RFC4 drives temozolomide resistance in glioblastoma by activating STK38-BECN1-dependent autophagy

Domain: biology
Published: 2026-03-23
Editorial outcome: major_revision

Abstract
Glioblastoma (GBM) remains a lethal brain tumor due to therapy resistance. While autophagy contributes to
temozolomide (TMZ) resistance, its regulation is incompletely understood. This study investigates the role of
replication factor RFC4, which is associated with poor prognosis and TMZ resistance in GBM. Multi-omics
analyses and molecular experiments reveal that TMZ-induced chromatin accessibility enables transcription
factor YY1 to bind the RFC4 promoter and upregulate its expression. RFC4, in turn, stabilizes the kinase STK38,
which is essential for autophagosome formation. The RFC4-STK38 interaction facilitates BECN1 recruitment,
thereby activating autophagy. Phosphorylation of STK38 at T444 stabilizes this complex, whereas a phospho-
deficient mutant impairs autophagy. In vivo, RFC4 overexpression confers TMZ resistance, reversible by
autophagy inhibition. Thus, our findings identify the RFC4-STK38-BECN1 axis as a mechanism underlying
TMZ resistance and a potential target for precision therapy in GBM.

Introduction
Glioblastoma (GBM) is the most prevalent and malignant primary brain tumor in adults. 1,2 It is
characterized by rapid proliferation and diffuse infiltration into adjacent healthy brain tissue, which significantly
hinders complete surgical resection. 3 Current therapeutic strategies for GBM involve maximal safe surgical
resection followed by adjuvant radiation therapy and chemotherapy. Temozolomide (TMZ), an alkylating agent,
remains the frontline chemotherapeutic option by inducing DNA damage and interfering with DNA replication.
4,5 Nevertheless, GBM treatment continues to face challenges such as TMZ resistance development, limited
therapeutic sensitivity, insufficient blood-brain barrier permeability, and systemic adverse effects. 6,7
Consequently, GBM demonstrates a strong propensity for recurrence, even after intensive therapeutic regimens.
6,7 Overcoming these obstacles requires further research efforts and the development of innovative treatment
approaches, including combination therapies and targeted strategies, to improve clinical outcomes for GBM
patients. 8

Accumulating evidence supports the notion that pharmacological disruption of autophagic homeostasis -
particularly via sustained inhibition of macroautophagy - elicits transformative therapeutic effects against GBM.
9,10 Autophagy is a cellular process that plays a crucial role in the degradation and recycling of impaired proteins
and organelles, thereby preserving cellular homeostasis. 11 This dynamic catabolic pathway usually involves
multiple steps: initiation through ULK1 kinase complex activation, elongation via ATG conjugation systems,
and lysosomal degradation mediated by autophagosome-lysosome fusion. 12 Notably, the PI3K/AKT/mTOR
signaling axis serves as a master regulator of nutrient-responsive autophagy, where mTORC1 suppression
triggers autophagosome formation under stress conditions. Central to this process is BECN-1/Beclin-1, a key
autophagy-related (ATG) protein that coordinates membrane nucleation through its interaction with class III
PI3K complexes. 13

In the context of GBM, autophagy can be activated as a survival mechanism in response to chemotherapy,
leading to the development of treatment resistance. 9,14 Advances in research have elucidated diverse signaling
pathways and molecular mechanisms implicated in autophagy-mediated resistance, including the
PI3K/AKT/mTOR pathway, BECN-1 expression, and lysosomal function. 15 Consequently, targeting autophagy
has emerged as a promising therapeutic strategy to enhance GBM susceptibility to TMZ. 16 While preclinical
studies demonstrate synergistic tumor suppression through combined autophagy inhibition and TMZ treatment,
clinical translation remains limited by an incomplete mechanistic understanding of context-dependent resistance
mechanisms. A comprehensive elucidation of autophagy activation in TMZ resistance, coupled with the
development of targeted interventions, holds potential for overcoming therapeutic limitations and improving
outcomes in GBM patients.

RFC4, Replication Factor C Subunit 4, serves as an essential component of the RFC complex, responsible
for facilitating DNA replication and repair through its role in loading the PCNA sliding clamp onto DNA.
Emerging evidence underscores its significant involvement in tumor progression and therapeutic resistance.
RFC4 has been consistently observed to exhibit preferential upregulation in cancer tissues compared to normal
counterparts, promoting carcinogenic processes across various malignancies. 17-20 In colorectal cancer, elevated
RFC4 expression demonstrates significant correlation with inadequate tumor regression and unfavorable
prognosis among locally advanced rectal cancer patients receiving neoadjuvant chemoradiotherapy, thereby
emphasizing its contribution to radiotherapy resistance. 17 In lung cancer models, RFC4 potentiates tumorigenic
capacity and metastatic potential through Notch signaling activation while simultaneously conferring therapeutic
resistance to cancer cells. 20 These findings collectively describe the functional impacts of RFC4 on cancer
biology and treatment resistance with a tumor type specificity. Regarding glioma, however, RFC4 remains
insufficiently characterized, with its potential involvement in tumor proliferation and treatment responsiveness
yet to be elucidated.

In this study, we observe a correlation between RFC4 upregulation and TMZ resistance in GBM cells in an
autophagy-dependent manner. Through coimmunoprecipitation (Co-IP) followed by mass-spectrometry (MS)
analysis, we identify an interaction between RFC4 and STK38. Mechanistic investigation reveals that RFC4
stabilizes STK38 and facilitates BECN1 recruitment by STK38, enhancing STK38-BECN1 complex formation
to induce autophagy. Thus, our findings herein established an RFC4-STK38-BECN1 regulatory axis underlying
TMZ resistance, providing a mechanistic foundation for targeting autophagy in GBM therapy.

Results
RFC4 upregulation in GBM is correlated with temozolomide resistance and poor prognosis.
Analysis on CGGA and TCGA_GBM glioma databases revealed that RFC4 mRNA level was markedly
elevated in GBM compared to non-tumor counterparts (Fig. 1a). Moreover, RFC4 mRNA level was positively
correlated with glioma progression (Fig. 1b). Using other glioma databases, we consistently observed
upregulation of RFC4 mRNA in tumor versus normal tissues (Supplementary Fig. 1a). Receiver operating
characteristic (ROC) curve analysis revealed high predictive accuracy of RFC4 on GBM in TCGA_GBM
database (Fig. 1c). Analysis on CGGA database further demonstrated increased RFC4 in recurrent glioma tissues
versus primary counterparts (Supplementary Fig. 1b). Immunohistochemistry (IHC) of a tissue microarray
confirmed this oncogenic upregulation (Fig. 1d). Kaplan-Meier survival analysis indicated that RFC4 predicted
adverse clinical outcomes, including reduced overall survival (OS) (Supplementary Fig. 1c) and reduced
recurrence-free survival (RFS) (Supplementary Fig. 1d) using TCGA_GBMLGG and CGGA databases. These
findings indicated that RFC4 was a prognostic marker and played oncogenic roles of RFC4 in glioma.

TMZ remains a major therapeutic regimen for glioma through inducing DNA alkylation damage, which
promoted us to explore whether RFC4 was involved in the TMZ response of glioma cells. Using a panel of
glioma cells, including one primary GBM cell line (091214) and four commercial GBM cell lines (A172,
DBTRG-05MG, LN229, and U251), we surprisingly observed an upregulation of RFC4 mRNA and protein with
TMZ treatment in a dose-dependent manner (Fig. 1e and 1f), implying that RFC4 was a TMZ-responsive gene.
In accordance with this notion, we found that glioma patients receiving pharmaceutical therapy harbored higher
RFC4 mRNA than those not receiving any intervention therapy using TCGA_GBMLGG database
(Supplementary Fig. 1e). High expression of RFC4 in patients receiving radio-chemo therapy or chemo therapy
was associated with shortened overall survival of glioma patients (Supplementary Fig. 1f).

Then, we used U251 and LN18 cells to construct RFC4 overexpression and depletion stable cells to measure
the IC of TMZ (Supplementary Fig. 1g and 1h). The result showed that increased or decreased RFC4
50
expression was associated with high or low TMZ IC , respectively, in the two GBM cell lines (Fig. 1g and 1h),
50
which suggested that RFC4 might be related with TMZ resistance in GBM cells. To examine this assumption,
we used LN229 cells to construct TMZ-resistant counterparts (LN229/RI) through treatment of TMZ with
gradually increasing concentration. Compared with parental LN229, LN229/RI showed remarkably increased
TMZ IC and RFC4 mRNA level (Fig. 1i and 1j).
50

To elucidate the mechanism by which TMZ regulates RFC4 expression, we performed ATAC-seq to profile
chromatin accessibility to profile candidate genes with transcriptional changes (Supplementary Fig. 2a). Genes
with increased openness were cross-referenced with transcription factors (TFs) predicted by JASPAR to
potentially regulate RFC4 and with genes positively correlated with RFC4 in the CGGA database. This integrated
analysis identified 26 candidate TFs (Supplementary Fig. 2b), among which 13 exhibited expression patterns
consistent with RFC4. We then measured the mRNA levels of these 13 TFs in TMZ-resistant cells, which
narrowed the candidates to YY1 and HOXA4 (Supplementary Fig. 2c). Subsequent western blotting analysis
confirmed that YY1 protein expression was elevated in the TMZ-resistant cell line (Supplementary Fig. 2d).
To delineate the mechanism by which YY1 regulated RFC4, we confirmed that manipulating YY1 expression
leads to corresponding changes in RFC4 protein (Supplementary Fig. 2e and 2f). This functional link led us to
hypothesize a direct interaction between YY1 and RFC4 promoter. We therefore performed CUT&RUN
targeting a JASPAR-predicted YY1- recognizing motifs in the RFC4 promoter (Supplementary Fig. 2g), which
demonstrated a strong binding of YY1 to the ‘GCCATC’ motif (Supplementary Fig. 2h). The critical role of
this interaction was solidified by a dual-luciferase reporter assay, where mutation of this motif eliminated YY1-
dependent transcriptional regulation (Supplementary Fig. 2i).

Together, these results demonstrated RFC4 as an oncogenic driver in GBM progression and responder to
TMZ treatment, highlighting its involvement in TMZ resistance and dampened prognosis of GBM patients.

RFC4 serves as a modulator for autophagy.
Autophagy serves as a crucial cellular survival mechanism during various stress conditions and has been
found to be critically involved in TMZ resistance of glioma cells. Indeed, geneset enrichment analysis (GSEA)
21 demonstrated that recurrent GBM dramatically enriched REACTOME_AUTOPHAGY geneset compared
with primary GBM using CGGA database (Supplementary Fig. 3a and 3b), indicating the involvement of
autophagy in treatment resistance of GBM. Interestingly, we also noticed significant positive correlation between
RFC4 mRNA and KUMAR_AUTOPHAGY_NETWORK geneset through Pearson correlation analysis using
TCGA_GBM and CGGA databases (Fig. 2a). Similarly, GSEA revealed that high expression of RFC4 enriched
REACTOME_AUTOPHAGY geneset compared with low expression of RFC4 using CGGA database
(Supplementary Fig. 3c and 3d). In addition, analysis on TCGA_GBM database revealed significant
correlations between RFC4 and several key autophagy-regulated genes (Fig. 2b). Moreover, RFC4
overexpression in GBM cells leaded to an increase of LC3B-Ⅱ (Fig. 2c), which could be further enhanced by
Bafilomycin A1 (BaA1), an autophagic flux inhibitor (Fig. 2d), indicating the activation of autophagy and
autophagic flux. The eGFP-mRFP-LC3B fluorescent reporter system and transmission electron microscope
(TEM) analysis clearly indicated the increase of autophagic flux (Fig. 2e and 2f) and autophagosomes with
RFC4 overexpression (Fig. 2g). Then, we explored the effects of RFC4 on TMZ-induced autophagy. In LN229
cells, long time treatment of TMZ induced upregulation of autophagic markers, while in RFC4 depleted LN229
cells, the TMZ-induced autophagy and autophagic flux was repressed (Fig. 2h and 2i). Consistently, the eGFP-
mRFP-LC3B fluorescent reporter system and TEM data demonstrated TMZ-induced autophagic flux and
autophagosomes were indeed suppressed by RFC4 depletion (Fig. 2j-2m). Thus, TMZ-induced autophagy
largely relied on RFC4 in GBM cells.

Blocking autophagy sensitizes GBM cells with RFC4 overexpression to TMZ.
Colony formation assay using GBM cells with forced expression of RFC4 showed that RFC4 rendered
resistance of the cells to TMZ (100µM), while combined administration of autophagy inhibitor, chloroquine
(CLQ, 25µM), significantly inhibited the cell proliferation of GBM cells (Fig. 3a). Then, we examined
autophagy-inhibition effects in orthotopic mouse model utilizing U251 cell line with stable expression of
luciferase (Fig. 3b). U251 cells with empty or RFC4 overexpression were orthotopically inoculated into NOD-
SCID mice to evaluate tumorigenicity. The results showed that RFC4 could effectively promote in vivo growth
of GBM cells in brain. Although TMZ (5mg/kg) treatment could inhibited the in vivo growth of U251/empty
cells but barely suppressed that of U251/RFC4 cells, confirming the TMZ-resistant role of RFC4 in vivo.
However, combination of TMZ and CLQ (80mg/kg) could effectively block the growth of U251/RFC4 cells
(Fig. 3c). Survival analysis suggested that combination of TMZ and CLQ could improve survival of mice bearing
GBM and reverse RFC4-induced TMZ resistance (Fig. 3d). Thereby, our results suggested that RFC4-related
TMZ resistance was mediated by autophagy of GBM cells.

STK38 interacts with RFC4 and plays oncogenic roles in GBM cells.
To further investigate the downstream target pathway regulated by RFC4, we employed
immunoprecipitation and mass spectrometry techniques (Co-IP-MS) to profile interactome of RFC4 (Fig. 4a).
Combining Co-IP-MS data from two cell lines identified 791 proteins as potential binding partners for RFC4
(Fig. 4b). Among 791 RFC4-binding proteins, we detected well-known RFC components, including RFC1,
RFC2, RFC3, RFC5, and CHTF18, confirming the Co-IP reliability. It was noted that among top 10 RFC4-
interacted non-RFC proteins, STK38 (serine/threonine kinase 38, also known as NDR1) and THRAP3 were
found to be closely associated with the autophagy process (Fig. 4c, Supplementary Fig. 4a). However, only
STK38 predicted worsen prognosis in glioma patients (Supplementary Fig. 4b). Moreover, STK38 has been
reported to be involved in the regulation of autophagy, 22-24 and therefore, we focused on STK38 for further
examination.

Co-transfection of RFC4 and STK38 in 293 T cells followed by Co-IP confirmed the interaction between
the two proteins (Fig. 4d). Consistently, Co-IP using U251 or LN18 with RFC4 overexpression cells recombinant
RFC4 protein further confirmed the interaction between RFC4 and endogenous STK38 (Fig. 4e). We also
incubated recombinant RFC4-GST and STK38-His proteins in reaction buffer followed by GST pulldown assay
and the result demonstrated the direct interaction between the two proteins (Fig. 4f). Notably,
immunofluorescence (IF) data revealed that the interaction between RFC4 and STK38 was significantly
enhanced with TMZ treatment via Manders coefficient analysis (Fig. 4g), which was further validated by the
Proximity Ligation Assay (PLA) with a significant increase in puncta (Fig. 4h and 4i).

Analysis on TCGA_GBM and CGGA databases demonstrated that the expression level of STK38 was
notably increased in glioma patients, and this elevation is further amplified as the tumor malignancy progresses
(Supplementary Fig. 5a). Moreover, STK38 mRNA level was progressively elevated with the glioma
histopathological grades (Supplementary Fig. 5b), significantly upregulated in IDH-wildtype versus mutant
subgroups (Supplementary Fig. 5c), and differentially regulated in chemoradiation-treated versus untreated
cohorts (Supplementary Fig. 5d). Survival analyses consistently exhibit shorter overall survival in STK38-high
patients across CGGA301, CGGA325, and CGGA693 cohorts (Supplementary Fig. 5e), implying STK38 as a
robust prognostic biomarker in GBM. Interestingly, we observed that TMZ treatment also induced STK38
mRNA upregulation (Supplementary Fig. 5f). The same bioinformatic strategy applied to RFC4 was used to
analyze STK38, which also identified YY1 as a putative transcriptional regulator (Supplementary Fig. 5g).
Experimental validation showed that YY1 expression positively modulated STK38 levels (Supplementary Fig.
5h and 5i). Intriguingly, the YY1-binding motif in the STK38 promoter was highly consistent with that in RFC4
(Supplementary Fig. 5j). CUT&RUN-qPCR confirmed that YY1 directly bound to the “ACCATG” motif
within the STK38 promoter, transcriptionally regulating STK38 under TMZ treatment (Supplementary Fig. 5k).
To further assess whether TMZ or TMZ-induced DNA damage could influence the RFC4-STK38 interaction
beyond transcriptional regulation, we exogenously co-expressed both proteins in 293FT cells and treated them
with either TMZ or irradiation. Neither TMZ nor irradiation significantly altered the interaction between
exogenous RFC4 and STK38 (Supplementary Fig. 6a and 6b), indicating that the binding affinity between
RFC4 and STK38 is not directly modulated by TMZ or DNA damage.

Therefore, STK38 likely functioned as a cofactor of RFC4 in autophagy regulation, and the TMZ-enhanced
RFC4-STK38 interaction was most likely attributable to the increased protein abundance of both RFC4 and
STK38 mediated through the TMZ-YIY1 axis.

STK38 functions as a cooperative regulator of RFC4-mediated autophagy.
Significantly positive correlation of RFC4 and STK38 was observed in glioma (Supplementary Fig. 7a).
Protein interaction network analysis revealed RFC4 as a central hub, directly connected to STK38, and forming
a functional module with replication factors (RFC3, RFC5, PCNA) (Supplementary Fig. 7b). Meanwhile, co-
upregulation of RFC4 and STK38 produced more worse survival than downregulation of each of/both the two
proteins (Supplementary Fig. 7c and 7d). These data suggested a relationship between RFC4 and STK38 in
GBM cells. To delineate the structure basis for the interaction between RFC4 and STK38, we constructed various
truncated forms for RFC4 and STK38, respectively (Fig. 5a). We found that RFC4 lacking N-terminal (1-250)
domains could not bind to STK38, while all three STK38 truncated forms could interact with N-terminus of
RFC4 (Fig. 5b). Notably, increased expression of RFC4 extended the lifetime of STK38 protein (Fig. 5c). To
elucidate the mechanism by which RFC4 regulates STK38 protein lifetime, we treated GBM cells with the
proteasome inhibitor MG132 and CLQ. Treatment with MG132, but not CLQ, led to a pronounced accumulation
of STK38, suggesting that STK38 was degraded through the proteasomal pathway (Fig. 5d). Notably, the
MG132-induced accumulation of STK38 was significantly attenuated in RFC4-overexpressing cells (Fig. 5e).
However, RFC4 knockdown resulted in a marked increase in polyubiquitination of STK38 (Fig. 5f). Furthermore,
STK38 deficiency considerably attenuated the RFC4-induced autophagy and autophagic flux, featured with
downregulation of LC3B-Ⅱ (Fig. 5g and 5h).

Collectively, these results indicated that RFC4-STK38 protein interaction inhibited STK38 ubiquitination
and subsequent proteasomal degradation to elongate STK38 protein lifetime, which was required for the
autophagy occurrence in TMZ-treated GBM cells.

RFC4 facilitates STK38-BECN1 interaction in a STK38-T444-dependent manner.
It has been reported that STK38 activates autophagy mainly through its interaction with the autophagosome
membrane-forming proteins, BECN1. 24 We performed His pull-down assay and confirmed a direct interaction
between STK38 and BECN1 (Fig. 6a). Moreover, we observed that RFC4 expression significantly enhanced
STK38 recruitment to BECN1 in a dose-dependent manner (Fig. 6b). Moreover, RFC4, STK38, and BECN1
were found to be co-localized in both primary and recurrent glioblastoma tissues (Fig. 6c). Since phosphorylation
of BECN1 at S15, S90, and S93 by STK38 was ruled out (Supplementary Fig. 8), the kinase might target other
residues or facilitate autophagy through a distinct, phosphorylation‑independent mechanism. This possibility
warranted further investigation.

As demonstrated in previous studies, phosphorylation at STK38-S281 and T444 sites represents its active
state. K118 and D230 mutations eliminate STK38’s kinase activity, while the S91 site is crucial for its stability.
25 In order to identify the specific functional domain or residue in STK38 responsible for its interaction with the
autophagy machinery, we engineered various mutant plasmids of STK38 and co-transfected them with RFC4
and BECN1 into 293FT cells. The result indicated that the T444A point mutation in STK38 specifically impaired
its ability to recruit BECN1 (Fig. 6d).

Additionally, STK38-T444A mutation suppressed autophagy activation (Supplementary Fig. 9). Thus,
RFC4-STK38-BECN1 axis was functionally required for autophagy in GBM cells.

STK38 is required for RFC4 biological functions in GBM cells with T444 as critical site.
To evaluate the involvement of STK38 in RFC4 functions, we constructed RFC4 forced expression cells in
combination with STK38 knockdown. Then, the STK38 expression was rescued by wild-type STK38 or STK38
T444A (Supplementary Fig. 10). Colony formation and CCK8 assays indicated loss of STK38 attenuated
RFC4-promoted growth of GBM cells (Fig. 7a and 7b). Overexpression using STK38 wild-type in GBM cells
with RFC4 and shSTK38 rescued RFC4 effects but not overexpression using STK38 T444A (Fig. 7a and 7b).
Increased TMZ IC of GBM cells by RFC4 high expression could be repressed by loss of STK38. However,
50
wild-type STK38, but not STK38 T444A mutant, could elevate the TMZ IC of GBM cells with RFC4
50
overexpression and STK38 depletion (Fig. 7c). Similarly, orthotopical mouse model using U251 cells
demonstrated depletion of STK38 leaded to growth inhibition of GBM cells with RFC4 overexpression (Fig.
7d). Rescue with wild-type STK38 consistently recovered in vivo growth of GBM cells but STK38 T444A failed
to do so (Fig. 7d). Survival analysis also supported the involvement of STK38 activity in RFC4-enhance growth
of GBM cells (Fig. 7e). Therefore, our data demonstrated that STK38 acted as an important mediator for RFC4
functions in GBM cells.

To further substantiate the RFC4-STK38-autophagy pathway in promoting tumor development, we
employed two autophagy inhibitors: SAR405, which targets VPS34 and inhibits early-stage autophagosome
formation by disrupting its complex with BECN1, and CLQ, a late-stage autophagy inhibitor. Both inhibitors
effectively suppressed the RFC4 and wild-type STK38-promoted colony formation (Supplementary Fig. 11a),
cell proliferation (Supplementary Fig. 11b), and xenograft tumor growth (Supplementary Fig. 11c), and
improved mouse survival (Supplementary Fig. 11d). However, STK38 T444A mutation rendered GBM cells
insensitivity to both inhibitors (Supplementary Fig. 11a-11d). Together, these results demonstrated the critical
role of the RFC4-STK38-BECN1-mediated autophagy pathway in GBM cells.

Methods
The study was conducted in accordance with the Declaration of Helsinki. This study protocol, including the use
of mice, was reviewed and approved by the Institutional Review Board of West China Fourth Hospital, Sichuan
University (Ethics: Gwll2025040).

Cell culture
DMEM (10,566,016), penicillin-streptomycin (15,140,122), fetal bovine serum (FBS) (10,270,106),
Dulbecco’s phosphate-buffered saline (DPBS; 14,190,094) and trypsin-EDTA (25,200,056) were purchased
from Gibco® (Life Technologies). The human glioma cell line U251, Ln18, LN-229, A172 and DBTRG-05MG
was obtained from Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Army Medical
University (Third Military Medical University), Chongqing, China. 32 Cells were cultivated in DMEM, high
glucose, 10% FBS and 100 U/mL penicillin-streptomycin at 37°C and 5% CO . Cells were exposed to
2
Temozolomide (MedChemExpress, HY17364) and Cycloheximide (MedChemExpress, HY12320), chloroquine
diphosphate salt (chloroquine; Sigma-Aldrich, C6628) for the indicated time periods.

Lentivirus packing in 293-FT cells
For depletion and overexpression of target genes, virus particles were generated in 293-FT cells transfected
with the control vector or the respective plasmid, the gag/pol plasmid pCMV-dr8.91 and the VSV-g envelope
plasmid pMD2. G (Addgene, 12,259). Cells were transfected by using Lipofectamine 3000 (Thermo Fisher
Scientific, L3000008) according to manufacturer’s instructions. The DNA mixture was added drop-wise to the
cells and incubated for 6-8 h at 37°C and 5% CO . After incubation the medium was replaced with fresh medium.
2
48 h and 72h after transfection the virus-containing supernatant was harvested and stored short-term at 4°C.
Lentivirus were added directly in cells with polybrene (MedChemExpress, HY112735). Medium was changed
after 24h and cells with stable integration of target genes were selected with puromycin or hygromycin.

Immunoblot analysis
For immunoblot analysis, cells were lysed with RIPA Lysis Buffer (Strong) (50 mM Tris (pH 7.4), 150 mM
NaCl, 1% Triton X-100, 1% sodium deoxycholate, 0.1% SDS, and general protease and phosphatase inhibitors
(cOmplete™ Protease Inhibitor, PhosSTOP) [Roche, 4693116001, 04906837001]. The protein amount was
determined with a Pierce BCA protein assay kit (Thermo Fisher Scientific, 23,225). The SDS gels (8% - 15%)
were loaded with 30 - 50 µg protein in 4x Laemmli protein sample buffer (250 mM Tris-HCl, pH 6.8, 10% SDS,
30% glycerol, 0.02% bromophenol blue (Bio-Rad, 1610747), after heating the samples for 5 min at 95°C.
Proteins were separated (80 V for 30 min, then 120 V) and blotted semi-dry (15 V, 35 min) onto a PVDF
membrane (Bio-Rad, 1620177). The membranes were blocked with 5% milk in TBS (150 mM NaCl [50 mM
Tris, pH 7.5) + 0.05% Tween 20 (TBS-T) for 1 h at room temperature, followed by incubation with the primary
antibodies overnight at 4°C, LC3B (1:3000, Abcam, ab63817), BECN-1 (1:1000, Cell Signaling Technology,
4122), RFC4 (1:1000, Novus biologicals, NBP2-45946), p62 (1:1000, Cell Signaling Technology, 23214),
Rabbit anti-Flag (1:1000, Cell Signaling Technology, 14793), Rabbit anti-Myc (1:1000, Cell Signaling
Technology, 2278), Mouse anti-Myc (1:1000, Cell Signaling Technology, 2276), GFP (1:1000, Cell Signaling
Technology, 2956), STK38 (1:1000, Novus Biologicals, H00011329-M01), β-actin (1:10000, Cell Signaling
Technology, 4970), YY1 (1:1000, Cell Signaling Technology, 46395), HOXA4 (1:1000, Novus Biologicals,
NBP2-32515). The primary and secondary antibodies were used at dilutions of 1:1000 and 1:3000, respectively.

Assessment of gene expression by quantitative real-time polymerase chain reaction (qRT-PCR)
For determination of gene expression by qRT-PCR, RNA was isolated with the EXTRACTME total RNA
Kit according to the manufacturer’s instructions (Bioscience, EM09.1-250). Next, 1 - 2 µg RNA, superscript III
reverse transcriptase (Thermo Fisher Scientific, 18,080,044) and random primers were used in a total volume of
20 µL for cDNA synthesis according to the manufacturer’s protocol. cDNA was diluted with 80 - 180 µL DEPC-
H O (Carl Roth GmbH + Co. KG, T143.1). For qRT-PCR, 5µL cDNA, 1μl 1xTaqMan Gene Expression Assay
2
primer and 10μl 1xFastStart Universal Probe Master-mix (Roche 3439 Diagnostics GmbH, 04913957001) were
applied. Relative gene expression levels were calculated by using the comparative CT method. Samples were
normalized to the reference gene TBP (TATA-box binding protein). The following primers (Thermo Fisher
Scientific) were used:
RFC4 (Forward): 5’-GCAACTCAGCTCGTCAATCAACTC-3’;
RFC4 (Reverse): 5’-AGGCATTTGTCAACTTCGGCAAG-3’;
STK38 (Forward): 5’-TCGTGCGGAGCGTGACATTC-3’;
STK38 (Reverse): 5’-CCAGGCAGGAACTCCATGATTAGG-3’.

Tissue Microarray and Immunohistochemical Analysis
The custom-made tissue microarray (TMA) chip was provided by Zhongke Guanghua Company, containing
159 sample spots (16 cases of normal adjacent tissues and 143 cases of glioblastomas). All samples were
pathologically validated. The TMA sections were baked at 60°C for 2 h, deparaffinized in xylene, and rehydrated
through graded ethanol. Antigen retrieval was performed using citrate buffer (pH 6.0) under high-pressure
heating. Sections were incubated with a primary antibody (RFC4, 1:100, Novus biologicals, NBP2-45946) at
4°C overnight, followed by an HRP-polymer secondary antibody for 30 min at room temperature. Detection was
carried out with DAB, and nuclei were counterstained with hematoxylin. Quantitative analysis of IHC staining
was performed using ImageJ software (v1.53t). Data are presented as mean ± SEM and were analyzed by
unpaired t-test using GraphPad Prism 9.0.

Generation of shRNA KD cell lines
For depletion of RFC4/STK38 virus particles were generated in HEK 293-T cells transfected with the
control vector or the respective shRNA plasmid (designed and synthesized by Shanghai Sangon
Biological Engineering Technology & Services Company） gag/pol plasmid pCMV-△8.91 and the VSV-g
envelope plasmid pMD2. G (Addgene, 12,259). The plasmid was transfected into HEK293 cells using
Lipofectamine 3000 (Thermo Fisher Scientific, L3000015). The plasmids were diluted in DMEM while blowing
air bubbles through a Pasteur pipette and incubated for 15 - 20 min. The DNA mixture was added drop-wise to
the cells and incubated for 6 - 8 h at 37°C and 5% CO2. After incubation the medium was replaced with fresh
medium. 24 h and 48 h after transfection the virus-containing supernatant was harvested and stored short-term
at 4°C. For transduction, 120,000 parental GBM cells were seeded out in 6-well plates. Next day cells were
incubated for 24 h with the virus-containing supernatant diluted in culture medium and 3µg/mL polybrene.
Afterward medium was changed and after additional 24h the medium was changed to selection medium
containing 1 µg/mL puromycin. KD was confirmed by immunoblot analysis.

Protein expression and purification
The plasmid was transfected into HEK293 cells using Lipofectamine 3000 (Thermo Fisher Scientific,
L3000015). After culture at 37℃ under 5% CO2 for 6 days, cells were collected and lysed by 1XPBS (pH 7.2-
7.4), and the insoluble fraction was removed by centrifugation at 30,000xg for 30 min. Supernatants were
incubated with protein A-agarose for 1-2 h and washed extensively. The protein was eluted by 0.1M glycine (pH
3.0) and neutralized with 1M Tris-HCl (pH8.5), and then concentrated to 1mg/ml and stored at -80℃.

Immunoprecipitation
The experimental procedure for immunoprecipitation commenced with cell harvesting and lysis using ice-
cold RIPA buffer supplemented with protease inhibitors. After incubation on ice for 30 minutes, the lysate was
centrifuged at 12,000×g for 15 minutes at 4°C to pellet cellular debris. The clarified supernatant was incubated
overnight at 4°C with VHH-Sepharose® magnetic beads (Antibody Life Biotechnology) pre-coupled with
specific antibodies under constant rotation. Magnetic bead complexes were isolated using a dedicated separator
and washed three times with chilled Tris-buffered saline containing 0.1% Tween-20. Proteins were dissociated
by boiling in Laemmli sample buffer at 95°C for 10 minutes, followed by immediate SDS-PAGE analysis.
Negative controls using antibody-free beads from the same manufacturer were included to validate target-
specific binding. All steps involving protein handling were performed at 4°C to preserve complex integrity, with
reagents from Antibody Life Biotechnology maintained at recommended storage temperatures prior to use.

The IP/MS analyses
The experiment aimed to identify molecules interacting with the RFC4 protein through FLAG affinity
purification coupled with mass spectrometry analysis. A total of 3 samples were analyzed, including one sample
each of RFC4-FLAG transfected LN18 cells and U251 cells, along with one negative control sample (used to
exclude non-specific binding). The experiment was performed with two independent biological replicates (n=2),
using distinct cell lines. The sample preparation process is summarized as follows: after transfection of RFC4-
FLAG expression plasmids into LN18 and U251 cells and expression for a specific duration, RFC4 protein and
its complexes were immunoprecipitated from cell lysates using FLAG magnetic beads, ultimately yielding
peptide samples for mass spectrometry analysis.

The IP/MS analyses were designed and analyzed by PTM Biological Company. Briefly, it was performed
using a triple quadrupole mass spectrometer QTrap 5500 (Sciex, Darmstadt, Germany) equipped with a Turbo
V Ion Source operating in positive electrospray ionization mode. The analysis was done in Multiple Reaction
Monitoring (MRM) mode. Precursor to product ion transitions (m/z) for analysis and internal standards are
shown in the supplementary methods. Data Acquisition was done using Analyst Software V1.6.2 and
quantification was performed with MultiQuant Software 3.0.2 (both Sciex, Darmstadt, Germany), employing
the internal standard method (isotope dilution mass spectrometry). Calibration curves were calculated by linear
or quadratic regression with 1/x or 1/x2 weighting. Variations in accuracy of the calibration standards were less
than 15% over the whole range of calibration, except for the lower limit of quantification, where a variation in
accuracy of 20% was accepted. For the acceptance of the analytical run, the accuracy of the QC samples had to
be between 85% and 115% of the nominal concentration for at least 67% of all QC samples.

GST pull down
The GST pull-down protocol was initiated by expressing GST-tagged recombinant proteins in BL21(DE3)
Escherichia coli through 0.5 mM IPTG induction at 16°C for 20 hours. Bacterial pellets were lysed in ice-cold
GST-binding buffer (50 mM Tris-HCl pH 8.0, 150 mM NaCl, 1% Triton X-100) supplemented with lysozyme
and protease inhibitors. Following sonication and centrifugation at 15,000×g for 30 minutes at 4°C, the clarified
lysate was incubated with pre-equilibrated GST-VHH nanomagnetic beads (Antibody Life Biotechnology, Cat#
GST-NM102) for 2 hours at 4°C with gentle agitation. The bead-protein complexes were magnetically captured
and sequentially washed with high-salt buffer (500 mM NaCl) and low-salt buffer (50 mM NaCl) to remove
nonspecific binders. Competitor proteins or cell lysates containing putative interaction partners were then
introduced to the immobilized GST fusion proteins and incubated for 4 hours under rotational mixing. After
three stringent washes with GST elution buffer lacking reducing agents, specifically bound proteins were
competitively eluted using 10 mM reduced glutathione in 50 mM Tris-HCl (pH 8.0) for downstream
immunoblotting or mass spectrometry analysis. Control experiments with untagged protein lysates and GST-
empty nanomagnetic beads from the same manufacturer were systematically included to exclude artifacts. All
Antibody Life Biotechnology reagents were quality-controlled for batch consistency, with nanomagnetic beads
stored at 4°C in glycerol-containing preservation buffer until use.

Construction of shRNA-resistant STK38 plasmid
To generate a shRNA-resistant STK38 expression plasmid, we designed a mutant construct with nucleotide
substitutions in the shRNA-targeted region to disrupt base pairing while preserving the encoded protein sequence.
The shRNA PLVE4507-1 targets the STK38 sequence CAGCAAGGGCCATGTGAAA (positions 663 - 683 in
the wild-type genomic DNA), which overlaps with the stem-loop (STEMP) and loop regions in the viral vector
backbone. Two-point mutations were introduced into the STEMP sequence: G9A (position 9, G→A), T15C
(position 15, T→C), converting the original sequence to CAGCAAGGACCATGCGAAA. This mutation
disrupts the stem-loop secondary structure critical for shRNA binding while maintaining the amino acid sequence
at the corresponding protein locus. The mutant fragment was synthesized via overlap extension PCR using
Phusion High-Fidelity DNA Polymerase (New England Biolabs) with the following primers:
Forward primer: 5’- CAAGGACCATGCGAAACTTTCTGACTTTGGTCTTTGCA -3’;
Reverse primer: 5’- AGTTTCGCATTGGTCCTTGCTGTCCAAAAGAAGGTT -3’.
The ligation product was transformed into Stbl3 chemically competent E. coli (Thermo Fisher Scientific),
and plasmid DNA was extracted using a HiSpeed Plasmid Midi Kit (Qiagen).

Site-directed mutagenesis
Using a high-fidelity PCR-based approach with Phusion DNA polymerase (New England Biolabs). The
plasmid template containing the wild-type STK38 gene was amplified with primers flanking the target site,
designed using SnapGene software. PCR products were treated with DpnI (NEB) to digest methylated template
DNA, followed by ligation using T4 DNA ligase (NEB). Competent E. coli cells (DH5α) were transformed with
ligated products, and positive colonies were selected via colony PCR. Plasmid DNA was extracted (QIAGEN
MinElute Kit), and mutations were confirmed by Sanger sequencing (Genewiz).

ATAC-seq data visualization using IGV
Processed ATAC-seq data were visualized using the Integrative Genomics Viewer (IGV v2.16.2) with strict
adherence to the GRCh37/hg19 reference genome assembly. 33 BAM alignment files and normalized BigWig
coverage tracks were loaded following coordinate conversion using CrossMap v0.6.3 to ensure compatibility
with the hg19 genomic framework. Visualization parameters were optimized through iterative adjustment of
sequencing depth thresholds (1-10× in collapsed view mode) and coverage normalization (0-100 reads per
million, RPM), while maintaining the diagnostic 200-bp Tn5 transposase cleavage periodicity at transcription
start sites (TSSs) as critical quality control criteria. Comparative analyses incorporated ENCODE Consortium
annotations (Version 3, hg19) of regulatory elements, with chromatin accessibility patterns contextualized
against Roadmap Epigenomics Project ATAC-seq datasets mapped to the same genome build. Spatial
relationships between differential accessibility regions and known genomic features were verified through multi-
track visualization using consistent color schemes (BAM: grayscale gradient; BigWig: royal blue) and layer
transparency settings (40-60%). High-resolution snapshots (300 DPI, 1600×900 pixels) of representative loci
were acquired using IGV’s batch image export function with standardized chromosome coordinate ranges.

Protein lifetime assay
To investigate the effect of RFC4 on STK38 protein lifetime, a cycloheximide (CHX) chase assay was
performed. Control (CTRL) and RFC4-overexpressing cells were treated with CHX (100 µM) to inhibit new
protein synthesis. Cell lysates were harvested at the indicated time points (0, 2, 4, 6, 8, 10 hours) post-treatment.
The protein levels of STK38 and the loading control β-actin were analyzed by Western blotting.

CUT&RUN-qPCR
The interaction between the transcription factor YY1 and the RFC4 promoter was analyzed using the
hyperactive pG-MNase CUT&RUN Assay Kit for PCR/qPCR (#HD101-01, Vazyme) following the
manufacturer’s protocol. Based on the top three binding sites for YY1 in the RFC4 promoter region predicted
by JASPAR, 34 three pairs of specific primers were designed to flank each putative binding site. qPCR
amplification was carried out with three specific primer pairs targeting the predicted YY1 binding sites in the
RFC4 or STK38 promoter (Supplementary Table. 1) . The enrichment of YY1-bound DNA fragments was
quantified by qPCR. Data were normalized to the corresponding Spike in DNA provided by the kit.

Dual-Luciferase reporter assay
To determine whether YY1 binds to specific sites within the RFC4 promoter and activates transcription, a
dual-luciferase reporter assay was performed. A wild-type fragment of the RFC4 promoter was cloned into the
pGL3-Basic vector to create the pGL3-RFC4-wt construct. Site-directed deletion mutagenesis was then used to
generate a deletion mutant construct (pGL3-RFC4-mut) by deleting the key motif within the top three predicted
YY1 binding sites. 35 Cells were co-transfected with either the wild-type or mutant reporter plasmid along with
the pRL-TK Renilla luciferase plasmid as an internal control. After 48 hours, luciferase activities were measured
using a dual-luciferase assay kit. The firefly luciferase signal was normalized to the Renilla signal for each
sample.

Orthotopic xenograft
The study was conducted using mice housed in a controlled, specific pathogen-free (SPF) environment.
Housing conditions included a 12-hour light/12-hour dark cycle, an ambient temperature maintained at 22 ± 2 °C,
and relative humidity of 50 - 60%. Unless otherwise specified by the experimental design, all animals were
provided with standard rodent chow (YC-6680, Nanning Yancheng Biotechnology Co., Ltd.) and water ad
libitum. The manufacturer certified that the nutritional composition of the diet was appropriate for standard
laboratory rodent maintenance.

NOD/ShiLtJGpt-Prkdcem26Cd52/Gpt mice (NOD-SCID, GemPharmatech, stock no. T001492) were used in
this study. NOD-SCID mice were injected intracranially with U251 cells (5×104) with pLVX-puro-linker-
luciferase lentivirus via the right frontal lobe on a 6-week-old basis (Animal Facility of West China Fourth
Hospital, China). The mice were housed in large plastic cages in groups of 10 and kept pathogen-free. An In
Vivo Image System (IVIS) (PerkinElmer, USA) was used to detect and quantify growing xenograft tumors.
Tumors were collected from mice with neurological signs or that were moribund.

In the in vivo experiments of chloroquine and SAR405, NOD-SCID mice (5 weeks old) were intracranially
injected with luciferase-expressing U251 cells into the right frontal lobe. On day 14 after tumor inoculation, the
mice were randomly assigned to different treatment groups and administered daily intraperitoneal injections for
14 consecutive days. Chloroquine was administered at a dose of 80 mg/kg, and SAR405 at 40 mg/kg. On day 28
after tumor inoculation (i.e., at the end of the treatment period), tumor growth was monitored via an in vivo
imaging system, and bioluminescence signals were quantified. The primary endpoint of the study was overall
survival and Kaplan-Meier curves were plotted to analyze survival status. In addition, the humane endpoints for
animals were predefined in accordance with our Institutional Animal Care and Use Committee-approved
protocol. Mice were euthanized prior to becoming moribund upon meeting ANY of the following criteria: i) >20%
body weight loss; ii) development of neurological deficits (such as persistent circling, seizures, or paralysis); iii)
signs of distress or pain (such as hunched posture, lethargy, or inability to reach food or water); iv) inability to
access food or water; v) lack of response to external stimuli. Animals were monitored daily (or twice daily during
peak disease progression), and euthanasia was performed immediately once any of the criteria were met to
prevent undue suffering. No animals were allowed to progress to a moribund state.

Sex was not considered as a biological variable in this study. The research focused on characterizing the
baseline tumorigenic potential of the cell lines, and the experiments were conducted using male mice only to
minimize variability in the initial model establishment. All animal procedures were conducted in compliance
with ethical regulations and were approved by the Institutional Review Board of West China Fourth Hospital,
Sichuan University (Ethics Approval No.: Gwll2025040). The maximum permitted tumor burden (2000 mm³,
not exceeding 5% of body weight) was not exceeded in any experiment.

Statistics
Analysis of proteome data was performed in Perseus. GraphPad Prism 9 (GraphPad Software, La Jolla CA,
USA) was used for statistical analysis of all other data. To determine if the data set is well-modeled by a normal
distribution, a Shapiro Wilk normality test was performed. In case of normally distributed data, the data set was
analyzed with a one- or two-way ANOVA. For non-normally distributed data, the nonparametric Mann-Whitney
U-test or Kruskal-Wallis test was used. The minimum level of statistical significance was set at P < 0.05 and
significances are depicted as *P < 0.05, **P < 0.01, and ***P < 0.001, or n.s. (not significant) between control
and treated cells or as indicated with brackets.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper is quite interesting, well controlled and done in different cancer cell lines and several convincing in vivo studies were conducted. Although, into my opinion for a publication in Nature Communications the paper should include more mechanistic and functional data: - In the figure 7, authors should investigate whether the involvement of STK38 activity in RFC4-enhance growth of GM cells in vivo, required the autophagy process, as it is proposed. - Why/how TMZ induced the upregulation of RFC4 ? and upregulated STK38 mRNA level ? - The authors take several shortcuts. For example, in Fig4C, the results displayed in Fig 4C did not really involved autophagy. Next why they picked up STK38 ? - In Fig 4G following TMZ treatment the level of STK38 was increased a lot. Is it expected ? can this have an impact on the Pearson coefficient ? it would be interesting to have the Manders coefficient instead. - The depletion of STK38 mediated by the shRNA in Figure 5E does not seem really efficient, however there is an impact on LC3-II level? how can the authors explain that ? can another shRNA targeting STK38 be used? - In order to properly measure autophagy flux in Figures 2F, 5E, authors should evaluate the difference in the amount of LC3-II in both the presence and absence of inhibitors that prevent lysosomal degradation (chloroquine, BAFA1 etc…) see “Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)”. Therefore, the following sentence “RFC4 overexpression in GBM cells leaded to an increase of LC3B-Ⅱ, indicating activation of autophagy » line 169-169 is wrong. Also: - Fig1K was not convincing. - Why the same geneset was not used in Fig2 and S2? - On Fig. 2D, in the text authors indicate Immunofluorescence but it is not the case as in fact it is a probe that it was used. Authors should be more precise and indicated that it is a tandem probe etc… - In several Figures panels are not well positioned, they should be displayed in the reading direction.", "reviewer_2": "Glioblastoma (GBM) is the most aggressive brain tumor, marked by inevitable recurrence and poor prognosis. This study identifies Replication Factor C subunit 4 (RFC4) as significantly upregulated in GBM, correlating with temozolomide (TMZ) resistance, enhanced autophagy, and reduced patient survival. These findings suggest that targeting the RFC4–STK38–BECN1 axis, especially the STK38-T444 site, may provide a novel therapeutic approach. While the study presents a coherent mechanism, further clarification is needed; therefore, a major revision is recommended. I believe that the revised content will further enhance the knowledge of readers of this journal. Reviewer Comments 1.In Figures 2D, E, and G, the IF and TEM images should be performed using TMZ-resistant cell lines, such as LN229-TMZ RI, to validate the correlation between RFC4 and autophagy, thereby avoiding off-target effects caused by TMZ induction. 2.In Figures 3C and D, why does treatment with CLQ, an autophagy inhibitor, fail to prolong survival in the RFC4 (TMZ+CLQ) group compared to the CTRL (TMZ+CLQ) group? Could other mechanisms beyond autophagy be involved in promoting tumor growth or contributing to TMZ resistance? 3.In Figure 4G, to better validate the interaction between RFC4 and STK38, a Proximity Ligation Assay (PLA) should be performed, as it offers in situ visualization of protein-protein interactions. 4. In Figure 5 and 6, it remains unclear whether TMZ affects the RFC4-STK38 interaction. Co-immunoprecipitation (IP) assays with and without TMZ treatment are recommended to address this question. 5. In Figure 5, the manuscript shows that RFC4 enhances STK38 stability, but the molecular mechanism remains unclear. Additional studies, such as ubiquitination assays, are needed to determine whether RFC4 functions as a chaperone or prevents STK38 degradation via the ubiquitin-proteasome pathway. 6. The identification of T444 phosphorylation on STK38 as a potential druggable site is promising. The authors should expand the discussion to include therapeutic strategies for targeting this site. Kinase prediction tools and inhibitor screening could help identify candidate kinases responsible for T444 phosphorylation. 7. Since BECN1 regulates autophagy initiation through its interaction with VPS34, inhibitors targeting VPS34 (e.g., SAR405) are more appropriate than late-stage inhibitors like chloroquine for dissecting early autophagy regulation. 8. Given RFC4’s role in DNA replication and repair, and TMZ’s ability to induce DNA damage, it is important to investigate whether DNA damage promotes RFC4-STK38 interaction. Correlation with DNA damage response pathways may help elucidate the functional relevance of the RFC4-STK38-BECN1 axis. 9. In Figure 7E, knockdown of endogenous STK38 followed by overexpression of the STK38 T444A mutant significantly prolongs mouse survival, suggesting that this mutant may exert additional effects beyond simply rescuing STK38 loss. Please provide an explanation in the discussion section.", "reviewer_3": "In this paper, Mao et al. demonstrate that RFC4, a subunit of the replication factor C (RFC) complex, is significantly upregulated in glioblastoma (GBM) and correlates with poor prognosis and resistance to temozolomide (TMZ), a frontline chemotherapy drug. Mechanistically, the authors identify a key interaction between RFC4 and the kinase STK38, which in turn recruits BECN1, promoting autophagy as a survival pathway for tumor cells. They report that RFC4 N-terminal region binds STK38, and that phosphorylation of STK38 at T444 is essential for forming the RFC4–STK38–BECN1 complex and enabling autophagy. Finally, they show that co-expression of RFC4 and STK38 identifies high-risk GBM patients. The work has potential clinical impact, as the STK38 phosphorylation at T444 was identified as a vulnerability that could be exploited therapeutically. My expertise is in structural bioinformatics and biochemistry, so my comments focus on these aspects of the manuscript: - Line 227 – The authors state that RFC4–GST was expressed in E. coli. To my knowledge, no prior work has described expression and purification of RFC4 from a bacterial system. Were solubility and proper folding verified? This is critical for downstream pull-down. RFC4 lacking residues 1–380 is reported as non-interacting with STK38. According to Figure 5, this should be 1–250. - Line 229 – The authors used Swiss-Model to \"fix\" RFC4 experimental structure, and AlphaFold for BECN1 and STK38. For kinases, AlphaFold predictions are more accurate when Mg2+ ions and ATP are included; this is not mentioned. Furthermore, experimental structures of full length BECN1 and STK38 exists. Why were these not used? - Figure 5C – The RFC4–STK38 docking appears to include only the STK38 kinase domain, whereas pull-down data (Fig. 5B) indicate interaction with both N- and C-terminal regions. Was full-length STK38 used in the docking simulations? In the model shown, RFC4 seems positioned far from the STK38 N-terminal region. Could the authors expand on how the docking results align with the experimental interaction data? - Line 234 – The narrative jumps from docking results to protein stability without explanation, and I could not find a detailed description of the stability assay in the Methods. It is also not immediately clear how the docking results support a protein stabilisation function. - Line 247 – The phrase “action site of STK38” is unclear. Could the authors clarify the sentence? - Line 252 – Can authors provide more information on why the T444A mutant was not modelled with AlphaFold? The claim that T444A induces local conformational changes is not supported by sufficient data; homology modelling alone cannot reliably predict conformational chenges. If the authors wish to include this claim, molecular dynamics simulations would be required. - Docking Approach - It is unclear why the RFC4–STK38–BECN1 docking was performed with AutoDock rather than ClusPro, which was used earlier. AutoDock and AutoDock Vina are intended for protein–ligand docking and they use a side chain rotamer scanning procedure which is not a true protein flexible docking. Their scoring functions are not optimised for protein–protein binding, and affinities should be estimated with more reliable tools (e.g., PPI-Graphomer). For best practice, authors should carry out flexible docking using HADDOCK or RosettaDock. Since AlphaFold was already used for monomer models, AlphaFold could also be applied to predict the structure of the complex. To increase reliability of data, I also recommend a final comparison of structures generated with rigid-body docking (CluPro), flexible docking (HADDOCK or RosettaDock), and alphafold. Experimental binding interface data from pull-downs should be incorporated as restraints in HADDOCK to improve docking accuracy. - Figure 6D – The RFC4–STK38–BECN1 complex shows STK38 sandwiched between RFC4 and BECN1. This structure is very interesting, as it suggests that BECN1 could be phosphorylated on it's N-terminal region. It is known that BECN1 can be phosphorylated on several residues on the N-temrinal, have the authors tested whether STK38 is able to phosphorylate BECN1? In the T444A complex, RFC4 appears to interact directly with BECN1, and BECN1 seems to engage a completely different STK38 surface than in the wild-type model. It is improbable that a single point mutation would fully remodel the binding interface and simultaneously promote RFC4–BECN1 interaction. This suggests that the docking predictions for the mutant complex may be unreliable. Several tools exist to predicting effects of mutations on protein–protein interactions (e.g., DDMut-PPI). Could the authors elaborate on how the presented structural model supports their proposed mechanism in which RFC4–STK38–BECN1 complexes promote autophagosome formation? I was wondering the authors could expand on the structural and functional context of RFC4. In cells, RFC4 is not typically a stand-alone protein but rather a subunit of the heteropentameric RFC complex. Do the database analyses indicate concurrent upregulation of other RFC subunits? Furthermore, is there evidence that the RFC4–STK38–BECN1 complex forms in vivo when RFC4 is unbound, or does it assemble only when RFC4 is incorporated into the full RFC complex? While the biological findings are compelling, the structural modelling and docking methodology require additional work to support the conclusions. Addressing these points would strengthen the mechanistic claims and ensure that the structural interpretations are consistent with both the biochemical data and best practices in protein–protein modelling."}, "author_response": "We sincerely thank the reviewers for their comments. Below please find our point-by-point response. We believe that by addressing these comments, our manuscript has been significantly enhanced in terms of scientific rigor and logical coherence. 1 - In the figure 7, authors should investigate whether the involvement of STK38 activity in RFC4-enhance growth of GBM cells in vivo, required the autophagy process, as it is proposed. Response: We appreciate the reviewer for the valuable suggestion. According to the suggestion, we established stable GBM cell lines expressing RFC4-shSTK38 together with either wild-type STK38 or the STK38 T444A mutant. Using CCK-8 assays, colony formation experiments, and in vivo imaging, we found that inhibiting autophagy via inhibitors targeting both early and late stages of the process consistently suppressed tumor progression in glioma cells expressing RFC4-shSTK38 with wild-type STK38. However, these inhibitors showed limited effects in cells expressing RFC4-shSTK38 with the STK38 T444A mutant. These results supported the notion that RFC4-shSTK38 with wild-type STK38 promoted GBM malignancy through autophagy. The new data were included in Supplementary figure 10 and Supplementary figure 11 and the related description were added in lines 338-346 in the revised manuscript. 2 - Why/how TMZ induced the upregulation of RFC4? and upregulated STK38 mRNA level? Response: We thank the reviewer for this comment. To provide stronger support for our conclusions and to clarify the potential mechanism by which TMZ regulates RFC4, we have replaced the original data in Figure 1K with a series of new experiments in the revised manuscript. Specifically, we found that RFC4 expression is significantly upregulated at both the mRNA and protein levels in a TMZ dose-dependent manner. To investigate the underlying mechanism, we performed ATAC-seq in TMZ-resistant cells and identified genes located in genomic regions with enhanced chromatin accessibility following TMZ treatment. These genes were then cross-referenced with transcription factors (TFs) predicted by the JASPAR database to bind the RFC4 promoter. From this overlapping set, we selected TFs whose expression positively correlated with RFC4 in the CGGA database for further validation. This multi-step screening identified 26 candidate TFs, 13 of which exhibited expression patterns consistent with RFC4. In TMZ-resistant cells, only HOXA4 and YY1 showed increased mRNA expression, but only YY1 was consistently upregulated at the protein level. We therefore focused on YY1 as a potential regulator of RFC4. Subsequent gain- and loss-of-function experiments confirmed that modulating YY1 expression positively regulates RFC4 levels. The new data were included in Supplementary figure 2a-2d and the related description were added in lines 149-158 in the revised manuscript. To determine whether YY1 directly binds the RFC4 promoter, we performed a CUT&RUN assay using primers designed for three potential binding sites predicted by JASPAR. This analysis revealed significant YY1 enrichment specifically at the second site, which contains the GCCATC motif. Consistent with this, a dual-luciferase reporter assay demonstrated that YY1 robustly enhanced RFC4 promoter activity, an effect that was abolished upon mutation of the GCCATC motif. Collectively, these findings demonstrated that YY1 directly bound to the GCCATC motif in the RFC4 promoter to drive its transcriptional upregulation following TMZ treatment. Thus, TMZ exposure increased chromatin accessibility at the YY1 promoter, upregulating YY1 expression, which in turn specifically bound the RFC4 promoter to activate transcription. The new data were included in Supplementary figure 2e-2i and the related description were added in lines 158-165 in the revised manuscript. Regarding the reviewer’s question on STK38 regulation, we sincerely appreciate this comment as it led us to an interesting observation: STK38 indeed shared a similar YY1-binding motif as RFC4. Therefore, the increase in YY1 expression induced by TMZ could enhance not only the transcriptional activity of RFC4 but also the transcription level of STK38. Both PCR and Western blotting confirmed that YY1 expression promotes the upregulation of STK38. CUT&RUN assay identified a critical YY1 motif (site 2) in the STK38 promoter, confirming its direct binding and functional importance. Moreover, we also found that RFC4 can enhance the protein stability of STK38. Thus, STK38 increased upon TMZ treatment, and its protein level was further stabilized by the concurrently elevated RFC4. The new data were included in Supplementary figure 5f-5k and the related description were added in lines 237-244 in the revised manuscript. 3 - The authors take several shortcuts. For example, in Fig4C, the results displayed in Fig 4C did not really involved autophagy. Next why they picked up STK38? Response: We thank the reviewer for this comment. In the original manuscript, we performed DAVID analysis on RFC4-interacting proteins but did not directly detect autophagy-related genesets. Since the lysosome is a key organelle regulating autophagy and often exhibits strong ATPase activity, we initially linked this activity to autophagy. However, prompted by the reviewer’s suggestion, we recognized that ATPase activity plays important roles in multiple cellular compartments beyond the lysosome, and we have therefore removed this description in the revised manuscript. Moreover, we have added clarification regarding the reliability of the RFC4 interactome. In this interactome, key components of the RFC complex, including RFC1, RFC2, RFC3, RFC4, RFC5, and CHTF18, were identified, supporting the credibility of the experimental data. Furthermore, we first selected non-RFC complex proteins that showed strong binding to RFC4 and analyzed their correlation with autophagy gene sets. The results revealed that both THRAP3 and STK38 exhibited correlation coefficients exceeding 0.8 with autophagy-related geneset. However, survival analysis indicated that high expression of STK38, but not THRAP3, was significantly associated with poor prognosis in glioma. Given that RFC4-mediated regulation of autophagy contributes to TMZ resistance and worse clinical outcomes, we reasoned that a downstream mediator of RFC4 function should also carry prognostic relevance. Based on this evidence, we hypothesized that STK38 might act as a mediator of RFC4 in regulating autophagy. Consistent with this hypothesis, STK38 has been reported to directly regulate autophagy through BECN1. Therefore, we selected STK38 for further investigation. The new data were included in Figure 4b, 4c, and Supplementary figure 4 and the related description were added in lines 213-219 in the revised manuscript. 4 - In Fig 4G following TMZ treatment the level of STK38 was increased a lot. Is it expected? can this have an impact on the Pearson coefficient? it would be interesting to have the Manders coefficient instead. Response: We appreciate the reviewer’s valuable comment. The substantial increase in STK38 levels upon TMZ treatment was not initially anticipated. In light of the reviewer’s comments, we fully agree that elucidating this regulatory relationship is of significant importance. Through ATAC-seq and promoter luciferase assays, we ultimately identified that TMZ induces chromatin remodeling, leading to the upregulation of YY1. The increased YY1 then functioned as a transcription factor to directly enhance STK38 transcription. Detailed description of TMZ-mediated STK38 regulation has been provided above in our response to the reviewer’s second comment. Moreover, we thank the reviewer’s important suggestion regarding the Manders coefficient. Accordingly, in the revised manuscript, we re-analyzed the co-localization data using the Manders coefficient (M1 and M2), which is particularly better suited for such scenarios as it quantifies the fraction of fluorescence signal from one channel that overlaps with signal from another channel, independent of intensity variations. Since RFC4 and STK38 could both be regulated by the same transcription factor YY1, and RFC4 further enhanced the protein stability of STK38, a positive correlation existed between RFC4 and STK38 at both the RNA and protein levels. The new data were included in Figure 4g-4i and the related description were added in lines 225-228 in the revised manuscript. 5 - The depletion of STK38 mediated by the shRNA in Figure 5E does not seem really efficient, however there is an impact on LC3-II level? how can the authors explain that? can another shRNA targeting STK38 be used? Response: We thank the reviewer for their careful observation regarding the STK38 knockdown efficiency and its functional impact. We acknowledge that while the knockdown efficiency was likely adequate, the original western blot data were not sufficiently clear due to issues of antibody specificity (STK38 Polyclonal Antibody, PA5-76351, ThermoFisher). To thoroughly address this concern and strengthen our conclusions, we have performed additional experiments as suggested. First, we obtained a new and highly specific anti-STK38 antibody (STK38 Monoclonal Antibody (2G8-1F3), H00011329-M01 Novus Biologicals), which provided clear and specific detection on STK38. We updated the antibody information in line 478 in the Methods section of the revised manuscript. Second, we employed two distinct shRNAs targeting independent sequences of STK38 to validate its knockdown and its effect on LC3-II levels. Using this optimized approach, the new data consistently demonstrated that both shRNAs effectively reduced STK38 protein expression. Importantly, corresponding changes in LC3-II levels were reliably observed in both independent knockdown experiments, reinforcing the conclusion that STK38 depletion affected autophagy flux. The new data were included in Figure 5h and 5i and the related description were added in lines 277-279 in the revised manuscript. 6 - In order to properly measure autophagy flux in Figures 2F, 5E, authors should evaluate the difference in the amount of LC3-II in both the presence and absence of inhibitors that prevent lysosomal degradation (chloroquine, BAFA1 etc…) see “Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)”. Therefore, the following sentence “RFC4 overexpression in GBM cells leaded to an increase of LC3B-Ⅱ, indicating activation of autophagy » line 169-169 is wrong. Response: We sincerely thank the reviewer for this critical comment, which guided us to the authoritative guidelines in the field. Following the recommendation, we conducted additional experiments in which cells were treated with BafA1 to inhibit lysosomal degradation. The new results clearly demonstrated that RFC4 overexpression resulted in a greater accumulation of LC3-II upon BaA1 treatment compared to the control, confirming an increase in autophagic flux rather than merely a static change in LC3-II levels. Accordingly, we added new data and revised the relevant text, including the indicated sentence by the reviewer, to accurately reflect the findings in accordance with established guidelines. Again, we apologized for the oversight and thank the reviewer once again for this insightful comment, which has greatly strengthened the rigor of our study. The new data were included in Figure 2i and 5i and the related description were added in lines 187-189 and 277-279 in the revised manuscript. 7 - Fig1K was not convincing. Response: We are grateful to the reviewer for their insightful comment. In the original manuscript, we sought to demonstrate that TMZ treatment upregulates RFC4 expression through the chromatin structural changes depicted in Fig. 1K. We agree with the reviewer that this interpretation was not convincing enough. To provide a more accurate and robust explanation for the TMZ-induced increase in RFC4 expression, we performed a series of additional, more rigorous experiments in the revised manuscript. The details of these experiments were thoroughly described in our response to the reviewer’s second comment. Briefly, TMZ treatment enhanced chromatin accessibility at the YY1 promoter, which upregulated YY1 expression. Subsequently, YY1 functioned as a transcription factor that specifically bound to the RFC4 promoter and activates its transcription. 8 - Why the same geneset was not used in Fig2 and S2? Response: We thank the reviewer for raising this point regarding the use of different gene sets in Fig. 2 and Fig. S2, which made us realize that our initial explanation for selecting distinct databases was not sufficiently clear. In Fig. 2, we used both CGGA_GBM and TCGA_GBM datasets to establish the foundational relationship between RFC4 expression and autophagy activation in a broad GBM population. In Fig. S2, however, we intentionally employed the CGGA database, as it includes a substantially larger number of recurrent GBM patient samples compared to TCGA. This enabled us to examine whether the association between the RFC4-autophagy also existed in recurrent GBM. In addition, prompted by the reviewer’s comment, we realized that only one subset of the CGGA database was used in Fig. 2, whereas two subsets were included in Fig. S2. To make our findings more robust, we have now incorporated the second CGGA subset into Fig. 2 as well. The new data were included in Figure 2a in the revised manuscript. 9 - On Fig. 2D, in the text authors indicate Immunofluorescence but it is not the case as in fact it is a probe that it was used. Authors should be more precise and indicated that it is a tandem probe etc… Response: We sincerely thank the reviewer for pointing out this error, which we had not recognized previously. According to the reviewer’s suggestion, we have corrected the issue by replacing the relevant immunofluorescence (IF) descriptions with the more accurate terms “the eGFP-mRFP-LC3B fluorescent reporter system” or “tandem probe.” 10 - In several Figures panels are not well positioned, they should be displayed in the reading direction. Response: We thank the reviewer for this valuable suggestion. As suggested by the reviewer, we repositioned the panels in the relevant figures to follow a clear left-to-right and top-to-bottom reading order in the revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided an important body of work and have addressed all of my comments. In my opinion, the manuscript is now suitable for publication in Nature Communications. However, I may have overlooked it, but I did not find information in the Materials and Methods section regarding the in vivo use of chloroquine and SAR405 (e.g., dosage, timing, and administration). This information should be added, as it concerns newly performed experiments.", "reviewer_2": "Thanks Authors making a lot of efforts on improving the manuscript according the the reviews'a comments. In the revised manuscript, Authors clearly made the modification and clarified my concens point by point. I have no further comments, and this version of manuscript is suitable for publication on Nature Communication.", "reviewer_3": "The manuscript has been substantially improved compared to the previous version, and several of the original concerns have been addressed. Many of the structural inconsistencies raised during the first round of review have been corrected or removed. In particular, the new data supporting the solubility and folding of E. coli–expressed RFC4 are convincing, and the overall quality of the structural models has improved (although, it should be noted that kinases bind two Mg²⁺ ions and one ATP molecule). However, a number of important issues remain and should be addressed before the manuscript can be considered further. 1) The authors addressed fully my query about “protein stability”. However, figure 5d shows protein presence/quantity over time, not thermodynamic stability (e.g. changes in melting temperature or folding free energy). The terminology should therefore be corrected to more accurate descriptions such as protein lifetime, protection from degradation, or reduced proteasomal turnover. 2) Regarding Figure 5c, I recommend that the authors carefully re-examine the interaction annotations (dotted lines). Some lines do not clearly connect residues from different proteins, while others appear to link chemically implausible residue pairs (e.g. charged residues such as D137 with hydrophobic residues such as W148, or K80 and P140). This likely suggest a sidechain to backbone interaction, but the backbone is not shown in the figure, hence the confusion. 3) Line 616 – The sentence is not accurate. The statement ‘The STK38 T444A mutant structure was generated through homology modeling using the AlphaFold web server with wild-type STK38 as template’ is incorrect. If the AlphaFold web server was used, this does not constitute homology modelling but rather an AI-based structure prediction. The authors should simply state that both the wild-type and T444A mutant structures were generated using the AlphaFold web server. The revised and simplified structural model of the RFC4–STK38–BECN1 complex reveals a conceptual inconsistency that was not evident in the initial version. If phosphorylation of T444 is required for binding, and the T444A mutation prevents this phosphorylation, what evidence supports the claim that T444A additionally induces changes that further destabilise the complex? The data presented do not support this distinction, as the observed effects can be fully explained by the absence of phosphorylation alone. Moreover, the authors do not present evidence that the non-phosphorylated wild-type protein is capable of forming the complex, for example through inhibition of upstream kinases. If the RFC4–STK38–BECN1 interaction mechanism is conserved with previously reported data, in which phosphorylation of T444 induces a conformational changes in the HM domain (e.g. https://doi.org/10.1016/j.cellsig.2014.04.011), then capturing phosphorylation-induced conformational effects (of just STK38) would require advanced simulation approaches. Given the limited reliability of such simulations, any conclusions would necessarily require experimental validation, which falls outside the primary scope and objectives of this study. More importantly, this undermines the rationale for generating a structural model of the complex: if an STK38 'open' conformation is required for binding, then in the 'closed' conformation (as in the T444A mutant), binding and complex formation would not be expected to occur in the first place. I would therefore advise removing this modelling section entirely. However, if the authors wish to retain a predictive analysis of the RFC4–STK38–BECN1 complex, there are additional isues to be considered: (i) The PPI-Graphomer analysis compares STK38-WT with STK38-T444A, whereas the biologically relevant comparison should be between phosphorylated STK38-T444, and either the non-phosphorylated or STK38-T444A. (ii) In Figure 6e, it is difficult to identify the location of the zoomed-in region within the full complex. I recommend: (i) retaining the protein backbone to provide spatial context; (ii) maintaining a consistent orientation between the overall structure and the zoomed region; and (iii) indicating the zoomed area on the full structure (e.g. with a bounding box). (iii) The interaction surfaces depicted in Figures 5c and 6e are still inconsistent. In Figure 5c, the interaction involves residues in the N-terminus of RFC4, whereas in Figure 6e STK38 appears to bind the C-terminal region of RFC4. The authors have presented no experimental data to suggest this is the case. The molecular dynamics simulations raise several significant concerns: a) In line 317, the authors refer to “fluctuations,” but the analysis presented corresponds to RMSD, not RMSF (Root Mean Square Fluctuation). While an RMSF analysis could provide additional information, its value would remain limited, as the RMSD traces suggest that the system has not yet reached a stable plateau, indicating incomplete equilibration. b) A simulation length of 100 ns is far too short to assess mutation-induced effects in a predicted model of a large multiprotein complex (hence the incomplete equilibration). c) Even when assessing the effect of the T444A mutation on STK38 alone (which was my original suggestion in the first review) this simulation timescale would still be insufficient. Recent studies addressing comparable questions typically employ simulations ranging from ~500 ns to 1-2 microseconds. d) For the full complex, all-atom MD at this timescale is unlikely to yield meaningful insight into global rearrangements due to severely limited sampling. More appropriate approaches would include enhanced sampling or coarse-grained methods. But as stated above, this would first require understanding of the phosphorylation induced conformational changes of STK38. e) Finally, the conclusions drawn from the MD simulations are overstated. The observed RMSD difference (~0.5 Å) is minimal and most likely reflects the equilibration of a large complex rather than mutation-specific effects. The convergence of wild-type and mutant trajectories to similar RMSD values at 100 ns further suggests the absence of meaningful structural differences within the sampled timeframe. This could be readily confirmed by direct comparison of the final structures, which are likely to be very similar."}, "author_response": "Response to Reviewer #1 and #3. Reviewer #1 The authors have provided an important body of work and have addressed all of my comments. In my opinion, the manuscript is now suitable for publication in Nature Communications. However, I may have overlooked it, but I did not find information in the Materials and Methods section regarding the in vivo use of chloroquine and SAR405 (e.g., dosage, timing, and administration). This information should be added, as it concerns newly performed experiments. Response: Thanks for the comment, and we have added the detailed information on the in vivo use of chloroquine and SAR405 in the revised Methods section (Line 641 - 650). Reviewer #3 The manuscript has been substantially improved compared to the previous version, and several of the original concerns have been addressed. Many of the structural inconsistencies raised during the first round of review have been corrected or removed. In particular, the new data supporting the solubility and folding of E. coli–expressed RFC4 are convincing, and the overall quality of the structural models has improved (although, it should be noted that kinases bind two Mg²⁺ ions and one ATP molecule). However, a number of important issues remain and should be addressed before the manuscript can be considered further. 1) The authors addressed fully my query about “protein stability”. However, figure 5d shows protein presence/quantity over time, not thermodynamic stability (e.g. changes in melting temperature or folding free energy). The terminology should therefore be corrected to more accurate descriptions such as protein lifetime, protection from degradation, or reduced proteasomal turnover. 2) Regarding Figure 5c, I recommend that the authors carefully re-examine the interaction annotations (dotted lines). Some lines do not clearly connect residues from different proteins, while others appear to link chemically implausible residue pairs (e.g. charged residues such as D137 with hydrophobic residues such as W148, or K80 and P140). This likely suggest a sidechain to backbone interaction, but the backbone is not shown in the figure, hence the confusion. 3) Line 616 – The sentence is not accurate. The statement ‘The STK38 T444A mutant structure was generated through homology modeling using the AlphaFold web server with wild-type STK38 as template’ is incorrect. If the AlphaFold web server was used, this does not constitute homology modelling but rather an AI-based structure prediction. The authors should simply state that both the wild-type and T444A mutant structures were generated using the AlphaFold web server. The revised and simplified structural model of the RFC4–STK38–BECN1 complex reveals a conceptual inconsistency that was not evident in the initial version. If phosphorylation of T444 is required for binding, and the T444A mutation prevents this phosphorylation, what evidence supports the claim that T444A additionally induces changes that further destabilize the complex? The data presented do not support this distinction, as the observed effects can be fully explained by the absence of phosphorylation alone. Moreover, the authors do not present evidence that the non-phosphorylated wild-type protein is capable of forming the complex, for example through inhibition of upstream kinases. If the RFC4–STK38–BECN1 interaction mechanism is conserved with previously reported data, in which phosphorylation of T444 induces a conformational changes in the HM domain (e.g. https://doi.org/10.1016/j.cellsig.2014.04.011), then capturing phosphorylation-induced conformational effects (of just STK38) would require advanced simulation approaches. Given the limited reliability of such simulations, any conclusions would necessarily require experimental validation, which falls outside the primary scope and objectives of this study. More importantly, this undermines the rationale for generating a structural model of the complex: if an STK38 'open' conformation is required for binding, then in the 'closed' conformation (as in the T444A mutant), binding and complex formation would not be expected to occur in the first place. I believe that the data presented, without the 3D structural model of the complex, are enough to state/speculate that T444 may either contribute directly to the interaction interface and/or regulate binding indirectly by modulating the open to close conformation. I would therefore advise removing this modelling section entirely. However, if the authors wish to retain a predictive analysis of the RFC4–STK38–BECN1 complex, there are additional issues to be considered: (i) The PPI-Graphomer analysis compares STK38-WT with STK38-T444A, whereas the biologically relevant comparison should be between phosphorylated STK38-T444, and either the non-phosphorylated or STK38-T444A. (ii) In Figure 6e, it is difficult to identify the location of the zoomed-in region within the full complex. I recommend: (i) retaining the protein backbone to provide spatial context; (ii) maintaining a consistent orientation between the overall structure and the zoomed region; and (iii) indicating the zoomed area on the full structure (e.g. with a bounding box). (iii) The interaction surfaces depicted in Figures 5c and 6e are still inconsistent. In Figure 5c, the interaction involves residues in the N-terminus of RFC4, whereas in Figure 6e STK38 appears to bind the C-terminal region of RFC4. The authors have presented no experimental data to suggest this is the case. The molecular dynamics simulations raise several significant concerns: a) In line 317, the authors refer to “fluctuations,” but the analysis presented corresponds to RMSD, not RMSF (Root Mean Square Fluctuation). While an RMSF analysis could provide additional information, its value would remain limited, as the RMSD traces suggest that the system has not yet reached a stable plateau, indicating incomplete equilibration. b) A simulation length of 100 ns is far too short to assess mutation-induced effects in a predicted model of a large multiprotein complex (hence the incomplete equilibration). c) Even when assessing the effect of the T444A mutation on STK38 alone (which was my original suggestion in the first review) this simulation timescale would still be insufficient. Recent studies addressing comparable questions typically employ simulations ranging from ~500 ns to 1-2 microseconds. d) For the full complex, all-atom MD at this timescale is unlikely to yield meaningful insight into global rearrangements due to severely limited sampling. More appropriate approaches would include enhanced sampling or coarse-grained methods. But as stated above, this would first require understanding of the phosphorylation induced conformational changes of STK38. e) Finally, the conclusions drawn from the MD simulations are overstated. The observed RMSD difference (~0.5 Å) is minimal and most likely reflects the equilibration of a large complex rather than mutation-specific effects. The convergence of wild-type and mutant trajectories to similar RMSD values at 100 ns further suggests the absence of meaningful structural differences within the sampled timeframe. This could be readily confirmed by direct comparison of the final structures, which are likely to be very similar. Response: We sincerely appreciate the reviewer's valuable suggestions and have revised the manuscript as below in response to the concerns. (1) According to the reviewer’s suggestion, we have replaced “protein stability” with “protein lifetime” in the 2nd revised manuscript (Line 253, 254, 262, 358, 599, and 600). (2) Regarding protein structure simulation, PPI-Graphomer analysis, and Molecular Dynamics, we sincerely thank the reviewer for the expert and insightful comments. Frankly, protein structure analysis is not our core expertise, and despite seeking assistance from specialized teams, our data, just as pointed out by the reviewer, still contain inconsistencies with the conclusions. Therefore, following the reviewer's suggestion, we have removed the sections containing uncertain structural predictions. Briefly, we have removed Fig. 5c, 6e, and 6f, as well as all contents associated with the predictive structural models of the RFC4-STK38 and RFC4-STK38-BECN1 complexes, the PPI-Graphomer analysis, and the molecular dynamics simulations in the sections of Results, Discussion, and Methods in the 2nd revised manuscript."}]

Decision letter