes (

Fig. 1c, g, h, and Supplementary Fig. 1g

). Overall, the KD

phenotypes suggested that EIF3D loss diminishes the primed PSC identity.

Given these results, we explored the potential roles of increased p53 levels and reduced

NANOG expression in the phenotypes arising from EIF3D KD. Introducing exogenous NANOG
did not restore pluripotency marker expression or resolve the proliferative impairment
(

Supplementary Fig. 2a-d

). Additionally, the concurrent KD of TP53 with EIF3D did not fully

restore pluripotency marker expression (

Supplementary Fig. 2e-h

). However, these experiments

showed partial recovery of cellular proliferation and a decrease in elevated p21 expression
(

Supplementary Fig. 2g, h

). These findings suggest that analyzing hallmark genes associated

with undifferentiated or differentiated states alone is insufficient to fully understand EIF3D's role
in pluripotency. Nonetheless, the data indicate the involvement of the p53-p21 pathway in
regulating the proliferation of primed PSCs, although other pathways likely contribute to EIF3D-
mediated self-renewal. In summary, the collective findings highlight the multifaceted role of EIF3D
in maintaining primed pluripotency through complex molecular interactions.

Next, we investigated the differentiation of naïve PSCs into a primed state to further

assess EIF3D's role in maintaining primed pluripotency. Prior to transitioning to the primed state,
we induced EIF3D KD in naïve PSCs for 3 days, which did not result in any observable
abnormalities (

Fig. 1i

). However, when we altered the culture conditions from the kinase-inhibiting

naïve state to the growth factor-rich primed state, the EIF3D KD naïve PSCs demonstrated an
inability to differentiate into the primed state. This was marked by significant cell death within 4
days (

Fig. 1i

). Given this evidence of the inability to self-renew primed PSCs following EIF3D KD,

we conclude that EIF3D is essential for maintaining primed pluripotency.

Loss of EIF3D Diminishes Primed Pluripotency with Limited Impact on Three Germ Layer
Specifications.

We then conducted a comprehensive genome-wide transcriptome analysis to further

investigate the underlying mechanisms from a broader perspective. To elucidate the cell fate
changes in primed PSCs induced by EIF3D KD, we compared the global gene expression profiles
of EIF3D KD cells with those of iPSCs differentiated through suppression of core transcription

factors (

Supplementary Fig. 3a-c

), and iPSC-derived cells directed towards endoderm (EN),

mesoderm (ME), and neuroectoderm (NE) lineages (

Supplementary Fig. 3d

Our findings revealed that EIF3D KD resulted in an incremental increase in differentially

expressed genes (DEGs) over successive days compared to the controls (

Fig. 2a and

Supplementary Fig. 3e

). Notably, despite significant changes in gene expression, the EIF3D KD

profile showed less similarity to the corresponding comparatives (

Fig. 2a and Supplementary Fig.

). Instead, it seemed to enter an independent state, marked by a lack of clear lineage

commitment to any of the three germ layers. This was accompanied by the downregulation of key
pluripotency and primed PSC markers, including ZIC2, CD24, and SFRP2 (

Fig. 2a, b, and

Supplementary Table 2

)

27-32

. GO analysis revealed that EIF3D KD upregulated genes associated

with inconsistent differentiation terms, while genes related to cell cycle and division were
downregulated (

Supplementary Fig. 3g

). These results align well with the observed EIF3D KD

phenotypes, including growth retardation and loss of pluripotency, with minimal contribution to
specific lineages.

To further investigate the effects of EIF3D KD in primed PSCs, beyond the typical three

germ layers derived from these cells, our study expanded to include marker genes related to
naïve PSCs and the trophoblast, an earlier diverging fate

. EIF3D KD also appears to weaken

naïve pluripotent signatures, marked by decreased expression of naïve PSC markers, though
without noticeable morphological changes in naïve PSC colonies (

Supplementary Fig. 2c, d

However, the impact of EIF3D KD on gene expression changes in naïve PSCs was less
pronounced compared to those in the primed state (

Fig. 2e, f

). These findings underscore the

significant role of EIF3D in the regulation of primed PSCs.

Cluster analysis of gene sets showed that EIF3D downregulation in primed PSCs

resulted in a significant divergence from the typical primed PSC cluster, as evidenced by reduced
expression of genes linked to pluripotency, post-implantation epiblast, and the formative state, an
intermediary between naïve and primed pluripotency

, predominantly in clusters 1 and 2 (

Fig.

A notable characteristic of EIF3D KD primed PSCs is the increased expression of

trophoblast genes, primarily found in cluster 8 (

Fig. 2g

). This cluster is distinct from clusters 4, 5,

and 7, which contain cells that have differentiated into the three germ layers. This suggests that

EIF3D KD phenotypes disrupt primed pluripotency, leading to mismanagement of cell fate, such
as trophoblast gene activation, rather than inducing straightforward differentiation in line with
developmental logic. The evidence collectively indicates that EIF3D maintains primed
pluripotency through mechanisms distinct from those of core transcription factors or the inhibition
of specific lineage commitments.

EIF3D Orchestrates Translation of Key Signaling Pathways in Primed Pluripotency.

Based on global transcriptome data, we observed a lesser degree of change induced by

EIF3D KD on day 3 compared to day 5 (

Fig. 2a, b, and Supplementary Fig. 3e, f

). To understand

the initial response to EIF3D loss, we analyzed translation statuses on day 3 post-KD induction.
Puromycin incorporation showed that EIF3D KD reduced de novo protein synthesis to 45%
relative to control cells (

Fig. 3a

). Polysome profile analysis indicated a significant accumulation of

the 80S ribosomal subunit with EIF3D KD, suggesting decreased translation initiation (

Fig. 3b, c

To determine if EIF3D selectively regulates translation, we identified 28,561 open

reading frames (ORFs) undergoing translation in human primed iPSCs, classified as follows: 45%
as annotated coding sequences (CDS), 42% as variant CDS, 2% as unidentified ORFs, and 8%
as upstream ORFs (uORFs) (see All in

Fig. 3d

). Our analysis revealed that EIF3D KD increased

translation efficiency (TE) in 284 ORFs and decreased it in 1,340 ORFs. The increased TE group
featured a higher percentage of uORFs (34.86%), whereas the reduced TE group showed no
significant preference in ORF classification. These findings suggest that EIF3D is involved in
regulating the translation of non-canonical ORFs in primed PSCs, though annotated ORFs like
CDS comprise the majority of EIF3D targets.

Therefore, we subsequently examined the status of annotated ORF translation following

EIF3D KD. Ribosome profiling demonstrated that EIF3D KD significantly altered the TE of 1,321
genes (increased in 402; decreased in 919), which we term differential translation efficiency genes
(DTEGs). This suggests a selective translation regulation by EIF3D (

Fig. 3e and Supplementary

Table 3, 4

). Pathway analysis indicated that downregulated DTEGs (dDTEGs) were linked to

several signaling pathways, including EGF, WNT, insulin, MAPK, and TGFβ, all critical in

maintaining pluripotency (

Fig. 3f

)

. In contrast, upregulated DTEGs (uDTEGs) did not show

significantly enriched terms. Given the EIF3D KD phenotype, which includes the loss of primed

pluripotency, it is plausible that these pluripotency-related signaling pathways are implicated in
dDTEG.

Following the pathway analysis results, we confirmed the TEs of EIF3D targets in each

enriched pathway. The beeswarm plots revealed significant TE alterations in the transcripts of the

EGF, WNT, Insulin, MAPK, and TGFβ pathways (

Fig. 3g and Supplementary Table 5-9

). Next,

we assessed the phosphorylation statuses of key proteins in these signaling pathways, potentially
regulated by EIF3D. EIF3D KD led to the hyperactivation of the MAPK, insulin (AKT, mTOR, and
p70S6K), and WNT pathways, while simultaneously

suppressing the TGFβ pathway through

SMAD2 (

Fig. 3h

). These findings confirm that EIF3D KD disrupts the balance of multiple signaling

activities in primed PSCs through selective translation regulation.

EIF3D Inhibits p53 Protein Expression Through Selective Translation of p53 Regulators.

Besides the dysregulation of multiple kinase pathways, there is a notable increase in p53

protein and subsequent activation of the p53 pathway due to EIF3D KD (

Fig. 1h and

Supplementary Fig. 1h, 2g

). However, ribosome profiling data revealed no significant change in

p53 translation efficiency following EIF3D KD (Likelihood ratio test, Fold Change=1.01, adjusted
p=0.92). This led us to hypothesize about an indirect regulatory mechanism. To deepen our
understanding, we conducted a comparative analysis between DTEGs and a compilation of post-
transcriptional regulators of p53 protein expression

. Setting a TE threshold of 1.5-fold change,

EIF3D KD resulted in significant translation dysregulation of 207 out of 818 genes, accounting for
25.3% of the list (

Fig. 4a and Supplementary Table 10

). We also confirmed the protein reduction

of p53 regulators in EIF3D KD primed PSCs (

Fig. 4b

), suggesting that EIF3D indirectly influences

p53 protein expression by regulating the translation of its regulators.

As an example, we identified RBBP6, a dDTEG, previously reported as a negative

regulator of p53 protein stability

. As expected, RBBP6 KD mimicked EIF3D KD phenotypes,

including cell growth defects with morphological changes, reduced expression of pluripotency
markers, increased p53 proteins, and elevated expression of CDKN1A/p21 and MDM2 (

Fig. 4c-

). These findings indicate that EIF3D plays a role in modulating p53 protein expression by

controlling the translation of its regulators.

Discussion

This study demonstrates that EIF3D is crucial in sustaining primed pluripotency through

selective translation, which finely balances kinase signaling and suppresses the p53 pathway.
EIF3D's role, inclusive of selective translation, has been recognized in relation to oncogenic and
stress responses. Recent research has clarified EIF3D's influence on the translation regulation of
the MAPK pathway in common cell lines, such as 293T and HeLa

38,39

. These prior findings bolster

our current results, showing that EIF3D's translation control can modulate specific signaling
pathways' activities.

Maintaining low p53 activity is essential in undifferentiated PSCs, though its exact

regulatory mechanism was previously unclear

. Our study sheds light on EIF3D's indirect

inhibition of p53 by targeted translation modulation of p53 regulators. Moreover, the marked
increase of p53 protein in EIF3D KD, combined with the inverse correlation between increased
p53 protein and decreased EIF3D expression during PSC differentiation, strongly suggests
EIF3D's pivotal role in suppressing p53.

EIF3D KD led to a notable phenotype in primed PSCs, yet its effect was limited in the

naïve state. This difference might be due to the abundant expression and EIF3D-insensitive
regulation of p53 protein in naïve PSCs. Additionally, kinase pathway-independent self-renewal
in kinase-inhibiting conditions might make naïve PSCs less susceptible to EIF3D's translation
regulation. Although further investigation is needed, the significant phenotype observed in EIF3D
KD naïve PSCs exposed to growth factor-rich media for primed PSCs supports this theory.

Research on transcription factor networks and signaling pathways has greatly enhanced

our understanding of pluripotency. This study highlights the significance of translation regulation
as a link between these two aspects in pluripotency. Our CRISPRi screening indicates that,
besides EIF3D, other translational regulators may play roles in maintaining primed pluripotency,
suggesting that deeper exploration into translational control will offer more insights into
pluripotency's fundamental nature.

PSCs (10 and 20 days post-FGF withdrawal) and HDFs.

, Representative images of Control and

EIF3D KD iPSCs, 5 days post-KD induction. Scale bars:

100 μm

, Cell counts on days 3

(p=4.70e-4) and 5 (p=3.53e-7) post-KD induction (mean ± SD, n=6). p-values determined via
unpaired t-test.

, Cell cycle phase distribution (mean ± SD, n=3). G1: p=2.14e-3; S: p=4.43e-7;

G2/M: p=1.99e-4, calculated by unpaired t-test.

, RNA expression of pluripotency and TP53-

related genes during EIF3D KD. n=3.

, Expression of pluripotency and p53-associated proteins

during EIF3D KD.

, Differentiation of naive PSCs to primed state. Induction of differentiation from

naive to primed PSCs, 3 days post-Dox addition, by altering culture conditions. Representative

images of specified cell lines and days are shown. Scale bars: 100 μm. See also

Supplementary

Fig. 1, 2

Fig. 2: Transcriptome dynamics in primed PSCs following EIF3D loss.

, Principal component analysis (PCA) of RNA-seq data. Each dot represents the average value

of replicates. n=3.

, Volcano plots displaying differentially expressed genes (DEGs) between

control and EIF3D KD iPSCs, 3 and 5 days after Dox addition. Understated-colored dots represent
DEGs. Highlighted-colored dots denote key pluripotency markers. Red and blue dots indicate
genes significantly upregulated and downregulated, respectively (|log

FC| > 1, adjusted p < 0.05).

n=3. See full gene list and FC in

Supplementary Table 2

, Representative images of control and

sgEIF3D naïve PSCs, 5 days post-

Dox addition. Scale bars: 100 μm.

, Protein expression in

control and EIF3D KD naïve (N) and primed (P) PSCs, 5 days post-Dox addition.

, Volcano plot

illustrating DEGs (|log2FC| > 1, adjusted p-value < 0.05) between control and sgEIF3D naïve
PSCs, 5 days post-Dox addition.

, Euclidean distance between control and sgEIF3D in both naïve

and primed PSCs (mean ± SD, n=9) (unpaired comparison of three each from control and
sgEIF3D groups). p=5.84e-4, determined by unpaired t-test.

, Sample clustering from

Fig. 2A

addition to control and sgEIF3D naïve PSCs, with emphasis on selected marker genes. n=3. See
also

Supplementary Fig. 3

3 determined by unpaired t-test.

, Representative polysome profiles of sgEIF3D primed PSCs

compared to control lines on day 3 post-Dox addition.

, Area under curve quantification for

specified ribosomal fractions (mean ± SD, n=3). Ratios 60S/40S: p=0.011; 80S/40S: p=0.012;
polysomes (PS)/40S: p=0.094, calculated using unpaired t-test.

, Categorization of ORFs with

varying translation efficiency during EIF3D KD. Displayed are all ORFs translated in human iPSCs
(All), and those downregulated (Down) or upregulated (Up) by EIF3D KD over 3 days.

, Volcano

plot showing upregulated Differentially Translated Expressed Genes (uDTEGs, red) and
downregulated DTEGs (dDTEGs, blue) (|log

FC| > 1, adjusted p < 0.05). See full gene list, FC,

and adjusted p-value in

Supplementary Table 3, 4

, Pathway enrichment analysis of dDTEGs

using WikiPathways.

, Beeswarm plots indicating log

TE of transcripts in specified pathways

according to WikiPathways (WP437, WP399, WP481, WP382, and WP366 for EGF, WNT, Insulin,
MAPK, and TGF

pathways, respectively) (mean ± SD). EGF: p<0.0001; WNT: p=0.0262; Insulin:

p<0.0001; MAPK: p<0.0001; TGF

β: p<0.0001, analyzed by Wilcoxon matched

-pairs signed-rank

test. See full gene list, FC, and adjusted p-value in

Supplementary Table 5-9

, Phosphorylation

status of key proteins in the signaling pathways across the timeline of EIF3D KD.

Fig. 4: Indirect regulation of p53 protein through EIF3D-mediated selective translation.

, Venn diagrams display the overlap between DTEGs with significant Translation Efficiency (TE

|log

FC| > 0.58) and p53 regulators. Genes with higher TE (|log

FC| > 1) are highlighted in red.

See full gene list, FC, and adjusted p-value in

Supplementary Table 10

, Protein expression of

p53 regulators translationally controlled by EIF3D. High TE (|log

FC| > 1) includes RBBP6, SSU72,

and TCP1; moderate TE (|log

FC| > 0.58) includes KDM5C, WDR5, WDR82, and REST.

Representative images of specified cells, 5 days post-

KD induction. Scale bars: 100 μm.

, Cell

counts of the cells depicted in

Fig. 4c

(mean ± SD, n=6).

, Relative gene expression in cells from

Fig. 4c

. Values normalized to GAPDH and compared to control without Dox. n=3.

, Protein

expression of specified proteins in cells from

Fig. 4c

Supplementary Fig. 1: Supporting Data for EIF3D KD in Primed PSCs (Related to Fig. 1).
a

, EIF3D expression on specified days post-KD induction, normalized to GAPDH and compared

to control on day 0 (mean

SD, n=3).

, Representative images of Hoechst 33342-stained control

and sgEIF3D primed PSCs, 5 days post-KD induction. Scale bars:

100 μm

, Nuclear size in

control (n=5287) and sgEIF3D (n=5257) iPSCs, 5 days after Dox addition. p=1.18e-47,
determined by unpaired t-test.

, Flow cytometry panels showing DNA content and EdU

incorporation in control (grey) and sgEIF3D (red) primed PSCs, 5 days post-KD induction.

Standard gating strategy used in flow cytometry analysis.

, Flow cytometry data of control (upper)

Supplementary Fig. 2: Impact of NANOG Overexpression and TP53 KD in EIF3D KD Primed
PSCs (Related to Fig. 1).

, Representative images of control and sgEIF3D primed PSCs, with and without NANOG

transgene (Tg), 5 days post-KD induction. Scale bars:

100 μm

, Relative expression of EIF3D

and pluripotency markers in cells from

Supplementary Fig. 2a

, normalized to GAPDH and

compared to the control without Dox. n=3.

, Protein expression of EIF3D and pluripotency

markers in cells depicted in

Supplementary Fig. 2a

, Cell counts from

Supplementary Fig. 2a

, 5

days post-KD induction (mean ± SD, n=6).

, Representative images of control and sgEIF3D

primed PSCs, with and without sgTP53, 5 days post-

KD induction. Scale bars: 100 μm.

, Relative

expression of specified transcripts in cells from

Supplementary Fig. 2e

. Values normalized to

GAPDH and compared to control without Dox. n=3.

, Protein expression in cells depicted in

Supplementary Fig. 2e

, Cell counts from

Supplementary Fig. 2e

, 5 days post-KD induction

(mean ± SD, n=6). Control vs. sgTP53: p<0.0001; Control+sgTP53 vs. sgEIF3D+sgTP53:
p<0.0002, determined by one-way ANOVA.

Methods

Cell Culture
Human iPSC lines (WTB6 and 1B4 are gifts from Bruce R. Conklin of the Gladstone Institutes)
were maintained on tissue culture plates coated by iMatrix 511 silk (Matrixome) using StemFit
AK02N media (Ajinomoto), as previously described

. For passaging, cells were washed once

with Dulbecco's Phosphate Buffered Saline (D-PBS, Nacalai Tesque) and incubated in TrypLE
Express (Thermo Fisher Scientific) for 10 min at 37°C. Subsequently, cells were dissociated into
single cells and washed in Dulbecco's Modified Eagle Medium/Ham's F-12 (DMEM/F-12, WAKO)
containing 0.1% bovine serum albumin (BSA, WAKO). After cell counting and centrifugation, the
cells were resuspended in StemFit AK02N media suppl

emented with 1.67 μg/mL iMatrix

-511 silk

and 10 μM Y

-27632 (Nacalai Tesque). G-banding tests conducted by Nihon Gene Laboratories

confirmed that all PSC lines used in this study showed no significant karyotypic abnormalities.
Human dermal fibroblasts (HDFs) derived from fetal (HDF1419, Cell Applications) and adult (TIG-
120, a gift from Kazuhiko Kaji) donors were maintained in DMEM (Nacalai Tesque) with 10% fetal
bovine serum (FBS, Cosmo Bio) and used within six passages for the study. Routine testing
confirmed the absence of mycoplasma infection.

Transposon-Mediated Gene Transfer

We transfected 1 μg of a plasmid containing

the inverted terminal repeats of either PiggyBac (PB)

or Sleeping Beauty (SB) transposons, together with 0.5 μg of a plasmid encoding a hyperactive

PB transposase (hyPBase) or SB transposase (SB100X), into 5 × 10

human PSCs. This was

accomplished using the P3 Primary Cell 4D-Nucleofector X Kit S (Lonza) and Program CA-137
on the 4D Nucleofector device (Lonza). Two days post-transfection, the transfectants were
selected with the appropriate drug until non-transfected cells were completely eradicated.
Subsequently, single cell-derived colonies that uniformly expressed the transduced fluorescent
protein were isolated and expanded.

CRISPR Interference (CRISPRi)
To generate inducible CRISPRi iPSC lines targeting a specific gene, we introduced a vector
containing U6 promoter-driven sgRNA along with CAG promoter-driven fluorescence protein and

a drug resistance marker into the 1B4 human iPSC line (P23-28) using the PB-mediated gene
transfer method previously described

. Starting on day 2 post-transfection, drug selection was

initiated and continued until non-transfected cells were eliminated. Subsequently, single cell-
derived colonies uniformly expressing the fluorescent protein were isolated and expanded. To
induce kno

ckdown, we administered 1 μg/mL of doxycycline (Dox, WAKO) for the specified

duration. Knockdown clones within 20 passages post-subcloning were utilized for the study. The
spacer sequences are listed in

Supplementary Table 11

Endoderm Differentiation
Endoderm differentiation was conducted as previously described, with minor modifications

42,43

1B4 iPSCs (P35, 36, and 37) were seeded at a density of 1 × 10

cells per well in iMatrix 511-

coated 6-

well plates using StemFit AK02N media, supplemented with 10 μM Y

-27632. The

following day, cells were washed once with DMEM/F-12 and the media was replaced with
Differentiation Media 1 (DM1) consisted of DMEM/F-12 (Thermo Fisher Scientific), 2% B27
supplement (Thermo Fisher Scientific), 1% MEM Non-Essential Amino Acids (NEAA, Thermo
Fisher Scientific), and 0.1 mM 2-mercaptoethanol (2-ME, Thermo Fisher Scientific),

supplemented with 100 ng/mL Activin A (Nacalai Tesque), 3 μM CHIR99021 (Nacalai Tesque),

20 ng/mL bFGF (Nacalai Tesque), and 50 nM PI-103 (Cayman Chemical). After 24 hours, the
cells were washed with DMEM/F-12 and the medium was replaced with DM1 supplemented with
100 ng/mL Activin A and 250 nM LDN193189 (Stemgent). Two days later, following another wash
with DMEM/F-12, the cells were cultured in DM1 with 100 ng/mL Activin A for an additional 48
hours.

Mesoderm Differentiation
Directed differentiation to mesoderm was carried out with minor modifications from previously
described methods

43,44

. A day prior to differentiation, 1B4 iPSCs (P35, P36, and P37) were

seeded at a density of 1 × 10

cells per well in iMatrix 511-coated 6-well plates using StemFit

AK02N media supplemented with 10 μM Y

-27632. The following day, cells were washed once

with DMEM/F-12 and then cultured in DM1 medium containing 30 ng/mL Activin A, 40 ng/mL
BMP4 (Peprotech)

, 6 μM CHIR99021, 20 ng/mL bFGF, and 100

nM PIK-90 (MedChemExpress)

for 24 hours. Subsequently, after a wash with DMEM/F-12, the medium was replaced with DM1

supplemented with 40 ng/mL BMP4, 1 μM A83

01, and 4 μM CHIR99021, and cells were

maintained for an additional 48 hours. Then, the cells were washed once more with DMEM/F-12
and then cultured in DM1 medium supplemented with 40 ng/mL BMP4 for another 48 hours.

Ectoderm Differentiation
Neuroectoderm differentiation was conducted as previously described

45,46

. A day prior to induction,

1B4 iPSCs (P35, 36, and 37) were seeded at a density of 1 × 10

cells per well in iMatrix 511-

coated 6-

well plates, using StemFit AK02N medium supplemented with 10 μM Y

-27632. The

following day, the cells were washed once with DMEM/F-12 and then cultured in Glasgow's MEM
(WAKO) containing 8% Knockout Serum Replacement (KSR, Thermo Fisher Scientific), 1 mM
sodium pyruvate (Sigma-Aldrich), 1% MEM Non-Essential Amino Acids (NEAA), 0.1 mM 2-ME, 1

μM A83

-01, and 250 nM LDN193189. This was maintained for five days, with daily media changes.

Generation and Maintenance of Naïve PSCs
Primed PSCs were converted to a naïve pluripotent state as previously described

. Prior to

conversion, we maintained primed PSCs on γ

-ray irradiated primary mouse embryonic fibroblasts

(MEFs) in DFK20 media, composed of DMEM/F-12, 20% KSR, 1% NEAA, 0.1 mM 2-ME, and 4
ng/mL bFGF. For harvesting, cells were treated with CTK solution (ReproCELL) and dissociated
into single cells. We then seeded 1.5 × 10

primed PSCs onto inactivated MEFs in a well of a 6-

well plate using DFK20 media supplemented with 10 μM Y

-27632. The cells were incubated at

37°C in a hypoxic environment (5% O

). The following day, the media was replaced with NDiff227

(Takara) supplemented with 1 μM PD325901 (Stemgent), 10 ng/mL LIF (EMD Millipore), and 1

mM Valproic acid (WAKO). After three days, we switched the media to PXGL, consisting of
NDiff227 supplemente

d with 1 μM PD325901, 2 μM XAV939 (WAKO), 2 μM Gö6983 (Sigma

Aldrich), and 10 ng/mL LIF. Upon the emergence of round-shaped colonies, the cells were
dissociated using a 1:1 mixture of TrypLE Express and 0.5 mM EDTA, and then plated onto fresh
inactivated M

EF feeders in PXGL media containing 10 μM Y

-27632. We replaced the media daily

and passaged the cells every 3–5 days. The cells were utilized for assays after a minimum of 30
days post-conversion.

Differentiation of Naïve PSCs to the Primed State
Prior to differentiating naïve PSCs into a primed state, we treated the cells, which were grown in
PXGL media on iMatrix 511-coated plates, with Dox for 3 days. Subsequently, the media was
replaced with StemFit AK02N, also supplemented with Dox, and the cells were incubated under
normoxic conditions (20% O

). The cells were passaged every 4 days.

RNA Isolation and Reverse-Transcription Polymerase Chain Reaction
Cells were washed once with D-PBS and lysed using QIAzol reagent (QIAGEN). Total RNA was
extracted using the Direct-zol RNA Miniprep kit (Zymo Research), including on-column genomic
DNA digestion as per the provided instructions. For reverse transcription (RT), one microgram of
RNA was utilized, employing the ReverTra Ace qPCR RT Master Mix (TOYOBO). Quantitative
RT-PCR was conducted with gene-specific primers (refer to

Supplementary Table 11

) using either

THUNDERBIRD Next SYBR qPCR Mix (TOYOBO) or TaqMan assays (Thermo Fisher Scientific)
with TaqMan Universal Master Mix II, no UNG (Thermo Fisher Scientific) on a QuantoStudio 5
Real-Time PCR System (Applied Biosystems). Raw Ct values were normalized against ACTB or
GAPDH expression using the delta-delta Ct method. Relative expression was then calculated as
fold-change relative to the control.

Size-Based Protein Analysis
Cells were washed once with D-PBS and lysed using RIPA buffer (Sigma-Aldrich) supplemented
with a protease inhibitor cocktail (Sigma-Aldrich). The crude lysates were centrifuged at 15,300 ×
g for 15 min at 4°C, and the cleared supernatant was transferred to a new tube. The concentration
of the cleared lysate was measured using a Pierce BCA Protein Assay Kit (Thermo Fisher
Scientific) and an EnVision 2104 plate reader (Perkin Elmer), following previously described
methods. For quantitative and specific detection of target proteins, we utilized either a Wes or
Jess automated capillary electrophoresis platform (ProteinSimple) with 12-230 kDa or 60-440 kDa

Separation Modules (ProteinSimple). We loaded 2 μg of cell lysate per detection, along with the

following antibodies: mouse monoclonal anti-OCT3/4 (1:500, Santa Cruz Biotechnology), goat
polyclonal anti-SOX2 (1:40, R&D Systems), goat polyclonal anti-NANOG (1:40, R&D Systems),
mouse monoclonal anti-p53 (DO-7) (1:200, Novus Biologicals), goat polyclonal anti-p53 (1:100,

R&D Systems), rabbit monoclonal anti-p21 (1:50, Cell Signaling Technology), rabbit monoclonal
anti-MDM2 (1:50, Cell Signaling Technology), rabbit polyclonal anti-EIF3D (1:250, Proteintech),
rabbit polyclonal anti-SOX11 (1:500, Proteintech), rabbit polyclonal anti-KLF17 (1:200, Sigma-
Aldrich), rabbit monoclonal anti-ERK1/2 (1:50, Cell Signaling Technology), rabbit monoclonal anti-
phospho-ERK1/2 (Thr202/Tyr204) (1:50, Cell Signaling Technology), rabbit monoclonal anti-
SMAD2 (1:50, Cell Signaling Technology), rabbit polyclonal anti-phospho-SMAD2
(Ser245/250/255) (1:50, Cell Signaling Technology), rabbit monoclonal anti-mTOR (1:50, Cell
Signaling Technology), rabbit monoclonal anti-phospho-mTOR (Ser2448) (1:50, Cell Signaling
Technology), rabbit monoclonal anti-AKT (1:50, Cell Signaling Technology), rabbit monoclonal
anti-phospho-AKT (Ser473) (1:50, Cell Signaling Technology), rabbit polyclonal anti-p70S6K
(1:50, Cell Signaling Technology), rabbit polyclonal anti-phospho-p70S6K (Thr389) (1:50, Cell
Signaling Technology), rabbit polyclonal anti-phospho-p70S6K (Thr421/Ser424) (1:50, Cell
Signaling Technology), rabbit monoclonal anti-

-Catenin (1:50, Cell Signaling Technology), rabbit

monoclonal anti-phospho-

-Catenin (Ser552) (1:50, Cell Signaling Technology), mouse

monoclonal anti-puromycin (1:20, Developmental Studies Hybridoma Bank), rabbit polyclonal
anti-RBBP6 antibody (1:50, Sigma-Aldrich), rabbit polyclonal anti-TCP1 (1:200, Proteintech),
rabbit polyclonal anti-SSU72 (1:100, Proteintech), rabbit polyclonal anti-REST (1:100,
Proteintech), rabbit polyclonal anti-KDM5C (1:250, Proteintech), rabbit polyclonal anti-WDR5
(1:100, Proteintech), rabbit polyclonal anti-WDR82 (1:50, Proteintech), rabbit monoclonal anti-
VINCULIN (1:250, Cell Signaling Technology), and rabbit polyclonal anti-alpha tubulin (1:200,
Proteintech). Data visualization and analysis were conducted using Compass for SW6.0 software
(ProteinSimple).

Immunocytochemistry
The cells were washed once with D-PBS and fixed with 4% paraformaldehyde (Nacalai Tesque)
for 15 min at room temperature. They were then blocked in D-PBS containing 1% BSA, 2% normal
donkey serum (Sigma-Aldrich), and 0.2% Triton X-100 (Teknova) for 45 min at room temperature.
Subsequently, the fixed cells were incubated overnight at 4°C with primary antibodies diluted in
D-PBS containing 1% BSA. Following this, the cells were washed with D-PBS and incubated for
45 min at room temperature in 1% BSA containing fluorescence-conjugated secondary antibodies

and 1 μg/mL Hoechst 33342 (Thermo Fisher Scientific). After a final wash in D

-PBS, fluorescence

was detected using a BZ-X810 imaging system (KEYENCE). Merged images were generated
using a BZ-X Analyzer (KEYENCE). Nuclear size was quantified by analyzing Hoechst images
with a Hybrid Cell Count Module (KEYENCE). The antibodies and their dilutions were as follows:
mouse monoclonal anti-OCT3/4 (1:200), goat polyclonal anti-SOX2 (1:100), goat polyclonal anti-
NANOG (1:100), goat polyclonal anti-p53 (1:200), rabbit monoclonal anti-p21 (1:400), Alexa 647
Plus anti-mouse IgG (1:500, Thermo Fisher Scientific), Alexa 647 Plus anti-rabbit IgG (1:500,
Thermo Fisher Scientific), and Alexa 647 Plus anti-goat IgG (1:500, Thermo Fisher Scientific).

Puromycin Incorporation
After washing the cells grown in three wells twice with pre-warmed D-PBS, we added StemFit

AK02N media containing 100 μg/mL Cycloheximide (CHX, Sigma

-Aldrich) to one well, and

StemFit AK02N media alone to the other two wells. Following a 10-min incubation at 37°C, we

added 1 μM puromycin to one well containing CHX

-treated cells and to one of the two non-treated

wells, then continued incubation for 30 min at 37°C. Post-incubation, cells were washed with ice-
cold D-PBS and lysed using RIPA buffer, supplemented with a protease inhibitor cocktail.
Subsequently, the samples underwent Size-based protein analysis as described previously.

Cell-Cycle Analysis
As previously described

, we conducted cell-cycle analysis using the Click-iT EdU Alexa Fluor

647 Flow Cytometry Assay Kit (Thermo Fisher Scientific). Cells seeded at a density of 5 × 10

cells per well in a 6-well plate were cultured for five days in StemFit AK02N media supplemented
with Dox. Subsequently, the cells were incubated in media containing 10 µM 5-ethynyl-

2′

deoxyuridine (EdU) for 135 min at 37°C. The cells were then harvested and washed with 1% BSA.
Following centrifugation, the cells were resuspended in Click-iT fixative and incubated for 15 min
at room temperature. After washing the fixed cells with 1% BSA, they were permeabilized with 1×
Click-iT Perm/Wash reagent for 15 min at room temperature. For EdU detection, we added D-
PBS containing Copper Protectant, Alexa Fluor 647 picolyl azide, and 1× Click-iT EdU buffer
additive to the cell suspension. The samples were then washed with 1× Click-iT Perm/Wash

reagent and stained with 1 μg/mL FxCycle Violet (Thermo Fisher Scientific) for 30 min at room

temperature. We analyzed 1 × 10

cells using a FACS Aria II (BD Biosciences) and BD FACSDiva

software (BD Biosciences). EdU (detected with Alexa 647) and DNA (detected with FxCycle
Violet) were analyzed using APC (650/660 nm) and Pacific Blue (405/455 nm) filters, respectively.
Data analysis was conducted using FlowJo software (FlowJo LLC).

Genome-wide CRISPRi Screens
Ten micrograms of the genome-wide CRISPRi library hCRISPRi-v2 (courtesy of Jonathan

Weissman: Addgene, #83969) along with 3.75 μg of psPAX2 (courtesy of Didier Trono: Addgene,
#12260) and 1.25 μg of pMD2.G (courtesy of Didier Trono: Addgene, #12259) were t

ransfected

into 293T/17 cells (P27, ATCC) using TransIT-Lenti Transfection Reagent (Mirus). Cells, plated
at 5 million per 100-mm collagen I-coated dish, were transfected the day before

. Two days post-

transfection, the virus-containing supernatant was filtered through a 0.45-

μm pore size PVDF filter

(Millipore), and lentiviral particles were concentrated using the Lenti-X Concentrator (Takara) as
per the instructions. The lentivirus was then infected into 1B4 iPSCs (P23) at a multiplicity of
infection (MOI) of <0.4 (as determined by TagBFP fluorescence in the lentiviral vector) to achieve
coverage of >1,000×. Three days post-

infection, cells were selected with 1.5 μg/mL puromycin

until all non-infected cells perished. Subsequently, the cells were plated at 10 million per 150-mm

dish in StemFiT AK02N containing 10 μM Y

-27632 and iMatrix-511 silk. The following day, the

media was replaced with StemFiT AK02N supplemented with Dox. Cells were split every two to
three days, maintaining a minimum of 100 million cells, corresponding to a 1,000× coverage. Cells
maintained without Dox (day 0) and those with Dox for 16 days were harvested, and SSEA-5 (+)
cells were collected using an autoMACS Pro Separator (Miltenyi biotec). Genomic DNA was
purified from at least 100 million cells of each sample using NucleoSpin Blood XL (Takara) or
QIAamp DNA Blood Midi Kit (QIAGEN). The purified DNA was digested overnight with SbfI-HF
(New England Biolabs) and separated on a 0.8% TAE agarose gel. Post-electrophoresis, DNA
fragments ranging from 350 to 700 bp were excised from the gel and purified using a QIAGEN
gel extraction kit (QIAGEN). PCR and library preparation were conducted as previously
described

. The libraries were sequenced using a NextSeq 500/550 High Output v2 Kit (Illumina)

with custom primers, following the manufacturer's protocol. Reads were aligned to the hCRISPRi-
v2 sequences, counted, and analyzed using MAGeCK (version 0.5.9.5), then visualized using the

MAGeCK flute (version 1.12.0) package in R (version 4.1.1)

48,49

. GO analysis was conducted

using clusterProfiler (version 4.2.2)

50,51

Polysome Profiling
The method used for polysome fractionation was based on a previously described method with
minor modifications

. A single semiconfluent well of a 10-cm dish containing either control or

sgEIF3D iPSCs was placed on a CoolBox XT Workstation (Biocision) to maintain a temperature
of 4°C. This was followed by one gentle wash with 5 mL of ice-cold DPBS. The cells were then
gently scraped and dissociated in 0.6 mL of ice-cold lysis buffer, consisting of 20 mM Tris-HCl
(pH 7.5), 150 mM NaCl, 5 mM MgCl

(Nacalai Tesque), 1 mM dithiothreitol (DTT, WAKO), a

protease inhibitor cocktail, 100 μg/mL cycloheximide (CHX), 100 μg/mL chloramphenicol, and 1%

Triton X-100. The cell suspension was collected into a pre-chilled 1.5-mL DNA LoBind Tube
(Eppendorf). The lysate was incubated for 15 min on ice with 25 units/mL Turbo DNase (Thermo
Fisher Scientific) before centrifugation at 20,000 ×

for 10 min at 4°C. The cleared supernatant

was then transferred to a fresh 1.5-mL tube. The samples were rapidly frozen using liquid nitrogen
and stored at -80°C.

A continuous sucrose gradient ranging from 10% to 45% was prepared using 10% and

45% sucrose solutions (Sigma-Aldrich) in a 14 × 95 mm polyclear tube (Seton). The gradient was

created in the presence of 100 μg/mL CHX and 1 mM DTT in polysome buffer (25 mM

Tris-HCl,

pH 7.5, 150 mM NaCl, and 15 mM MgCl

) using the Biocomp Gradient Master program (Biocomp).

Thawed cell lysates were measured for RNA concentration using the Qubit RNA BR Assay Kit
(Thermo Fisher Scientific). A consistent volume of cell lysate con

taining 40 μg of RNA from each

sample (300 μL) was layered onto the continuous sucrose gradient. The polysomes were

separated by centrifugation in a himac ultracentrifuge using a P40ST rotor (himac) at 36,000 rpm
for 2.5 hours at 4°C. The relative RNA abundance in ribosomal subunits, monosomes, and
polysomes was detected using a 254-nm ultraviolet light with the Biocomp Piston Gradient
Fractionator (Biocomp). AUC were calculated using GraphPad Prism 8.0.2.

RNA Sequencing
Cells were lysed using QIAzol reagent, and total RNA was purified according to the protocol

mentioned earlier. RNA quality was assessed using an Agilent RNA6000 Pico Kit on a
Bioanalyzer 2100 (Agilent). The library preparation and subsequent analysis were carried out
following methods outlined in previous studies

53,54

. Briefly, 100 ng of DNase-treated total RNA

was used for library preparation with the Illumina Stranded Total RNA Prep Ligation with Ribo-
Zero Plus kit (Illumina). The libraries were evaluated using an Agilent High-Sensitivity DNA Kit
(Agilent) and then sequenced using either a NextSeq 500/550 High Output v2 Kit (Illumina),
NextSeq 1000/2000 P2 Reagents (100 cycles) v3 (Illumina), or HiSeq X (Illumina). The adapter
sequence was trimmed using cutadapt-1.12

. Reads mapping to ribosomal RNA were excluded

using SAM tools (version 1.10)

and Bowtie 2 (version 2.2.5)

. Reads were aligned to the hg38

human genome using STAR Aligner (version 2.7.10b)

. Quality checks were performed using

RSeQC (version 4.0.0)

. Reads were counted with HTSeq (version 0.13.5)

using the

GENCODE annotation file (version 35)

. Counts were normalized using DESeq2 (version 1.34.0)

in R (version 4.1)

. The DESeq2 package was also used to perform Wald tests. PCA and

heatmaps were generated using prcomp and pheatmap, respectively. GO analysis was
conducted and visualized using clusterProfiler

50,51

Ribosome Profiling
Ribosome profiling was conducted as previously outlined

63,64

. Cells were lysed in a buffer

containing 20 mM Tris-HCl (pH 7.5), 150 mM NaCl, 5 mM MgCl

, 1 mM DTT, 1% Triton X-100,

100 μg/mL chloramphenicol, and 100 μg/mL CHX, followed by a 15

-minute DNase treatment on

ice. RNA concentrations in the lysate were measured using the Qubit RNA BR assay kit (Thermo

Fisher Scientific). We treated 10 μg of RNA w

ith RNase I (Epicentre) for 45 min at 25°C. The

ribosome footprint RNA was then concentrated via ultracentrifugation using a sucrose cushion
(20 mM Tris-HCl, pH 7.5, 150 mM NaCl, 5 mM MgCl

, 1 mM DTT, 20 U/mL SUPERase-In (Thermo

Fisher S

cientific), 1 M Sucrose, 100 μg/mL chloramphenicol, and 100 μg/mL CHX). The resulting

pellets were resuspended in pellet buffer (20 mM Tris-HCl, pH 7.5, 300 mM NaCl, 5 mM MgCl

1 mM DTT, 1% Triton X-100, and 20 U/mL SUPERase-In) and purified using the Direct-zol RNA
Microprep kit (Zymo Research). The RNA samples were separated by electrophoresis, and
fragments ranging from 17-34 nt were excised and purified using Dr. GenTLE Precipitation Carrier
(Takara). These purified ribosome footprint RNAs were ligated with linker oligonucleotides

containing an inner index sequence and a unique molecular identifier (UMI), followed by rRNA
depletion using riboPOOLs for Ribo-seq (siTOOLs). The residual RNAs were reverse transcribed
using ProtoScript II (New England Biolabs) and circularized with circLigase2 (Epicentre). The
cDNA templates were amplified using Phusion polymerase (New England Biolabs) with index-
sequenced primers.

To calculate translational efficiency, corresponding RNA-seq experiments were

performed using RNA extracted from the lysis buffer. We utilized TRIZOL LS reagent (Thermo
Fisher Scientific) and the Direct-zol RNA Microprep kit for RNA extraction. The RNA-seq libraries
were prepared as instructed by manufacturer's protocol, except using RiboPOOLs for RNA-seq
(siTOOLs) in the rRNA depletion step and xGen

UDI-UMI Adapters (Integrated DNA

Technologies) in adapter ligation step. The cDNA libraries were sequenced following the RNA
sequencing protocol. The reads were demultiplexed using the inner index and adapters were
removed using fastp (version 0.22.0) and fastx-split

. To filter out reads mapping to rRNA,

Bowtie2 and SAMtools were used. The remaining reads were aligned to the human genome
(hg38) using STAR (version 2.7.10b), and duplicates were removed based on UMI using bam-
suppress-duplicates. Quality control statistics were calculated using fp-framing. Read counting
and normalization were performed using fp-count and DESeq2, respectively. Translation
efficiency (TE) and fold change values were calculated using the average values of replicates as
follows (

indicates a gene):

𝑇𝑇𝑇𝑇

𝑖𝑖

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅

𝑝𝑝𝑝𝑝𝑅𝑅𝑝𝑝𝑅𝑅𝑝𝑝𝑅𝑅𝑝𝑝𝑝𝑝

𝑝𝑝𝑅𝑅𝑝𝑝𝑅𝑅𝑛𝑛𝑝𝑝𝑅𝑅𝑛𝑛𝑅𝑅𝑛𝑛

𝑐𝑐𝑅𝑅𝑐𝑐𝑝𝑝𝑐𝑐

𝑖𝑖

𝑅𝑅𝑅𝑅𝑅𝑅

𝑅𝑅𝑅𝑅𝑠𝑠𝑐𝑐𝑅𝑅𝑝𝑝𝑐𝑐𝑅𝑅𝑝𝑝𝑝𝑝

𝑝𝑝𝑅𝑅𝑝𝑝𝑅𝑅𝑛𝑛𝑝𝑝𝑅𝑅𝑛𝑛𝑅𝑅𝑛𝑛

𝑐𝑐𝑅𝑅𝑐𝑐𝑝𝑝𝑐𝑐

𝑖𝑖

𝐹𝐹𝑅𝑅𝑝𝑝𝑛𝑛

𝑐𝑐ℎ𝑛𝑛𝑝𝑝𝑝𝑝𝑅𝑅

𝑖𝑖

𝑇𝑇𝑇𝑇

𝑖𝑖

𝑅𝑅𝑝𝑝

𝑇𝑇𝐸𝐸𝐹𝐹

𝐷𝐷

𝑇𝑇𝑇𝑇

𝑖𝑖

𝑅𝑅𝑝𝑝

𝑐𝑐𝑅𝑅𝑝𝑝𝑐𝑐𝑝𝑝𝑅𝑅𝑝𝑝

For identifying differentially translated transcripts, we employed DESeq2, utilizing a likelihood ratio
test (model: Experiment + Target + Experiment:Target; reduced model: Experiment + Target). In
pathway enrichment analysis, Enrichr was used with the WikiPathways_2021_Human database.
To analyze upstream, downstream, and newly identified open reading frames (ORFs), we first
obtained a bed file using ORF-RATER with ribosome profiling data of the parental iPSC line WTB6,

treated with CHX and harringtonine

66,67

. Using this bed file, reads from control and sgEIF3D (Dox+

3 days) samples were counted by fp-count. We then conducted a statistical analysis to identify
significantly different transcripts between these conditions, employing DESeq2 with a Wald test.

Statistics
The quantitative measurement results are presented as individual data points, depicted by colored
dots, with means indicated by bars. In some instances, bar graphs with individual data points and
error bars representing standard deviations are used. Statistical analyses included unpaired two-
tailed t-tests to calculate p-values, assessing differences between two groups. Furthermore, one-
way analysis of variance (ANOVA) was utilized for multiple comparisons. These analyses were
performed using GraphPad Prism version 8.0.2 (GraphPad) and Excel (Microsoft). Statistical
significance was determined by p-values or adjusted p-values less than 0.05, denoted by
asterisks in the figures. The specific values are detailed in the figure legends.

Key resources table

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Antibodies

Mouse monoclonal anti-OCT3/4

Santa Cruz Biotechnology

Cat# sc-5279,
RRID:AB_628051

Goat polyclonal anti-SOX2

R&D Systems

Cat# AF2018,
RRID:AB_355110

Goat polyclonal anti-NANOG

R&D Systems

Cat# AF1997,
RRID:AB_355097

Rabbit polyclonal anti-EIF3D

Proteintech

Cat# 10219-1-AP,
RRID:AB_2096880

Goat polyclonal anti-SOX17

R&D Systems

Cat# AF1924,
RRID:AB_355060

Goat polyclonal anti-HAND1

R&D Systems

Cat# AF3168,
RRID:AB_2115853

Rabbit polyclonal anti-PAX6

BioLegend

Cat# 901301,
RRID:AB_2565003

Goat polyclonal anti-p53

R&D Systems

Cat# AF1355,
RRID:AB_354749

Mouse monoclonal anti-p53

Novus biologicals

Cat# NBP2-34308,
RRID:AB_964897

Rabbit monoclonal anti-p21

Cell Signaling Technology

Cat# 2947,
RRID:AB_823586

Rabbit monoclonal anti-MDM2

Cell Signaling Technology

Cat# 86934,
RRID:AB_2784534

Rabbit monoclonal anti-ERK1/2

Cell Signaling Technology

Cat# 4695,
RRID:AB_390779

Rabbit monoclonal anti-phospho-
ERK1/2 (Thr202/Tyr204)

Cell Signaling Technology

Cat# 4370,
RRID:AB_2315112

Rabbit monoclonal anti-SMAD2

Cell Signaling Technology

Cat# 5339,
RRID:AB_10626777

Rabbit polyclonal anti-phospho-
SMAD2 (Ser245/250/255)

Cell Signaling Technology

Cat# 3104,
RRID:AB_390732

Rabbit monoclonal anti-mTOR

Cell Signaling Technology

Cat# 2983,
RRID:AB_2105622

Rabbit monoclonal anti-phospho-
mTOR (Ser2448)

Cell Signaling Technology

Cat# 5536,
RRID:AB_10691552

Rabbit monoclonal anti-AKT

Cell Signaling Technology

Cat# 4691,
RRID:AB_915783

Rabbit monoclonal anti-phospho-
AKT (Ser473)

Cell Signaling Technology

Cat# 4058,
RRID:AB_331168

Rabbit polyclonal anti-p70S6K

Cell Signaling Technology

Cat# 9202,
RRID:AB_331676

Rabbit polyclonal anti-phospho-
p70S6K (Thr389)

Cell Signaling Technology

Cat# 9205,
RRID:AB_330944

Rabbit polyclonal anti-phospho-
p70S6K (Thr421/Ser424)

Cell Signaling Technology

Cat# 9204,
RRID:AB_2265913

Rabbit monoclonal anti-

-Catenin

Cell Signaling Technology

Cat# 9582,
RRID:AB_823447

Rabbit monoclonal anti-phospho-

Catenin (Ser552)

Cell Signaling Technology

Cat# 5651,
RRID:AB_10831053

Rabbit polyclonal anti-SOX11

Proteintech

Cat# 29395-1-AP,
RRID:AB_2918291

Rabbit polyclonal anti-KLF17

Sigma-Aldrich

Cat# HPA024629,
RRID:AB_1668927

Rabbit polyclonal anti-RBBP6

Sigma-Aldrich

Cat# HPA041725,
RRID:AB_2677639

Rabbit polyclonal anti-TCP1

Proteintech

Cat# 10320-1-AP,
RRID:AB_10694136

Rabbit polyclonal anti-SSU72

Proteintech

Cat# 15434-1-AP,
RRID:AB_2878138

Rabbit polyclonal anti-KDM5C

Proteintech

Cat# 14426-1-AP
RRID:AB_10837073

Rabbit polyclonal anti-REST

Proteintech

Cat# 22242-1-AP,
RRID:AB_2879044

Rabbit polyclonal anti-WDR5

Proteintech

Cat# 15544-1-AP
RRID:AB_2257220

Rabbit polyclonal anti-WDR82

Proteintech

Cat# 21354-1-AP

Rabbit monoclonal anti-VINCULIN Cell Signaling Technology

Cat# 13901,
RRID:AB_2728768

Rabbit polyclonal anti-alpha tubulin Proteintech

Cat# 11224-1-AP,
RRID:AB_2210206

Mouse monoclonal anti-puromycin

Developmental Studies
Hybridoma Bank

Cat# PMY-2A4
RRID:AB_2619605

FITC mouse monoclonal anti-
SSEA-5

Biolegend

Cat# 355208,
RRID:AB_2561827

Anti-FITC Microbeads

Miltenyi biotec

Cat# 130-048-701,
RRID:AB_244371

Alexa 488 Plus donkey anti-mouse
IgG

Thermo Fisher Scientific

Cat# A32766,
RRID:AB_2762823

Alexa 647 Plus donkey anti-mouse
IgG

Thermo Fisher Scientific

Cat# A32728,
RRID:AB_2633277

Alexa 647 Plus donkey anti-rabbit
IgG

Thermo Fisher Scientific

Cat# A32795,
RRID:AB_2762835

Alexa 647 Plus donkey anti-goat
IgG

Thermo Fisher Scientific

Cat# A32849,
RRID:AB_2762840

Chemicals, peptides, and recombinant proteins

StemFit AK02N

Ajinomoto

Cat# AK02

NDiff227

Takara

Cat# Y40002

DMEM high glucose

Nacalai Tesque

Cat# 08459-35

DMEM/F-12

WAKO

Cat# 048-29785

DMEM/F-12

Thermo Fisher Scientific

Cat# 10565018

Glasgow's MEM

WAKO

Cat# 078-05525

Fetal bovine serum

Cosmo Bio

Cat# CCP-FBS-BR-500

7.5% Bovine Serum Albumin (BSA) WAKO

Cat# 012-23881

B27 supplement

Thermo Fisher Scientific

Cat# 17504044

Knockout Serum Replacement

Thermo Fisher Scientific

Cat# 10828028

MEM Non-Essential Amino Acids
Solution

Thermo Fisher Scientific

Cat# 11140050

Sodium pyruvate solution

Sigma-Aldrich

Cat# S8636

2-mercaptoethanol

Thermo Fisher Scientific

Cat# 21985023

iMatrix-511 silk

Matrixome

Cat# 892021

Geltrex

Thermo Fisher Scientific

Cat# A1413301

Doxycycline Hydrochloride

WAKO

Cat# 045-31123

Puromycin Dihydrochloride

Thermo Fisher Scientific

Cat# A1113803

Blasticidin S Hydrochloride

WAKO

Cat# 022-18713

Hygromycin B

WAKO

Cat# 084-07681

Dulbecco's Phosphate Buffered
Saline

Nacalai Tesque

Cat# 14249-95

Antibiotic-Antimycotic

Thermo Fisher Scientific

Cat# 15240062

Antibiotic-Antimycotic Mixed
Solution

Nacalai Tesque

Cat# 02892-54

BD FACS Pre-Sort Buffer

BD biosciences

Cat# 563503

UltraPure 0.5 M EDTA, pH8.0

Thermo Fisher Scientific

Cat# 15575020

TrypLE Express

Thermo Fisher Scientific

Cat# 12604013

AccuMax

Innovative Cell Technologies Cat# AM105

Dissociation solution (CTK
solution)

ReproCELL

Cat# RCHETP002

Activin A, Human recombinant

Nacalai Tesque

Cat# 18585-81

BMP4, Human recombinant

Peprotech

Cat# 120-05ET

bFGF, Human recombinant

Nacalai Tesque

Cat# 19155-36

LIF, Human recombinant

WAKO

Cat# 125-06661

EGF, Human recombinant

WAKO

Cat# 059-07873

Y-27632

Nacalai Tesque

Cat# 18188-04

PIK-90

Cayman Chemical

Cat# 10010749

PI-103 Hydrochloride

MedChemExpress

Cat# HY-10115A

A83-01

Stemgent

Cat# 04-0014

LDN193189

Stemgent

Cat# 04-0074

CHIR99021

Nacalai Tesque

Cat# 18764-44

PD325901

Stemgent

Cat# 04-0006

XAV939

WAKO

Cat# 247-00951

Gö6983

Sigma-Aldrich

Cat# G1918

Valproic acid

WAKO

Cat# 227-01071

SC79

Sigma-Aldrich

Cat# SML0749

MHY1485

MedChemExpress

Cat# HY-B0795

Nutlin-3a

Sigma-Aldrich

Cat# SML0580

FxCycle Violet Stain

Thermo Fisher Scientific

Cat# F10347

Hoechst 33342

DOJINDO

Cat# H342

0.4% Trypan blue solution

WAKO

Cat# 207-17081

4% Paraformaldehyde Solution

Nacalai Tesque

Cat# 09154-85

10% Triton X-100 solution

Teknova

Cat# T1105

Normal donkey serum

Sigma-Aldrich

Cat# D9663

Lenti-X Concentrator

Takara

Cat# 631232

Cycloheximide

Sigma-Aldrich

Cat# C4859

Chloramphenicol

WAKO

Cat# 030-19452

1 M Tris-HCl, pH 7.5

WAKO

Cat# 318-90225

5 M Sodium chloride

Nacalai Tesque

Cat# 06900-14

1 M Magnesium chloride

Nacalai Tesque

Cat# 20942-34

1 M dithiothreitol

WAKO

Cat# 044-33871

Sucrose

WAKO

Cat# 198-13525

Critical commercial assays

P3 Primary Cell 4D-Nucleofector X
Kit S

Lonza

Cat# V4XP-3032

TransIT-Lenti Transfection
Reagent

Mirus

Cat# MIR6600

Click-iT EdU Alexa Fluor 647 Flow
Cytometry Assay Kit

Thermo Fisher Scientific

Cat# C10424

QIAzol lysis reagent

QIAGEN

Cat# 79306

TRIZOL LS reagent

Thermo Fisher Scientific

Cat# 10296028

Direct-zol RNA Miniprep kit

Zymo Research

Cat# R2052

Direct-zol RNA Microprep kit

Zymo Research

Cat# R2062

ReverTra Ace qPCR RT Kit

TOYOBO

Cat# FSQ-101

THUNDERBIRD Next SYBR qPCR
Mix

TOYOBO

Cat# QPX-201

TaqMan Universal Master Mix II,
no UNG

Thermo Fisher Scientific

Cat# 4440040

NucleoSpin Blood XL

Takara

U0950B

QIAamp DNA Blood Midi Kit

QIAGEN

Cat# 51185

QIAquick Gel Extraction Kit

QIAGEN

Cat# 28706

SbfI-HF

New England Biolabs

Cat# R3642

KOD One Master Mix

TOYOBO

Cat# KMM-101

NEBuilder HiFi DNA Assembly
Master Mix

New England Biolabs

Cat# E2621

riboPOOLs for Ribo-seq (Homo
sapiens)

siTOOLsBiotech

Cat# 042

riboPOOLs (Homo sapiens)

siTOOLsBiotech

Cat# 054

xGen UDI-UMI Adapters

Integrated DNA Technologies Cat# 10005903

ProtoScript II

New England Biolabs

Cat# M0368L

CircLigaseII ssDNA ligase

Epicentre

Cat# CL9025K

Turbo DNase

Thermo Fisher Scientific

Cat# AM2238

RNase I

Epicentre

Cat# N6901K

SUPERase-In

Thermo Fisher Scientific

Cat# AM2696

Phusion polymerase

New England Biolabs

Cat# M0530S

Dr. GenTLE Precipitation Carrier

Takara

Cat# 9094

Anti-Rabbit Detection Module for
Jess, Wes, Peggy Sue or Sally Sue

ProteinSimple

Cat# DM-001

Anti-Mouse Detection Module for
Jess, Wes, Peggy Sue or Sally Sue

ProteinSimple

Cat# DM-002

Anti-Goat Detection Module for
Jess, Wes, Peggy Sue or Sally Sue

ProteinSimple

Cat# DM-006

Agilent RNA6000 Pico Kit

Agilent

Cat# 5067-1513

Agilent High-Sensitivity DNA Kit

Agilent

Cat# 5067-4626

Qubit RNA BR Assay Kit

Thermo Fisher Scientific

Cat# Q10211

IDT for Illumina RNA UD Indexes
Set A, Ligation

Illumina

Cat# 20040553

Illumina Stranded Total RNA Prep,
Ligation with Ribo-Zero Plus

Illumina

Cat# 20040529

NextSeq 500/550 High Output v2
Kit

Illumina

Cat# FC-404-2005

NextSeq 1000/2000 P2 Reagents
(100 cycles) v3

Illumina

Cat# 20046811

Experimental models: Cell lines

WTB6 human iPSC line

Miyaoka et al.

RRID:CVCL_VM30

1B4 (CRISPRi Gen1B) human
iPSC line

Mandegar et al.

RRID:CVCL_VM35

TIG-120 human dermal fibroblast

Kazuhiko Kaji

RRID:CVCL_320

HDF1419 human dermal fibroblast Cell Applications, Inc.

RRID:CVCL_DP65

293T/17

ATCC

RRID:CVCL_1926

Oligonucleotides

Primers

eurofins

See

Supplementary

Table 11

CDKN2A (Hs02902543_mH)
TaqMan assay

Thermo Fisher Scientific

Cat# 4331182

CDKN2A (Hs99999189_m1)
TaqMan assay

Thermo Fisher Scientific

Cat# 4331182

GAPDH (Hs02786624_g1)
TaqMan assay

Thermo Fisher Scientific

Cat# 4331182

Recombinant DNA

PB-U6-CNCB

Takahashi et al.

N/A

PB-U6-CNCB_sgEIF3D-1

This study

See

Supplementary

Table 11

PB-U6-CNCB_sgEIF3D-4

This study

PB-U6-CNKB_sgEIF3D-1

This study

See

Supplementary

Table 11

PB-U6-CNCH_sgTP53-1

This study

PB-U6-CNCH_sgTP53-3

This study

PB-U6-CNCH_sgTP53-5

This study

PB-2G-CNCB_sgRBBP6-68

This study

SB-CAG-Clover-P2A-NANOG-IP

This study

N/A

pCW-hyPBase

Takahashi et al.

N/A

pCW-SB100X

Takahashi et al.

N/A

pMD2.G

Didier Trono

RRID:Addgene_12259

psPAX2

Didier Trono

RRID:Addgene_12260

Human Genome-wide CRISPRi-v2
Libraries

Jonathan Weissman

RRID:Addgene_83969

Software and algorithms

BZ-X Analyzer BZ-H3A

https://www.keyence.com/glo
bal.jsp

KEYENCE

Hybrid Cell Count Module BZ-H3C

https://www.keyence.com/glo
bal.jsp

KEYENCE

Compass for SW6.0

https://www.proteinsimple.co
m/compass/downloads/

ProteinSimple

cutadapt-1.12

http://gensoft.pasteur.fr/docs/
cutadapt/1.18/index.html

Martin et al.

bowtie 2 (version 2.2.5)

http://bowtie-
bio.sourceforge.net/bowtie2/i
ndex.shtmll

Langmead et al.

SAM tools (version 1.10)

https://sourceforge.net/projec
ts/samtools/files/samtools/1.7
/

Li et al.

STAR Aligner (version 2.7.10b)

https://github.com/alexdobin/
STAR

Dobin et al.

RSeQC (version 4.0.0)

http://rseqc.sourceforge.net/

Wang et al.

HTSeq (version 0.13.5)

https://htseq.readthedocs.io/e
n/master/

Anders et al.

DESeq2 (version 1.26.0)

https://bioconductor.org/pack
ages/release/bioc/html/DESe
q2.html

Love et al.

FlowJo (version 10.9.0)

https://www.flowjo.com/

FlowJo, LLC

Office 365

https://www.office.com/

Microsoft

Adobe Creative Cloud

https://www.adobe.com/

Adobe

GraphPad Prism (version 8.0.2)

https://www.graphpad.com/sc
ientific-software/prism/

GraphPad

R (version 4.1.1)

https://www.r-project.org/

MAGeCK (version 0.5.9.5)

https://sourceforge.net/p/mag
eck/wiki/Home/

Li et al.

MAGeCKFlute (version 1.12.0)

https://www.bioconductor.org/
packages/release/bioc/html/
MAGeCKFlute.html

Wang et al.

clusterProfiler (version 4.2.2)

https://bioconductor.org/pack
ages/release/bioc/html/cluste
rProfiler.html

Yu et al.

fastp

https://github.com/OpenGene
/fastp

Wu et al.

fastx-split

https://github.com/ingolia-
lab/RiboSeq/

bam-suppress-duplicates

https://github.com/ingolia-
lab/RiboSeq/

fp-framing

https://github.com/ingolia-
lab/RiboSeq/

fp-count

https://github.com/ingolia-
lab/RiboSeq/

Enrichr

https://maayanlab.cloud/Enric
hr/

References

1. Thomson, J. A.

et al.

Embryonic stem cell lines derived from human blastocysts.

Science

282

1145-1147 (1998).

2. Evans, M. J. & Kaufman, M. H. Establishment in culture of pluripotential cells from mouse

embryos.

Nature

292

, 154-156 (1981).

3. Martin, G. R. Isolation of a pluripotent cell line from early mouse embryos cultured in medium

conditioned by teratocarcinoma stem cells.

Proc Natl Acad Sci U S A

, 7634-7638 (1981).

4. Brons, I. G.

et al.

Derivation of pluripotent epiblast stem cells from mammalian embryos.

Nature

448

, 191-195 (2007).

5. Tesar, P. J.

et al.

New cell lines from mouse epiblast share defining features with human

embryonic stem cells.

Nature

448

, 196-199 (2007). https://doi.org/10.1038/nature05972

6. Nichols, J. & Smith, A. Naive and primed pluripotent states.

Cell Stem Cell

, 487-492 (2009).

https://doi.org/10.1016/j.stem.2009.05.015

7. Takashima, Y.

et al.

Resetting transcription factor control circuitry toward ground-state

pluripotency in human.

Cell

158

, 1254-1269 (2014). https://doi.org/10.1016/j.cell.2014.08.029

8. Theunissen, T. W.

et al.

Systematic identification of culture conditions for induction and

maintenance of naive human pluripotency.

Cell Stem Cell

, 471-487 (2014).

https://doi.org/10.1016/j.stem.2014.07.002

9. Bayerl, J.

et al.

Principles of signaling pathway modulation for enhancing human naive

pluripotency induction.

Cell Stem Cell

, 1549-1565.e1512 (2021).

https://doi.org/10.1016/j.stem.2021.04.001

10. Ying, Q. L.

et al.

The ground state of embryonic stem cell self-renewal.

Nature

453

, 519-523

(2008). https://doi.org/nature06968 [pii]10.1038/nature06968

11. Guo, G.

et al.

Epigenetic resetting of human pluripotency.

Development

144

, 2748-2763

(2017). https://doi.org/10.1242/dev.146811

12. Bendall, S. C.

et al.

IGF and FGF cooperatively establish the regulatory stem cell niche of

pluripotent human cells in vitro.

Nature

448

, 1015-1021 (2007).

https://doi.org/10.1038/nature06027

13. James, D., Levine, A. J., Besser, D. & Hemmati-Brivanlou, A. TGFbeta/activin/nodal signaling

is necessary for the maintenance of pluripotency in human embryonic stem cells.

Development

132

, 1273-1282 (2005). https://doi.org/10.1242/dev.01706

14. Vallier, L., Alexander, M. & Pedersen, R. A. Activin/Nodal and FGF pathways cooperate to

maintain pluripotency of human embryonic stem cells.

J Cell Sci

118

, 4495-4509 (2005).

https://doi.org/10.1242/jcs.02553

15. Sumi, T., Tsuneyoshi, N., Nakatsuji, N. & Suemori, H. Defining early lineage specification of

human embryonic stem cells by the orchestrated balance of canonical Wnt/beta-catenin,
Activin/Nodal and BMP signaling.

Development

135

, 2969-2979 (2008).

https://doi.org/10.1242/dev.021121

16. Schuldiner, M., Yanuka, O., Itskovitz-Eldor, J., Melton, D. A. & Benvenisty, N. From the cover:

effects of eight growth factors on the differentiation of cells derived from human embryonic
stem cells.

Proc Natl Acad Sci U S A

, 11307-11312. (2000).

17. Singh, A. M.

et al.

Signaling network crosstalk in human pluripotent cells: a Smad2/3-regulated

switch that controls the balance between self-renewal and differentiation.

Cell Stem Cell

312-326 (2012). https://doi.org/10.1016/j.stem.2012.01.014

18. Huang, T. S.

et al.

A Regulatory Network Involving β

-Catenin, e-Cadherin, PI3k/Akt, and Slug

Balances Self-Renewal and Differentiation of Human Pluripotent Stem Cells In Response to
Wnt Signaling.

Stem Cells

, 1419-1433 (2015). https://doi.org/10.1002/stem.1944

19. Sampath, P.

et al.

A hierarchical network controls protein translation during murine embryonic

stem cell self-renewal and differentiation.

Cell Stem Cell

, 448-460 (2008).

https://doi.org/10.1016/j.stem.2008.03.013

20. Iwasaki, M.

et al.

Multi-omics approach reveals posttranscriptionally regulated genes are

essential for human pluripotent stem cells.

iScience

, 104289 (2022).

https://doi.org/10.1016/j.isci.2022.104289

21. Corsini, N. S.

et al.

Coordinated Control of mRNA and rRNA Processing Controls Embryonic

Stem Cell Pluripotency and Differentiation.

Cell Stem Cell

, 543-558.e512 (2018).

https://doi.org/10.1016/j.stem.2018.03.002

22. Takahashi, K.

et al.

Critical Roles of Translation Initiation and RNA Uridylation in Endogenous

Retroviral Expression and Neural Differentiation in Pluripotent Stem Cells.

Cell Rep

107715 (2020). https://doi.org/10.1016/j.celrep.2020.107715

23. Bartsch, D.

et al.

mRNA translational specialization by RBPMS presets the competence for

cardiac commitment in hESCs.

Sci Adv

, eade1792 (2023).

https://doi.org/10.1126/sciadv.ade1792

24. Mandegar, M. A.

et al.

CRISPR Interference Efficiently Induces Specific and Reversible Gene

Silencing in Human iPSCs.

Cell Stem Cell

, 541-553 (2016).

https://doi.org/10.1016/j.stem.2016.01.022

25. Horlbeck, M. A.

et al.

Compact and highly active next-generation libraries for CRISPR-

mediated gene repression and activation.

Elife

(2016). https://doi.org/10.7554/eLife.19760

26. Lee, A. S., Kranzusch, P. J., Doudna, J. A. & Cate, J. H. eIF3d is an mRNA cap-binding protein

that is required for specialized translation initiation.

Nature

536

, 96-99 (2016).

https://doi.org/10.1038/nature18954

27. Adewumi, O.

et al.

Characterization of human embryonic stem cell lines by the International

Stem Cell Initiative.

Nat Biotechnol

, 803-816 (2007). https://doi.org/10.1038/nbt1318

28. Takahashi, K.

et al.

Induction of pluripotent stem cells from adult human fibroblasts by defined

factors.

Cell

131

, 861-872 (2007). https://doi.org/10.1016/j.cell.2007.11.019

29. Mair, B.

et al.

Essential Gene Profiles for Human Pluripotent Stem Cells Identify

Uncharacterized Genes and Substrate Dependencies.

Cell Rep

, 599-615.e512 (2019).

https://doi.org/10.1016/j.celrep.2019.02.041

30. Tsuneyoshi, N.

et al.

PRDM14 suppresses expression of differentiation marker genes in

human embryonic stem cells.

Biochem Biophys Res Commun

367

, 899-905 (2008).

https://doi.org/S0006-291X(08)00003-X [pii]10.1016/j.bbrc.2007.12.189

31. Wong, R. C.

et al.

L1TD1 is a marker for undifferentiated human embryonic stem cells.

PLoS

One

, e19355 (2011). https://doi.org/10.1371/journal.pone.0019355

32. Messmer, T.

et al.

Transcriptional Heterogeneity in Naive and Primed Human Pluripotent

Stem Cells at Single-Cell Resolution.

Cell Rep

, 815-824.e814 (2019).

https://doi.org/10.1016/j.celrep.2018.12.099

33. Io, S.

et al.

Capturing human trophoblast development with naive pluripotent stem cells in vitro.

Cell Stem Cell

, 1023-1039.e1013 (2021). https://doi.org/10.1016/j.stem.2021.03.013

34. Kinoshita, M.

et al.

Capture of Mouse and Human Stem Cells with Features of Formative

Pluripotency.

Cell Stem Cell

, 453-471.e458 (2021).

https://doi.org/10.1016/j.stem.2020.11.005

35. Dalton, S. Signaling networks in human pluripotent stem cells.

Curr Opin Cell Biol

, 241-

246 (2013). https://doi.org/10.1016/j.ceb.2012.09.005

36. Lü, Y.

et al.

Genome-wide CRISPR screens identify novel regulators of wild-type and mutant

p53 stability.

bioRxiv

, 2022.2003.2013.483372 (2022).

https://doi.org/10.1101/2022.03.13.483372

37. Li, L.

et al.

PACT is a negative regulator of p53 and essential for cell growth and embryonic

development.

Proc Natl Acad Sci U S A

104

, 7951-7956 (2007).

https://doi.org/10.1073/pnas.0701916104

38. Herrmannová, A.

et al.

eIF3e and eIF3d control expression of riboproteins and key

components of the MAPK signaling pathway.

bioRxiv

, 2023.2006.2029.547003 (2023).

https://doi.org/10.1101/2023.06.29.547003

39. Lamper, A. M., Fleming, R. H., Ladd, K. M. & Lee, A. S. Y. A phosphorylation-regulated eIF3d

translation switch mediates cellular adaptation to metabolic stress.

Science

370

, 853-856

(2020). https://doi.org/10.1126/science.abb0993

40. Ayaz, G., Yan, H., Malik, N. & Huang, J. An Updated View of the Roles of p53 in Embryonic

Stem Cells.

Stem Cells

, 883-891 (2022). https://doi.org/10.1093/stmcls/sxac051

41. Takahashi, K.

et al.

A stress-reduced passaging technique improves the viability of human

pluripotent cells.

Cell Rep Methods

, 100155 (2022).

https://doi.org/10.1016/j.crmeth.2021.100155

42. Loh, K. M.

et al.

Efficient endoderm induction from human pluripotent stem cells by logically

directing signals controlling lineage bifurcations.

Cell Stem Cell

, 237-252 (2014).

https://doi.org/10.1016/j.stem.2013.12.007

43. Martin, R. M.

et al.

Improving the safety of human pluripotent stem cell therapies using

genome-edited orthogonal safeguards.

Nat Commun

, 2713 (2020).

https://doi.org/10.1038/s41467-020-16455-7

44. Loh, K. M.

et al.

Mapping the Pairwise Choices Leading from Pluripotency to Human Bone,

Heart, and Other Mesoderm Cell Types.

Cell

166

, 451-467 (2016).

https://doi.org/10.1016/j.cell.2016.06.011

45. Chambers, S. M., Mica, Y., Studer, L. & Tomishima, M. J. Converting human pluripotent stem

cells to neural tissue and neurons to model neurodegeneration.

Methods Mol Biol

793

, 87-97

(2011). https://doi.org/10.1007/978-1-61779-328-8_6

46. Doi, D.

et al.

Isolation of human induced pluripotent stem cell-derived dopaminergic

progenitors by cell sorting for successful transplantation.

Stem Cell Reports

, 337-350 (2014).

https://doi.org/10.1016/j.stemcr.2014.01.013

47. Maurissen, T. L. & Woltjen, K. Synergistic gene editing in human iPS cells via cell cycle and

DNA repair modulation.

Nat Commun

, 2876 (2020). https://doi.org/10.1038/s41467-020-

16643-5

48. Li, W.

et al.

MAGeCK enables robust identification of essential genes from genome-scale

CRISPR/Cas9 knockout screens.

Genome Biol

, 554 (2014).

https://doi.org/10.1186/s13059-014-0554-4

49. Wang, B.

et al.

Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute.

Nat Protoc

, 756-780 (2019). https://doi.org/10.1038/s41596-018-0113-7

50. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological

themes among gene clusters.

Omics

, 284-287 (2012).

https://doi.org/10.1089/omi.2011.0118

51. Wu, T.

et al.

clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.

Innovation (Camb)

, 100141 (2021). https://doi.org/10.1016/j.xinn.2021.100141

52. McGlincy, N. J. & Ingolia, N. T. Transcriptome-wide measurement of translation by ribosome

profiling.

Methods

126

, 112-129 (2017). https://doi.org/10.1016/j.ymeth.2017.05.028

53. Okubo, C.

et al.

Expression dynamics of HAND1/2 in in vitro human cardiomyocyte

differentiation.

Stem Cell Reports

(2021). https://doi.org/10.1016/j.stemcr.2021.06.014

54. Okubo, C., Narita, M., Yamamoto, T. & Yoshida, Y. RNA-Sequencing Analysis of Differentially

Expressed Genes in Human iPSC-Derived Cardiomyocytes.

Methods Mol Biol

2320

, 193-217

(2021). https://doi.org/10.1007/978-1-0716-1484-6_19

55. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads.

2011

, 3 (2011). https://doi.org/10.14806/ej.17.1.200

56. Li, H.

et al.

The Sequence Alignment/Map format and SAMtools.

Bioinformatics

, 2078-

2079 (2009). https://doi.org/10.1093/bioinformatics/btp352

57. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2.

Nat Methods

357-359 (2012). https://doi.org/10.1038/nmeth.1923

58. Dobin, A.

et al.

STAR: ultrafast universal RNA-seq aligner.

Bioinformatics

, 15-21 (2013).

https://doi.org/10.1093/bioinformatics/bts635

59. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments.

Bioinformatics

, 2184-2185 (2012). https://doi.org/10.1093/bioinformatics/bts356

60. Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput

sequencing data.

Bioinformatics

, 166-169 (2015).

https://doi.org/10.1093/bioinformatics/btu638

61. Frankish, A.

et al.

GENCODE reference annotation for the human and mouse genomes.

Nucleic Acids Res

, D766-d773 (2019). https://doi.org/10.1093/nar/gky955

62. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for

RNA-seq data with DESeq2.

Genome Biol

, 550 (2014). https://doi.org/10.1186/s13059-

014-0550-8

63. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome profiling of mouse embryonic stem

cells reveals the complexity and dynamics of mammalian proteomes.

Cell

147

, 789-802

(2011). https://doi.org/10.1016/j.cell.2011.10.002

64. Mito, M., Mishima, Y. & Iwasaki, S. Protocol for Disome Profiling to Survey Ribosome Collision

in Humans and Zebrafish.

STAR Protoc

, 100168 (2020).

https://doi.org/10.1016/j.xpro.2020.100168

65. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor.

Bioinformatics

, i884-i890 (2018). https://doi.org/10.1093/bioinformatics/bty560

66. Fields, A. P.

et al.

A Regression-Based Analysis of Ribosome-Profiling Data Reveals a

Conserved Complexity to Mammalian Translation.

Mol Cell

, 816-827 (2015).

https://doi.org/10.1016/j.molcel.2015.11.013

67. Miyaoka, Y.

et al.

Isolation of single-base genome-edited human iPS cells without antibiotic

selection.

Nat Methods

, 291-293 (2014). https://doi.org/10.1038/nmeth.2840

Acknowledgments

We thank B. Conklin, M. Iwasaki, K. Kaji, M. Khurram, S. Perli, Y. Sato, K. Tomoda, D. Trono, J.
Weismann, and S. Yamanaka for sharing materials and equipment, M. Hamao, K. Mitsunaga, A.
Ogawa, and T. Wada for technical assistance and discussion, Y. Ishida and S. Takeshima for
administrative support, and Editage for English language editing. This work was supported by
Grants-in-Aid for Scientific Research from the Japanese Society for the Promotion of Science
(JSPS) 20K20585 (KT), Grants-in-Aid for Scientific Research from JSPS 21H02155 (KT), Grants-
in-Aid for Scientific Research from JSPS 22K14829 (CO), ACT-X grant from Japan Science and
Technology Agency JPMJAX2222 (CO), Core Center for Regenerative Medicine and Cell and
Gene Therapy from Japan Agency for Medical Research and Development (AMED)
JP23bm1323001 (KT), Core Center for iPS Cell Research from AMED JP21bm0104001 (KT), a
grant from the Takeda Science Foundation (KT), a grant from the Uehara Memorial Foundation
(KT), a grant from the Mochida Memorial Foundation (KT), and the iPS Cell Research Fund from
the Center for iPS Cell Research and Application, Kyoto University (CO, KT).

Author Contributions

Conceptualization: C.O., K.T.; Methodology: C.O., M.S., Y.S., M.M., Y.T., S.I., K.T.; Investigation:
C.O., M.N., K.T.; Visualization: C.O., K.T.; Funding acquisition: C.O., K.T.; Project administration:
C.O., K.T.; Supervision: K.T.; Writing – original draft: C.O., K.T.; Writing – review & editing: all
authors

Competing Interests

K.T. is on the scientific advisory board of I Peace, Inc. with no salary, and all other authors declare
no competing interests.