KIF11 prevents retinal endothelial ferroptosis in familial exudative vitreoretinopathy by inhibiting phosphorylation-driven PRDX1 phase separation

Domain: biology
Published: 2026-03-24
Editorial outcome: major_revision

Abstract
Familial exudative vitreoretinopathy is a hereditary disorder predominantly affecting infants and young
children, often leading to severe vision loss. Approximately 40% of patients carry mutations in Norrin/β-
catenin pathway genes. Nevertheless, the downstream pathogenic mechanisms remain unclear. Here, by
using bulk RNA sequencing and single-cell RNA sequencing analyses, we identify KIF11 as a key
downstream effector in retinal endothelial cells. Lentivirus-mediated KIF11 overexpression partially
restores vascular defects in endothelial cell-specific Ctnnb1 knockout mice. Functional and multi-omics
studies reveal that β-catenin/KIF11 deficiency induces autophagy-accompanied ferroptosis.
Mechanistically, KIF11 binds PRDX1, and the disrupted β-catenin/KIF11 axis releases the competitive
restraint of KIF11 on Src-mediated PRDX1 phosphorylation, triggering subsequent liquid-liquid phase
separation. Treatment with the ferroptosis inhibitor ferrostatin-1 or lentiviral overexpression of non-
phosphorylatable PRDX1 partially rescues vascular defects in familial exudative vitreoretinopathy-
associated mice. Overall, we elucidate a β-catenin/KIF11/PRDX1 axis-dependent ferroptosis mechanism
in familial exudative vitreoretinopathy, highlighting ferroptosis-targeting and antioxidant strategies as
potential therapies.

Introduction
Familial exudative vitreoretinopathy (FEVR) is a hereditary eye disease (0.46% in Chinese
newborns) driven primarily by mutations in the Norrin/β-catenin (~40% of cases, FZD4, LRP5, NDP,
TSPAN12, or CTNNB1)1, 2, as well as the cadherin/catenin (CTNNA1 and CTNND1)3, 4 and Jag1/Notch
signaling (JAG1)5. Despite the central role of Norrin/β-catenin signaling, the key downstream target of
Norrin/β-catenin is unknown, hindering the development of efficient therapies for FEVR.

KIF11 was initially identified as a kinesin motor protein with end-directed motor activity and is
known to associate with spindle microtubules6. Overexpression of KIF11 has been associated with solid
tumors and poor prognosis7, whereas its deficiency prevents the separation of duplicated centrosomes,
leading to the formation of monoastral spindles and subsequent mitotic block8. KIF11 mutations are
associated with FEVR, and a recent study demonstrated that endothelial cell (EC)-specific knockout of
Kif11 recapitulated FEVR-like phenotypes9. However, in contrast to most FEVR-associated genes, the
regulatory role of KIF11 in Norrin/β-catenin signaling remains unclear.

Ferroptosis is a form of programmed cell death characterized by the accumulation of cellular iron
and lipid peroxidation10. Beyond its involvement in pathological conditions like cancer, sepsis, and
degenerative diseases, emerging evidence indicates that ferroptosis is associated with vascular damages
like pulmonary arterial hypertension (PAH), atherosclerosis, and lung fibrosis11, 12, 13. However, whether
the Norrin/β-catenin signaling or KIF11 exerts a similar effect against ferroptosis in FEVR remains
unresolved. Our present study aimed to elucidate the pathogenic mechanisms downstream of decreased
β-catenin signaling. To achieve this, we conducted a combination of multi-omics analysis with cell-based
models and animal models to reveal that KIF11 is a pivotal downstream regulator of the β-catenin
signaling pathway. We also demonstrate that loss of KIF11 function in endothelial cells led to autophagy-
accompanied ferroptosis by releasing its competitive role on the Src-mediated phosphorylation and
triggering liquid-liquid phase separation (LLPS) of PRDX1, ultimately causing defective vascularization
in FEVR.

Results
KIF11 is a key downstream target of Norrin/β-catenin signaling
To identify genes strongly associated with reduced Norrin/β-catenin signaling, we performed unbiased
bulk RNA sequencing (RNA-seq) to analyze the global transcriptional landscape of human retinal
microvascular endothelial cells (HRECs) following knockdown of genes in the Norrin/β-catenin signaling
pathway, specifically CTNNB1, FZD4, TSPAN12, or LRP5 (Fig. 1a, b and Supplementary Data 1).
Depletion of these genes markedly altered the expression of representative β-catenin downstream targets14,
15, 16, characterized by downregulation of APCDD1, DKK1, and SLCS2A1, along with the upregulation of
PLVAP (Fig. 1c). Dimension reduction via 3D principal coordinate analysis (PCoA) revealed convergent
transcriptional responses of the CTNNB1, FZD4, TSPAN12, and LRP5 knockdown cells (Supplementary
Fig. 1a). Moreover, Venn analysis showed that approximately 25% of differentially expressed genes
(DEGs) in each group were shared among all four knockdown groups, indicating a common
transcriptomic response to Norrin/β-catenin pathway inhibition (Supplementary Fig. 1b-d and
Supplementary Data 2). Notably, KIF11 was among the top 76 downregulated genes across the
knockdown conditions (Supplementary Data 2).

Weighted coexpression network analysis (WGCNA) identified 13 gene modules, among which the
blue and dark grey modules were selected for further analysis owing to their strongest and most consistent
associations with knockdown-induced transcriptomic changes (Fig. 1d, e). The blue module showed a
robust negative correlation with the control group (ME = -0.71, p = 0.003) and uniformly positive
correlations across four knockdown groups, compared to the opposite regulation pattern in the dark gray
module (ME = 0.98, p = 3e-10) (Fig. 1d, e). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
analysis showed significant enrichment of blue module genes in cell-death pathways, especially
ferroptosis (Fig. 1f).

As β-catenin activates transcription with TCF/LEF proteins17, genes within the dark grey module
were regarded as the primary targets regulated by the loss of β-catenin signaling. KEGG pathway analysis
revealed the enrichment of these genes with cell cycle and meiosis (Fig. 1g), which aligns with the
established link between compromised beta-catenin signaling and cell proliferation18. PPI network
analysis of genes in the dark grey module using CytoHubba maximal clique centrality (MCC) algorithm
revealed KIF11, a known FEVR-associated gene, as one of the top five-ranked downstream targets of
Norrin/β-catenin signaling (Fig. 1h, i). Quantitative real-time PCR (RT-qPCR) and western blot
consistently confirmed a robust downregulation of KIF11 following CTNNB1, FZD4, TSPAN12, or LRP5
knockdown (Fig. 1j-q, Supplementary Fig. 1e-h). Furthermore, exogenous Norrin increased KIF11
expression in HRECs (Supplementary Fig. 1i). Nevertheless, KIF11 remained significantly
downregulated upon knockdown of these genes (Supplementary Fig. 1j). These results suggest KIF11 as
a potential core target of Norrin/β-catenin signaling.

Kif11 is expressed in proliferative mouse retinal ECs and transcriptionally regulated by Norrin/β-
catenin signaling
We next sought to delineate the cellular expression landscape of Kif11 in postnatal and adult mouse
retinal ECs with or without Norrin/β-catenin inactivation. We constructed EC-specific Ctnnb1 conditional
knockout (Ctnnb1flox/flox; Pdgfb-iCreER19, Ctnnb1 cKO) and Tspan12 knockout (Tspan12 KO) mice,
which showed efficient gene deletion and characteristic FEVR-like retinal vascular defects (Fig. 2a and
Supplementary Fig. 2a, b). We next performed single-cell RNA sequencing (scRNA-seq) on retinal
vascular cells isolated from postnatal day 6 (P6) wild-type (WT), Ctnnb1 cKO, and Tspan12 KO mice,
and integrated these data with publicly available datasets from P26 WT and Tspan12 KO retinas
(GSE213887)16, P60 WT and P60 Ndp KO retinas (GSE125708)20, as well as ECs of P6 WT retinas
(GSE175895)21 (Fig. 2b). After stringent quality control, a total of 78,830 retinal cells were annotated
based on published markers20, including 5,338 ECs (Fig. 2c and Supplementary Fig. 2c).

Focusing on ECs, we observed consistent downregulation of Apcdd1, and upregulation of Plvap in
the cKO or KO groups compared with their corresponding WT, confirming pathway suppression upon
gene ablation (Fig. 2d). Kif11 expression in ECs of postnatal (P6) mice, rather than in adult (P26, and P60)
mice, were downregulated upon inactivation of Norrin/β-catenin signaling (Fig. 2d). Notably, Kif11 was
predominantly expressed in postnatal retinas compared with adult retinas (Fig. 2d and Supplementary Fig.
2d), suggesting a critical role for KIF11 during retinal development. Further reclustering of the ECs
identified seven EC subpopulations based on previously established arterio-venous (A-V) zonation
markers22, including arterial capillary EC, arterial EC, capillary EC, tip EC, proliferative EC, venous
capillary EC, and venous EC (Fig. 2e and Supplementary Fig. 2e, f). As expected, proliferative ECs were
exclusively derived from postnatal retinas, where Kif11 was selectively enriched and downregulated upon
inactivation of Norrin/β-catenin (Fig. 2f-h and Supplementary Fig. 2e). Therefore, KIF11 is likely
expressed during postnatal retinal development, as validated by RT-qPCR and western blot analyses of
retinas collected from P1, P7, P14, P21, and P28 (Fig. 2i and Supplementary Fig. 2g).

We next turned to a publicly available transposase-accessible chromatin using sequencing (ATAC-
seq) dataset of cerebellar endothelial cells isolated from control mice and mice with endothelial cell-
specific β-catenin stabilization23. β-catenin stabilization led to a genome-wide increase in chromatin
accessibility at transcription start sites (TSSs), with increased chromatin accessibility at the promoter
region of Kif11, as well as Axin2 (Fig. 2j, k and Supplementary Fig. 2h), a known β-catenin downstream
target23. Further analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data for TCF7L224, a
major cofactor and transcriptional effector of Norrin/β-catenin signaling pathway, revealed evident
binding of TCF7L2 near the TSSs of both Kif11 and Axin2 (Fig. 2l-n). Collectively, these results reveal
that Kif11 is specifically expressed in proliferative mouse retinal ECs and transcriptionally downregulated
upon impaired β-catenin signaling during vascular development.

Pathogenic KIF11 mutations and functional rescue in FEVR
In previous studies, mutations in the KIF11 gene were shown to be associated with FEVR25. We
applied targeted gene panel on the genomic DNA samples from FEVR patients and identified six predicted
deleterious KIF11 mutations, including NM_004523.4: c.466_469del (p.Val156LysfsTer38), c.1387dupA
(p.Thr463AsnfsTer10), c.2342delA (p.Gln781ArgfsTer18), c.2579C>A (p.Ser860Ter), c.2738del
(p.Leu913ArgfsTer5), and c.2906_2907del (p.Lys969ArgfsTer19) (Supplementary Fig. 3a and
Supplementary Table 1). Clinically, probands in families 1, 3, and 5 experienced exudative retinal
detachment of both eyes, while the probands in families 4 and 6 exhibited retinal proliferative lesions
accompanied by localized tractional retinal detachment in both eyes (Supplementary Fig. 3b-i). Western
blot analysis revealed truncation and moderate degradation of these overexpressed mutant proteins in
HEK293T cells (Fig. 2o), which may underlie the pathogenesis of FEVR.

We next asked whether restoring KIF11 could rescue the retinal vascular defects in Ctnnb1 cKO mice.
Retro-orbital venous sinus delivery of lentivirus exhibited efficient and targeted transduction in retinal
vascular cells, as revealed by immunofluorescence of GFP (Supplementary Fig. 3j). Remarkably,
lentiviral overexpression of KIF11 via retro-orbital venous sinus injection exhibited partial restoration of
retinal vascular progression in Ctnnb1 cKO mice without affecting their body weight (Fig. 2p-r and
Supplementary Fig. 3k). Collectively, these results provide direct evidence that KIF11 functions as a
critical downstream effector of Norrin/β-catenin signaling. Downregulation of KIF11 due to impaired
Norrin/β-catenin signaling, or KIF11 loss-of-function mutations leading to protein truncation and
degradation, may represent a primary mechanism underlying FEVR.

KIF11 deficiency leads to decreased proliferation and increased HREC death
To explore how KIF11 dysfunction contributes to FEVR, we knocked down KIF11 in HRECs using
an shRNA-expressing lentivirus (Supplementary Fig.4a-c). KIF11 depletion inhibited proliferation and
triggered cell death, as revealed by real-time cellular analysis (RTCA) (Fig. 3a) and EdU incorporation
assays (Supplementary Fig. 4d, e). While TUNEL assays detected no overt apoptosis in KIF11-
knockdown HRECs (Supplementary Fig. 4f), transmission electron microscopy (TEM) revealed that
KIF11 depletion induced increased autophagosome formation and ferroptosis-like mitochondrial
alterations, including cristae loss and outer membrane fragmentation (Fig. 3b). Thus, the alterations
observed in KIF11 knockdown HRECs are strongly related to the molecular pathways perturbed by
reduced β-catenin signaling (Fig. 1f, g).

To further investigate the molecular consequences of KIF11 deficiency, we performed bulk RNA-seq
(Supplementary Data 1). Differential expression and KEGG analyses indicated that genes downregulated
upon KIF11 knockdown were predominantly involved in the cellular process of cell cycle (Supplementary
Fig. 4g, h). Gene set enrichment analysis (GSEA) revealed that KIF11-deficient cells were negatively
correlated with cell cycle and DNA replication pathways (Supplementary Fig. 4i-o), recapitulating the
transcriptional effects of compromised β-catenin signaling (Fig. 1g). Moreover, several motor proteins
were also downregulated, highlighting a regulatory role for KIF11 in motor activity (Supplementary Fig.
4n, o). Conversely, genes upregulated upon KIF11 knockdown were enriched in cellular processes related
to necroptosis, phagosome, and lysosome signaling pathways but were less associated with ferroptosis
(Supplementary Fig. 5a). Consistently, GSEA revealed robust enrichment of lysosome and autophagy
pathways, in contrast to ferroptosis (Supplementary Fig. 5b-f). RT-qPCR further validated increased
expression of autophagy-related genes (ATG5, ATG7, and ATG14), and core components of autophagic
machinery (BECN1, MAP1LC3B, TFEB, and CLEAR), as well as lysosomal marker LAMP1
(Supplementary Fig. 5g), confirming activation of autophagy and lysosome signaling in KIF11-deficient
HRECs.

Integrated analysis revealed the involvement of KIF11 in autophagy-accompanied ferroptosis
We next used mass spectrometry to profile proteomic changes upon KIF11 knockdown. A total of 555
proteins were upregulated and 538 were downregulated in the absence of KIF11 (Fig. 3c). GSEA and
western blot validation revealed strong negative correlations between KIF11-deficient cells and cell cycle,
DNA replication, and motor protein signaling (Supplementary Fig. 6a-h), consistent with the
transcriptomic data. In contrast, KEGG pathway analysis of all differentially expressed proteins identified
ferroptosis, peroxisome, phagosome, and lysosome as the top-enriched pathways, while the cycle and
DNA replication were less significantly enriched (Fig. 3d). Accordingly, GSEA revealed strong positive
correlations between KIF11-depleted cells and lysosome, autophagy, ferroptosis, and peroxisome
pathways (Fig. 3e and Supplementary Fig. 6i-n), indicating increased autophagy and ferroptosis-like
alterations upon KIF11 depletion. We then integrated the transcriptomic and proteomic data using a nine-
quadrant diagram (Fig. 3f) and found that most of the altered genes (in quadrants four and six) exhibited
significant expression changes at the protein level but minimal changes at mRNA level (Fig. 3g). KEGG
pathway analysis of genes with unchanged transcription but altered protein levels (i.e., those in quadrants
four and six) was largely consistent with those shown in Fig. 3d (Supplementary Fig. 7a). These findings
not only confirm the role of KIF11 in regulating cell proliferation but also suggest that KIF11 participates
in regulating cell death-related pathways, particularly ferroptosis, mainly at the protein level.

We then first investigated the regulatory role of KIF11 in autophagy. Western blot analysis revealed
that KIF11 deficiency increased the expression of core autophagy- and lysosome-related proteins,
including ATG5/7/14, Beclin-1, and LAMP1, in addition to decreasing SQSTM1/p62 levels and
increasing the conversion of LC3I to LC3II, strongly indicating enhanced autophagic flux (Supplementary
Fig. 7b, c). Immunofluorescence further confirmed a marked increase in the number of LC3B puncta and
elevated LAMP1 expression in KIF11 knockdown cells (Supplementary Fig. 7d-f). Taken together, these
results suggest that the loss of KIF11 might trigger autophagy in HRECs.

Ferroptosis requires selective autophagic degradation of anti-ferroptotic proteins such as glutathione
peroxidase (GPX) 4, thereby facilitating the generation of ROS and lipid peroxides26. Consistent with this,
transcriptomic and proteomic analyses revealed a pronounced decrease in GPX4 protein levels with
minimal changes in mRNA following KIF11 knockdown (Fig. 3h). RT-qPCR further revealed
considerable increases in the transcription of the ferroptosis sensitivity marker ACSL427 and ferroptosis
regulatory factor HMOX1 (HO-1)28, while most other ferroptosis-related genes showed minimal
alterations at mRNA level (Supplementary Fig. 7g). Conversely, the protein levels of xCT, PCBP1, GPX4,
and SLC3A2 were downregulated, while ACSL4 and HO-1 were upregulated (Fig.3i, j). Additionally, the
protein levels of the ferroptosis regulator, ferroptosis suppressor protein 1 (FSP1)29 and dihydroorotate
dehydrogenase (DHODH)30, remained largely unchanged (Fig. 3i, j), indicating that KIF11 may
selectively regulate ferroptosis via xCT/GSH/GPX4 axis. Consistently, the alterations of the core
ferroptosis-related proteins were recapitulated with another KIF11 knockdown shRNA (Supplementary
Fig. 7h-j).

Glutathione (GSH) is thought to be the primary coordinating ligand for cytosolic ferrous iron and
serves as the substrate for GPX431. The enzymatic activity of GPx was measured using a kinetic
spectrophotometric assay based on the reaction between GSH and 5,5’-dithio-bis-(2-nitrobenzoic acid)
(DTNB)32, which revealed an approximately 50% decrease in GPx activity following KIF11 knockdown
(Fig. 3k). Consistently, knockdown of KIF11 significantly increased the cellular level of labile ferrous
iron (Fig. 3l, m). Ferroptosis is characterized by the accumulation of ROS and lipid peroxidation11, 33. In
line with this, the levels of malondialdehyde (MDA), BODIPYTM 665/676, and CellROX were markedly
elevated in KIF11-knockdown HRECs (Fig. 3n-r). Liquid chromatograph-tandem mass spectrometer (LC-
MS/MS) targeted analysis demonstrated that KIF11 depletion in HRECs enhanced the peroxidation of
phosphatidylethanolamines (PEs), phosphatidylcholines (PCs), and their ether-linked forms (Fig. 3s),
which are associated with ferroptosis11, 33. Time-lapse real-time live-cell imaging using Annexin V and
propidium iodide (PI) showed increased cell death upon KIF11 knockdown (Supplementary Fig. 8a-c),
which was corroborated by elevated lactate dehydrogenase (LDH) release (Supplementary Fig. 8d). These
results imply that KIF11 depletion increases ferroptotic vulnerability in HRECs.

We next asked whether treatment with the ferroptosis inhibitor ferrostatin-1 (Fer-1) could restore
endothelial functions. Fer-1 treatment effectively rescued the ferroptotic phenotypes induced by KIF11
depletion in HRECs, including impaired viability, lipid peroxide accumulation, elevated ROS and labile
iron, reduced GPx activity, and dysregulated ferroptosis-related proteins (Fig. 4a-m). To assess whether
KIF11 plays a broader role in ferroptosis beyond endothelial cells, we conducted additional experiments
in HT1080 cells, a model system commonly used for ferroptosis studies10. As expected, KIF11-depleted
cells exhibited enhanced ferroptosis, with reduced xCT, PCBP1, GPX4, and SLC3A2 expression,
increased lipid peroxidation, and elevated cell death (Supplementary Fig. 8e-k). Fer-1 treatment also
effectively rescued the ferroptotic phenotypes in KIF11-deficient HT1080 cells (Supplementary Fig. 8l,
m). Collectively, these data demonstrate that the absence of KIF11 triggers autophagy-accompanied
ferroptosis.

KIF11 interacts with PRDX1 to suppress the LLPS of PRDX1
We next performed immunoprecipitation-mass spectrometry in KIF11-FLAG-overexpressing HRECs to
examine whether KIF11 protects against ferroptosis via protein interactions (Fig. 4n). The mass
spectrometry identified nine top-ranked KIF11 binding partners (Fig. 4o), among which IK and PRDX1
remained unchanged following KIF11 knockdown or overexpression (Supplementary Fig. 9a-c).
Nevertheless, Co-IP confirmed that PRDX1, but not IK, interacts with KIF11 (Fig. 4p). Importantly,
FEVR-associated truncated KIF11 variants nearly eliminated its interaction with PRDX1 (Fig. 4q).
Since PRDX1 is a thiol-dependent peroxidase with a conserved cysteine (Cys) residue that serves as the
site for peroxide-mediated oxidation34, we hypothesized that KIF11 loss induces ferroptosis via inhibition
of PRDX1 function. Intriguingly, we observed a prominent formation of cytosolic PRDX1 condensates
in both HRECs and HEK293T cells depleted with KIF11 (Fig. 4r, s and Supplementary Fig. 10a, b).
PRDX1 has recently been reported to undergo LLPS, which has an inhibitory effect on its peroxidase
activity35. We therefore asked whether loss of KIF11 triggers the LLPS of PRDX1. The fluorescence
recovery after photobleaching (FRAP) analysis revealed that, in the presence or absence of KIF11, the
PRDX1-EYFP puncta in HEK293T cells recovered comparably to 30% of the initial level within 160 s
after photobleaching (Fig. 4t, u). Notably, however, the puncta size was larger following KIF11
knockdown (Fig. 4v). Additionally, treatment of cells with 10% 1,6-hexanediol (1,6-HD), a reagent used
to disrupt LLPS condensates36, successfully dissolved the PRDX1-EYFP puncta regardless of KIF11
status (Fig. 4w).

According to previous reports, PRDX1 monomers undergo oxidation to form peroxidase-inactive
homodimers, a transformation facilitates assembly into chaperone-active polymers and trigger PRDX1
LLPS37, 38, 39, 40. Consistently, non-reducing SDS PAGE revealed a shift of PRDX1 from low-molecular-
weight (LMW) monomers to high-molecular-weight (HMW) dimers upon depletion of KIF11 in HRECs
(Fig. 4x, y). Taken together, these data identify PRDX1 as a core binding partner of KIF11, and disruption
of their interaction promotes PRDX1 LLPS.

KIF11 inhibits Src-mediated PRDX1 phosphorylation-driven LLPS via competitive binding
Given that the phosphorylation of PRDX1 at Tyr194 leads to its inactivation and the accumulation of
H2O2 41, we next evaluated whether KIF11 influences PRDX1 phosphorylation. As expected, KIF11
knockdown significantly increased the phosphorylation of PRDX1 at Tyr194 in HRECs (Fig. 5a, b). Since
phosphorylation is well known to regulate protein LLPS40, we asked whether PRDX1 phosphorylation
promotes its LLPS in the absence of KIF11. The overexpressed non-phosphorylatable PRDX1 mutant,
Tyr194Gln (Y194Q), showed reduced condensates, and the shift of most HWM dimers to LMW
monomers compared to wild-type protein in HRECs and HEK293T cells (Fig. 5c-h and Supplementary
Fig. 10c-f).

Bioinformatic analysis predicted intrinsically disordered regions (IDRs), known drivers of LLPS42, in the
N- and C-terminal regions of PRDX1 (Fig. 5i). Unexpectedly, Y194Q mutation led to a slight
upregulation, rather than downregulation, of the IDR confidence in the C-terminus of PRDX1 compared
to the wild-type protein (Fig. 5i). Thus, substitution of Tyr194 with Gln did not lead to disruption of
disordered regions, suggesting that phosphorylation might be a critical trigger for PRDX1 LLPS.

To further investigate the role of phosphorylation in PRDX1 LLPS, we treated HRECs with gradient
concentrations of larotinib, a tyrosine kinase inhibitor43. We identified 100 μM as the minimum effective
dose that robustly inhibited PRDX1 Tyr194 phosphorylation (Supplementary Fig. 10g, h). Treatment of
KIF11 knockdown HRECs with larotinib markedly reduced the levels of HMW PRDX1 dimers while
increasing the levels of LMW PRDX1 monomers (Fig. 5j, k). Additionally, Co-IP assays revealed an
increase in the interaction between Y194Q-mutant PRDX1 and KIF11 (Fig. 5l, m). These results
demonstrate that KIF11 inactivation prevents the inhibition of PRDX1 phosphorylation, thereby impairing
the peroxidase activity of PRDX1 via the induction of HWM-mediated LLPS.

A previous report indicated that Src, a tyrosine kinase, serves as a critical upstream regulator of
PRDX1-Y194 phosphorylation44, therefore, we asked whether Src participates in PRDX1
phosphorylation in HRECs depleted with KIF11. This was confirmed by the dose-dependent reduction of
PRDX1 phosphorylation upon treatment with PP1 (Supplementary Fig. 10i, j), a Src kinase inhibitor44.
We then treated HRECs with 100 μM PP1 (Supplementary Fig. 10i, j), which effectively diminished
PRDX1 Tyr194 phosphorylation, leading to a shift from HWM dimers to LMW monomers, in KIF11-
deficient HRECs (Fig. 5n, o).

We next performed miniTurboID-based proximity labeling to capture transient kinase-substrate (Src-
PRDX1) interactions and test whether KIF11 modulates PRDX1 phosphorylation through regulating Src-
PRDX1 interaction. We co-expressed Src-miniTurboID-FLAG and PRDX1-HA in HEK293T cells in the
presence or absence of KIF11-FLAG (Fig. 5p). Biotinylated proteins were enriched from the lysates using
streptavidin beads (Fig. 5p). Src-miniTurboID-FLAG successfully labeled PRDX1-HA with biotin
compared to Vector-miniTurboID-FLAG (Fig. 5q, r), confirming the interaction between Src and PRDX1.
Interestingly, exogenous overexpression of KIF11 markedly decreased the amount of biotin-labeled
PRDX1, along with reduced P-PRDX1 in cell lysates (Fig. 5s-u), suggesting that KIF11 competes with
Src for PRDX1 binding and phosphorylation.

Depletion of PRDX1 or CTNNB1 drives autophagy-accompanied ferroptosis that could be rescued
by Fer-1
Given that loss of KIF11 leads to impaired PRDX1 function, we tested whether knockdown of PRDX1
phenocopies the defects associated with KIF11 deficiency. Similarly, efficient PRDX1 knockdown in
HRECs exhibited decreased proliferation and subsequent death, accompanied by autophagy- and
ferroptosis-like mitochondrial changes (Fig. 6a-g). PRDX1 knockdown led to a ~40% reduction in GPx
activity, with increased labile ferrous iron, oxidative stress, and lipid peroxidation (Fig. 6h-m),
indicating that PRDX1 deficiency triggers autophagy-accompanied ferroptosis in HRECs.

We next performed bulk RNA-seq to analyze transcriptomic alterations following PRDX1 knockdown
(Fig. 6n, Supplementary Data 1). 3D PCoA revealed that PRDX1-knockdown and KIF11-knockdown cells
clustered closely, indicating similar transcriptional changes (Fig. 6o). Furthermore, the transcriptional
profiles of PRDX1-knockdown and KIF11-knockdown HRECs resembled those depleted with CTNNB1,
FZD4, TSPAN12, and LRP5 (Fig. 6p). GSEA revealed a strong negative correlation of PRDX1-knockdown
cells with gene clusters associated with the cell cycle and DNA replication, which were validated at the
mRNA and protein levels (Supplementary Fig. 11a-g). Conversely, PRDX1 knockdown positively
correlated with autophagy, ferroptosis, and lysosome signaling, with corresponding modulators of
autophagy and lysosome altered at both the protein and the mRNA levels (Supplementary Fig. 12a-g),
whereas ferroptosis modulators were predominantly regulated at the protein level rather than mRNA level
(Fig. 6q-s). The alterations of core ferroptosis-related genes were validated by another shRNA
(Supplementary Fig. 12h-j). Moreover, immunofluorescence analysis revealed an increased number of
LC3B puncta and elevated LAMP1 expression in PRDX1-deficient cells (Supplementary Fig. 12k-m).
Consistent with those observed upon KIF11 depletion, Fer-1 reversed PRDX1 depletion-induced
ferroptotic phenotypes, including reduced viability, lipid peroxide accumulation, elevated ROS and labile
iron, decreased GPx activity, and dysregulated ferroptosis-related proteins (Fig. 6t-z). These findings
suggest that, similar to compromised KIF11 function, PRDX1 deficiency drives autophagy-accompanied
ferroptosis in endothelial cells.

We next examined whether ferroptotic alterations related to KIF11 and PRDX1 deficiency could be
recapitulated in CTNNB1-deficient HRECs. CTNNB1 knockdown induced cell death, lipid peroxidation,
accompanied by ferroptosis-like mitochondrial changes and alterations in ferroptosis-associated proteins
(Fig. 7a-h). Moreover, CTNNB1 depletion promoted formation of PRDX1 dimers, increased
phosphorylation of PRDX1 at Tyr194 and cytosolic RPDX1 puncta, mirroring the effects observed upon
KIF11 knockdown (Fig. 7i-n). Fer-1 treatment significantly restored the cell death, lipid peroxidation, and
changes in ferroptosis-related proteins (Fig. 7o-u). Collectively, these findings underscore a pathway in
which impaired Norrin/β-catenin signaling drives PRDX1 dysfunction-induced ferroptosis through
KIF11.

Inhibition of KIF11 compromises mouse retinal vascular development
To determine the mechanisms of KIF11-associated retinal vascular defects in vivo, we constructed an
inducible endothelial cell-specific Kif11 knockout mouse model19. Kif11flox/flox; Pdgfb-iCreER (Kif11
cKO) mice exhibited comparable body weight with control littermates at P7 (Supplementary Fig. 13a).
In line with the findings of Wang et al.9, Kif11 cKO mice presented compromised vascular progression and
decreased vascular density at P7 (Fig. 8a-c). However, leakage of neither erythrocytes nor the low-
molecular-weight tracer Sulfo-N-hydroxysuccinimide (NPHS)-biotin was detectable in Kif11 cKO mice
(Supplementary Fig. 13b, c). At P10, capillaries in the outer plexiform layer (OPL) were fully developed
in WT mice but largely absent in Kif11 cKO mice (Fig. 8d). In agreement with observations in HRECs,
TEM analysis of retinal ECs revealed increased autophagosome formation and ferroptosis-like
mitochondrial alterations upon Kif11 knockout (Fig. 8e), supporting the occurrence of autophagy-
accompanied ferroptosis.

Given the high abundance of vascular endothelial cells in mouse lung tissue45, we then investigated the
molecular changes in the lung lysates. Kif11-depletion in ECs led to significantly increased PRDX1
phosphorylation at Tyr194 (Fig. 8f, g). Notably, Kif11 cKO mice presented alterations in protein
expression suggestive of increased autophagic flux (Fig. 8h, i) and ferroptosis (Fig. 8j, k). These findings
suggest that dysfunction of KIF11 in mouse ECs induces autophagy-accompanied ferroptosis, possibly
impairing defective retinal vascularization.

Ferroptosis inhibition or PRDX1 restoration partially rescues defects in mice
Given the rescue effect of Fer-1 on the dysfunction of β-catenin/KIF11/PRDX1 axis in HRECs, we next
examined whether Fer-1 treatment could restore vascular defects in FEVR mice. As anticipated, daily
administration of Fer-1 from P2 to P6 partially rescued the delayed retinal vascular development in Kif11
cKO and Ctnnb1 cKO mice at P7, without affecting the body weight (Fig. 9a-g and Supplementary Fig.
14a, b).

Since KIF11 dysfunction inhibits the peroxidase activity of PRDX1 by triggering its phosphorylation at
Tyr194, we asked whether restoration of PRDX1 could reverse the impairment of endothelial function.
RTCA revealed that overexpression of either wild-type PRDX1 or the Y194Q mutant increased cell
viability, with the Y194Q mutant yielding a significant higher cell index than the wild-type, indicative of
enhanced peroxidase activity (Fig. 9h). Consistently, retro-orbital venous sinus injection of a lentivirus
carrying the Y194Q mutant significantly improved vascular development in Kif11 cKO mice compared
with vehicle treatment (Fig. 9i-l). In contrast, a lentivirus carrying wild-type PRDX1 produced a minimal
rescue effect (Fig. 9i-l). Neither treatment affected the body weight (Supplementary Fig. 14c). These
findings further support the role of β-catenin/KIF11/PRDX1 deficiency-induced ferroptosis in
compromised retinal vascularization.

In summary, we propose that KIF11 serves as a core downstream target of Norrin/β-catenin signaling.
Loss-of-function mutations in either KIF11 or upstream components of this pathway impair the peroxidase
activity of PRDX1 by releasing the inhibitory effect of KIF11 on Src-mediated Tyr194-PRDX1
phosphorylation, thereby triggering PRDX1 LLPS. This process induces autophagy-accompanied
ferroptosis in retinal endothelial cells, ultimately causing defective retinal vascular development and
contributing to the pathogenesis of FEVR (Fig. 9m).

Methods
Mice
The Kif11-flox mice (Strain S-CKO-03251, Cyagen) and Ctnnb1-flox mice (VSM4101958, Viewsolid)
were crossed with Pdgfb-iCre-ER mice62 to obtain vascular endothelial cell-specific Kif11 or Ctnnb1
knockout mice, namely Kif11loxp/loxp; Pdgfb-iCre-ER (Kif11 cKO) and Ctnnb1loxp/loxp; Pdgfb-iCre-ER
(Ctnnb1 cKO). Mice were induced through daily intraperitoneal injection with 50 μg tamoxifen (T6906,
Topscience) from postnatal day 1 for 3 consecutive days. Tspan12 knockout (Tspan12 KO, Strain S-KO-
08729, Cyagen) mice were used for single-cell RNA sequencing analysis. All mice were on a C57BL/6
background. The mice were maintained in an SPF animal facility under a controlled environment with a
12-hour light/dark cycle, free access to food and water, at Sichuan Provincial People's Hospital. The
ambient temperature was maintained at 22–26°C with a relative humidity of 40–70%. The primers used
for genotyping are shown in Supplementary Table 2.

For rescue experiments, lentiviruses (20 μl at a titer of 1 × 109 TUS/mL) expressing KIF11, wild-type
PRDX1, mutant PRDX1, or an empty vector (control) were obtained from Genechem Co. (Shanghai,
China). For retro-orbital venous sinus injection63, the P1 mice were anesthetized with isoflurane using a
mouthpiece. A 31G needle was inserted into the area of retro-orbital sinus at an angle of approximately
40 °. After gentle injection, the needle was withdrawn slowly to avoid backflow. GFP fluorescence was
used as an indicator of successful injection and efficient endothelial transduction. For rescue experiments
targeting ferroptosis, mice were administered Fer-1 (30 mg/kg) or DMSO via intraperitoneal injection
from P2 to P6. Both male and female mice were used for all experiments. Since familial exudative
vitreoretinopathy is an inherited disorder driven by specific genetic mutations that do not exhibit
documented sex-dependent phenotypic bias, sex was not considered as a primary biological variable in
this study, and data from both sexes were pooled for analysis.

Retinal vascular cell isolation and Single-cell RNA-sequencing
Due to the limited number of mice per litter, the wild-type samples were not derived from the same litters
as the Ctnnb1 cKO or Tspan12 KO mice. Retinal tissues from five P6 mice per litter with the same
genotype were immediately transferred to 5 mL centrifuge tubes and rinsed with 1640 medium containing
1% bovine serum albumin (BSA). The tissue was finely minced and enzymatically digested at 37 °C for
12 minutes using collagenase type II (4 mg/ml) and DNase I (0.05 mg/ml). The resulting suspension was
filtered through a 70 μm cell strainer to remove debris and aggregated material. Cells were pelleted by
centrifugation at 500 × g for 10 minutes, and single-cell suspensions were confirmed under a microscope.
Vascular cells, including endothelial cells (ECs) and microglia, were enriched using CD31 and CD11b
MicroBeads (130-097-418 and 130-126-725, Miltenyi Biotec) following the manufacturer’s protocol.
Sorted cells were resuspended in culture medium for inspection. Single-cell libraries were prepared using
the Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 (10× Genomics) and sequenced on an Illumina
NovaSeq 6000 platform (PE150) by OE Biotech (Shanghai, China).

Public scRNA-seq data of P6 WT, P26 WT and Tspan12 KO retinas, as well as P60 WT and P60 Ndp KO
retinas were retrieved from Gene Expression Omnibus (GEO) database (GSE175895, GSE213887,
GSE125708). FASTQ sequencing reads were aligned to the mouse reference genome (mm10-3.0.0) using
Cell Ranger software (version 9.0.0, 10× Genomics), which also generated counts of unique molecular
identifiers (UMIs) per cell barcode. The resulting UMI count matrices were processed in R using the
Seurat package (version 4.0.0) for downstream analyses. Cells with fewer than 250 detected genes and
500 UMIs, or > 15% of reads mapping to mitochondrial genes, were removed.

Gene expression normalization was performed with the NormalizeData function using the
"LogNormalize" method, which scales the total expression per cell to 10,000 followed by log
transformation. Highly variable genes (HVGs, n = 2,000) were identified using FindVariableGenes with
FastExpMean and FastLogVMR as the mean and dispersion functions, respectively. Dimensionality
reduction was conducted via PCA using the RunPCA function. To correct batch effects, Harmony
integration was applied using RunHarmony from the Harmony R package (version 1.0). Graph-based
clustering was performed with FindClusters, and two-dimensional visualization of cellular states was
generated using UMAP through RunUMAP. Cluster-specific marker genes were identified using
FindAllMarkers with test.use = presto. Differential expression analysis between groups was carried out
with FindMarkers using the same test, and p-values were adjusted with the Bonferroni method.

Patient recruitment and clinical evaluation
The following characteristics were recorded as potential covariates to ensure a comprehensive
understanding of the genotype-phenotype correlations: (1) Age: The cohort includes participants ranging
from 4 months to 53 months. (2) Sex: The cohort includes 5 males and 1 female. While age and sex were
collected for all participants, they were not treated as primary experimental variables, as familial
exudative vitreoretinopathy is an inherited vascular disorder driven by genetic mutations with no
documented sexual dimorphism or sex-dependent phenotypic bias in its clinical presentation. Patients
were evaluated at the Xinhua Hospital, Shanghai Jiaotong University School of Medicine. Written
informed consent was obtained from all participants or the legal guardians of minors. Participants did not
receive financial compensation for their involvement in this study. The diagnostic criteria for familial
exudative vitreoretinopathy (FEVR) primarily consist of the following: (1) full-term birth with normal
birth weight and no history of oxygen support; (2) presence of an avascular peripheral retina; (3) displaced
or dragged retinal vessels and macula; (4) retinal folds (falciform) or retinal detachment; (5)
neovascularization or subretinal exudates.

Targeted gene sequencing and Sanger sequencing
Genomic DNA was isolated from peripheral blood using the QIAamp DNA Mini Kit (Qiagen), following
the manufacturer's guidelines. DNA quantification was performed with a Nanodrop 2000 (Thermo Fisher
Scientific). Approximately 1-3 μg of genomic DNA was then fragmented to an average fragment size of
150 bp using an S220 Focused-ultrasonicator (Covaris). Library preparation was carried out with the DNA
Sample Prep Reagent Set (MyGenostics), involving end repair, adapter ligation, and PCR amplification.
Amplified DNA was then enriched using the GenCap OT004/OT021A capture kit (MyGenostics). Biotin-
labelled capture probes were designed in 100 bp that cover coding exons along with 50 bp flanking regions
of all target genes. The capture process was executed as per the manufacturer's instructions. Finally, the
enriched libraries were sequenced on an Illumina Nextseq 500 platform (Illumina).

The raw data were saved in FASTQ format following sequencing. Illumina sequencing adapters and low-
quality reads (< 80 bp) were removed using Cutadapt (http://code.google.com/p/cutadapt/). The filtered,
clean reads were then aligned to the UCSC hg19 human reference genome with BWA (http://bio-
bwa.sourceforge.net/). Duplicate reads were eliminated with Picard tools
(http://broadinstitute.github.io/picard/), and the aligned reads were subsequently analyzed for variant
detection. SNP and InDel variants were identified using HaplotypeCaller in GATK
(https://software.broadinstitute.org/gatk/) and filtered using VariantFiltration in GATK based on the
following criteria: a) mapping quality of variants < 30; b) total mapping quality zero reads < 4; c)
approximate read depth < 5; d) QUAL score < 50.0; e) Fisher's exact test phred-scaled p-value for strand
bias > 10.0. Finally, the data were converted to VCF format. Variants were annotated using ANNOVAR
(http://annovar.openbioinformatics.org/en/latest/) and cross-referenced with multiple databases, including
1000 Genomes, ESP6500, dbSNP, EXAC, MyGenostics Inhouse, HGMD, and further analyzed using the
prediction tool of MutationTaster. The candidate pathogenic variants were validated using Sanger
sequencing and analyzed for cosegregation within families. PCR products were sequenced on an ABI
3730XL DNA Sequencer (Applied Biosystems, Thermo Fisher Scientific). The primers for validation
were listed in the Supplementary Table 3.

Cell culture, lentivirus-mediated knockdown, real-time cell analysis, and time-lapse live-cell
imaging
Human microvascular endothelial cells (HRECs, ANCBRI-181, Cell-systems) were cultured in a complete
classic medium (4Z0-500, Cell-systems). HEK293T cells (CRL-3216™, ATCC) and HT1080 cells (CCL-
121™, ATCC) were cultured in Dulbecco's Modified Eagle Medium (DMEM) (11995040, Gibco) and
Minimum Essential Medium (MEM), respectively. Both media were supplemented with 10% fetal bovine
serum (C04001-500, VivaCell) and 1% penicillin/streptomycin (15070063, Thermo Fisher Scientific).
Gene knockdown was performed using lentivirus-mediated shRNA obtained from Genechem Co.
(Shanghai, China). The shRNA sequences are listed in Supplementary Table 4. The Agilent xCELLigence
Real-Time Cell Analysis systems 64 and xCELLigence RTCA eSight were used for live cell analysis and
time-lapse live-cell imaging. Briefly, for time-lapse live-cell imaging, cells were seeded on E-plate VIEW
24 hours after lentivirus infection. At 24 hours post-seeding, 50 μl of culture medium was replaced with
medium containing lipid peroxidation sensor BODIPY™ 665/676 (B3939, Thermo Fisher Scientific),
Annexin V (A35122, Thermo Fisher Scientific), and Propidium Iodide (PI, HY-D0815, Med Chem
Express). Time-laspe imaging was performed with scans acquired every 30 minutes. 4 μM Fer-1 (HY-
100579, Med Chem Express) or DMSO was added to the culture medium 24 hours after lentivirus
infection. 50 ng/ml Norrin (3014-NR-025, R&D) was added to the culture medium 72 hours after
lentivirus infection for 24 hours.

EdU labeling, TUNEL fluorescent assays, ROS staining, intracellular iron measurement, and LDH
release assays
For EdU labeling, HRECs were treated with 10 μM EdU for 3 h and then harvested for staining using
Click-iT EdU Alexa Fluor-594 Imaging Kit (C10339, Thermo Fisher Scientific). The TUNEL Apoptosis
Detection Kit (40308ES20, YEASEN) was used for TUNEL apoptosis detection in HRECs. To detect the
cellular ROS, the HRECs were incubated with the CellROX® Deep Red Reagent (C10422, Life
Technologies) at a final concentration of 5 μM for 30 minutes at 37 ℃, followed by three PBS rinses
before microscopy. To detect intracellular Fe2+, the HRECs were washed three times with HBSS and
incubated with FerroOrange (1 μM) (F374, DOJINDO) for 30 minutes at 37 ℃ before microscopy. LDH
release assays were performed Cytotoxicity LDH Assay Kit (HY-K1090, MedChenExpress) according to
the manufacturer's instructions. The cultured HRECs were fixed in 4% PFA for 15 min and rinsed with
PBS 3 times before being blocked with 5% fetal bovine serum. The antibodies used for
immunofluorescence staining were listed in Supplementary Table 5. The images were obtained using a
Zeiss LSM900 confocal microscope, Zeiss Axio Observer 7, and processed with Adobe Illustrator.

Measure of lipid peroxidation malondialdehyde (MDA) and activity of total glutathione peroxidase
in HRECs
The levels of MDA were measured according to the manufacturer's instruction (S0131S, Beyotime).
Briefly, about 3 × 106 HRECs were lysed, and the supernatants were mixed with the working solution.
Then, the mixtures were heated at 100 ℃ for 40 min, and the absorbance was detected by SuperMax
3000AL Microplate reader (FLASH) at 532 nm after cooling down to room temperature. The activity of
cellular glutathione peroxidase was measured using a kit with DTNB (S0055S, Beyotime). The cells were
lysed, and the supernatant was collected for subsequent procedures; the absorbance was measured using
a spectrophotometer at 412 nm.

Plasmids, site-directed mutagenesis, and transfection
Plasmids encoding full-length wild-type KIF11 (NM_004523.4) with FLAG tag, PRDX1 (NM_002574.4)
with HA or EYFP tag, and plasmids expressing FLAG-tagged miniTurboID, with or without an N-
terminal Src fusion, were purchased from Youbio (China). Plasmids encoding the KIF11 or PRDX1
(Tyr194Gln) mutants were synthesized by YouBio (Changsha, China). HEK293T cells were transfected
with plasmids using Lipofectamine 3000 (L3000015, Invitrogen), and cells were collected 48 h after
transfection.

Fluorescence recovery after photobleaching (FRAP) assay
FRAP assay was performed on HEK293T cells transfected with wild-type or mutant PRDX1-EYFP using
a Zeiss LSM900 confocal microscope equipped with an incubation chamber (5% CO2, 37 °C). Cell nuclei
were stained with Hoechst (C1027, Beyotime) for at least 20 min before imaging. Fluorescence recovery
was recorded every 5 s for 5 min and analyzed using Zen microscopy software. Images were processed
and quantified by the FRAP profiler v2 plugin using Image J (version 2.16.0).
To dissolve the LLPS condensates, 1,6-Hexanediol (1,6-HD, 240117, Sigma) was dissolved in the DMEM
culture medium (10% concentrations) and warm at 37 °C incubator before adding to cells. The LSM900
confocal microscope (Zeiss) was used to visualize live-cell imaging for cells treated with 10% 1,6-HD.

RNA extraction, quantitative real-time PCR, and bulk RNA-sequencing
The total RNA was extracted from HRECs or lung tissues using TransZol (ET101-01-V2, Transgen
Biotech) and then reverse transcribed using the EasyScript One-Step RT-PCR SuperMix (AT411-02,
TransGen Biotech). The quantitative real-time PCR was conducted using TransStart Tip Green qPCR
SuperMix (AQ142-21, TransGen Biotech) on the ABI 7500. The primers were listed in Supplementary
Table 6. The bulk RNA-sequencing transcriptome was conducted by Biozeron (Shanghai). Briefly, the
libraries were constructed using TruSeq RNA Sample Preparation Kit (Illumina) with 1 μg of total RNA.
mRNA was isolated through polyA selection using oligo(dT) beads and then fragmented with
fragmentation buffer. cDNA synthesis, end repair, A-base addition, and adaptor ligation were completed
according to protocol. Libraries were size-selected to obtain cDNA fragments between 200–300 bp on 2%
Low Range Ultra Agarose and then PCR-amplified with Phusion DNA polymerase (New England Biolabs)
for 15 cycles. After quantification by TBS380, paired-end libraries were sequenced using the Illumina
NovaSeqXplus or NovaSeq 6000 system (150 bp × 2, Shanghai BIOZERON Co., Ltd).

The raw paired-end reads were trimmed and quality-controlled with Trimmomatic (version 0.39). The
cleaned reads were then aligned to the reference genome using HISAT2 (version 2.1.0). Data quality was
assessed with Qualimap (version 2.2.1), and gene counts were generated using HTSeq (version 0.11.1).
For the knockdown experiments targeting FZD4, LRP5, TSPAN12, and CTNNB1, the same batch of cells
infected with a lentivirus carrying non-targeting shRNA was used as a negative control. The KIF11 and
PRDX1 knockdown data set was compared to its own corresponding non-targeting shRNA control.
Differential expression analysis to identify DEGs was conducted using the edgeR package (version 3.34.0)
based on the FPKM (Fragments Per Kilobase of transcript per Million mapped reads). The transcripts
with a |log2 FoldChange| > 1 and p-value < 0.05 were classified as differentially expressed.

Weighted gene co-expression network analysis (WGCNA), protein-protein interacting (PPI)
network construction, and hub gene identification
The ComBat function in the sva R package (version 3.44.0) was used to combine different gene expression
profiles by removing the batch effects based on FPKM65. WGCNA was conducted using the WGCNA R
packages (version 1.71) to identify a correlation between modules and samples. Briefly, the Pearson
correlation coefficient matrix was created from the integrated gene expression data and then was
transformed into a weighted adjacency matrix. The topological overlap matrix (TOM) was created using
the TOM similarity function and the TOM-based dissimilarity calculated by the "hclust" function. The
hierarchical clustering dendrogram was then built based on TOM dissimilarity, and the modules were
identified using a dynamic tree cut with a minimum module size of 30. Then, the module eigengene (ME)
dissimilarity was calculated and the cut-off for merging similar modules was set as 0.3. The protein-
protein interacting data for genes in interested modules was extracted from the Search Tool for the
Retrieval of Interacting Genes (STRING). Subsequently, CytoHubba-a plugin from Cytoscape (version
3.10.1) was utilized to identify hub genes using maximal clique centrality (MCC).

Over-representation analysis (ORA)
Genes from the dark grey and blue modules of WGCNA, upregulated or downregulated genes from KIF11
KD transcriptome, differentially expressed proteins from KIF11 KD proteome, or differentially expressed
proteins in quadrants 4 and 6 (|log2 FoldChange| < 0.585 for transcriptome, |log2 FoldChange| > 0.585 for
proteome, p-value < 0.05) were enriched by KEGG pathways using the clusterProfiler (version 3.14.3).

Gene set enrichment analysis (GSEA)
The GSEABase package (version 1.44.0) was used to load the gene set file which was downloaded and
processed from KEGG database (https://www.kegg.jp/). Gene Set Enrichment Analysis (GSEA) was used
to complete KEGG term enrichment analysis on genes detected in KIF11 KD and PRDX1 KD
transcriptomes, and KIF11 KD proteome.

Nine-quadrant diagram for integrated transcriptome and proteome data
The log2 FoldChange and p-value of genes following KIF11 knockdown in transcriptome and proteome
data were integrated using a nine-quadrant diagram by OmicShare tool (https://www.omicshare.com). The
threshold for the division of the nine quadrants was |log2 FoldChange| > 1 for transcriptome and
|log2 FoldChange| > 0.585 for proteome.

ATAC-seq and ChIP-seq analysis
The raw data of ATAC-seq (GSE122117) and ChIP-seq (GSE164442) were obtained from the GEO
database and analyzed as previously described23. Briefly, the reads were trimmed in silico to remove
adapter sequences, low-quality reads, and 50-bp length using trimGalore (version 1.18) and aligned to the
mm10 reference genome using the Bowtie2 alignment tool (version 2.3.5.1). Mitochondrial reads were
filtered for the subsequent analyses. All peak calling of ATAC-seq data was performed using MACS2
(version 2.2.7.1) with callpeak-format BAMPE parameters. BigWig files were generated and normalized
using RPKM by Deeptools (version 3.4.3). Heatmaps were created by Deeptools using the RPKM
normalized bigwig files.

Western blot analysis and mass spectrometry analysis
Total proteins were extracted from HRECs or lung tissues using RIPA lysis buffer (R0010, Solarbio) and
denatured using loading buffer (P1040, Solarbio). The proteins were separated by electrophoresis on 4-
20% SDS PAGE and transferred onto the nitrocellulose membrane. The membranes were blocked with
5% skimmed milk in TBST for 1 h at room temperature and incubated with primary antibodies overnight
at 4 ℃, followed by incubation with secondary antibodies for 1 h at room temperature. The protein signals
were detected using SuperSignalTM West Dura Extended Duration Substrate (34075, Thermo Fisher
Scientific) by Touch Imager (e-BLOT) and analyzed using Image J software (version 2.16.0).

Mass spectrometry analysis was performed by Shanghai GENECHEM Co., Ltd and Applied Protein
Technology. Briefly, total proteins for proteomic analysis were extracted from control and KIF11-depleted
HRECs (n = 3 biological replicates per group). For IP-MS analysis, proteins were pulled down using anti-
Flag magnetic beads from HRECs either overexpressing Flag-tagged KIF11 (n = 1) via lentiviral
transduction or a non-overexpressing control (n = 1). All protein samples were sonicated and solubilized
in SDT buffer (4% SDS, 100 mM Tris-HCl, pH 7.6). The samples were then boiled and centrifuged at 4℃
for 15 min to remove debris. The proteins were then reduced with 100 mM DTT for 5 min at 100℃, and
the UA buffer (8 M urea, 150 mM Tris-HCl, pH 8.5) was used to remove the detergent, DTT, and low-
molecular-weight components. After blocking the reduced cysteine residues with 100 μl iodoacetamide
(100 mM IAA in UA buffer), the protein solution was subsequently digested with 4 μg trypsin in 40 μl 50
mM NH4HCO3 buffer overnight at 37℃. Peptides from each sample were desalted using C18 cartridges,
concentrated by vacuum centrifugation, and reconstituted in 40 μL of 0.1% (v/v) formic acid. The peptide
concentration was estimated by measuring the UV absorbance at 280 nm. An extinction coefficient of 1.1
for a 0.1% (w/v) solution was used, which was calculated based on the frequency of tryptophan and
tyrosine residues in vertebrate proteins.

For proteomics, the LC-MS/MS analysis was conducted on an Orbitrap Exploris 480 or a Q Exactive
mass spectrometer (Thermo Fisher Scientific) coupled to Easy nLC (Thermo Fisher Scientific). 2 μg of
peptides were loaded onto a C18 reversed-phase analytical column (Acclaim PepMap RSLC, 50 μm × 15
cm, nanoViper, P/N 164943; Thermo Fisher Scientific) in buffer A (0.1% formic acid) and separated with
a linear gradient of buffer B (80% acetonitrile and 0.1% formic acid) at a flow rate of 300 nL/min. MS
data were acquired using a data-dependent top-10 method, dynamically selecting the most abundant
precursor ions from the survey scan (m/z 350–1200) for higher-energy collisional dissociation (HCD)
fragmentation. Full MS1 scans were acquired at a resolution of 120,000 (at m/z 200) with an automatic
gain control (AGC) target of 300% and a maximum injection time (IT) of 50 ms. The data-dependent
mode was set to cycle time (1.5 s). MS2 scans were acquired at a resolution of 15,000 (at m/z 200) with
an AGC target of 75% and a maximum IT of 35 ms. The isolation window was set to 1.6 m/z, and
microscans were set to 1. Only ions with a charge state between 2 and 6 were selected for fragmentation.
Dynamic exclusion was set to 30 s, and the normalized collision energy (NCE) was 33%.

For IP-MS, LC-MS/MS analysis was performed on a timsTOF Pro mass spectrometer (Bruker) coupled
to an Evosep One liquid chromatography system (Denmark). The peptides were loaded onto a C18
reversed-phase analytical column (15 cm length, 150 μm inner diameter, 1.9 μm resin) in buffer A (0.1%
formic acid in water) and separated with a linear gradient of buffer B (99.9% acetonitrile and 0.1% formic
acid) at a flow rate of 220 nL/min. The mass spectrometer was operated in positive ion mode. The
electrospray voltage applied was 1.6 kV. Precursors and fragments were analyzed using the TOF detector
over a mass range of m/z 100–1700. The timsTOF Pro was operated in parallel accumulation serial
fragmentation (PASEF) mode. PASEF mode data collection was performed based on the following
parameters: the ion mobility coefficient (1/K0) value was set from 0.75 to 1.35 V·s/cm2; each cycle
included 1 MS and 8 PASEF MS/MS scans. Active exclusion was enabled with a release time of 24 s.

The MS data were analyzed by MaxQuant (version 1.6.14) and searched against the
Uniprot_homo_20221024_20402_9606_swiss_prot. The global false discovery rate (FDR) threshold for
peptide and protein identification was 0.01. Protein abundance was quantified based on the normalized
spectral protein intensity (LFQ intensity). Proteomics data contain a significant number of missing values
that severely hinder proper differential analysis. Based on a previous work that suggested a proper
imputation method66, we first categorized the proteins into two groups: (1) one group includes proteins
with at least two non-missing values within the same sample group; (2) the other group includes proteins
with at least two non-missing values in one sample group, while all data in the other sample group are
entirely missing. Then, the missing values of both groups were filled with the protein minimum and 1/2
sample minimum, respectively. The differential analysis was performed using the Limma (version 3.40.6)
package. Proteins with a |log2 FoldChange| > 0.585 and p-value < 0.05 were classified as differentially
expressed.

LC-MS/MS targeted analysis of ferroptosis-related phospholipid peroxidation
HRECs depleted with KIF11 were collected for lipid extraction, which were subsequently subjected to
targeted LC-MS/MS analysis of ferroptosis-related phospholipid peroxidation11, 33. For lipid extraction,
the collected HRECs were resuspended in 1 mL of a solvent mixture containing methanol, tert-butyl
methyl ether (MTBE), butylated hydroxytoluene (BHT), along with internal standards. Lipid extracts
were separated on a Thermo Accucore C30 column (2.6 µm, 2.1 × 100 mm) using the following gradient
at 0.35 mL/min: 0 min (80% A, 20% B), 2 min (70% A, 30% B), 4 min (40% A, 60% B), 9 min (15% A,
85% B), 14 min (10% A, 90% B), 15.5 min (5% A, 95% B), 17.3 min (5% A, 95% B), and 17.5 min (80%
A, 20% B). Mobile phase A consisted of acetonitrile/water (60:40, v/v) with 0.1% formic acid and 10 mM
ammonium formate, while mobile phase B was acetonitrile/isopropanol (10:90, v/v) with the same
additives. MRM mode was employed for detection on a 6500 Q-Trap mass spectrometer (Sciex, UK),
monitoring parent-to-daughter ion transitions with the following settings: TEM 500 °C, GS1 45, GS2 55,
CUR 35, IS -4500 V, DP -50 V, EP -10 V, and CE -38 V. The peak area of each transition was integrated
and normalized to the corresponding internal standard for quantification of lipid species. All lipid species
analyzed had been previously identified and their structures confirmed by MS/MS33, 67.

Co-immunoprecipitation (Co-IP) assay
The HRECs and HEK293T cells overexpressed with KIF11-FLAG were lysed with 300 μl cold lysis
buffer for 20 min at 4℃. For proximity-labeling, HEK293T co-overexpressing Src-miniTurboID-FLAG
or Vec-miniTurboID-FLAG, KIF11-FLAG or Vector-FLAG, and PRDX1-HA were incubated with biotin
at final concentration of 500 μM for 30 min at 37 ℃. After labeling, cells were immediately placed on ice
and washed 3 × with ice-cold PBS to remove free biotin. Then the cells were lysed with 300 μl cold lysis
buffer for 20 min at 4 ℃. After centrifugation, the soluble supernatants were incubated overnight at 4 ℃
with anti-FLAG Magnetic Beads for the KIF11-FLAG samples, or Streptavidin Magnetic Beads for the
miniTurboID-based proximity-labeling (HY-K0207 and HY-K0208, Med Chem Express). The beads were
washed three times with lysis buffer and resuspended in loading buffer before being heated for 5 min at
95 ℃. The supernatants were loaded onto SDS PAGE and processed for western blot analysis. The
antibodies used for western blot analysis were listed in Supplementary Table 5.

Transmission electron microscopy (TEM)
The eyeballs and HRECs were prefixed with 3% glutaraldehyde, followed by postfixed with 1% osmium
tetroxide, dehydrated in acetone, and embedded in Epon 812. Semithin sections were stained with
toluidine blue for optical localization, while ultrathin sections were prepared using a diamond knife and
stained with uranyl acetate and lead citrate. Observations were conducted using a JEM-1400 FLASH
transmission electron microscope.

Retinal leakage evaluation and immunofluorescence
For leakage evaluation, 100 µl EZ-Link™ Sulfo-NHS-Biotin (2 mg/ml, 21217, Thermo Fisher Scientific)
were intraperitoneally injected into P7 mice 1 h before sacrifice. The retinal flat mounts were prepared
and processed for immunofluorescence staining as described previously3, 4. Briefly, the retinas were
isolated from 4% FPA-fixed eyeballs and then fixed in methanol at -20 ℃ for 20 min, followed by rinsing
with PBS 3 times and blocking with 5% donkey serum for 30 min at room temperature. The retinas were
then incubated overnight at 4℃ with Isolectin GS-IB4 (I21413 or I21411, Invitrogen) and primary
antibody, followed by 3 washes and incubation with secondary antibody or Streptavidin, Alexa Fluor™
488 Conjugate (S11223, Thermo Fisher Scientific). The retinas were then flat-mounted using
FluoromountTM Aqueous Mounting Medium (F4680, Merck). The vascular progression and vessel density
were measured using Zen Software (blue edition, version 2.6; Carl Zeiss) and Angiotool (version 0.5 beta).
The images were obtained using a Zeiss LSM900 confocal microscope, Zeiss Axio Observer 7, and
processed with Adobe Illustrator and Adobe Photoshop.

Statistical and Reproducibility
Statistical analyses were performed using GraphPad Prism 9.0. For comparisons between two groups,
unpaired parametric Student's t-tests were applied if the data passed normality and lognormality tests;
otherwise, unpaired nonparametric tests with Welch's correction were used. For comparisons involving
more than two groups and not otherwise specified in the figure legend, one-way or two-way ANOVA
followed by Dunnett's or Tukey's multiple-comparison tests was conducted. Data are shown as mean ±
SD unless otherwise specified. All experiments were independently repeated at least three times.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Yang et al. report that KIF11 is a downstream target of beta-catenin signaling in HRMVECs and regulates the activity of the antioxidant protein Peroxiredoxin 1 by a physical interaction that promotes T194 PRDX1 phosphorylation and LLPS. Kif11 or PRDX1 or CTNNB1 deficiency all induce ferroptosis in HRMVECs. In mice, Kif11 endothelial KO angiogenesis phenotypes are restored by the ferroptosis inhibitor ferrostatin or viral overexpression of non-phosphorylated PRDX1. Conceptually, there is major progress in the model that KIF11 is downstream of beta-catenin signaling because it is not known why KIF11 is a FEVR gene and if it has a role within the norrin/frizzled4 pathway. There is also a major advance in the model that KIF11 regulates PRDX1 and ferroptosis, with implications for FEVR. This is a major study based on the novelty of the findings and models. This is also a massive set of data, generally with appropriate controls and quantifications. Therefore, this reviewer will focus on a few key issues.\n\nThe claim that KIF11 is a downstream target of beta-catenin dependent signaling.\n- This claim will be controversial, because scRNAseq data from Ndp KO and Tspan12 KO mice did not identify KIF11 as a downstream target of beta-catenin signaling (Heng et al 2019 (Nathans lab), Zhang et al 2023 (Junge lab)). In addition, transcriptomics in HRMVECS and other CNS EC cell lines +/- norrin stimulation was conducted by multiple groups and there is no report that Kif11 is a downstream target. This could be due to differences in developmental timing, i.e., maybe KIF11 is a target of norrin signaling only in developing (non-quiescent) endothelial cells. This is a key claim of the study, because it links ferroptosis to loss of beta-catenin signaling and FEVR. Therefore, the authors should examine one of the KO strains in which mediators of the norrin/frizzled4 pathway are gene ablatated (e.g., Ndp KO mice or Fzd4 KO mice) and confirm that KIF11 is downregulated in the ECs of these mice and that a ferroptosis inhibitor alleviates phenotypes (at least some of the phenotypes) in these mice. This experiment is critical to substantiate the model in this manuscript.\n- In addition, to rule out that Kif11 is dysregulated as response to lentiviral infection or as off-target effect of the shRNA, the qPCR data in Figure 2A should be repeated with an independent siRNA (which targets a different region of the target), without using lentivirus for delivery.\n\nWeaknesses in the analyses of the transcriptomic data.\nThe following can be addressed without new experiments:\n- The authors should highlight and discuss that the knockdown of FZD4, LRP5, TSPAN12, and CTNNB1 was apparently done in the absence of norrin stimulation. This is important because target genes of beta-catenin-dependent signaling may not be affected by the KD in the absence of norrin, and neither HRMVECs nor serum are a source of norrin.\n- The supplemental transcriptomic data in the excel file are not readily accessible because the accession numbers but no gene names are provided. This makes it difficult to critically evalaute the data, as every gene of interest has to be translated into an accession number before the gene can be looked up in this data.\n- A clarification is lacking whether the same control samples were used for all KD comparisons, or if different control samples were used for each KD experiment (apparently the latter based on variable RPK in sets of control samples).\n- The data don’t look good with respect to consistency across CTNNB1, FZD4, TSPAN12, and LRP5 KD for key genes, despite the claim in Fig 1C that there are consistent differences. Note also that in Fig 1B there is little to no overlap in the top upregulated and top downregulated genes, this shhould be discussed in the manuscript.\n- There is no discussion on what genes are expected to be regulated by beta-catenin siglaning (e.g., Axin2 and Apcdd1), and how these markers of Wnt signaling change across the various KD conditions. Contrary to expectations, Axin2 is upregulated in Tspan12 KD cells, and it is not downregulated in CTNNB1 KD or LRP5 KD cells. Also contrary to expectations, APCDD1 is upregulated in Tspan12 KD cells. Together, the evidence is not strong that knock down conditions would affect beta-catenin signaling.\nThere is no discussion in the manuscript of how many fold KIF11 was downregulated in each KD experiment, if the change was signficant (no statistics shown for the RNAseq in the suppmemental table), and how Kif11 ranks relative to other downregulated genes in each knock down condition (is it in the top10? top 100? )\n- It is unclear why the authors selected the dark magenta module, whas the negative correlation with control and the trend towards a postive correlation with knock down stronger than for any of the other 23 gene modules? This is not described, was the module selected based on subjective interest?\n\nIn vivo experiments\n- Figure 10 p and 10 t. Clarify in the manuscript if littermates were used. Add the control group (Cre-) for the qualitiative and quantitative data so the extent of rescue vs this control can be quantified.\n- Unclear how a retroorbital viral delivery in mouse pups works (with closed eyelids at this stage), and how effective it is. An experiment with a GFP virus can show how many ECs are transfected and validate this delivery method at this age.\n\nMinor:\n- some of the fluorescent cell images are too dim (e.g., Hoechst in Fig 8h and multiple others)\n- Fig 6K should show the full native gel so that all MW specimens of PRDX1 can be seen", "reviewer_2": "In the presented manuscript, Yang, Zhao Peng and Lv et al. discovered KIF11 as a novel downstream target of the Norrin/beta-catenin pathway in HRECs in a screen. Mutations in KIF11 have been previously reported in the context of human eye diseases. Besides their models of endothelial cells of the eyes and mouse retinas (e.g. Fig. 9a), the authors might want to step a little further and test KIF11 in general models of ferroptosis, standard cells and assays that might expand their discovery from eye-specific to generally important to ferroptosis. I therefore suggest a couple of cell culture experiments in my list of major concerns below. Once this is performed, the manuscript will be of interest not only to eye specialists, but also to the broader readership of a journal such as Nature Communications.\nIn summary, at the current stage, the manuscript is quite specialized, but I can envision a broader role for their discovery in ferroptosis which I recommend to be tested in some detail.\n\nMajor concerns\n• What is the role of KIF-11 in ferroptosis, if any? This is easy to investigate by knocking out and back in AKR1 in standard ferroptosis cell lines (HT1080, Pfa1) treated with various ferroptosis inducers of all classes (erastin, RSL3, FIN56, FINO2). As readout systems, time lapse imaging, PI staining, LDH release, any other vital dye, annexin V etc should be investigated?\n• What is the baseline expression and overall relevance of the reported Kif-11-system? Western blots should be provided with established (ko-controlled) antibodies. How does the absence of KIF-11 relate to the known ferroptosis-controlling systems such as GPX4, FSP1 etc.? How does it affect the baseline levels of hydropersulfides, ether lipids etc?\n• How does KIF-11 regulate ferroptosis? This requires the monitoring of cellular systems (HT1080, NIH3T3, Pfa1 and potentially the cells used in the screen in Fig. 1) over time. How plastic are the above-mentioned systems once induced in the absence of KIF-11? How does the peroxidative lipid profile assessed by LC/MS change over time? How does this phenocopy MALDI-imaging of the corresponding lipids in section of these eyes?\n• Fig. 7-9 are a strong part of this manuscript. But which kinase is responsible for the LLPS and PRDX1 phosphorylation, and how does KIF-11 regulate it?\n• The authors conclude that autophagy dependent ferroptosis is key here. However, they do not demonstrate the requirement of the “autophagy” part. Therefore, I highly recommend to reduce this controversial term “ferroptosis” instead of “autophagy-dependent ferroptosis”. As with other regulated cell death pathways, autophagy is triggered mostly in parallel to the cell death pathways of most kinds. In that sense, autophagy is rather a counterregulatory attempt of the cell to improve its energy profile and may be interpreted as a rescue attempt, also during the process of ferroptosis.", "reviewer_3": "The overall study has positive significance for the mechanistic research of KIF11. The authors conclude that: modulation of the Norrin pathway induces changes in KIF11; KIF11 regulates alterations in the autophagy and ferroptosis pathways; KIF11 interacts with PRDX1, and the loss of the KIF11/PRDX1 interaction leads to PRDX1 polymerization and triggers liquid-liquid phase separation (LLPS). Additionally, ferroptosis inhibitors and PRDX1 overexpression can partially rescue the vascular developmental defects caused by KIF11.\n\nHowever, this study has several notable limitations:\nLogical Inconsistencies: The experimental design is fragmented, with many proposed connections relying excessively on bioinformatic correlations. The authors have not established a complete mechanistic chain through functional experiments, and some of the bioinformatic analyses appear redundant.\nWeak Evidence for Autophagy-Dependent Ferroptosis: Although KIF11 is shown to induce autophagy and ferroptosis separately, there is insufficient experimental evidence to definitively demonstrate that KIF11 triggers ferroptosis through autophagy-dependent mechanisms. The reliance on a single shRNA for knockdown experiments undermines the reliability of these findings.\nAbrupt Presentation of KIF11 Mutation Data: The inclusion of KIF11 mutation results lacks contextual integration with the overall narrative, appearing disconnected from the core mechanistic exploration.\nIncomplete Animal Experiments: The methodology for drug treatment in animal models is inadequately described. Phenotypic analysis is superficial, focusing primarily on vascular developmental status while neglecting critical assessments (e.g., blood-retinal barrier integrity). Given the close relationship between vascular development and overall growth in mice, the authors should provide body weight statistics for all experimental groups in the supplementary materials."}, "author_response": "We thank the reviewers for their thorough and constructive feedback, which guided us to conduct multiple new experiments over the past few months, resulting in a more comprehensive mechanistic framework. The major advancements include:\n1. Incorporation of scRNA-seq data: We have performed scRNA-seq on retinal vascular cells from Ctnnb1 cKO and Tspan12 KO mice. Integrated with public datasets and validated by additional experiments, these data confirmed that Kif11 is specifically expressed in proliferative ECs of postnatal retinas, but largely absent in adult retinas, and is downregulated in Ctnnb1 cKO and Tspan12 KO retinal ECs. These findings strengthen the conclusion that KIF11 is regulated by β-catenin signaling and explain the early-onset nature of KIF11-related FEVR.\n2. Ferroptosis-related experiments: In HRECs, a combination of assays—including time-lapse live-cell imaging, LDH release, and LC-MS/MS phospholipid peroxidation profiling—demonstrated that KIF11 knockdown induces ferroptosis. Parallel studies in HT1080 cells yielded consistent results, suggesting a broader role of KIF11 in ferroptosis regulation beyond endothelial cells.\n3. Mechanistic studies on PRDX1 phosphorylation: Western blot and inhibitor assays confirmed that PRDX1 phosphorylation in KIF11-depleted HRECs is mediated by Src kinase. miniTurboID-based Co-IP further revealed that KIF11 disrupts the Src-PRDX1 interaction, likely through spatial occupancy of the Src-binding site, thereby preventing Src-mediated phosphorylation.\n4. In vitro and in vivo rescue experiments: We have incorporated additional rescue experiments. Fer-1 treatment exhibits partial rescue effects on CTNNB1-depleted HRECs and Ctnnb1 cKO mice. In addition, retro-orbital sinus delivery of KIF11-expressing lentivirus partially restored retinal vascular progression in Ctnnb1 cKO mice.\nConclusion: Together, these new results provide strong in vitro and in vivo evidence that the β-catenin/KIF11/PRDX1 axis plays a critical role in ferroptosis and retinal vascular development, highlighting the potential of targeting ferroptosis or restoring KIF11 function as therapeutic strategies for related retinal vascular disorders.\n\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nYang et al. report that KIF11 is a downstream target of β-catenin signaling in HRMVECs and regulates the activity of the antioxidant protein Peroxiredoxin 1 by a physical interaction that promotes T194 PRDX1 phosphorylation and LLPS. Kif11 or PRDX1 or CTNNB1 deficiency all induce ferroptosis in HRMVECs. In mice, Kif11 endothelial KO angiogenesis phenotypes are restored by the ferroptosis inhibitor ferrostatin or viral overexpression of non-phosphorylated PRDX1. Conceptually, there is major progress in the model that KIF11 is downstream of β-catenin signaling because it is not known why KIF11 is a FEVR gene and if it has a role within the norrin/frizzled4 pathway. There is also a major advance in the model that KIF11 regulates PRDX1 and ferroptosis, with implications for FEVR. This is a major study based on the novelty of the findings and models. This is also a massive set of data, generally with appropriate controls and quantifications. Therefore, this reviewer will focus on a few key issues.\n1. The claim that KIF11 is a downstream target of β-catenin dependent signaling.\n- This claim will be controversial, because scRNAseq data from Ndp KO and Tspan12 KO mice did not identify KIF11 as a downstream target of β-catenin signaling (Heng et al 2019 (Nathans lab), Zhang et al 2023 (Junge lab)). In addition, transcriptomics in HRMVECS and other CNS EC cell lines +/- norrin stimulation was conducted by multiple groups and there is no report that Kif11 is a downstream target. This could be due to differences in developmental timing, i.e., maybe KIF11 is a target of norrin signaling only in developing (non-quiescent) endothelial cells. This is a key claim of the study, because it links ferroptosis to loss of β-catenin signaling and FEVR. Therefore, the authors should examine one of the KO strains in which mediators of the norrin/frizzled4 pathway are gene ablated (e.g., Ndp KO mice or Fzd4 KO mice) and confirm that KIF11 is downregulated in the ECs of these mice and that a ferroptosis inhibitor alleviates phenotypes (at least some of the phenotypes) in these mice. This experiment is critical to substantiate the model in this manuscript.\nResponse:\nWe sincerely appreciate the reviewer’s insightful comments and valuable suggestions.\nTo address this controversial claim, we have incorporated data from endothelial cell-specific Ctnnb1 (encode β-catenin) conditional knockout (Ctnnb1 cKO) and Tspan12 (encode TSPAN12) global knockout (Tspan12 KO) mice, and have performed single-cell RNA sequencing (scRNA-seq) on P6 retinal vascular cells isolated from both models. Furthermore, as mentioned by the reviewer, we have integrated the relevant public datasets (P60 WT and Ndp KO from Heng et al 2019 of Nathans lab; P26 WT and Tspan12 KO from Zhang et al 2023 of Junge lab) with our data.\nConsistent with the interpretation proposed by the reviewer that KIF11 might be a target of Norrin/β-catenin signaling only in developing (non-quiescent) ECs, the integrated scRNA-seq analysis confirmed that Kif11 is predominantly expressed in proliferative ECs of postnatal retinas (non-quiescent), but largely absent in adult retinas (quiescent, P26 and P60) (Fig. 2d-h and Supplementary Fig. 2d, e). We have also incorporated western blot and RT-qPCR analyses on P1, P7, P14, P21, and P28 retinas and confirmed the marked reduction of KIF11 expression after P7 (Fig. 2i and Supplementary Fig. 2g). Therefore, it is not applicable to test whether KIF11 is altered in quiescent retinal ECs from adult mice with defective Norrin/β-catenin signaling.\nIn the postnatal retinal ECs, scRNA-seq revealed significant downregulation of Kif11 in total EC (Fig. 2d) and proliferative EC (Fig. 2h) of Ctnnb1 cKO and Tspan12 KO groups compared with the WT. The raw scRNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) database under accession GSE305756.\nAdditionally, to further test the role of KIF11 in postnatal vascular development in the absence of endothelial β-catenin, we have applied retro-orbital injection of KIF11-expressing lentivirus, which resulted in specific expression of KIF11 in retinal vascular cells and partially rescued vascular progression in Ctnnb1 cKO mice (Fig. 2p). These findings support the conclusion that KIF11 functions downstream, at least in part, of Norrin/β-catenin signaling to promote retinal vascular development.\nFurthermore, in response to the reviewer's suggestion, we have conducted rescue experiments in Ctnnb1 cKO mice using Fer-1 treatment. The results demonstrated partial rescue effects on the retinal vascular progression of Ctnnb1 cKO (Fig. 9e, f), suggesting that ferroptosis contributes to the vascular defects observed in FEVR.\n2. In addition, to rule out that Kif11 is dysregulated as response to lentiviral infection or as off-target effect of the shRNA, the qPCR data in Figure 2A should be repeated with an independent siRNA (which targets a different region of the target), without using lentivirus for delivery.\nResponse:\nWe appreciate the reviewer’s thoughtful suggestion. To rule out the possibility that the downregulation of KIF11 results from lentiviral infection or off-target effects of the original shRNA, we have conducted knockdown experiments in HRECs using an independent shRNA targeting a different region of CTNNB1, FZD4, LRP5, or TSPAN12. These experiments reproduced the reduction in KIF11 expression (Supplementary Fig. 1e-h).\nAdditionally, the reviewer mentioned delivering siRNA without using lentivirus.\nHowever, transient transfection in HRECs does not achieve high delivery efficiency, as has been reported previously1. To rule out potential non-specific effects of viral infection on HRECs, the lentivirus-delivered non-targeting shRNA was used as the control. Taken together, these results indicate that the observed KIF11 downregulation following CTNNB1, FZD4, LRP5, or TSPAN12 knockdown is not attributable to viral infection or off-target effects of the original construct.\nWeaknesses in the analyses of the transcriptomic data.\nThe following can be addressed without new experiments:\n3. The authors should highlight and discuss that the knockdown of FZD4, LRP5, TSPAN12, and CTNNB1 was apparently done in the absence of norrin stimulation. This is important because target genes of β-catenin-dependent signaling may not be affected by the KD in the absence of norrin, and neither HRMVECs nor serum are a source of norrin.\nResponse:\nWe thank the reviewer for this comment. We agree with the reviewer that, although our RNA-seq and further analyses identifying KIF11 as a hub gene downregulated upon knockdown of FZD4, LRP5, TSPAN12, or CTNNB1 were originally performed without Norrin stimulation, it is indeed important to assess KIF11 expression in the presence of Norrin. To address this, we have incorporated RT-qPCR analysis with recombinant Norrin protein treatment (50 ng/ml for 24 h). Consistent with the findings obtained in the absence of Norrin, KIF11 was downregulated by more than two-fold upon knockdown of FZD4, LRP5, TSPAN12, or CTNNB1 in the presence of Norrin (Supplementary Fig. 1i). The related description has been added in the main text. Therefore, KIF11 expression was reduced upon silencing of core components of Norrin/β-catenin signaling, regardless of Norrin stimulation.\n4. The supplemental transcriptomic data in the excel file are not readily accessible because the accession numbers but no gene names are provided. This makes it difficult to critically evaluate the data, as every gene of interest has to be translated into an accession number before the gene can be looked up in this data.\nResponse:\nWe thank the reviewer for pointing this out. To improve data accessibility, we have revised the supplemental transcriptomic data file to include gene symbols with FPKM values for each sample (Supplementary Table S1). The updated file has been uploaded with the revised submission. Additionally, the corresponding raw and processed data have been deposited in the NCBI Gene Expression Omnibus (GEO) database under accession GSE306946.\n5. A clarification is lacking whether the same control samples were used for all KD comparisons, or if different control samples were used for each KD experiment (apparently the latter based on variable RPK in sets of control samples).\nResponse:\nWe thank the reviewer for raising this critical point. In the previous version of the manuscript, the different knockdown groups were compared with different control samples from separate batches, which could have introduced discrepancies in downstream analyses. Therefore, we have newly performed transcriptome sequencing for the knockdown of FZD4, LRP5, TSPAN12, and CTNNB1 using the same control samples. The KIF11 and PRDX1 knockdown datasets were compared to their own corresponding non-targeting shRNA control. This clarification has been added to the Methods part.\n6. The data don’t look good with respect to consistency across CTNNB1, FZD4, TSPAN12, and LRP5 KD for key genes, despite the claim in Fig 1C that there are consistent differences. Note also that in Fig 1B there is little to no overlap in the top upregulated and top downregulated genes, this should be discussed in the manuscript.\nResponse:\nWe thank the reviewer for this important suggestion. As mentioned above, we have performed RNA-seq for FZD4, LRP5, TSPAN12, and CTNNB1 knockdown groups using the same control samples within a single experimental batch. Volcano plots of the newly generated bulk RNA-seq data revealed partial overlap among the top five upregulated and downregulated genes across the CTNNB1, FZD4, TSPAN12, or LRP5 knockdown.\nNotably, TNF and SMIM ranked among the top 5 downregulated genes in three of four groups (Fig. 1b). Additionally, several classical Wnt downstream targets—including PLVAP (upregulated) and APCDD1, DKK1 and SLC2A1 (downregulated)—have been incorporated in Fig. 1c and highlighted in Fig. 1b.\nFurthermore, to assess the consistency of transcriptomic change following knockdown of CTNNB1, FZD4, TSPAN12, or LRP5, we have performed a Venn diagram analysis of the differentially expressed genes (DEGs) in each condition. This analysis revealed that approximately 25% of the DEGs (|log2FC|>0.585, FDR<0.05) were commonly altered across groups (Supplementary Fig. 1b-d), indicating a substantial degree of overlap and supporting the presence of a core transcriptomic response associated with disruption of the β-catenin pathway, even in the absence of Norrin. The Log FC values of each gene across all groups analyzed by Venn diagram have been provided in Supplementary Table 2. The corresponding description has been added to the main text.\n7. There is no discussion on what genes are expected to be regulated by β-catenin signaling (e.g., Axin2 and Apcdd1), and how these markers of Wnt signaling change across the various KD conditions. Contrary to expectations, Axin2 is upregulated in Tspan12 KD cells, and it is not downregulated in CTNNB1 KD or LRP5 KD cells. Also contrary to expectations, APCDD1 is upregulated in Tspan12 KD cells. Together, the evidence is not strong that knock down conditions would affect β-catenin signaling.\nThere is no discussion in the manuscript of how many fold KIF11 was downregulated in each KD experiment, if the change was significant (no statistics shown for the RNAseq in the supplemental table), and how Kif11 ranks relative to other downregulated genes in each knock down condition (is it in the top10? top 100? )\nResponse:\nWe are grateful to the reviewer for highlighting this important point. We have analyzed the newly generated RNA-seq data and found consistent regulation of several genes known to be regulated by the β-catenin pathway2, 3, 4 —including APCDD1, DKK1, PLVAP, and SLC2A1—across the various knockdown conditions. In line with previous findings, APCDD1, DKK1, and SLC2A3 were consistently downregulated, while PLVAP was significantly upregulated following knockdown of either CTNNB1, FZD4, TSPAN12, or LRP5 (Fig. 1b, c). The corresponding description has been added to the main text.\nHowever, AXIN2 showed only a slight, non-significant downregulation in the CTNNB1 and FZD4 knockdown groups (see the figure below), which may reflect variability due to its inherently low expression levels in HRECs (the average AXIN2 FPKM level in control group of HRECs is 0.5, compared with 51.9 and 5.5 of DKK1 and SLC2A1, respectively). The FPKM values in each sample could be found in Supplementary Table S1.\nAs to the expression levels of KIF11 in the bulk RNA-seq data, we have incorporated a dot-box plot of KIF11 expression levels (FPKM value) across all groups in Fig. 1i, which revealed a significant (FDR<0.05) reduction of KIF11 upon knockdown of either CTNNB1, FZD4, TSPAN12, or LRP5 with Log FC of -1.62, -0.92, -1.22, -1.23, respectively. The relevant description has been added to the figure legend.\nIn terms of ranking, KIF11 was among the top 76 downregulated genes when using a more stringent Venn analysis parameter (log FC<-0.91, FDR<0.05) across the knockdown conditions. The relevant description has been added to the main text. As mentioned in the response to comment 6, the Log FC values of each gene across all groups included in the Venn diagram have been provided in Supplementary Table S2.\nAlthough KIF11 was not among the most highly downregulated genes, we considered it a potential downstream target of the β-catenin pathway for two main reasons: (i) WGCNA followed by CytoHubba analysis identified KIF11 as a top-five hub gene ranked by MCC (Maximal Clique Centrality) score, and (ii) KIF11 has been previously identified as a FEVR-associated gene.\n8. It is unclear why the authors selected the dark magenta module, whas the negative correlation with control and the trend towards a positive correlation with knock down stronger than for any of the other 23 gene modules? This is not described, was the module selected based on subjective interest?\nResponse:\nWe appreciate the reviewer’s thoughtful question regarding our selection of the dark magenta module. In the previous version, this module was selected because it showed a distinct and statistically significant correlation pattern across conditions, with the strongest negative correlation with the control group (ME = -0.96), compared with other modules such as midnight blue (ME = -0.77) and yellow (ME = -0.58). Therefore, the dark magenta module was selected for further analysis due to its robust association with knockdown-induced transcriptomic changes.\nIn the revised version, however, the blue module was selected, as it exhibited the strongest negative correlation with the control group (ME = -0.71). Notably, further enrichment analysis yielded results similar to those in the previous version (Fig. 1f).\nIn vivo experiments\n9. Figure 10 p and 10 t. Clarify in the manuscript if littermates were used. Add the control group (Cre-) for the qualitative and quantitative data so the extent of rescue vs this control can be quantified.\nResponse:\nWe thank the reviewer for this comment. In the previous version, littermates were used for phenotypic comparisons of the mouse models, and the corresponding descriptions have been added to the manuscript. In the revised version, most of the newly added experiments were also performed using littermate controls. However, the scRNA-seq data for Ctnnb1 cKO, Tspan12 KO, and control samples were not obtained from littermates, as more than four mice are required to obtain sufficient retinal vascular cells for scRNA-seq, which exceeded the number available per litter.\nIn addition, we have added figures showing non-targeting lentivirus–treated littermate controls (Flox/Flox) for the KIF11-expressing lentivirus rescue experiments on Ctnnb1 cKO mice (Fig. 2p-r), as well as DMSO-treated littermate controls for the Fer-1 rescue experiments on Kif11 cKO (Fig. 9b-d) and Ctnnb1 cKO mice (Fig. 9e-g). These analyses demonstrated partial rescue effects of KIF11-expressing lentivirus in Ctnnb1 cKO mice (Fig. 2p-r) and of Fer-1 treatment in Kif11 cKO (Fig. 9b-d) and Ctnnb1 cKO mice (Fig. 9e-g) FEVR mice.\n10. Unclear how a retroorbital viral delivery in mouse pups works (with closed eyelids at this stage), and how effective it is. An experiment with a GFP virus can show how many ECs are transfected and validate this delivery method at this age.\nResponse:\nWe thank the reviewer for pointing this out. Retro-orbital venous sinus injection is a widely used method for compound intravenous administration and vessel visualization in neonatal mice with closed eyelids5, 6. As suggested, we have now included images of flat-mounted retinas from P7 mice that received retro-orbital sinus injections of GFP-expressing lentivirus at P1, alongside uninjected controls. Immunofluorescence confirmed robust GFP expression in the retinal vasculature, demonstrating efficient viral delivery and transduction (Supplementary Fig. 3j).\nMinor:\n11. some of the fluorescent cell images are too dim (e.g., Hoechst in Fig 8h and multiple others)\nResponse:\nWe thank the reviewer for this comment. In the revised version, we have optimized the imaging settings and replaced the representative images of Fig. 3l, q; Fig. 4b; Fig. 6h, k, u; and Fig. 7k.\n12. Fig 6K should show the full native gel so that all MW specimens of PRDX1 can be seen\nResponse:\nWe thank the reviewer for this suggestion. Due to space limitations, the full uncropped images of all native gels showing all molecular weight species of PRDX1 have been provided in the Souce Data file.\nReviewer #2 (Remarks to the Author):\nIn the presented manuscript, Yang, Zhao Peng and Lv et al. discovered KIF11 as a novel downstream target of the Norrin/β-catenin pathway in HRECs in a screen. Mutations in KIF11 have been previously reported in the context of human eye diseases. Besides their models of endothelial cells of the eyes and mouse retinas (e.g. Fig. 9a), the authors might want to step a little further and test KIF11 in general models of ferroptosis, standard cells and assays that might expand their discovery from eye-specific to generally important to ferroptosis. I therefore suggest a couple of cell culture experiments in my list of major concerns below. Once this is performed, the manuscript will be of interest not only to eye specialists, but also to the broader readership of a journal such as Nature Communications.\nIn summary, at the current stage, the manuscript is quite specialized, but I can envision a broader role for their discovery in ferroptosis which I recommend to be tested in some detail.\nMajor concerns\n1. What is the role of KIF-11 in ferroptosis, if any? This is easy to investigate by knocking out and back in AKR1 in standard ferroptosis cell lines (HT1080, Pfa1) treated with various ferroptosis inducers of all classes (erastin, RSL3, FIN56, FINO2). As readout systems, time lapse imaging, PI staining, LDH release, any other vital dye, annexin V etc should be investigated?\nResponse:\nWe thank the reviewer for this important comment.\nIt is indeed necessary to investigate the role of KIF11 in standard ferroptosis cell lines. Due to the unavailability of Pfa1 cells, we were unable to conduct the corresponding experiments. Instead, we have performed experiments on HRECs and HT1080 cells, which we believe provide supporting evidence for our conclusions.\nIn accordance with the reviewer's suggestion, we have incorporated time-lapse live-cell imaging of PI, Annexin V, as well as BODIPY staining in control and KIF11-knockdown HRECs and HT1080 cells. Since KIF11 knockdown is sufficient to induce hallmarks of ferroptosis and trigger rapid cell death in HRECs and HT1080 cells, we did not apply additional ferroptosis inducers. The KIF11-depleted HRECs and HT1080 cells exhibited hallmark features of ferroptosis, including increased cell death and accumulated lipid peroxidation (Fig. 3p, q and Supplementary Fig. 8a-c, g-k). We have also performed LDH release assays and LC-MS/MS targeted analysis of peroxidized lipids in control and KIF11 knockdown HRECs. As expected, the results revealed that knockdown of KIF11 resulted in increased LDH release (Supplementary Fig. 8d) and elevated phospholipid peroxidation (Fig. 3s).\nAdditionally, in agreement with those observed in HRECs, KIF11-depleted HT1080 cells also exhibited alterations of ferroptosis-related proteins, including a prominent downregulation of GPX4, xCT, PCBP1, and SLC3A2. The related results have been incorporated in Supplementary Fig. 8e, f.\nHowever, as efficient gene manipulation in HRECs requires viral transduction, we were unable to perform KIF11 restoration experiments in KIF11-knockdown cells. Also, due to the limited proliferative capacity of primary cells and the rapid inhibition of proliferation and cell death following KIF11 knockout, we were unable to establish and select a stable KIF11 knockout HREC using the CRISPR-Cas9 system. Despite that, we have incorporated ferroptosis inhibitor Fer-1 treatment assays using time-lapse live-cell imaging of PI, Annexin V, as well as BODIPY staining, to assess role of β-catenin/KIF11 axis in ferroptosis. The results revealed marked attenuation of lipid peroxidation and prevention of cell death in KIF11- (Fig. 4l, m) or CTNNB1-depleted (Fig. 7t, u). Moreover, Fer-1 treatment also revealed partial rescue effects of retinal vascular development in Kif11 cKO (Fig. 9b-d) and Ctnnb1 cKO (Fig. 9e-g) mice. These findings establish a mechanistic link between β-catenin/KIF11 deficiency and ferroptosis.\nCollectively, the revised and previous results demonstrat that KIF11 deficiency leads to ferroptosis in endothelial cells via downregulation of the xCT/GPX4 axis, independent of ferroptosis inducers.\n2. What is the baseline expression and overall relevance of the reported Kif-11-systemi? Western blots should be provided with established (ko-controlled) antibodies. How does the absence of KIF-11 relate to the known ferroptosis-controlling systems such as GPX4, FSP1 etc.? How does it affect the baseline levels of hydropersulfides, ether lipids etc?\nResponse:\nWe thank the reviewer for highlighting this important concern. We have incorporated the data relating to baseline expression of KIF11 in mouse retinas and the disturbance of ferroptosis upon KIF11 depletion:\n1) Baseline expression of KIF11 in mouse retinas: As described in our response to reviewer 1’s first comment, we have performed scRNA-seq on P6 retinal vascular cells isolated from wild-type, Ctnnb1 cKO, and Tspan12 KO mice. The resulting data were integrated with relevant public datasets from adult mice (P60 WT and Ndp KO, P26 WT and Tspan12 KO), which revealed that Kif11 expression is predominantly restricted to proliferative endothelial cells in postnatal retinas and is largely absent in adult retinas (P26 and P60) (Fig. 2d-g and Supplementary Fig. 2d, e). We have also performed RT-qPCR and western blot analyses using a KO-validated antibody on wild-type retinas collected at P1, P7, P14, P21, and P28. These experiments revealed prominent baseline KIF11 expression peaking in early postnatal retinas (P1 and P7) and declining rapidly thereafter (Fig. 2i and Supplementary Fig. 2g). The same KO-validated antibody confirmed the ablation of KIF11 in KIF11 knockdown HRECs (Supplementary Fig. 4a-c), HT1080 cells (Supplementary Fig. 8e, f), and Kif11 cKO mice lung lysates (Fig. 8f, g).\n2) The role of KIF11 in ferroptosis-regulating systems: The previous version of this manuscript only assessed alterations in the GPX4 axis following KIF11 ablation in HRECs. As noted by the reviewer, we have demonstrated the disturbance of ferroptosis-related proteins within the GPX4 system in KIF11-depleted HT1080 cells (Supplementary Fig. 8e, f) and Kif11 cKO mice lung lysates (Fig. 8f, g). Considering the pivotal roles of ferroptosis suppressor protein 1 (FSP1)7, 8 and dihydroorotate dehydrogenase (DHODH)9 in ferroptosis regulation, it is indeed important to examine whether these axes are also affected upon KIF11 deletion. Western blot analysis confirmed that the expression levels of both FSP1 and DHODH remained largely unchanged upon KIF11 knockdown in HRECs (Fig. 3i, j). The antibodies targeting ferroptosis-related proteins have been used in multiple previously published studies and validated by the respective manufacturers, including anti-DHODH10, anti-FSP111, anti-GPX412, anti-xCT13, anti-SLC3A214, anti-ACSL415, and Anti-HO-116.\nAs mentioned by the reviewer, it is indeed essential to evaluate the baseline of ether lipids and hydropersulfides upon KIF11 depletion. However, the insufficient number of KIF11-knockdown HRECs, due to compromised proliferation and activated ferroptosis, precluded direct quantification of ether lipids and hydropersulfides using untargeted LC-MS/MS. Instead, we have performed targeted LC-MS/MS analysis of ferroptosis-associated phospholipid peroxidation7, 17, 18, 19 and revealed that KIF11 knockdown enhanced the peroxidation of phospholipids (phosphatidylethanolamines [PEs] and phosphatidylcholines [PCs]) and their associated ether-linked forms (Fig. 3t). Notably, GSSH is a major cellular hydropersulfide that serves as a reducing equivalent for GPX4 to detoxify lipid hydroperoxides. In KIF11-depleted cells, both GPX4 protein levels and enzymatic activity were significantly reduced, providing indirect evidence of impaired hydropersulfide-dependent antioxidant defense. Hydropersulfides can inhibit lipid peroxidation by scavenging reactive aldehydes such as MDA20. Consistently, MDA levels were significantly increased in KIF11-knockdown cells, further supporting the notion of functionally reduced hydropersulfides. Taken together, these results provide indirect but converging evidence that KIF11 depletion compromises the hydropersulfide system, contributing to increased ferroptosis susceptibility.\n3. How does KIF-11 regulate ferroptosis? This requires the monitoring of cellular systems (HT1080, NIH3T3, Pfa1 and potentially the cells used in the screen in Fig. 1) over time. How plastic are the above-mentioned systems once induced in the absence of KIF-11? How does the peroxidative lipid profile assessed by LC/MS change over time? How does this phenocopy MALDI-imaging of the corresponding lipids in section of these eyes?\nResponse:\nWe thank the reviewer for highlighting this important concern. In line with the reviewer's suggestion, we have incorporated time-lapse live-cell imaging of PI, annexin V, as well as BODIPY staining in control and KIF11-knockdown HRECs and HT1080 cells. These assays revealed hallmark features of ferroptosis, including increased cell death and accumulated lipid peroxidation in both cells depleted with KIF11 (Fig. 3p, q and Supplementary Fig. 8a-c, g-k). Notably, KIF11 knockdown is sufficient to trigger rapid cell death with phospholipid peroxidation in HRECs and HT1080 cells without additional ferroptosis inducers, indicative of its susceptibility to ferroptosis (Fig. 3p, q and Supplementary Fig. 8a-c, g-k).\nFurthermore, as suggested by the reviewer, we have also performed targeted LC-MS/MS analysis of ferroptosis-associated phospholipid peroxidation in control and KIF11 knockdown HRECs. As expected, KIF11 knockdown in HRECs led to elevated phospholipid peroxidation and their associated ether forms (Fig. 3t).\nWe agree that confirming the elevation of the corresponding lipid peroxidation in Kif11 cKO mouse retinal ECs using MALDI-imaging would be valuable. However, due to technical limitations, we were unable to perform lipid peroxidation analysis using MALDI at this stage. Despite that, other evidence—including TEM of mouse retinas (Fig. 8e) and western blot analysis of mouse lung lysates (Fig. 8j, k)—confirmed the activation of ferroptosis in Kif11 deficient mice.\nTaken together, these findings strongly support the occurrence of ferroptosis upon Kif11 depletion in mice, despite the current lack of direct MALDI-imaging evidence.\n4. Fig. 7-9 are a strong part of this manuscript. But which kinase is responsible for the LLPS and PRDX1 phosphorylation, and how does KIF-11 regulate it?\nResponse:\nWe greatly appreciate the reviewer’s insightful questions. A previous report identified Src as a tyrosine kinase that serves as a critical upstream regulator of PRDX1-Y194 phosphorylation21. Therefore, we have performed western blot analysis to assess PRDX1 phosphorylation in KIF11-depleted HRECs treated with the Src kinase inhibitor PP1. As anticipated, increasing concentrations of PP1 resulted in a dose-dependent reduction of PRDX1 phosphorylation (Supplementary Fig. 10i, j). Furthermore, PP1 treatment in HRECs resulted in a shift of PRDX1 aggregation from HWM complexes to LMW proteins (Fig. 5n, o). These results indicate that Src kinase activity is responsible for the phosphorylation and subsequent LLPS of PRDX1 upon KIF11 knockdown.\nTo investigate how KIF11 regulates the phosphorylation of PRDX1 by Src, we have incorporated miniTurboID based co-immunoprecipitation (Co-IP) analysis to capture transient kinase-substrate interactions and examine whether KIF11 spatially occupies the Src-binding site on PRDX1, thereby preventing Src-mediated phosphorylation. The results confirmed the interaction between Src-miniTurboID-FLAG and PRDX1-HA (Fig. 5p-r). Notably, KIF11 overexpression markedly reduced the amount of biotin-labeled PRDX1, together with decreased P-PRDX1 levels in cell lysates (Fig. 5s-u), suggesting that KIF11 competes with Src for PRDX1 binding and phosphorylation. Collectively, these data suggest that KIF11 exerts an inhibitory effect on the Src/PRDX1 interaction, possibly through its spatial occupancy of the Src-binding site on PRDX1.\n5. The authors conclude that autophagy dependent ferroptosis is key here. However, they do not demonstrate the requirement of the “autophagy” part. Therefore, I highly recommend to reduce this controversial term “ferroptosis” instead of “autophagy-dependent ferroptosis”. As with other regulated cell death pathways, autophagy is triggered mostly in parallel to the cell death pathways of most kinds. In that sense, autophagy is rather a counterregulatory attempt of the cell to improve its energy profile and may be interpreted as a rescue attempt, also during the process of ferroptosis.\nResponse:\nWe agree with the reviewer that the term “autophagy-dependent ferroptosis” is controversial. As suggested, we have replaced this term with either “ferroptosis” or “autophagy-accompanied ferroptosis”, and most of the autophagy-related images have been moved to the Supplementary Figures. Additionally, we have reduced the descriptions related to “autophagy” and “autophagy-dependent ferroptosis” in the main text to improve focus and clarity.\nReviewer #3 (Remarks to the Author):\nThe overall study has positive significance for the mechanistic research of KIF11. The authors conclude that: modulation of the Norrin pathway induces changes in KIF11; KIF11 regulates alterations in the autophagy and ferroptosis pathways; KIF11 interacts with PRDX1, and the loss of the KIF11/PRDX1 interaction leads to PRDX1 polymerization and triggers liquid-liquid phase separation (LLPS). Additionally, ferroptosis inhibitors and PRDX1 overexpression can partially rescue the vascular developmental defects caused by KIF11.\nHowever, this study has several notable limitations:\n1. Logical Inconsistencies: The experimental design is fragmented, with many proposed connections relying excessively on bioinformatic correlations. The authors have not established a complete mechanistic chain through functional experiments, and some of the bioinformatic analyses appear redundant.\nResponse:\nWe thank the reviewer for this critical comment.\n(1) To improve logical consistency, we have removed or relocated redundant bioinformatic analyses to the Supplementary Figures: 1) GSEA corresponding to quadrants 4 and 6 in the nine-quadrant plot previously shown in Fig. 5d–g has been removed; 2) the volcano plot, enrichment dot plot, heatmaps, and GSEA running score plots related to the transcriptome of KIF11 knockdown, previously shown in of Fig. 4e–k and o–u, have been moved to the Supplementary Figures to maintain the overall flow and coherence of the manuscript.\n(2) In response to the reviewer’s constructive feedback, we have conducted multiple new experiments over the past few months to complete a mechanistic chain. Significant advancements in our experiments include:\nIncorporation of scRNA-seq data: We have incorporated scRNA-seq on retinal vascular cells of classical FEVR-associated Ctnnb1 EC-specific conditional knockout (Ctnnb1 cKO) and Tspan12 global knockout (Tspan12 KO). Integrated scRNA-seq analysis, together with multiple validation experiments, confirmed that Kif11 is expressed specifically in proliferative (non-quiescent) endothelial cells in the postnatal mouse retina, but not in adult retinal ECs (Fig. 2d-g and Supplementary Fig. 2d, e). These findings are consistent with the observation that loss-of-function mutations in KIF11 lead to inherited diseases such as FEVR, which typically manifest during infancy or early childhood. More importantly, scRNA-seq analysis confirmed the downregulation of Kif11 levels in total ECs (Fig. 2d) and proliferative ECs (Fig. 2h) of both Ctnnb1 cKO and Tspan12 KO mice retina. Therefore, the new results incorporated in the current version further solidify the conclusion that KIF11 is regulated by the β-catenin signaling.\nAdditional ferroptosis-related experiments: We have incorporated multiple experiments to further validate the relationship between the β-catenin/KIF11/PRDX1 axis and ferroptosis. In HRECs, we have performed time-lapse live-cell imaging with PI, annexin V (Supplementary Fig. 8a-c), and BODIPY staining (Fig. 3o, p), along with LDH release assays (Supplementary Fig. 8d) and LC-MS /MS targeted analysis of phospholipid peroxidation (Fig. 3t). These results support the conclusion that loss of KIF11 function is associated with ferroptosis in HRECs. To assess whether KIF11 plays a broader role in ferroptosis beyond endothelial cells, we conducted additional experiments in HT1080 cells, a model system commonly used for ferroptosis studies. Time-lapse live-cell imaging with PI, annexin V, and BODIPY staining (Supplementary Fig. 8g-k), together with western blot analysis of core ferroptosis-related proteins (Supplementary Fig. 8e, f), yielded results consistent with those observed in HRECs, indicating that KIF11 contributes to the regulation of ferroptosis across multiple cell types.\nAdditional function experiments to validate the mechanistic role of KIF11 on the phosphorylation of PRDX1: Our new results show that PRDX1 phosphorylation in KIF11-depleted HRECs is primarily mediated by Src, as evidenced by dose-dependent inhibition with PP1 (Supplementary Fig. 10i, j). Additionally, we have performed miniTurboID-based Co-IP experiments to capture transient interaction between Src and PRDX1 (Fig. 5p-u), which indicate that KIF11 inhibits the Src/PRDX1 interaction, likely through spatial occupancy of the Src-binding site on PRDX1, thereby preventing Src-mediated phosphorylation.\nAdditional in vitro and in vivo rescue experiments: To further address the role of the β-catenin/KIF11/PRDX1 axis in ferroptosis, we have performed ferroptosis inhibitor (Fer-1) treatment assays in HRECs, combined with time-lapse live-cell imaging using PI, annexin V, and BODIPY staining. The results demonstrated a marked attenuation of lipid peroxidation and prevention of cell death in HRECs depleted of KIF11 (Fig. 4l, m) or CTNNB1 (Fig. 7t, u). We have also conducted rescue experiments in vivo, in which Fer-1 treatment partially alleviated the vascular defects observed in Ctnnb1 cKO retinas (Fig. 9e-g). Furthermore, retro-orbital injection of KIF11-expressing lentivirus into Ctnnb1 cKO mice similarly resulted in partial restoration of retinal vascular progression (Fig. 2p-r).\nCollectively, our results demonstrate that loss of KIF11 function in endothelial cells led to autophagy-accompanied ferroptosis by releasing its competitive role on the Src-mediated phosphorylation and triggering liquid-liquid phase separation (LLPS) of PRDX1, ultimately leading to defective vascularization in FEVR.\n2. Weak Evidence for Autophagy-Dependent Ferroptosis: Although KIF11 is shown to induce autophagy and ferroptosis separately, there is insufficient experimental evidence to definitively demonstrate that KIF11 triggers ferroptosis through autophagy-dependent mechanisms.\nResponse:\nWe thank the reviewer for raising this concern. As also suggested by Reviewer 2, we replaced the term “autophagy-dependent ferroptosis” with either “ferroptosis” or “autophagy-accompanied ferroptosis”, and most of the autophagy-related images have been moved to the Supplementary Figures. Additionally, we have reduced autophagy-related descriptions in the main text to improve focus and clarity.\n3. The reliance on a single shRNA for knockdown experiments undermines the reliability of these findings.\nResponse:\nWe thank the reviewer for the insightful suggestion. As was also suggested by Reviewer 1, we have incorporated a second shRNA targeting another site for the knockdown of CTNNB1, LRP5, FZD4, or TSPAN12 to confirm the downregulation of KIF11 in HRECs; the related statistical analyses have been incorporated in the Supplementary Fig. 1e-h. Additionally, a second shRNA targeting another site for KIF11 or PRDX1 was performed to validate consistency by western blot analyses concerning ferroptosis-related proteins. The related images and statistical analyses for KIF11- and PRDX1-depletion have been incorporated in the Supplementary Fig. 7h-j and Supplementary Fig. 12h-j, respectively.\n4. Abrupt Presentation of KIF11 Mutation Data: The inclusion of KIF11 mutation results lacks contextual integration with the overall narrative, appearing disconnected from the core mechanistic exploration.\nResponse:\nWe thank the reviewer for raising this concern. We agree with the reviewer that the identification of novel frameshift or nonsense KIF11 mutations is less central to the mechanistic focus of this article. Accordingly, the pedigree and clinical images have been moved to Supplementary Fig. 3a-i. However, the western blot results for the expression of the mutated KIF11 proteins have been retained in Fig. 2o, as these mutations were tested for their interactions with PRDX1 in Fig. 4q. We believe this inclusion is important for establishing a clinical link between the disrupted PRDX1/KIF11 interaction and FEVR.\n5. Incomplete Animal Experiments: The methodology for drug treatment in animal models is inadequately described. Phenotypic analysis is superficial, focusing primarily on vascular developmental status while neglecting critical assessments (e.g., blood-retinal barrier integrity).\nResponse:\nWe thank the reviewer for highlighting this issue and apologize for the previous omission of the drug treatment methodology in animal models. We have carefully reviewed the article and added the missing description to the Method section.\nRegarding the phenotypic analysis, we agree that assessing blood-retinal barrier (BRB) integrity is pivotal for vascular research. Accordingly, we have incorporated Ter119 staining of erythrocytes, which reflects transcellular BRB integrity, and Sulfo-biotin, a small molecular tracer indicating paracellular BRB integrity, to evaluate the BRB in the presence or absence of KIF11. However, the results revealed no obvious leakage of either signal, indicating undisturbed vascular integrity in Kif11 cKO retinas (Supplementary Fig. 13b, c).\n6. Given the close relationship between vascular development and overall growth in mice, the authors should provide body weight statistics for all experimental groups in the supplementary materials.\nResponse:\nWe thank the reviewer for raising this concern and have incorporated the body weight statistics in the Supplementary Materials. Neither EC-specific depletion of Ctnnb1 or Kif11, nor retro-orbital lentivirus or intraperitoneal Fer-1 injection, affected mouse body weight. Specifically, the body weight for P7 wild-type and Ctnnb1 cKO treated with control lentivirus, and Ctnnb1 cKO treated KIF11-expressing lentivirus have been incorporated in Supplementary Fig. 3k. The body weight for P7 wild-type and Kif11 cKO mice have been incorporated in Supplementary Fig. 13a. The body weight for P7 wild-type and Kif11 cKO treated with DMSO, and Kif11 cKO treated Fer-1 have been incorporated in Supplementary Fig. 14a. The body weight for P7 wild-type and Ctnnb1 cKO treated with DMSO, and Ctnnb1 cKO treated Fer-1 have been incorporated in Supplementary Fig. 14b. The body weight for P7 Kif11 cKO treated with control, PRDX1-WT, and PRDX1-mut lentivirus have been incorporated in Supplementary Fig. 14c."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors conducted additional experiments that strengthen the conclusions of the study. With respect to the claim that KIF11 is a target gene of norrin signaling, the authors focussed on proliferating ECs and found a reduction of KIF11 mRNA in P6 proliferating ECs in Tspan12 and Ctnnb1 mutant cells (Fig. 2h, statistics not described in the figure legend). Lentivirus mediated delivery of KIF11 partially rescued retinal vascular outgrowth in Ctnnb1 mutant retinas. Whether norrin induces KIF11 in HRMVECs remains unclear. Together, concerns raised by this reviewer were sufficiently addressed.", "reviewer_2": "The authors have greatly improved their manuscript. However, I recommend to test one more important control in the HT1080 cells (Supplementary Figure 8). Would the cell death observed upon knockdown of KIF11 be reversed by the addition of a ferroptosis inhibitor (e.g. Fer-1)?\nIf the authors cannot provide a crCas9-mediated knockout of Kif-11 in HT1080 cells, they should at least discuss their limitations (PMID38489367).\nOtherwise, I recommend this manuscript for publication!", "reviewer_3": "The manuscript has significantly improved in this revised version. Both the experimental design and the writing are more logically structured than before. The authors have incorporated an additional shRNA to assess their key conclusion, and they have reorganized the section discussing the relationship between KIF11 and autophagy in conjunction with ferroptosis. Moreover, the mechanisms underlying how the LoF of KIF11 impairs endothelial cells through interaction with PRDX1 in the context of FEVR are now well addressed. However, there are several minor issues that still require refinement.\n1. Lines 246-247: The authors conclude, \"Taken together, these results suggest that the loss of KIF11 might trigger autophagy during retinal vascular development.\" However, in the in vitro experiments and bioinformatics analysis involving Primary Human Retinal Microvascular Endothelial Cells, the donor’s age is not provided on the official website of the primary cell line. Therefore, it is imprecise to attribute this phenotype to \"retinal vascular development.\" This conclusion would be more appropriate in the context of the in vivo results.\n2. Lines 278-279: The statement regarding the protective effect of KIF11 against ferroptosis is inaccurate, as no experimental data regarding the overexpression of KIF11 to mitigate ferroptosis are presented in this section.\n3. The MWs of PRDX1’s LMW and HWM forms should be mentioned, or at least an approximate scale should be provided."}, "author_response": "Response to Reviewer’s Comments\nGeneral response to reviewers’ comments:\nWe sincerely appreciate the reviewers' positive feedback and are grateful for the constructive suggestions during the previous round of revision, which have significantly improved our manuscript.\nWe have carefully considered the issues raised and have addressed each one comprehensively in the revised manuscript. Detailed explanations of the changes made can be found in our point-by-point responses.\n\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors conducted additional experiments that strengthen the conclusions of the study. With respect to the claim that KIF11 is a target gene of norrin signaling, the authors focussed on proliferating ECs and found a reduction of KIF11 mRNA in P6 proliferating ECs in Tspan12 and Ctnnb1 mutant cells (Fig. 2h, statistics not described in the figure legend). Lentivirus mediated delivery of KIF11 partially rescued retinal vascular outgrowth in Ctnnb1 mutant retinas. Whether norrin induces KIF11 in HRMVECs remains unclear. Together, concerns raised by this reviewer were sufficiently addressed.\nResponse:\nWe appreciate the reviewer’s thoughtful comments. We have incorporated the statistical method (Unpaired student’s t-test) for Fig. 2h in the figure legend.\nAccording to reviewer's suggestion, we have incorporated RT-qPCR analysis of KIF11 in the presence or absence of exogenous Norrin in HRECs (Supplementary Fig. 1i), which demonstrated that exogenous Norrin significantly increased KIF11 expression in HRECs. We have added this information in the revised manuscript page 5.\n\nRreviewer #2 (Remarks to the Author):\nThe authors have greatly improved their manuscript. However, I recommend to test one more important control in the HT1080 cells (Supplementary Figure 8). Would the cell death observed upon knockdown of KIF11 be reversed by the addition of a ferroptosis inhibitor (e.g. Fer-1)? If the authors cannot provide a crCas9-mediated knockout of Kif-11 in HT1080 cells, they should at least discuss their limitations (PMID38489367). Otherwise, I recommend this manuscript for publication!\nResponse:\nWe thank the reviewer for these important comments. It is indeed necessary to investigate whether a ferroptosis inhibitor could reverse the cell death observed in KIF11 KD HT1080 cells. Thus, in accordance with the reviewer's suggestion, we have performed live-cell imaging of PI, Annexin V, and BODIPY staining in KIF11 KD HT1080 cells treated with ferroptosis inhibitor Fer-1 or vehicle. The results revealed marked prevention of lipid peroxidation and cell death in KIF11-depleted HT1080 cells after treated with ferroptosis inhibitor Fer-1 (Supplementary Fig. 8l, m). The related descriptions have added in the revised manuscript page 11.\nFurthermore, we agree with the reviewer that, in light of recent findings showing that siRNA treatment can significantly sensitize cells to ferroptosis independently of target protein knockdown1, generating stable KIF11 knockout cell lines using the CRISPR-Cas9 system in HRECs and HT1080 cells would be highly informative. However, we were unable to generate the stable KIF11-knockout HRECs or HT1080 cell lines owing to the rapid proliferation arrest and increased cell death upon KIF11 depletion. Nevertheless, in this study, a lentiviral vector expressing a non-targeting shRNA was used as a negative control (the \"CTRL\" group in the figures) to minimize potential confounding effects of RNAi treatment. Crucially, this control exhibited no detectable effect on lipid peroxidation or cell death in HRECs and HT1080 cells. We have revised the manuscript to acknowledge this limitation, which is now addressed in the Discussion section (Page 19).\n\nReviewer #3 (Remarks to the Author):\nThe manuscript has significantly improved in this revised version. Both the experimental design and the writing are more logically structured than before. The authors have incorporated an additional shRNA to assess their key conclusion, and they have reorganized the section discussing the relationship between KIF11 and autophagy in conjunction with ferroptosis. Moreover, the mechanisms underlying how the LoF of KIF11 impairs endothelial cells through interaction with PRDX1 in the context of FEVR are now well addressed. However, there are several minor issues that still require refinement.\n1. Lines 246-247: The authors conclude, \"Taken together, these results suggest that the loss of KIF11 might trigger autophagy during retinal vascular development.\" However, in the in vitro experiments and bioinformatics analysis involving Primary Human Retinal Microvascular Endothelial Cells, the donor’s age is not provided on the official website of the primary cell line. Therefore, it is imprecise to attribute this phenotype to \"retinal vascular development.\" This conclusion would be more appropriate in the context of the in vivo results.\nResponse:\nWe thank the reviewer for the valuable suggestions. In response, we have removed the phrase \"during retinal vascular development\" and revised the statement as follows: \"Taken together, these results suggest that the loss of KIF11 might trigger autophagy in HRECs\".\n2. Lines 278-279: The statement regarding the protective effect of KIF11 against ferroptosis is inaccurate, as no experimental data regarding the overexpression of KIF11 to mitigate ferroptosis are presented in this section.\nResponse:\nWe sincerely appreciate the reviewer's constructive comments. Accordingly, we have revised this statement as follows: \"These results imply that KIF11 depletion increases ferroptotic vulnerability in HRECs\".\n3. The MWs of PRDX1’s LMW and HWM forms should be mentioned, or at least an approximate scale should be provided.\nResponse:\nWe thank the reviewer for the valuable suggestions. According to previous reports, PRDX1 monomers undergo oxidation to form peroxidase-inactive homodimers, a transformation facilitates assembly into chaperone-active polymers and trigger PRDX1 LLPS2, 3, 4, 5. The HMW and LMW PRDX1 in our non-reducing PAGE analysis are peroxidase-inactive dimeric and peroxidase-active monomeric forms, respectively. We have included the approximate molecular weights of PRDX1's LMW and HWM forms in Fig. 4x, Fig. 5g, Fig. 5j, Fig. 5n, Fig. 7i, and Supplementary Fig. 10e."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have adequately responded to all concerns raised by this referee, but still, I believe that a crCas9-based knockout is required for the conclusions. An shRNA or an siRNA in both cases may affect ferroptosis. However, this is discussed as a limitation of this work, so I recommend to publish the manuscript."}, "author_response": "Response to Reviewer’s Comments\nREVIEWER COMMENTS\nReviewer #2 (Remarks to the Author):\nThe authors have adequately responded to all concerns raised by this referee, but still, I believe that a crCas9-based knockout is required for the conclusions. An shRNA or an siRNA in both cases may affect ferroptosis. However, this is discussed as a limitation of this work, so I recommend to publish the manuscript.\nResponse:\nWe sincerely thank the referee’s positive recommendation and the constructive final feedback. As noted, we have explicitly discussed this as a limitation in the manuscript. We appreciate the referee’s professional recognition of our efforts to ensure a transparent discussion of these methodologies."}]

Decision letter