Clarisse Orniacki

Annalisa Verrico

Stéphane Pelletier

Benoit Souquet

Fanny Coulpier

Laurent Jourdren

Serena Benetti

Valérie Doye
email: valerie.doye@ijm.fr

Stéphane Pelleɵer

Y-complex nucleoporins independently contribute to nuclear pore assembly and gene regulation in neuronal progenitors

Keywords: Nucleoporin, Nup133, Seh1, neuronal progenitors, Nup210L, Lhx1

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INTRODUCTION

As channels embedded in the nuclear envelope, the nuclear pore complexes (NPCs) constitute the only gateway for selective transport of macromolecules between the cytoplasm and the nucleus. These impressive structures are composed of proteins called nucleoporins (Nups) that assemble in a highly organized and modular manner (reviewed in [START_REF] Dultz | The Nuclear Pore Complex: Birth, Life, and Death of a Cellular Behemoth[END_REF]). The Y-complex -also named Nup107-160 complex -that comprises in vertebrates nine distinct proteins, is a key structural subunit of the NPC scaffold. 16 copies of this complex assemble on the nuclear and cytoplasmic sides of the NPC to build up its outer rings, to which cytoplasmic filaments and the nuclear basket are anchored.

In addition to their canonical nuclear transport function, many Nups are also known to have other cellular functions, notably in cell cycle progression or as key regulators of chromatin organization and gene expression (reviewed in [START_REF] Buchwalter | Coaching from the sidelines: the nuclear periphery in genome regulation[END_REF][START_REF] Hezwani | The functional versatility of the nuclear pore complex proteins[END_REF]. In line with these multiple functions, mutations in many Nups have been identified as primary causes of rare genetic diseases. Despite the presence of NPCs in all nucleated cells, most of these diseases specifically affect one or a few organs (reviewed in [START_REF] Jühlen | Moonlighting nuclear pore proteins: tissue-specific nucleoporin function in health and disease[END_REF]. Such tissue or cell-type specific alterations may reflect variable Nup stoichiometry at NPCs, as notably reported for several integral membrane Nups and peripheral Nups [START_REF] Ori | Cell type-specific nuclear pores: A case in point for context-dependent stoichiometry of molecular machines[END_REF]. For instance, increased levels of the transmembrane protein Nup210 in myoblasts and neuronal progenitors was shown to be critical for their differentiation [START_REF] D'angelo | A change in nuclear pore complex composition regulates cell differentiation[END_REF]. Likewise, depletion of the basket nucleoporin Nup50 reduces the differentiation efficiency of C2C12 myoblasts [START_REF] Buchwalter | Nup50 is required for cell differentiation and exhibits transcription-dependent dynamics[END_REF]. In contrast, another basket nucleoporin, Nup153, which is highly expressed in pluripotent cells and neuronal progenitors compared to differentiated cells, is required for the maintenance of their identities, notably by regulating epigenetic gene silencing and transcriptional programs [START_REF] Jacinto | The nucleoporin Nup153 regulates embryonic stem cell pluripotency through gene silencing[END_REF][START_REF] Toda | Nup153 Interacts with Sox2 to Enable Bimodal Gene Regulation and Maintenance of Neural Progenitor Cells[END_REF]. More recently, the Y-complex constituent Seh1, which is highly expressed in oligodendrocyte progenitor cells, was shown to be required for oligodendrocyte differentiation and myelination by regulating the assembly of a transcription complex at the nuclear periphery [START_REF] Liu | Nucleoporin Seh1 Interacts with Olig2/Brd7 to Promote Oligodendrocyte Differentiation and Myelination[END_REF]. However, individual Ycomplex Nups also contribute to earlier stages of differentiation, as underscored by the impaired neuroectodermal differentiation of Nup133 -/-, Seh1 -/-and Nup43 -/-mouse embryonic stem cells (mESCs) (Gonzalez-Estevez, Verrico et al., 2021;[START_REF] Lupu | Nuclear Pore Composition Regulates Neural Stem/Progenitor Cell Differentiation in the Mouse Embryo[END_REF].

RESULTS

Nup133 is required for the regulation of a subset of genes during neuroectodermal differentiation We first determined if the impaired differentiation of Nup133 -/-mESCs, initially described in cells derived from merm (behaving as Nup133 -/-) blastocysts [START_REF] Lupu | Nuclear Pore Composition Regulates Neural Stem/Progenitor Cell Differentiation in the Mouse Embryo[END_REF], was also observed in an independent genetic background. As Lupu and colleagues, we observed altered growth and increased cell death upon neuroectodermal differentiation of Nup133 -/- mESCs derived by CRISPR/Cas9-editing from HM1 mESCs (previously described in Souquet et al., 2018) (Fig. 1A,B). To evaluate the potential effect of Nup133 deficiency in gene regulation upon neuroectodermal differentiation, we assessed the mRNA levels of genes expressed in pluripotent cells (Oct4 and Nanog) and in early neuronal progenitors (Sox1 and Pax6) that are considered markers for the respective states. RT-qPCR analyses showed that these genes were properly repressed and activated, respectively, in Nup133 -/-cells stimulated to differentiate towards neuroectoderm (Fig. 1C). This indicated that despite their impaired viability at early stages of differentiation (Fig. 1A,B) the surviving Nup133 -/- cells are able to exit pluripotency and to commit towards the neuronal lineage, without overt defects in the expression of these markers.

To more broadly explore the impact of Nup133 on gene expression, we compared the transcriptome of WT and Nup133 -/-mESCs at the pluripotent state and after 2 or 3 days of differentiation towards neuroectodermal lineage. We therefore used WT and Nup133 -/- mouse cell lines from two distinct genetic backgrounds, namely the 129/ola blastocystderived HM1 control cell line (Selfridge et al., 1992) and its isogenic CRISPR/Cas9-edited Nup133 -/-derivatives (#14 and #19; Souquet et al., 2018), and the 129/Sv blastocyst-derived control (#1A4) and Nup133 mutant -(merm, #319) mESC lines (Lupu et al., 2018) (Table S1).

This analysis revealed that the transcriptomes of pluripotent WT and Nup133 -/-mESCs were overall similar, with 43 down-and only 2 up-regulated genes, based on an adjusted p-value<0.05 and a |logFold Change|>2, whereas an increasing number of genes were misregulated (and notably upregulated) at day 3 of differentiation, with 57 down-and 61 upregulated genes, using the same cutoffs (Figs. 1D and S1A).

We assayed by RT-qPCR the altered expression of a subset of these genes, filtered by criteria of differential expression (log2FC>2 or <-2), significance (adjusted p-value<0.05) and lastly by average expression level (average number of reads with a log2(CPM)>1, to ensure proper detection by RT-qPCR) (Fig. S1B-E). In addition to WT (HM1) and Nup133 -/-(#14) used for the initial RNA-seq experiment, these analyses were also conducted on samples from Nup133 "Rescue" cell lines generated by inserting the GFP-Nup133 transgene in Nup133 -/-(#14) mESCs at the permissive Tigre locus [START_REF] Zeng | An inducible and reversible mouse genetic rescue system[END_REF]. As an additional control, we used an HM1-derived cell line that carries a transgene (OsTIR) similarly inserted in the Tigre locus (Fig. S2A and Table S1). In contrast to the impaired viability of Nup133 -/-mESCs upon neuroectodermal differentiation, the survival of the Rescue and WT (OsTIR) cell lines were similar, confirming the functionality of the GFP-Nup133 transgene (Fig. 2A-C).

For the candidate genes localized on the short arm of the Y chromosome (Ddx3y and Eif2s3y, also located in close proximity to the loci of Uty, Uba1y, Kdm5d and Zfy [START_REF] Subrini | Y chromosome functions in mammalian spermatogenesis[END_REF]) we observed clone-dependent expression variations (Fig. S1B). This suggests that their apparent shared misregulations, also reported in Tet1 and Tet2 mutant mESCs (Huang et al., 2013), might be in our case caused by partial loss or silencing of this genomic region, independently of Nup133 deficiency.

In contrast, we could validate the increased mRNA levels in Nup133 -/-compared to WT of Nuggc at day 0, and of Nup210L and Lhx1 at day 3 of differentiation (Fig. S1C). We also confirmed the reduced mRNA levels in Nup133 -/-compared to WT for Magohb and Wfikkn1 (but not Acta2) at day 3 of differentiation (Fig. S1D). Finally, reduced mRNA levels of the assayed candidate genes at the pluripotent state (day 0) were not significant due to high variability among replicates or between control cell lines (HM1 and OsTIR) (Fig. S1E).

Importantly, among the validated candidate genes, Lhx1, Nup210L, Nuggc and Magohb were all efficiently restored to wild-type levels by the GFP-Nup133 transgene.

Lhx1 is a transcription factor involved in kidney and brain differentiation [START_REF] Costello | Lhx1 functions together with Otx2, Foxa2, and Ldb1 to govern anterior mesendoderm, node, and midline development[END_REF][START_REF] Delay | Tissue-specific gene inactivation in xenopus laevis: Knockout of lhx1 in the kidney with CRISPR/Cas9[END_REF][START_REF] Mcmahon | Mechanistic insights from the LHX1-driven molecular network in building the embryonic head[END_REF][START_REF] Shawlot | Lim1 is required in both primitive streak-derived tissues and visceral endoderm for head formation in the mouse[END_REF], two organs affected in rare genetic diseases linked to Nup133 mutations, namely steroid-resistant nephrotic syndrome and Galloway Mowat syndrome [START_REF] Braun | Mutations in multiple components of the nuclear pore complex cause nephrotic syndrome[END_REF][START_REF] Fujita | Homozygous Splicing Mutation in NUP133 Causes Galloway-Mowat Syndrome Atsushi[END_REF]. We further focused on this gene because of its complex misregulation in Nup133 -/-cells. Indeed, while we confirmed by RT-qPCR the upregulation of Lhx1 expression at day 3 of differentiation (Fig. S1C), Lhx1 was subsequently downregulated again at later time-points (days 5 and 7 of differentiation) in Nup133 -/-cells compared to WT and Rescue cells (Fig. 2D).

The other candidate gene we further characterized, Nup210L, is the differentially expressed gene (DEG) with the most significant p-value at day 3 of differentiation (Fig. 1D). It is also one of the rare DEGs whose expression already increased at day 2 compared to WT cells (Fig. S1A). In mice, Nup210L mRNA is mainly detected in the testis and to a lesser extent in the embryonic brain (https://www.ncbi.nlm.nih.gov/gene/77595); in humans, besides testis, Nup210L expression was also detected in the prefrontal cortex neurons of rare individuals [START_REF] Gusev | Epigenetic-genetic chromatin footprinting identifies novel and subject-specific genes active in prefrontal cortex neurons[END_REF]. Analyses at later stages (day 5 and 7) of differentiation towards neuroectoderm showed that Nup210L was still more expressed in Nup133 -/-compared to WT and Rescue cells, although a progressive increase of its expression was also observed in the latter cell lines (Fig. 2E).

As its name implies, Nup210L encodes a potential homologue of the transmembrane nucleoporin Nup210/gp210 with 43% overall amino acid identity between the two proteins (Fig. 2F). However, its putative NPC localization had never been established. To address this issue, we generated a GFP-tagged construct encompassing the minimal NPC targeting determinants previously established for gp210/Nup210 [START_REF] Wozniak | The single transmembrane segment of gp210 is sufficient for sorting to the pore membrane domain of the nuclear envelope[END_REF], namely Nup210L predicted signal peptide, transmembrane domain and C-terminal domain (Fig. 2F). This Nup210L-mini construct when expressed at low levels in mESCs is targeted to the nuclear envelope where it colocalizes with the nucleoporin Tpr. This indicates that, like its homolog, Nup210L is indeed a nucleoporin (Fig. 2F).

The middle-domain of Nup133 is required for mESC differentiation, gene regulation and nuclear basket assembly in neuronal progenitors Having established the requirement of Nup133 for cell viability upon differentiation and for the regulation of a subset of genes, we next aimed to determine how Nup133 contributes to these processes. We first focused on the middle domain of Nup133 that is necessary for the proper assembly of the nuclear pore basket in pluripotent mESCs (Souquet et al., 2018). We therefore integrated the pCAG-GFP-Nup133∆mid transgene in HM1-derived Nup133 -/- mESCs at the Tigre locus (Fig. S2A). The GFP-Nup133∆mid protein levels in the resulting Nup133∆mid cells were comparable to those of endogenous Nup133 and of GFP-Nup133 in the Rescue cell lines throughout differentiation (Fig. 2B). Cell counts upon monolayer differentiation towards neuroectodermal lineage showed for the Nup133∆mid cell lines a viability phenotype intermediate between WT and Nup133 -/-, indicating that the middle domain of Nup133 is required for some, but not all, of the functions of this nucleoporin upon differentiation (Fig. 2C).

In contrast, RT-qPCR analysis showed that Nup210L and Lhx1 were similarly misregulated upon neuronal differentiation in Nup133∆mid and in Nup133 -/-cells (Fig. 2D andE).

The improved survival upon differentiation of Nup133∆mid compared to Nup133 -/-cells enabled us to perform immunofluorescence analyses to determine whether the NPC basket assembly defects, previously observed in pluripotent mESCs lacking Nup133 or its middle domain, also occurred at the differentiated stage. Quantitative immunofluorescence analyses, performed after 5 days of differentiation, showed that the intensity of Tpr at the nuclear envelope was comparable between the WT and Rescue cell lines. In contrast, a twofold decrease was observed in Nup133∆mid neuronal progenitors (Fig. 3A,B), a defect comparable to the one previously observed at the pluripotent state (Souquet et al., 2018). In addition, we also measured an increased Nup153 intensity at the nuclear envelope in Nup133∆mid progenitors compared to neuronal progenitors from WT or Rescue cell lines (Fig. 3C). This increased level of Nup153 is unlikely to solely reflect a global increase in NPC number as reported upon Tpr depletion in other cell lines [START_REF] Mccloskey | Tpr regulates the total number of nuclear pore complexes per cell nucleus[END_REF], since similar quantifications revealed a milder increase of Nup98 intensity at the nuclear envelope compared to Nup153 (Fig. 3D). Likewise, an increased accessibility of the Nup153 epitope when Tpr is absent seems unlikely, as such an effect was not previously observed in Nup133 - /-mESCs at the pluripotent stage (Souquet et al., 2018). The high level of Nup153 observed may therefore reflect an increased stoichiometry of Nup153 per NPC in Nup133∆midcompared to control-derived neuronal progenitors, possibly reflecting different stages of differentiation as previously described [START_REF] Toda | Nup153 Interacts with Sox2 to Enable Bimodal Gene Regulation and Maintenance of Neural Progenitor Cells[END_REF].

Nup133-dependent gene regulation and nuclear basket assembly can be uncoupled Having identified a critical function for the middle domain of Nup133 in gene regulation, we next aimed to determine the levels of Nup133 required for this process. We therefore established Nup133-degron cell lines that allow auxin-mediated degradation of a GFP-tagged allele of Nup133 in an OsTIR-expressing mESC line (Gonzalez-Estevez, Verrico et al., (2021), see Materials and Methods and Fig. S2B).

The resulting Nup133-degron cell lines maintained normal Nup133 mRNA expression during differentiation (Fig. S3A), but actual Nup133 protein levels (without Auxin treatment) were only ~25% of that found in WT cells (Figs. 4A and S3B). This could be due to leaky OsTIRinduced degradation as previously reported [START_REF] Mendoza-Ochoa | A fast and tuneable auxin-inducible degron for depletion of target proteins in budding yeast[END_REF][START_REF] Yesbolatova | The auxin-inducible degron 2 technology provides sharp degradation control in yeast, mammalian cells, and mice[END_REF], decreased stability of the tagged nucleoporin, or impaired export or translation of its mRNA. Nevertheless, these cells properly differentiated in the absence of auxin (Figs. 4C, S3D and S3E). As anticipated, addition of auxin to the medium throughout differentiation led to a Nup133 -/--like phenotype: normal growth at the pluripotent state but massive cell death in neuronal differentiation (Figs. 4C, S3C and S3D).

Importantly, the lower Nup133 levels observed in the degron cell lines in the absence of auxin was not accompanied by the altered expression of Nup210L or Lhx1 during neuronal differentiation (Fig. 4E). In contrast, more extensive depletion of Nup133 upon continuous auxin treatment mimicked the effect of Nup133 inactivation on these genes (Fig. 4E).

Quantification of the nuclear basket protein Tpr in the Nup133-degron cell lines revealed that, even in the absence of auxin, Tpr levels at the nuclear envelope were already reduced to ~60% of the WT levels both in pluripotent cells and in neuronal progenitors (Figs. 4D and S4A). In contrast, the levels of Nup98 were not reduced in Nup133-degron cells (Fig. S4C), consistent with a largely unaltered NPC density and a specific alteration of the nuclear basket. The minor -i.e., less than 10% -increase in Nup98 intensity observed in one of the two cell lines (Nup133-degron #1) may reflect modest clonal-dependent variations of NPC density. Finally, the levels of Nup153 were very mildly increased only in the Nup133-degron #1 cells, with a similar trend in both undifferentiated and differentiated cells (Fig. S4B,D).

These results indicate that a 4-fold reduction of Nup133 protein levels in untreated Nup133degron cells is sufficient to severely impair Tpr recruitment or stabilization at nuclear pores, without major additional impact on NPC density.

Finally, although most of the GFP-mAID-Nup133 protein was already degraded after 30 minutes of auxin treatment in differentiated cells (Fig. 4B), a 16 to 24h auxin treatment of these cells only led to a modest additional decrease of Tpr levels at the nuclear envelope compared to the untreated Nup133-degron cells (Figs. 4D andS4A).

Overall, these results thus demonstrated that a correct Nup133 stoichiometry is critical for nuclear basket assembly, yet is not required for cell viability or gene regulation upon neuroectodermal differentiation. Taken together these data also reveal that a properly assembled nuclear basket at all NPCs is not required to regulate the expression of Nup133target genes.

Nup210L mRNA levels rapidly increase in response to Nup133 or Seh1 depletion.

We next aimed to determine if the altered expression of Nup210L and Lhx1 was specific for Nup133 or was shared by other Y-complex constituents. In view of the requirement for Seh1 in global NPC assembly, distinct from the specific basket assembly defect of Nup133 mutant cells (Gonzalez-Estevez, Verrico et al., 2021), we chose to assess its implication at early stages of differentiation. Despite a very extensive death of the Seh1 -/-mutant cells upon differentiation, (Gonzalez-Estevez, Verrico et al., 2021), we could recover some mRNAs from these cells at day 3 of differentiation. As observed for Nup133 -/-mESCs (Fig. 1A), Seh1 -/-cells properly repressed pluripotency markers and were able to induce early differentiation markers (Fig. S5A). Importantly, mRNA levels of both Nup210L and Lhx1 were aberrantly increased in Seh1 -/-cells at day 3 of differentiation as also observed in differentiating Nup133 -/-cells (Fig. 5A).

The low viability of Seh1 -/-cells upon differentiation (Gonzalez-Estevez, Verrico et al., 2021) did not allow us to perform quantitative immunofluorescence studies at the differentiated stage. We therefore established and validated new Seh1-degron cell lines (see Materials and Methods and Fig. S2C), in which the C-terminally-tagged form of Seh1 was properly and homogeneously expressed upon differentiation (Figs. 5B andS5B,C). In the resulting Seh1degron cells, 24h addition of auxin at day 2 or 4 of differentiation led to impaired viability of the cells (Fig. 5C). This indicates that Seh1 is not solely required at the early onset of neuronal progenitor differentiation, but also for the proper growth or viability of the progenitors themselves. Analyses of nuclear pore assembly in Seh1-degron-derived neuronal progenitors (at day 5 of differentiation) did not reveal major defects in the absence of auxin (Fig. 5D). In contrast, a 16h-treatment with auxin led to a ~30% decrease of both Tpr and Nup98 intensities at the nuclear envelope of neuronal progenitors compared to the control cells (Figs. 5D andS4E). This suggests a global decrease in pore number upon Seh1 depletion, comparable to the observations previously made in pluripotent Seh1 -/-and GFP-mAID-Seh1 mESCs (Gonzalez-Estevez, Verrico et al., 2021). Note that unlike Tpr and Nup98, Nup153 levels were not altered in auxin-treated Seh1-degron cells, suggesting, as also observed in Nup133∆mid cells, an increased stoichiometry of Nup153 per NPC (Fig. S4F).

We also note that a 24h auxin treatment, applied to Seh1-mAID-GFP cells at day 2 of neuronal differentiation, was sufficient to cause a significant increase in Nup210L mRNA levels (Fig. 5E). Likewise, a 24h auxin treatment of Nup133-degron cells induced Nup210L expression (Fig. 5E). In contrast, 24h of auxin treatment did not led to an altered regulation of Lhx1 in Seh1-degron or Nup133-degron cells at day 3 (Fig. 5F). Together, these data indicate that Nup210L and Lhx1 are shared downstream targets of Nup133 and Seh1, with Nup210L appearing to be a gene induced early upon loss of these Y-complex Nups. On the other hand, Lhx1 seems to need a prolonged depletion of these Y-complex Nups to become misregulated, indicating that it is likely an indirect target of Y-complex Nup-dependent regulations.

DISCUSSION

In this study, we have shown in differentiating mESCs that a subset of genes is deregulated in the absence of Nup133. We found that neuronal progenitors lacking either Nup133 or just its middle domain share a defect in nuclear basket assembly and altered expression of Nup210L and Lhx1. While this indicates a dual function for the middle domain of Nup133, this domain is large enough (416 aa, 17 α-helices) to possibly allow simultaneous interactions with another nucleoporin and a gene expression regulator. Alternatively, this large deletion, by shortening the length of Nup133, may also alter the head-to-tail interactions between consecutive Y-complexes, in turn affecting nuclear basket assembly or stability.

However, the basket assembly and altered gene expression phenotypes can be uncoupled. Indeed, Nup210L and Lhx1 were not misregulated in our Nup133-degron cell lines that display a constitutive nuclear basket assembly defect, and conversely, they were both similarly misregulated in Seh1-deficient cells in which nuclear basket assembly is not specifically altered. As the untreated Nup133-degron cell lines exhibit lower Nup133 protein levels than the control cell lines, our data further argue that a limited amount of Nup133 is sufficient to keep Nup210L repressed in differentiating mESCs and to induce the proper and timely expression of Lhx1. Because this function in gene regulation is shared by Nup133 and Seh1, two physically distant members of the Y-complex, it likely involves the whole Ycomplex rather than each of these two individual subunits.

The rather short lag time (below 24h) between auxin-induced degradation of Nup133 or Seh1 and Nup210L activation suggests that there could be a direct contact between the Ycomplex and the Nup210L genomic locus. Although the Y-complex is mainly visualized at NPCs to which it is stably anchored [START_REF] Rabut | Mapping the dynamic organization of the nuclear pore complex inside single living cells[END_REF], a diffuse fraction is also likely present in the nucleus, as previously described in HeLa cells [START_REF] Morchoisne-Bolhy | Intranuclear dynamics of the Nup107-160 complex[END_REF].

Hence, Y-complex dependent gene regulation may take place either at NPCs or "off-pore".

Browsing available data of LaminB1-Dam-ID tracks [START_REF] Peric-Hupkes | Molecular Maps of the Reorganization of Genome-Nuclear Lamina Interactions during Differentiation[END_REF], we noticed that the Nup210L locus is adjacent to a lamin-associated domain (LAD) in mESCs and neural progenitor cells. A location near the nuclear periphery would be consistent with a regulation of Nup210L taking place at NPCs. In line with this hypothesis, Seh1 was shown to recruit to the NPC the transcription factor Olig2 and the chromatin remodeler Brd7 to promote the expression of differentiation genes in oligodendrocytes [START_REF] Liu | Nucleoporin Seh1 Interacts with Olig2/Brd7 to Promote Oligodendrocyte Differentiation and Myelination[END_REF]. Additionally, Nup133 was proposed to promote the expression of Myc in cancer cells by anchoring its superenhancer to the NPCs [START_REF] Scholz | WNT signaling and AHCTF1 promote oncogenic MYC expression through super-enhancer-mediated gene gating[END_REF]. This Y-complex-mediated gene regulation may also involve epigenetic mechanisms, as reported for Nup153, which interacts with PRC1 to repress developmental genes [START_REF] Jacinto | The nucleoporin Nup153 regulates embryonic stem cell pluripotency through gene silencing[END_REF]. Along these lines, it is noteworthy that human NUP210L, initially thought to be a testis-specific gene, was found to be expressed in prefrontal cortex neurons of some individuals. This regulation was linked to the epigenetic allele-specific activation of NUP210L, namely the deposition of the permissive histone mark H3K4me3 at its promoter [START_REF] Gusev | Epigenetic-genetic chromatin footprinting identifies novel and subject-specific genes active in prefrontal cortex neurons[END_REF]. In addition, another epigenetic mechanism, the DNA methylation state of Nup210L, has been linked to psychologic development disorders in patients carrying a hemizygous 22q11.2 microdeletion [START_REF] Starnawska | Differential DNA methylation at birth associated with mental disorder in individuals with 22q11.2 deletion syndrome[END_REF]. Considering its possible link with normal or pathological cognitive abilities, the mechanisms of Nup133-and Seh1dependent Nup210L activation warrant further investigations. It is quite remarkable that Nup210L, a gene encoding a nucleoporin, is one of the earliest and most robustly upregulated genes when Y-complex deficient mESCs are induced to differentiate towards neuronal fates. Although not sufficient to prevent the impaired differentiation of Nup133and Seh1-deficient mESCs, Nup210L induction might be a tentative mechanism to compensate for improper Y-complex (and NPC) assembly in differentiating cells. Nup210L might for example help to stabilize NPC structure or to promote NPC assembly in challenging conditions. As described for its homolog, Nup210, it may also participate in gene regulation at NPCs, or contribute via its conserved large N-terminal domain to nuclear envelope/ER homeostasis [START_REF] Raices | Nuclear pores regulate muscle development and maintenance by assembling a localized Mef2C complex[END_REF][START_REF] Gomez-Cavazos | The nucleoporin gp210/Nup210 controls muscle differentiation by regulating nuclear envelope/ER homeostasis[END_REF]. Finally, Nup210L may also indirectly impact chromatin compaction, as hypothesized in the context of impaired spermiogenesis [START_REF] Arafah | Defect in the nuclear pore membrane glycoprotein 210-like gene is associated with extreme uncondensed sperm nuclear chromatin and male infertility: a case report[END_REF]. More studies are needed to elucidate its functions in normal and pathological situations. mESCs culture and neuroectodermal differentiation Cell lines used in this study are listed in Table S1 and were grown as previously described (Gonzalez-Estevez, Verrico et al., 2021). Briefly, mESCs were grown at 37°C and 5% CO2 on Mitomycin-C inactivated feeder cells (DR4-mouse embryonic fibroblast) plated on 0.1% gelatin (Sigma-Aldrich, St Louis, MO, USA) in serum/leukemia inhibitory factor (LIF, ESGRO, Millipore, Burlington, MA, USA)-containing stem cell medium. mESCs were used at passages below 30. Lack of contamination in-between the mutant cell lines was assessed by PCR on genomic DNA, proper GFP expression when pertinent, and western blots analyses. Frequent DAPI staining ensured lack of major contamination by mycoplasms.

The neuroectodermal differentiation protocol used in this study was adapted from [START_REF] Abranches | Neural differentiation of embryonic stem cells in vitro: A road map to neurogenesis in the embryo[END_REF][START_REF] Ying | Conversion of embryonic stem cells into neuroectodermal precursors in adherent monoculture[END_REF], as previously described (Gonzalez-Estevez, Verrico et al., 2021). Briefly, following trypsinization and feeder removal, mESCs were resuspended in N2B27 medium [Neurobasal, DMEM-F12, 7.5% BSA, N2 supplement, B27 supplement, Pen/Strep, L-glutamin, ß-mercaptoethanol] and plated at a density of ∼ 0.85x10 4 or 3x10 4 cells/cm 2 on gelatin-coated dishes (day 0). Medium was changed every day from day 2 on. To stimulate neuronal differentiation, 1μM RA (all-trans-Retinoic acid, Sigma-Aldrich) was added to the medium for 24h on day 2.

For annexin V/propidium iodide (PI) apoptosis/viability assays, cells were trypsinized, counted, and 10 5 cells were centrifuged at 400 x g for 3 minutes. Cells were resuspended in 500µL of Annexin V binding buffer (ab14084, Abcam) and incubated with 1µL annexin V-Cy5 (ab14147, Abcam) and 10 µg/mL propidium iodide for 5min at room temperature in the dark. Cells were then analyzed by flow cytometry using a CyanADP Cytomation (Beckman-Coulter), using SS (side-scatter) and FS (forward scatter) to remove debris and exclude cell doublets, and 488 nm and 635 nm excitation lasers. At least 10.000 cells were acquired and data were then processed using the Summit software.

To induce degradation of the GFP-mAID-Nup133 (in Nup133-degron cells) and Seh1-mAID-GFP (in Seh1-degron cells), 500µM auxin (Sigma-Aldrich) was added to the medium (from a 280 mM stock in EtOH). The same final concentration of EtOH was added for control experiments.

RNAs were extracted at days 0, 2 and 3 of differentiation from 3 independent Nup133 -/- clones (KO#1, HM1-derived Nup133 -/-#14; KO#2, HM1-derived Nup133 -/-#19; KO#3, blastocyst-derived #319 Nup133 merm/merm mESCs), and from 3 control samples (WT#1; HM1; WT#2, HM1; WT#3, blastocyst-derived #1AA Nup133 +/+ ) (See Table S1). Library preparation and Illumina sequencing were performed at the Ecole normale supérieure genomics core facility (Paris, France). Messenger (polyA+) RNAs were purified from 1 µg of total RNA using oligo(dT). Libraries were prepared using the strand specific RNA-Seq library preparation TruSeq Stranded mRNA kit (Illumina). Libraries were multiplexed by 9 on 2 flowcells. Two 75 bp single read sequencing runs were performed on a NextSeq 500 device (Illumina). A mean of 53.14 ± 14.72 million passing Illumina quality filter reads was obtained for each of the 18 samples.

The analyses were performed using the Eoulsan pipeline version 2.0-alpha7 [START_REF] Jourdren | Eoulsan: A cloud computingbased framework facilitating high throughput sequencing analyses[END_REF], including read filtering, mapping, alignment filtering, read quantification, normalization and differential analysis: Before mapping, poly N read tails were trimmed, reads ≤40 bases were removed, and reads with quality mean ≤30 were discarded. Reads were then aligned against the Mus musculus genome from Ensembl version 81 using STAR (version 2.4.0k) [START_REF] Dobin | STAR: Ultrafast universal RNA-seq aligner[END_REF]. Alignments from reads matching more than once on the reference genome were removed using Java version of samtools [START_REF] Li | The Sequence Alignment/Map format and SAMtools[END_REF]. To compute gene expression, Mus musculus GFF genome annotation version 81 from Ensembl database was used. All overlapping regions between alignments and referenced exons were counted and aggregated by genes using the HTSeq-count algorithm [START_REF] Anders | HTSeq-A Python framework to work with highthroughput sequencing data[END_REF]. A first analysis revealed that one of the samples (KO#2 day2) featured an abnormally high level of ribosomal transcripts; this dataset was therefore excluded from subsequent analyses.

The rest of the analysis was carried out using the bioinformatics software R (R v4.1.2 (2021.11.01)), and open access packages, using the publicly available bioinformatics course DIYtranscriptomics.com [START_REF] Berry | An open-source toolkit to expand bioinformatics training in infectious diseases[END_REF]. Mapped raw counts were transformed in counts per million (cpm) using the cpm function from the EdgeR package (v3.34.1). We filtered the genes that had a log2(cpm) above 1 for more than 3 samples, and then normalized their cpm using the TMM method [START_REF] Robinson | A scaling normalization method for differential expression analysis of RNA-seq data[END_REF]. The mean-variance relationship of the filtered normalized data was modeled by voom transformation, and a linear model was fitted to the data using the lmfit function from the limma package (v3.48.3). Bayesian statistics for the chosen pair-wise comparisons (average KO expression compared to average WT expression for each time point) were then calculated using the eBayes function from limma, and adjusted with the BH correction. An exhaustive list of differentially expressed genes (p. value<0.05 and |log2FC|>1.5) was pulled-out using the decideTests function. Plots in Figs. 1D and S1A were generated in R using ggplot2 (v.3.3.5).

Transfection and CRISPR/Cas9 genome editing mESCs were transfected as previously described (Souquet et al., 2018) using Lipofectamin 2000 (Invitrogen, Carlsbad, CA, USA) according to manufacturer instructions. To establish the Nup133-Rescue, Nup133-∆mid, Nup133-degron and Seh1-degron cell lines, 5•10 5 mESCs were co-transfected with 3µg of a plasmid directing the expression of a gRNA and high fidelity (HF) Cas9 fused to mCherry and with 3 µg of DNA sequences of interest flanked by homology directed repair arms (PCR product or linearized plasmid, see Fig. S2). Plasmids are listed in Table S2, gRNAs designed using the Benchling website (https://benchling.com) are listed in Table S3, and PCR primers used to generate homology-directed repair templates are listed in Table S4. 3 days after transfection, GFP-positive cells were FACS-sorted to select for cells expressing the tagged nucleoporin and plated on culture dishes. Individual clones were picked 6-7 days after sorting and characterized using immunofluorescence, western blot, PCR on genomic DNA and sequencing. Ploidy was assessed using chromosome spreads. Cell line characteristics are summarized in Table S1.

RT-qPCR

RNA extraction was performed using NucleoSpin RNAII isolation kit (Mascherey-Nagel) according to the manufacturer's instructions. Reverse-transcription (RT) was done with the transcriptase inverse Superscript II (Invitrogen) and random hexamers (Amersham Pharmacia), using at least 150 ng of RNA per sample. Real-time quantitative PCR was performed with SybrGreen reagents (Applied Biosystems) on a LightCycler 480 instrument (Roche Life Sciences). All mRNA level results are presented as relative to the TATA-binding protein (TBP) mRNA levels. qPCR primers used in this study are listed in supplementary Table S5.

Western blots analyses

Whole cell lysate preparations and western blot analyses were performed as previously described (Gonzalez-Estevez, Verrico et al., 2021), using 4-10% SDS-PAGE gels (Mini-Protean TGX Stain free precast gels, Bio-Rad, Hercules, CA, USA) and nitrocellulose membranes (GE healthcare). Incubations with primary antibodies were carried overnight at 4°C. Signals from HRP-conjugated secondary antibodies were detected by enhanced chemiluminescence (SuperSignal® Pico or Femto, ThermoScientific) using ChemiDoc (Bio-Rad). Antibodies used in this study are listed in Table S6. The uncropped membranes are presented in Fig. S6.

Immunofluorescence and quantification of nucleoporin intensity at the nuclear envelope.

Cells grown on glass coverslips coated with 0.1% gelatin were fixed for 20 minutes in 3% paraformaldehyde (VWR, Radnor, PA) (resuspended in PBS and brought to pH 8.0 with NaOH), permeabilized 30 minutes in H-Buffer (PBS, BSA 1%, Triton 0.2%, SDS 0.02%) and incubated with the primary and secondary antibodies for 1h at room temperature in H-Buffer, with washes in H-Buffer in-between. Antibodies used in this study are listed in supplementary Table S5. Coverslips were then incubated 5 min with DAPI (Sigma-Aldrich, 280nM in PBS) and mounted using Vectashield (Vector, Maravai Life Sciences, San Diego, CA). Images were acquired on a DMI8 microscope (Leica Microsystems), equipped with a CSU-W1 spinning-diskhead (Yokogawa, Japan) and 2 Orca-Flash 4 V2+ sCMOS cameras (Hamamatsu), using 100x/1.4 oil objectives Quantification of nucleoporin intensities at the nuclear envelope (NE) was performed essentially as described (Souquet et al., 2018), by mixing the cell line of interest with a reference cell line, either the WT (OsTIR) cell line or the Nup133-Rescue line, as indicated.

For each field, we measured the mean intensity of 8-pixel-thick lines drawn on the nuclear rims and of a background area. After subtraction of the background, the NE intensity value obtained for each cell was normalized to the mean value obtained for the reference cells acquired in the same field. Box plots were generated using GraphPad Prism (GraphPad p-value<0.05, log2FC>2) and downregulated DEGs (adj. p-value<0.05, log2FC<-2) is indicated at the top of each colored square. These DEGs are represented by red and blue dots, respectively, if their average normalized expression in log2(CPM) is above 1. The other genes are represented as black dots when their average normalized expression is above 1 and otherwise as grey dots. The names of DEGs assessed by RT-qPCR (genes with adj. p-value<10 - 2 or |log2FC|>4) are indicated in blue or red. The names of other relevant DEGs (i.e., genes that fulfilled these criteria but were not assayed by qRT-PCR, namely Krt19, Pdgfb, Cntd1, Pik3ip1, and Fam196a, and genes that belong to the same Y chromosome locus as Eif2s3y and Ddx3y, namely Kdm5d, Zfy1, Uba1y and Uty) are indicated in dark grey. For each domain, the % of identity between mouse Nup210L and Nup210 is indicated.

Numbers below the schematics correspond to amino acid residues. Right: WT (HM1) mESCs transiently expressing the GFP*-mini-Nup210L construct were fixed and processed for immunofluorescence with anti-Tpr antibodies. A single confocal section is shown. A threefold magnification of the nuclear envelope area highlights the colocalization of GFP*-mini-Nup210L with the NPC marker Tpr within defined dots. Scale bar, 5µm. or not (continuous lines) with auxin from day 0 on. For each cell line (represented by distinct labels) and each time point, the number of independent experiments is indicated. A. Volcano plot of the RNA-seq analysis carried out in cells at day 2 of neuroectodermal differentiation, showing differentially expressed genes (DEGs), by fold change (log2FC of Nup133 -/- compared to WT) and significance (adj. p.Val presented on a -log10 scale). The number of significantly upregulated DEGs (adj. p-value<0.05, log2FC>2) and downregulated DEGs (adj. p-value<0.05, log2FC<-2) is indicated at the top of each colored square. For this time point, the p-value of several genes is higher (less significant) due to the missing sample (see Materials and Methods). These DEGs are represented by red and blue dots, respectively, if their average normalized expression in log2(CPM) is above 1. The other genes are represented as black dots when their average normalized expression is above 1 and otherwise as grey dots. The names of DEGs assessed by RT-qPCR (genes with adj. p-value<10 -2 or |log2FC|>4) are indicated in blue or red. The names of other relevant DEGs that were not assayed by qRT-PCR (i.e., Kcnc2) or that are part of the same Y chromosome locus as Eif2s3y and Ddx3y are indicated in dark grey. B-E. mRNA levels of the indicated candidate genes (normalized to TBP) were measured by RT-qPCR in pluripotent mESCs or at day 3 of differentiation (see text for details). Data are presented as the mean±S.D. Each dot represents an individual experiment. Fragment lengths are indicated in base pairs. mAID: 7kDa mini-auxin Inducible Degron (mAID) sequence [START_REF] Natsume | Rapid Protein Depletion in Human Cells by Auxin-Inducible Degron Tagging with Short Homology Donors[END_REF]. Note that two different GFP-coding sequences were used in this study: GFP*, initially described in Harkins et al. (2017), contains, in addition to the S65T and F64L substitutions present in EGFP, the V163A substitution that is expected to confer it a brighter and more stable signal. In addition, the leucine introduced at position 231 during the creation of EGFP [START_REF] Tsien | The green fluorescent protein[END_REF] is reverted to the original Histidine. Note that Nup153 levels at the NE are mildly affected in a similar manner in pluripotent and differentiated Nup133-degron cell lines, with a mild increase observed mainly in Nup133-degron #1 cells treated or not with auxin (compare panels B and D). In contrast, Tpr levels are strongly decreased in both cell lines (compare panel B with Figure 1E). Note also that in auxin-treated Seh1-degron cells, the decreased NE level of Nup98 (panel E) (and of Tpr, see Figure 3D) but not of Nup153 (panel F) is consistent with a decreased NPC density combined with an increased stoichiometry of Nup153 per NPC. A, related to Fig. 2B. Rescue clones b11 and f5 were renamed #1, and #2 respectively and Nup133∆Mid clones c5 and d3 and f4 were renamed #3, #1, and #2 respectively. Unlike the clones used in Fig. 2B, the "Rescue" lines d7 and g8 had the pCAG-GFP-Nup133 transgene inserted at both Tigre loci and were therefore not used in this study. B, C, related to Figs. 4A,B, were used to characterize the Nup133-degron cell lines in neuronal progenitors. D, related to Fig. S3, was used to characterize the Nup133-degron cell lines in pluripotent mESCs. E, related to Fig. S5, was used to characterize the Seh1-degron cell lines at day 5 of differentiation. Note that the membrane was successively probed with the anti-Seh1 and anti-g-tubulin antibodies, explaining the residual labelling in the anti-g-tubulin panel. The black dotted box on the anti-Seh1 gel points to the area used to detect g-tubulin. Plasmids used in this study were either previously published or generated using standard molecular cloning techniques as previously described in (Gonzalez-Estevez, Verrico et al., 2021). Plasmid maps are available upon request to V. Doye and plasmids will be shared subject to the signature of a UBMTA. See details about GFP* in the legend to Figure S2.

WT HM1 Nup 133 -/- #2 Rescue Nup133∆mid #3 #1 #2 WT (OsTIR) Nup133 -/- Nup133Dmid #1 Nup133Dmid #2 Nup133Dmid #3 Rescue #1 Rescue #2 Rescue #3 C TPR mini-NUP210L-GFP F GFP*
Table S3. gRNA sequences used in this study

  Software, Inc.): each box encloses 50% of the normalized values obtained, centered on the median value. The bars extend from the 5 th to 95 th percentiles. Values falling outside of this range are displayed as individual points. Statistical analyses were performed using unpaired nonparametric Mann-Whitney tests. p<0.0001=****, p<0.001=***, p<0.01=**, p<0.05=*.

  FIGURE LEGENDS

Figure 2 .

 2 Figure 2. Altered expression of Lhx1 and Nup210L in Nup133 mutant cell lines A. Schematics of the GFP-Nup133 (Rescue) and GFP-Nup133∆mid fusion proteins (see also Table S1 and Fig. S2A). B. Western blot showing the expression of endogenous Nup133 or its GFP-tagged forms in WT, Nup133 -/-, Nup133-Rescue and Nup133∆mid cells at day 7 of differentiation. γ-tubulin is used as loading control. Molecular weights are indicated (kilodaltons). C. Growth curve obtained from cell counts in neuroectodermal differentiation presented in a logarithmic scale. Cells were seeded at a density of ∼0.85x10 4 cells/cm 2 (3.10 4 cells in p12 wells). For the Nup133∆mid and Rescue cells, the lines correspond to the mean of the cell counts from the various clones (each depicted by a different symbol). The number of independent experiments for each clone is indicated (n=). n=2*: data for the Nup133∆mid #3 and Rescue #3 cell lines correspond to technical replicates (independent wells but within the same experiment). D, E. Lhx1 and Nup210L mRNA levels were analyzed by RT-qPCR at the indicated time points of neuroectodermal differentiation in WT (OsTIR), Nup133 -/-, Rescue and Nup133∆mid cell lines. Each cell line is represented by a distinct label. The number of biological replicates for a given cell type is indicated below each time point (n=).

F

  . Left: Schematics of Nup210L and of the GFP*-mini-Nup210L construct that comprises: Nup210L predicted signal peptide (SP) and part of the luminal domain [aa 1-40], GFP* (see details about GFP* in the legend to Fig. S2) and aa 1791-1884 of Nup210L, encompassing part of its predicted luminal domain and its transmembrane TM and cytosolic (Cyt) domains.

Figure 3 .

 3 Figure 3. Nup133∆mid cells display a nuclear basket assembly defect at the neuronal

Figure 4 .

 4 Figure 4. Nuclear basket integrity and gene regulation are uncoupled in Nup133-degron

Figure 5 .

 5 Figure 5. Seh1 depletion leads to Lhx1 and Nup210L misregulation and altered NPC density

BFigure

  Figure S1, related to Figure 1: DEGs analysis in differentiating Nup133 -/-cells.

Figure S2 ,

 S2 Figure S2, related to Materials and Methods and Tables S1, S3 and S4: Schematics of CRISPR-Cas9mediated cell line establishment via homologous recombination. Nup133 Rescue and Nup133Dmid (A), Nup133-degron (B), and Seh1-degron (C).Fragment lengths are indicated in base pairs. mAID: 7kDa mini-auxin Inducible Degron (mAID) sequence[START_REF] Natsume | Rapid Protein Depletion in Human Cells by Auxin-Inducible Degron Tagging with Short Homology Donors[END_REF]. Note that two different GFP-coding sequences were used in this study: GFP*, initially described inHarkins et al. (2017), contains, in addition to the S65T and F64L substitutions present in EGFP, the V163A substitution that is expected to confer it a brighter and more stable signal. In addition, the leucine introduced at position 231 during the creation of EGFP[START_REF] Tsien | The green fluorescent protein[END_REF] is reverted to the original Histidine.

Figure S3 ,

 S3 Figure S3, related to Figure 4: Characterization of the Nup133-degron cell lines. A. mRNA levels of Nup133 during differentiation, quantified by RT-qPCR and normalized to TBP level, are comparable in the WT (OsTIR) and Nup133-degron cell lines. B. Western blot showing the different expression levels of Nup133 in the OsTIR, Nup133-degron and Rescue neuronal progenitors at day 5. g-tubulin was used as loading control. 1/2 and 1/4 dilutions of the WT (OsTIR) and Rescue extracts were also loaded. C. Growth of Nup133-degron is comparable to WT and not altered by addition of auxin (added at t=0) in pluripotent mESCs. Confluence values were obtained using the IncuCyteâ live cell imager software (Essen Biosciences, Ann Arbor, MI) as previously described (Gonzalez-Estevez, Verrico et al., 2021). The graph corresponds to the mean of n= 2 independent experiments. D. Images of Nup133-degron cells, treated or not with auxin from day 0, were acquired at day 7 of differentiation using a widefield microscope. Neuronal rosettes and axons are visible in untreated cells while major cell death is observed in auxin-treated Nup133-degron cells. Scale bars, 100µm. E. RT-qPCR analyses show that auxin-treated Nup133-degron cells properly repress the pluripotency marker Oct4 and induce the neuronal progenitor marker Pax6 when induced to differentiate towards neuroectoderm. Each dot corresponds to an individual experiment.

Figure S4 ,

 S4 Figure S4, related to Figure 3, B-D and Figure 4D: Nucleoporin densities at the nuclear envelope in Nup133-degron and Seh1-degron cell lines. Fluorescence intensity at the nuclear envelope of Tpr (A), Nup153 (B, D, F) and Nup98 (C, E) was quantified in Nup133-degron cells at the pluripotent stage (A, B), in Nup133-degron cells at day 5 of differentiation (C, D), and in Seh1-degron cells at day 5 of differentiation (E, F) and is presented as box-plots. Cells were treated with auxin (+) or ethanol (-) as control for 24 h or 16h as indicated. Values were normalized in each field to the Nup133-Rescue (A, B, D, E, F) or WT (OsTIR) cells (C) and are presented as box-plots. Standard deviation (SD), number of analyzed cells (N) and of experiments (n) are indicated. ****: p-value<0.0001; ***: p-value<0.001; **: p-value<0.01; *: p-value<0.05; n.s.: non-significant in Mann-Whitney test.Note that Nup153 levels at the NE are mildly affected in a similar manner in pluripotent and differentiated Nup133-degron cell lines, with a mild increase observed mainly in Nup133-degron #1 cells treated or not with auxin (compare panels B and D). In contrast, Tpr levels are strongly decreased in both cell lines (compare panel B with Figure1E). Note also that in auxin-treated Seh1-degron cells, the decreased NE level of Nup98 (panel E) (and of Tpr, see Figure3D) but not of Nup153 (panel F) is consistent with a decreased NPC density combined with an increased stoichiometry of Nup153 per NPC.

Figure S5 ,

 S5 Figure S5, related to Figure 5: Characterization of the Seh1 -/-and Seh1-degron cell lines A. mRNA levels of pluripotency (Oct4, Nanog) and neuronal progenitor (Pax6, Sox1) markers were analyzed by RT-qPCR in WT (HM1) and Seh1 -/-mESCs at the pluripotent stage (n=1) and after 3 days of neuroectodermal differentiation (n=3). B. Seh1 mRNA levels during differentiation, quantified by RT-qPCR and normalized to TBP level, are comparable in the WT (OsTIR) and the two Seh1-degron cell lines (#1 and #2). Each dot corresponds to an individual experiment. C. Western blot showing Seh1 levels upon auxin treatment in cells at day 5 of differentiation. The membrane was first hybridized with the Seh1 antibody, then stripped to perform g-tubulin hybridization.

Figure S6 ,

 S6 Figure S6, related to Materials and Methods: Entire gels used for western blot FiguresImages of total protein levels, visualized either by ponceau staining of the nitrocellulose membrane or by the Stain-free staining of the TGX-gel (Bio-Rad) before transfer are also presented. The black rectangles on the nitrocellulose membranes correspond to the areas of the gels that were incubated with the indicated antibodies. Red rectangles correspond to the areas presented in the final figures. A, related to Fig.2B. Rescue clones b11 and f5 were renamed #1, and #2 respectively and Nup133∆Mid clones c5 and d3 and f4 were renamed #3, #1, and #2 respectively. Unlike the clones used in Fig.2B, the "Rescue" lines d7 and g8 had the pCAG-GFP-Nup133 transgene inserted at both Tigre loci and were therefore not used in this study. B, C, related to Figs.4A,B, were used to characterize the Nup133-degron cell lines in neuronal progenitors. D, related to Fig.S3, was used to characterize the Nup133-degron cell lines in pluripotent mESCs. E, related to Fig.S5, was used to characterize the Seh1-degron cell lines at day 5 of differentiation. Note that the membrane was successively probed with the anti-Seh1 and anti-g-tubulin antibodies, explaining the residual labelling in the anti-g-tubulin panel. The black dotted box on the anti-Seh1 gel points to the area used to detect g-tubulin.

  Estevez, Verrico et al, 2021) Seh1 -/-#1 A18 As described in (Gonzalez-Estevez, Verrico et al, pCAG-GFP*-Nup133 in one of the Tigre alleles ND -GFP*-Nup133Dmid in one of the Tigre alleles 40 Chr (71%) As described in (Gonzalez-Estevez, Verrico et al, 2021), but a distinct clone Parental cell line: HM1 Cas9 & gRNA plasmid: pU6-sgTIGRE_CBh-Cas9-T2A-mCherry-3UTR (#2061) HR template: linearized (with EcoRV) TIGRE HR-pCAG-OsTir-T2A-NeoR-TIGRE HR (#2064) OsTIR 5F Insertion of pCAG-OsTir-T2A-NeoR in one of the Tigre alleles. HR template: PCR product: amplification of plasmid #2120 [Seh1 intron8/exon9 HR-mAID-EGFP-mSeh1 exon9 UTR (gRNA resistant)-HR] with primers AV48/AV49 (for clone D2) OR #2120 digested with SnaBI and HindII (clone E9) 2: Seh1-mAID-EGFP (only one band detected and sequenced, corresponding to the AID predicted allele). Note that both alleles carry a 207 bp endoduplication in intron 8, 28 bp before exon 9 that however did not affect Seh1 expression compared to Seh1-mAID-GFP #*-miniNup210L (SP-GFP*-TM-Cter) This paper #2130

Table S1 , related to Experimental Procedures: Cell lines used in this study

 S1

	NAME	SOURCE (IF PUBLISHED OR COMMERCIAL) OR DESIGN FOR CELL LINES GENERATED IN THIS STUDY	IDENTIFIER	Plate ref.	MUTATIONS (genomic sequences set at 1 for the first ATG)	Chromosome count/ Karyotype
	MEF (DR4)	Applied StemCell	ASF-1001	-	-	-
	WT (1A4)	LACY lab (Souquet et al, 2018)	#1A4	-	Sox1 gfp/+	40 Chr (68%) (XY)
	merm #319	LACY lab (Souquet et al, 2018)	#319	-	Nup133 merm/merm ; Sox1 gfp/+	42 Chr XX, +8, +12
	WT mESCs (HM1)	ThermoFisher Scientific (Selfridge et al., 1992)	MES4303	-	-	40 Chr (XY)
		DOYE lab -Generated by CRISPR/Cas9 editing.	Nup133 -/-	#14	Allele 1: 35 bp deletion in exon 6 (∆11094-111128), leading to a
		#14 was described in (Souquet et al., 2018);	(#14)		frameshift after aa 259.
		#19 is a distinct clone that arose from the same editing			Allele 2: large deletion encompassing exons 2-6 (∆2525-11143) causing a frameshift after aa 59.	40 Chr
		Parental cell line: HM1			Very low mRNA levels due to premature stop codons. No
	Nup133 -/-	Cas9 & gRNA plasmids:			protein detected by western blot.
		pX-102: (two tru gRNAs flanking Nup133 exon 2	Nup133 -/-	#19	Allele 1: 34 bp deletion in exon 6 (∆11095-11128), leading to a
		andCas9-mCherry)	(#19)		frameshift after aa 260.
		AND pX-98: two gRNAs within mNup133 exon 6			Allele 2: deletions removing (i) part of exon 2 including its 5'
		and nCas9-EGFP			splice site (∆2539-2719) and (ii) exon 3-6 (∆4075-11094).
					Expected to cause a frameshift after aa 59.
					Very low mRNA levels due to premature stop codons. No
					protein detected by western blot.

Table S4 . Primers used to generate homology-directed repair templates for Nup133-degron and Seh1-degrons

 S4

	Identifier		Sequence	Source
	sg-TIGRE		ACTGCCATAACACCTAACTT	(Gonzalez-Estevez, Verrico et al.,
				2021)
	gRNA-degron-Nup133 GTTCGCGGAGAGGAGACGCT This paper
	gRNA-Seh1Cterm	AGCTGAGTACAAGCTAGC	This paper
	Name		Sequence	Source
	CO69 (FW,	CCTCAGGTGTTCAAGCTCCGGGCGCGCGAGGTTCTCGCTATTAGCCC	This
	plasmid	GCGAGTGCCGCTTCCTCCACGCTCTCTGCAAACATGGTGAGCAAGGT	paper
	#2108)	AAGTATC
	CO34 (RV,	GTGGACGTGGGCCCGATCCCGACTAGCGGACCGCGTCGGGTCCCCG	This
	plasmid	GCCCTGGGGTTCGCGGGCTGGAGACGGACGGAAAGGATCTGAGTCC	paper
	#2108)	GGATTTATA
	AV48 (FW,	GAACAAATTATTTTATGAAGGAAAGCATAGTAGAGTTAATTTTTAAA	This
	#2120	AATCTGGTTTTAGGTTACAGCTTTAGTTCTGTGTTATTTTCTTCCATAA	paper
	plasmid)	ATAGC
	AV49 (RV,	GGCTGACTAGTACAAAAGTTATATACACTGTCACTTTAAAGG	This
	#2120	CCTTTTGGACTGTGTGTAGTAGTGAACGGGCTGCTGGTGCATCT	paper
	plasmid)	TGGAAGAGATCAAAC

Acknowledgements:

We are grateful to Vedrana Andric, Alessandro Berto, Marta Boira and Salomé Neuvendel for help in mESCs culture, differentiation, cell line establishment, or western blot analyses. We thank Charlène Boumendil and Pierre Therizols for helpful discussions, and Benoit Palancade, Roger Karess, Charlène Boumendil and the Doye lab members for critical reading of the manuscript. We also acknowledge the ImagoSeine core facility of the Institut Jacques Monod, member of the France BioImaging infrastructure (ANR-10-INBS-04) and GIS-IBiSA, for help with cell sorting, FACS analyses, and spinning disk imaging.

Funding Work in the laboratory of VD was supported by the Centre national de la recherche scientifique (CNRS), the "Fondation pour la Recherche Médicale" (FRM, Foundation for Medical Research) under grants No DEQ20150734355, "Equipe FRM 2015" and EQU202003010205, "Equipe FRM 2020" to VD, and by the Labex Who Am I? (ANR-11-LABX-0071; Idex ANR-11-IDEX-0005-02). CO received PhD fellowships from Ecole Doctorale BioSPC, Université Paris Cité and from the "Fondation pour la Recherche Médicale" (fourth year), AV received a post-doc grant from the Labex Who Am I? (2019 post-doc call) and B.S. was supported by ''la Fondation ARC pour la Recherche sur le Cancer'' (PDF 20130606747). The ImagoSeine core facility was supported by founds from GIS-IBISA and the France-Bioimaging (ANR-10-INBS-04) infrastructures and la Ligue contre le cancer (R03/75-79). The GenomiqueENS core facility was supported by the France Génomique national infrastructure, funded as part of the "Investissements d'Avenir" program managed by the Agence Nationale de la Recherche (contract ANR-10-INBS-0009).

Data availability

The RNASeq gene expression data and raw fastq files are available on the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218080) under accession number: GSE218080.

Competing interests

The authors declare that they have no conflict of interest. C.O., A.V., S.P., L.J. and V.D. analyzed the data C.O., A.V., and V.D. wrote the manuscript with contribution of L. J. for the method section. Validation of the anti-Nup153 and rat anti-Nup133 monoclonal antibodies is described in the indicated publications. Information on the commercial antibodies used in this study is provided by the manufacturers. In addition, the commercial Seh1 antibody was previously validated by western blot using corresponding KO mESCS (Gonzalez-Estevez et al. 2021).

Inventory of Supplementary Material

Supplemental Figures and Figure legend


Hernan Carrillo
email: hernan.carrillo-lindado@labri.fr

Michaël Clément

Aurélie Bugeau

Edgar Simo-Serra

Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models

Colorization of line art drawings is an important task in illustration and animation workflows. However, this highly laborious process is mainly done manually, limiting the creative productivity. This paper presents a novel interactive approach for line art colorization using conditional Diffusion Probabilistic Models (DPMs). In our proposed approach, the user provides initial color strokes for colorizing the line art. The strokes are then integrated into the conditional DPM-based colorization process by means of a coupled implicit and explicit conditioning strategy to generates diverse and high-quality colorized images. We evaluate our proposal and show it outperforms existing state-of-the-art approaches using the FID, LPIPS and SSIM metrics.

Introduction

Line art colorization is a popular technique used in various fields, such as art, animation, and graphic design. It involves adding color to grayscale line drawings to make them visually appealing and expressive. However, this process is laborious as it is typically carried out manually for traditional animation, mainly using software illustration tools such as Photoshop, ClipStudio, and Krita. Automated colorization have the potential to significantly enhance an artist's workflow.

In recent years, various methods have explored userguided automatic line art colorization using deep learning. In particular, Generative Adversarial Network (GANs) architectures have been proposed [START_REF] Ci | User-guided deep anime line art colorization with conditional adversarial networks[END_REF][START_REF] Sangkloy | Scribbler: Controlling deep image synthesis with sketch and color[END_REF][START_REF] Yliess | Paintstorch: A user-guided anime line art colorization tool with double generator conditional adversarial network[END_REF][27][START_REF] Zhang | User-Guided Line Art Flat Filling with Split Filling Mechanism[END_REF]. These methods couple color hints as input with learned color priors from large-scale datasets to colorize the line art images. GAN architectures can achieve impressive and high-quality outputs. However, certain issues remain problematic, for example, ensuring color consistency with user color inputs and reaching color harmony between small image regions. In addition, GAN architectures can be challenging to train due to instabilities [START_REF] Arjovsky | Wasserstein generative adversarial networks[END_REF]7]. To overcome these issues, Diffu- sion Probabilistic Models (DPMs) [9,[START_REF] Song | Generative modeling by estimating gradients of the data distribution[END_REF] propose a framework capable of generating high-fidelity images by training a U-Net [START_REF] Ronneberger | U-net: Convolutional networks for biomedical image segmentation[END_REF] like generator architecture and sampling from a Markov chain. These methods have been applied to various computer vision problems such as image synthesis [START_REF] Dhariwal | Diffusion models beat gans on image synthesis[END_REF], superresolution [16,[START_REF] Saharia | Image superresolution via iterative refinement[END_REF], and automatic image colorization [START_REF] Saharia | Palette: Image-to-image diffusion models[END_REF] achieving better qualitative and quantitative results than previous state-of-the-art GANs architectures.

In this paper, we introduce a new user-guided line art colorization model based on a conditional diffusion model that achieves better results than state-of-the-art methods. In addition, we explore the use of a coupled implicit and explicit conditioning strategy on the diffusion model for this application. An in-depth evaluation corroborates the efficiency of our approach.

Related work

Automatic methods for line art colorization have been primarily classical image processing optimization-based, as described in [START_REF] Qu | Manga colorization[END_REF][START_REF] Sýkora | Lazybrush: Flexible painting tool for hand-drawn cartoons[END_REF]. These approaches typically involve using image features such as pattern and intensity continuity to propagate color hints over regions. However, their effectiveness is limited when applied to complex line drawings where a high amount of user color hints are needed.

Deep learning methods, and in particular GAN architectures [13, 27, 29], have been used for user-guided line art colorization where color scribbles are propagated based on neural networks trained with large amounts of data. In [START_REF] Ci | User-guided deep anime line art colorization with conditional adversarial networks[END_REF], Ci et al. propose a method to enhance the generalization capability of the neural network by introducing a local features network independent of synthetic data. Yliess et al. [START_REF] Yliess | Paintstorch: A user-guided anime line art colorization tool with double generator conditional adversarial network[END_REF] improves the visual fidelity of results by using a double generator approach. Other methods explore the use of reference color images to transfer a particular artist style [START_REF] Furusawa | Comicolorization: semi-automatic manga colorization[END_REF][START_REF] Lee | Reference-based sketch image colorization using augmented-self reference and dense semantic correspondence[END_REF]12]. Although GANs can produce high-fidelity images, these architectures can be unstable to train which could produce perceptually unsatisfying results.

Diffusion Probabilistic Models (DPMs) [9,[START_REF] Song | Generative modeling by estimating gradients of the data distribution[END_REF] seem to have the potential to overcome GANs issues in many applications. These methods have recently emerged as a class of generative models for high-dimensional data such as images and audio. DPMs transform the input data through a series of controlled noising/denoising steps to predict new data distributions. These methods have achieved state-of-the-art results in various image-to-image generation tasks, including image synthesis [START_REF] Dhariwal | Diffusion models beat gans on image synthesis[END_REF], super-resolution [START_REF] Li | Srdiff: Single image super-resolution with diffusion probabilistic models[END_REF][START_REF] Saharia | Image superresolution via iterative refinement[END_REF], and colorization [START_REF] Saharia | Palette: Image-to-image diffusion models[END_REF]. Inspired by these new methods, we propose a novel conditional diffusion model for line art colorization that can be guided by user color scribbles.

Proposed method

The objective is to colorize a grayscale line art image from user color scribbles. Our proposal uses a diffusion model, which learns to generate a colorized line art image x0 given grayscale line art l and color scribbles s (see Figure 1).

Our framework is composed of two main parts: a denoising model ϵ θ from the main denoising pipeline and an application-specific encoder g θ for extracting color scribbles information (see Figure 2). The first learns to denoise noisy images from an unknown distribution conditioned to a line art image l. The second part, g θ , extracts color features previously inputted by the user to guide the line art colorization. Finally, the predicted image x0 is retrieved using the DDPM sampling algorithm [9].

Color image SketchKeras Sketch Simplification

Diffusion Models

Diffusion models, which have been used in various image generation applications, convert standard Gaussian distribution samples to empirical data distribution samples by employing a Markov chain denoising process of T steps. Given an initial image x 0 from the real distribution, the diffusion process successively adds Gaussian noise with vari-ance β t and mean µ t = √ 1 -β t x t-1 to obtain intermediate noisy image x t , this is called forward process. The idea is to learn a parametric approximation to the unknown conditional distribution p θ (x t-1 | x t ) by utilizing a stochastic iterative refinement process. During inference, the aim is to reverse the Gaussian diffusion process through a reverse Markov chain according to a learned transition distribution p θ :

p θ (x 0:T ) = p θ (x T ) T t=1 p θ (x t-1 | x t ) . (1)
Finally, to learn the reverse chain, we use a neural denoising model ϵ θ .

Conditioning Diffusion Models

Diffusion models such as those proposed by [9,[START_REF] Song | Generative modeling by estimating gradients of the data distribution[END_REF] operate in a non-conditional setting (Equation ( 1)). Instead, we jointly use two approaches to condition our diffusion model. First, as in [16], we jointly train an applicationspecific encoder g θ , which extracts semantic features from color scribbles and line art images. These features are then introduced to the denoising model ϵ θ by means of crossattention mechanism [START_REF] Vaswani | Attention is all you need[END_REF]. For the second approach, and inspired by [2, 18], we explicitly condition the predicted distribution on the denoising neural network ϵ θ by directly concatenating line art image s to the noisy input x t . Finally, by joining both conditioning in the inference process (1) changes to

p θ (x 0:T | (l, s)) = p θ (x T ) T t=1 p θ (x t-1 | x t , (l, s)) . (2)
Given (2), the training process of our proposal on conditioned image pairs is trained using the L 1 loss as

L 1 = E l,ϵ∼N (0,1),t [∥ϵ -ϵ θ (l, x t , t, g θ (l, s))∥ 1 ] , (3)
both the denoising model ϵ θ and the encoder g θ are jointly trained.

Experiments

Dataset preparation

Synthetic Line Art. We conducted experiments using a subsample of color illustrations from the dataset safe Dan-booru2021 [START_REF] Danboorucommunity | Danbooru2021: A large-scale crowdsourced and tagged anime illustration dataset[END_REF]. To filter out grayscale images, we utilized tags such as "grayscale" and "monochrome". We use 200k training images and 13k images for test. For creating synthetic line art, we rely on two extraction methods: SketchKeras [14] and Sketch simplification [START_REF] Simo-Serra | Mastering Sketching: Adversarial Augmentation for Structured Prediction[END_REF]. Figure 3 depicts an example of synthetic data generated by previously mentioned methods. Finally, the type of sketch at training time is randomly sample by a uniform distribution with a 50% probability of choosing SketchKeras or the Sketch simplification methods.

Simulated Color Scribbles. To achieve a model that can handle user color inputs, we simulate human scribbles by randomly sampling vertical, horizontal, and diagonal lines. We use three parameters: the number of scribbles sampled from the uniform distribution U(4, 25), scribble thickness sampled from U(1, 4) pixels, and scribble length sampled from U(5, 30) pixels. Additionally, as a high amount of illustrations in the dataset present white backgrounds, there is a high probability that the synthetic scribbles would bias the model toward the color white. Therefore, we only use the sampled synthetic scribbles when they contain less than 60% white pixels.

Implementation details

Our implementation was inspired by [9]. To reduce computational cost, we only use self-attention and crossattention mechanisms on the bottleneck of the denoising model ϵ θ . We use the Adam optimizer with a learning rate of 2e -5 , a cosine warm-up schedule for 5k training steps, and a batch size of 40. We introduce a color feature extraction encoder g θ that uses the same encoder architecture as ϵ θ with only one Residual block per layer instead of two. Both the denoising model ϵ θ and encoder g θ are jointly trained from scratch. All line art and color scribble images are fixed to the resolution 256 × 256, and values are normalized to the range [-1, 1]. Our final method was trained for 80 epochs with 10 NVIDIA RTX 2080Ti GPUs.

Experimental validation

To evaluate the effectiveness of our line art colorization framework, we compare our results quantitatively and qualitatively with two other state-of-the-art user-guided line art colorization approaches [START_REF] Ci | User-guided deep anime line art colorization with conditional adversarial networks[END_REF][START_REF] Yliess | Paintstorch: A user-guided anime line art colorization tool with double generator conditional adversarial network[END_REF]. In order to do a fair comparison, we retrained all the methods with the same dataset and used the default parameters presented in their methods.

Quantitative Evaluation. We use three metrics to compare different methods quantitatively. The first metric is the Structural Similarity (SSIM) [25], which examines the model's ability to reconstruct the content of the original image. The second metric is the Learned Perceptual Image Patch Similarity (LPIPS) [START_REF] Zhang | The unreasonable effectiveness of deep features as a perceptual metric[END_REF], which measures perceptual similarities between two images and correlates strongly with human perception. The last metric, the Fréchet Inception Distance (FID) [START_REF] Heusel | Gans trained by a two time-scale update rule converge to a local nash equilibrium[END_REF], is used to measure a perceptual similarity between two sets of images. All three metrics are calculated on 13k test images, between the color illustration image as ground-truth and generated images with fixed color hints from the different methods.

As shown in Table 1 our results retain 15% and 2% more structural information than the other two state-of-the-art methods. For the LPIPS metric, our method surpasses [START_REF] Ci | User-guided deep anime line art colorization with conditional adversarial networks[END_REF] and achieves comparable results to [START_REF] Yliess | Paintstorch: A user-guided anime line art colorization tool with double generator conditional adversarial network[END_REF]. Finally, on the FID metric, we outperform both methods. In addition, using only implicit conditioning reduces the performance compared to our full method.

Qualitative Evaluation. Figure 4 shows the results from the two state-of-the-art methods [START_REF] Ci | User-guided deep anime line art colorization with conditional adversarial networks[END_REF], [START_REF] Yliess | Paintstorch: A user-guided anime line art colorization tool with double generator conditional adversarial network[END_REF], and ours. We can see that our qualitative results are consistent with quantitative scores, showing more high-quality details, a visually appealing colorization, and a more accurate representation of color shades compared to the other two methods.

Conclusion

In this paper, we introduced a novel approach for userguided line art colorization using conditional Diffusion Models. Our proposal exploits a coupled implicit and explicit conditioning strategy that ensures a robust structural generation of details and an accurate representation of user colors. Experimental evaluation on a large-scale dataset shows that our method outperforms existing techniques both quantitatively and qualitatively.

Figure 1 .

 1 Figure 1. Diffusart enables the colorization of line arts created by an artist l (left) using color scribbles s (center). On the right is the result of our proposal x0.

Figure 2 .

 2 Figure 2. Overview of our proposed user-guided line art colorization. The framework is composed of two main components: a denoising model ϵ θ , which learns to generate a denoised image, and an application-specific encoder g θ for extracting user color scribbles information.

Figure 3 .

 3 Figure 3. Example of synthetic line art of a color image generate using SketchKeras [14], and Sketch Simplification [21].

Figure 4 .

 4 Figure 4. Comparison of our proposed method with different user-guided line art colorization methods: AlacGAN [3] and PaintsTorch [26].

Table 1 .

 1 Quantitative comparison with state-of-the-art user-guided line art colorization methods.

	Method	SSIM↑ LPIPS ↓ FID↓
	AlacGAN [3]	0.66	0.26	13.27
	PaintsTorch [26]	0.79	0.14	8.79
	Ours (w/o explicit cond.)	0.77	0.15	7.91
	Ours (full)	0.81	0.14	6.15

Acknowledgements. This study has been carried out with financial support from the French Research Agency through the PostProdLEAP project (ANR-19-CE23-0027-01).