Targeting Prolyl 3-hydroxylase 1 inhibits pancreatic cancer progression and macrophage immunity

Domain: biology
Published: 2026-03-13
Editorial outcome: major_revision

Abstract
Pancreatic ductal adenocarcinoma remains one of the most formidable challenges in oncology, with limited treatment options and a poor prognosis. Understanding the key pathways affecting cancer progression is crucial for the development of therapeutic strategies. Here, we reveal a pivotal role of Prolyl 3-hydroxylase 1 in pancreatic ductal adenocarcinoma using transcriptome sequencing, proteomic analyses and engineered mouse model. Mechanistically, our findings indicate that this effect is, at least in part, through the regulation of Polo-like kinase 1 and Polo-like kinase 1-mediated β-catenin signaling. Restoration of either Prolyl 3-hydroxylase 1 or Polo-like kinase 1 expression in Prolyl 3-hydroxylase 1-deficient cells reverse the defects of β-catenin signaling, facilitates tumor cell proliferation and elicits macrophage infiltration. In addition, pharmacological inhibition of Polo-like kinase 1 strongly increases the therapeutic efficacy of chemotherapeutic response against pancreatic ductal adenocarcinoma, alleviating tumor burden in mice. Our findings suggest a promising therapeutic strategy for treating pancreatic ductal adenocarcinoma.

Introduction
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide with over 467,005 death cases in 20221. More concerningly, it is estimated that PDAC will surpass colorectal cancer by 2040 as the second leading cause of cancer-related death 2, 3. The overall 5-year survival rate for pancreatic cancer is approximately 10%, primarily due to the absence of reliable biomarkers for early detection, the lack of effective late-stage treatments and widespread resistance to chemotherapy 4, 5. Despite revolutionized advances in immunotherapy and targeted treatments improving outcomes in various cancers, these approaches have achieved limited success in PDAC6. Gemcitabine-based chemotherapy has been the primary chemotherapeutic treatment for pancreatic cancer patients since 1997, and current clinical guidelines still recommend either gemcitabine alone or in combination therapies 7, 8. Unfortunately, combination chemotherapy generally results in only short-term, partial remission in newly diagnosed PDAC patients 9. Given these limitations, there is an urgent need to identify novel therapeutic targets and to develop more effective and durable treatment strategies for PDAC.

PDAC has a unique tumor immune microenvironment (TIME) with numerous immunosuppressive factors 10. These factors include infiltrating immune cells and cytokines that create an immunologically "cold" environment, hindering the effectiveness of therapeutic interventions. Among the immune cells, tumor-associated macrophages (TAMs) are closely related to worse prognosis, distant metastasis, and drug resistance in pancreatic cancer 11, 12. TAMs are recruited to the tumor site by tumor-secreted cytokines such as CXCL1, CXCL5 and CXCL1213. Therefore, targeting TAMs offers a promising strategy for enhancing immune-based therapies and potentially impeding tumor progression14, 15.

Post-translational modifications are fundamental drivers of cancer pathogenesis by influencing protein functionality16. The prolyl-hydroxylase family are involved in the post-translational modification of target proteins, particularly in the hydroxylation of proline residues17. Prolyl 3-hydroxylase 1 (P3H1), belonging to the P3Hs family, was identified as a basement membrane-associated proteoglycan with a core protein of 804 residues (90 kDa) 18. It contains an RGD attachment site for cell-matrix interactions and a KDEL sequence for endoplasmic reticulum retention, indicating its roles in both extracellular matrix interactions and intracellular secretory pathways 19. Previous studies indicate that P3H1 is crucial for bone development and skin integrity through regulating intracellular modification of type I collagen20, 21, 22, 23. Recently, limited evidence suggests that P3H1 is upregulated in various cancers and may play roles in cancer progression according to a large of bioinformatics analyses24, 25, 26. However, to date, the role of P3H1 on PDAC progression and TIME regulation and the related mechanisms have rarely been investigated.

Serine/threonine kinases are key players in the development of pancreatic cancer, impacting cell proliferation and metastasis by phosphorylating their substrate proteins27, 28, 29. Polo-like kinase 1 (PLK1), a highly conserved serine/threonine protein kinase, has been reported to be overexpressed in various cancers30, 31, 32 and contribute to tumor initiation, migration and resistance to therapy32, 33. PLK1 promotes tumorigenesis and drug resistance through phosphorylating various substrates 34 which are involved in numerous cellular events including DNA replication, transcription, translation, chromosomes dynamics, microtubule dynamics, apoptosis and organogenesis35. Therefore, characterization of the potential PLK1 substrates will provide valuable insights into understanding the PLK1-mediated cellular events and help to develop anti-cancer agents.

Aberrant activation of the Wnt/β-catenin signaling pathway has been associated with tumor progression and metastasis of pancreatic cancer 36, 37, 38. As a core component of Wnt signaling, β-catenin is tightly regulated by various post-translational modifications, including phosphorylation 39, 40. Previous studies reported that the C-terminal phosphorylation of β-catenin at Ser191 and Ser605 controls its nuclear translocation 41. After entering into the nucleus, β-catenin binds to transcriptional factors of the T-cell factor (TCF)/lymphoid enhancer factor (LEF) family, thereby inducing the transcription of oncogenic genes, such as c-Myc and Cyclin D1, for tumor initiation and progression 42, 43, 44. In addition, β-catenin is a critical regulator of tumor immune microenvironment (TIME) by influencing immune cell infiltration and modulating cytokine secretion, including CXCL1 45, 46, 47. More importantly, growing evidences show Wnt/β-catenin signaling contributes to gemcitabine resistance in PDAC 48. Therefore, the role of β-catenin played in tumor progression, TIME remodeling and drug resistance highlights its therapeutic value in PDAC.

In this study, we demonstrate that P3H1 is overexpressed in PDAC and is associated with poor patient prognosis. Genetic ablation of P3H1 delays PDAC progression and reduces tumor-associated macrophage infiltration by inhibiting the PLK1-β-catenin pathway. Targeting P3H1 or pharmacologically inhibiting PLK1 enhances the efficacy of chemotherapy in PDAC. These results highlight the role of P3H1-PLK1-β-catenin axis in PDAC progression and suggest a promising therapeutic strategy for PDAC treatment.

Results
P3H1 is upregulated and associated with poor prognosis of PDAC
Due to the critical role of tumor-secreted proteins in influencing tumor growth and the tumor immune microenvironment (TIME), our study aimed to identify secreted proteins that drive the pathogenesis of pancreatic ductal adenocarcinoma (PDAC). On the basis of our recent published the glycosylated secreted and membrane protein (S-PM) database resource 49, we conducted a re-analysis from a perspective and applied a more stringent filtering criterion (log₂FC > 0, FDR < 0.05) to identify high-confidence subsets of 226 upregulated secreted proteins in the human S-PM protein PDAC dataset and 233 upregulated secreted proteins in the LSL-KrasG12D/+; p53fl/fl; Pdx1-Cre (KPC) mouse S-PM protein dataset (Figure 1A and Supplementary Figure 1). Additionally, we curated a focused screening set of 227 ECM-annotated proteins from the human S-PM protein PDAC dataset (Figure 1B). The intersection of these three independent subsets yielded a consensus set of 34 candidate proteins whose expressions were significantly and consistently elevated in both human and mouse samples (Figure 1, A and B). One of the identified candidates, Prolyl 3-hydroxylase 1 (P3H1), an essential enzyme implied in collagen metabolism and bone homeostasis22, 23, was chosen for further investigation due to its consistent inducible expression patterns observed throughout the development of human and mouse pancreatic ductal adenocarcinoma (PDAC) (Figure 1, B and C). Additionally, P3H1 displayed higher differential expression in human PDAC versus normal tissues, as evidenced by the human S-PM protein dataset49 (Figure 1, B and C, P = 3.96e-13) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort RNA-seq dataset50 (Supplementary Figure 2 and 3, P = 9.0e-17). It also showed a stronger positive correlation with stromal abundance (Supplementary Figure 2 and 4, ρ = 0.42, P = 1.96e-8) and a higher hazard ratio for disease-free survival (DFS) (Supplementary Figure 2 and 5, 2.07; 95% CI 1.39 ~ 3.09) compared to other candidates (Figure 1D). To further validate the elevated expression of P3H1 in human PDAC, we conducted P3H1 immunohistochemistry (IHC) analysis on an independent cohort of human tissue microarray (TMA) containing 72 PDAC and 39 non-tumor tissues. Consistent with the S-PM proteome analysis, P3H1 protein expression was found to be minimal in non-tumor human pancreatic tissues, while significantly upregulated in human PDAC samples (Figure 1E, Supplementary Figure 6 and Supplementary Dataset 1). These observations were further confirmed by staining mouse pancreatic tumor samples from KPC mice (Figure 1F). Next, we assessed the clinical relevance of P3H1 expression in the development of pancreatic ductal adenocarcinoma (PDAC) by analyzing the association between P3H1 protein levels and clinicopathologic parameters of pancreatic cancer patients, such as gender, age, tumor node and metastasis (TNM) stage, and lymph node involvement. Our findings revealed a significant correlation between P3H1 expression and TNM stage (P = 0.0216), but no significant associations observed with other clinical parameters (Figure 1G). Furthermore, we investigated the impact of P3H1 mRNA expression on the clinical outcomes of human patients with PDAC using Kaplan-Meier plots from the dataset in KM plotter web server51. The analysis revealed that higher level of P3H1 was associated with a shorter disease-free survival (Figure 1H). Concurrently, Kaplan-Meier analysis indicated that pancreatic ductal adenocarcinoma (PDAC) patients exhibiting higher P3H1 protein expression experienced poorer prognoses compared to those with lower P3H1 levels (Figure 1I). Collectively, these findings strongly suggest a crucial role of P3H1 in the pathogenesis of pancreatic cancer.

P3H1 deletion hinders PDAC progression
To experimentally examine whether P3H1 contributes to PDAC malignancy, we generated pancreatic cell-specific P3H1 knockout mice by crossing P3H1fl/+ mice with Pdx1-Cre mice. First, we investigated the impacts of P3H1 deletion on normal pancreas development. Histologic analysis revealed that the morphology of endocrine and exocrine glands from 1-year-old P3H1 cKO mice (P3H1fl /fl; Pdx1-Cre) did not exhibit any abnormalities as compared with WT control mice (P3H1+/+; Pdx1-Cre), suggesting depletion of P3H1 did not impair normal pancreatic tissue development (Supplementary Figure 7). Next, we intercrossed P3H1fl/+; Pdx1-Cre mice with LSL-KrasG12D/+;p53fl/fl; Pdx1-Cre (KPC) mice to generate two experimental groups of mice: LSL-KrasG12D/+;p53fl/fl;Pdx1-Cre;P3H1+/+ (KPC;WT) and LSL-KrasG12D/+;p53fl/fl; Pdx1-Cre;P3H1fl/fl (KPC; P3H1 cKO) littermate mice (Figure 2A). Both tail genome PCR analysis and immunohistochemistry staining of mouse pancreatic tumor sections indicated that KPC;P3H1 cKO mice were successfully established (Figure 2, B and C). Immunoblotting analysis of isolated mouse primary pancreatic cancer cells (PCCs) confirmed that P3H1 was specifically diminished in tumor cells from KPC;P3H1 cKO mice (Figure 2D). Then, we examined the gross phenotypes of KPC;WT and KPC;P3H1 cKO mice. Consistent with the previous reports52, 53, the median survival of KPC;WT mice was around 64 days. Whereas KPC;P3H1 cKO mice had a remarkably extended median survival of 76 days. Exceeding 15% survival extension are considered significant and clinically relevant in aggressive PDAC mouse models 54, 55, 56. The observed 12-day median survival increase (from 64 days to 76 days) in KPC P3H1 cKO mice represented approximately a 20% extension in lifespan and should be considered a marked survival benefit, suggesting P3H1 deletion in KPC mice greatly prolonged survival (Figure 2E).

To determine the cause of prolonged survival in KPC;P3H1 cKO mice, we isolated the pancreas from 7-week-old KPC mice, a time point when the pancreatic tissues are occupied by both low- and high-grade pancreatic tumors 57. The results showed that the weight of pancreatic tumors isolated from KPC;P3H1 cKO mice was significantly reduced compared with that from KPC;WT mice (Figure 2F). Moreover, histological analysis revealed that a predominant increase of normal pancreatic histology in KPC;P3H1 cKO mice (Figure 2G). Approximately 92% pancreas were replaced by pancreatic intraepithelial neoplasias (PanINs) or PDAC in 7-week-old KPC;WT mice, whereas only 66% pancreas with altered acinar architecture in KPC;P3H1 cKO mice (Figure 2G). Accordingly, the immunohistochemical (IHC) staining of Cytokeratin 19 (CK19) and Epithelial cell adhesion molecule (EPCAM), two well-known markers for epithelial tumors, showed that a dramatic reduction of tumor areas in KPC;P3H1 cKO mice (Figure 2, H and I). The fibrotic areas, measured by α-smooth muscle actin (α-SMA) and type I collagen, were also decreased in tumor sections from KPC;P3H1 cKO mice (Supplementary Figure 8). Collectively, these results strongly suggested P3H1 deletion significantly alleviates PDAC progression in KPC mice.

P3H1 deletion inhibits pancreatic cancer cell proliferation
Pancreatic cancer cells exhibit excessive proliferation abilities 6. To dissect whether P3H1 depletion delayed PDAC progression through influencing pancreatic cancer cell proliferation, we performed Ki-67 staining. The results showed that a significant decrease in the percentage of Ki-67-positive cells in KPC;P3H1 cKO tumor lesions compared to KPC;WT tumors (Figure 2J). Consistently, staining of another proliferative marker protein proliferating cell nuclear antigen (PCNA) also showed a reduction of the percentage of proliferative cells in KPC;P3H1 cKO tumor lesions (Figure 2K). To confirm these findings in human PDAC, we knocked down P3H1 in MIA PaCa-2 and PANC-1 cells, two widely used human-derived PDAC cells (Figure 2L), and performed in vitro cell proliferation assays. Results indicated that pancreatic cancer cells lacking P3H1 displayed lower proliferation rate and lower frequencies of colony formation, indicating that loss of P3H1 has a strong inhibitory effect on pancreatic cancer cell proliferation (Figure 2, M-O). Collectively, these results indicated that loss of P3H1 significantly impedes pancreatic cancer cell proliferation and delays PDAC progression.

P3H1 deletion decreases CXCL1/CXCL5/CXCL8-mediated M2-like TAMs infiltration in PDAC
Growing evidences indicated that cancer cells could reshape the tumor immune microenvironment (TIME), especially the tumor-infiltrating immunosuppressive cells, to facilitate their growth 10, 58. To clarify whether the reduced PDAC progression, upon P3H1 deficiency, were associated with changes in TIME, we analyzed the relationship between P3H1 expression and tumor-infiltrating immunosuppressive cells in pancreatic cancers through TIMER2.0 database 59. The results showed that P3H1 expression exhibited positively correlated with the infiltration of tumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs), but was not correlated with other immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells in PDAC (Supplementary Figure 9). We then validated the correlation between P3H1 and the abundance of macrophages and neutrophils by immunohistochemistry (IHC) staining and found a strong reduction of F4/80+ macrophages, but not Ly6G+ neutrophils infiltration in tumor sections in KPC;P3H1 cKO mice (Figure 3, A and B). Previous studies report that macrophages in PDAC are primarily composed of immunosuppressive M2-like macrophages 60, 61. Indeed, IHC analysis revealed that KPC;P3H1 cKO tumors exhibited much fewer M2-like macrophages (represented by CD68 and Arginase I) as compared to KPC;WT tumors (Figure 3, C and D), suggesting that loss of P3H1 remodels the TIME, especially by inhibiting the number of M2-like TAMs. Given that M2-like TAMs facilitate cancer cell proliferation by secreting tumor-promoting factors such as PDGF and FGF262, 63, we investigated whether P3H1 deletion could modulate the functional state of M2-like TAMs and thereby impact tumor growth. Notably, tumor sections from KPC;P3H1 cKO mice demonstrated a significant reduction in PDGF-B and FGF2 expression compared to KPC;WT controls (Figure 3, E and F). To validate this, macrophages treated in vitro with conditioned media from P3H1-deficient MIA PaCa-2 cells showed decreased transcriptional levels of PDGF-B and FGF2 (Supplementary Figure 10). These findings suggested that P3H1 depletion not only decreased the abundance of M2-like TAMs but also diminished their pro-tumorigenic activity.

To comprehensively uncover how depletion of P3H1 in tumor cells inhibits TAMs abundance and pro-tumorigenic activity, we conducted RNA sequencing analysis with P3H1 knockdown or control pancreatic cancer cells. The Gene Ontology (GO) enrichment analysis conducted on down-regulated genes in P3H1-depleted cells revealed significant enrichment for gene sets related to cell cycle, regulation of protein phosphorylation, and cytokine-mediated signaling pathway (Figure 3G and Supplementary Figure 11). Moreover, gene set enrichment analysis (GSEA) revealed that PDAC with low level of P3H1 exhibited downregulation of cytokine production (Figure 3H). Considering the important role of secreted cytokines from tumor cells in the regulation of immune cell infiltration and activities, we performed the secretome proteomics approaches to unveil the difference of secreted cytokine levels between P3H1-deficient pancreatic cancer cells and wild-type cells (Figure 3I). Very interestingly, the results showed that depletion of P3H1 in MIA PaCA-2 cells significantly reduced the secretion of a series of cytokines, including CXCL1,CXCL5, CXCL8 and TGF-β (Figure 3J and Supplementary Dataset 2), which are well-known for their capability to induce TAMs infiltration and polarization 64, 65, 66, 67, 68, 69, 70. These findings were confirmed by analyzing the total and secreted CXCL1/CXCL5/CXCL8 proteins from P3H1-depleted pancreatic cancer cells using immunoblotting analysis and enzyme-linked immunosorbent assay (ELISA) (Figure 3, K-P). However, P3H1 knockdown did not significantly alter total TGF-β protein level and only modestly decreased secreted TGF-β (Supplementary Figure 12), indicating that TGF-β may not be the primary cytokine involved in P3H1-mediated regulation of TAMs abundance. Concordantly, P3H1 ablation led to a pronounced decrease of CXCL1 and CXCL5 in mouse tumor tissues isolated from KPC; P3H1 cKO mice than that from KPC; WT mice (Figure 3, Q and R). The mRNA levels of CXCL1, CXCL5 and CXCL8 in tumor cells were also diminished by P3H1 ablation (Supplementary Figure 13). The next question to investigate is the role of CXCL1, CXCL5 and CXCL8 in P3H1-mediated regulation of TAMs abundance. Given CXCL1, CXCL5 and CXCL8 are key cytokines responsible for TAMs recruitment, we first explored whether P3H1-deficiency-induced inhibition of TAMs recruitment was dependent on these cytokines. To do this, we conducted chemotaxis assays and found that the culture media from P3H1-deficient cells exhibited reduced ability to recruit macrophages compared with that from wild-type cells (Figure 3S). Importantly, forced increase of CXCL1, CXCL5 or CXCL8 levels by adding the recombinant cytokines in the culture media from P3H1-deficient cells effectively reversed the P3H1-deficiency-induced inhibition of macrophage migration (Figure 3S). In addition to recruiting TAMs to the tumor site, CXCL1, CXCL5, and CXCL8 are known to play critical roles in inducing TAMs polarization 68, 69, 70. Therefore, we next assessed whether P3H1 depletion influenced TAMs polarization, and whether these effects were mediated by CXCL1, CXCL5 or CXCL8. The results demonstrated that P3H1 knockdown in tumor cells decreased the proportion of M2-like TAMs (CD14+CD163+ TAMs), while M1-like TAMs (CD14+CD86+) remained unaffected, consistent with our previous in vivo observations (Figure 3C, 3D, 3T, and Supplementary Figure 14). Importantly, reintroducing recombinant CXCL1, CXCL5 or CXCL8 into the culture media from P3H1-depleted cancer cells restored M2-like TAMs polarization, and modestly enhanced tumor cell proliferation (Figure 3T, 3U and Supplementary Figure 14). Collectively, these findings strongly indicate that P3H1 loss reshapes the TIME by inhibiting CXCL1, CXCL5/CXCL8-dependent M2-like TAMs infiltration in PDAC.

P3H1 deletion reduces PLK1 protein expression
To elucidate the mechanisms by which P3H1 regulates pancreatic cancer progression and resets TIME, we performed proteomic analysis to identify potential downstream molecules regulated by P3H1. The results showed that 172 proteins were up-regulated and 110 proteins were down-regulated in wild-type MIA PaCa-2 cells compared to P3H1 siRNA#1 cells (Figure 4A and Supplementary Dataset 3). We also confirmed these observations in P3H1 siRNA#2 MIA PaCa-2 and in another human pancreatic cancer cells PANC-1 (Figure 4, B and C and Supplementary Dataset 3). Of note, analyses of both cell lines revealed a significant down-regulation of five proteins, including P3H1, in P3H1 knockdown pancreatic cancer cells (Figure 4C and Supplementary Dataset 3). Among these candidates, Polo-like kinase 1 (PLK1) was selected for further study because it was one of the most significantly down-regulated proteins in P3H1 knockdown cells (Figure 4D and Supplementary Figure 15A). Additionally, PLK1 is a serine/threonine kinase that phosphorylates a number of substrates to facilitate cell proliferation 71. Our above-mentioned RNA-seq data indicated that the differentially expressed genes in P3H1 knockdown cells were highly related to those involved in regulation of protein serine/threonine kinase activity (Figure 3G). Moreover, as previously acknowledged 32, 72, higher level of PLK1 expression was associated with poorer clinical outcomes and shorter survival in PDAC patients. Consistently, PLK1 knockdown exerted a more pronounced inhibitory effect on pancreatic cancer cell growth than other candidates (Supplementary Figure 15, B-D), implying that PLK1 plays a critical role in P3H1-mediated PDAC progression. To test this hypothesis, we explored the effect of P3H1 on PLK1 expression level. Consistent with the knockdown results (Figure 4D), overexpression of P3H1 increased PLK1 protein expression in both MIA PaCa-2 and PANC-1 cells (Figure 4E). We validated these findings in mouse pancreatic tumor tissues and obtained similar results. Depletion of P3H1 resulted in a pronounced decrease of PLK1 expression in tumor sections isolated from KPC; P3H1 cKO mice (Figure 4F). Furthermore, human PDAC samples evidenced a significantly higher expression levels of both P3H1 and PLK1 compared to normal pancreatic tissues (Figure 4G). Quantitative analysis indicated a strong positive correlation between P3H1 and PLK1 expression levels in human PDAC (R = 0.53, P = 1.4e-5) (Figure 4G). Although our findings confirmed that P3H1 regulates PLK1 expression, the mechanism by which P3H1 affects PLK1 protein levels remains unclear. To explore this, we tested whether P3H1 stabilized PLK1 through direct hydroxylation, considering P3H1's prolyl hydroxylase activity 17. However, the results demonstrated that P3H1 did not physically interact with PLK1 (Supplementary Figure 16A). Moreover, PLK1 lacked the specific proline residues within the Gly-Pro-Hyp repeating motif, which is known to be targeted by P3H1 17, 22, indicating that P3H1 is unlikely to hydroxylate PLK1 directly (Supplementary Figure 16B). To further assess whether P3H1 indirectly affected PLK1 protein stability, a cycloheximide (CHX) chase assay was conducted. The results indicated no significant difference in the degradation rate of PLK1 between P3H1 knockdown cells and wild-type cells after CHX treatment (Supplementary Figure 16, C and D), suggesting that P3H1 did not influence PLK1 protein stability. We then hypothesized that P3H1 regulated PLK1 expression at the transcriptional level rather than by affecting protein stability. Interestingly, P3H1 depletion resulted in a significant decrease in PLK1 mRNA levels, supporting a potential role for P3H1 in modulating PLK1 transcription (Supplementary Figure 16E).

P3H1 deletion suppresses PLK1-mediated downstream events
Since depletion of P3H1 reduced PLK1 protein expression, we speculated that P3H1 influenced the phosphorylation of PLK1 substrates. To identify the substrates of PLK1 involved in PDAC progression, we applied the phosphoproteomics analysis. A total of 111 phosphorylated proteins were detected in wild-type MIA PaCa-2 cells compared to that in P3H1 depleted cells (Figure 5, A and B and Supplementary Dataset 4). To better delineate PLK1 downstream substrates, we complied these 111 up-regulated phosphorylated proteins in MIA PaCa-2 with 490 PLK1 interactors analyzed by BioGRID database73, and obtained 7 overlapped proteins that might be PLK1 direct substrates (Figure 5B and Supplementary Dataset 4). The heatmap confirmed the increased phosphorylation levels of seven selected proteins in control MIA PaCa-2 cells (Figure 5C). These phosphorylated proteins are known to be involved in a variety of biological processes including cell proliferation (AKAP12 and CTNNB1)74, 75, 76, 77, cell cycle (MCM2)78, 79 , angiogenesis (AKAP12)80, 81 and regulation of TME (CTNNB1) 82, 83 (Figure 5C). Among these candidates, we prioritized CTNNB1 (β-catenin) for further study because it is the only protein known to be involved in both tumor cell proliferation and cytokine secretion modulation, thereby contributing to TME reshaping 75, 76, 82, 83, 84. Phosphoproteomic analysis validated that depletion of P3H1 reduced the phosphorylation level of β-catenin at S191 (Figure 5, D and E). Because phosphorylation of β-catenin at S191 has been reported to be essential for its nuclear localization and target gene regulation 41, we then investigated the impact of P3H1 on β-catenin nuclear expression. The results showed that loss of P3H1 decreased β-catenin nuclear expression analyzed by immunofluorescence staining (Figure 5, F and G). We further discovered that P3H1 deletion inhibited the transcription of β-catenin target genes involved in cell proliferation and cytokine production, such as C-MYC, CCND1, CXCL1, CXCL5 and CXCL8 48, 65, 85 (Figure 5H and Supplementary Figure 13). Consistently, an obvious reduction of c-Myc, Cyclin D1, CXCL1, CXCL5 or CXCL8 protein expression was observed in P3H1 knockdown MIA PaCa-2 cells (Figure 5I, 5J, 3K, 3M, 3O). These findings were further verified in PDAC tumor sections from P3H1 cKO mice (Figure 5K, 5L, 3Q and 3R). In summary, these above data imply a potential signaling axis of P3H1-PLK1-β-catenin in PDAC.

PLK1 expression level is crucial for P3H1-regulated PDAC progression and TIME remodeling
To further investigate whether P3H1-regulated PDAC progression is dependent on PLK1 expression level, we restored P3H1 or PLK1 expression in P3H1 knockdown MIA PaCa-2 cells. As expected, re-expression of P3H1 rescued the down-regulation of PLK1 expression in P3H1-deficient cells, confirming the aforementioned results that P3H1 controlled PLK1 protein expression (Figure 6A). Intriguingly, re-introduction of PLK1, like that of P3H1, into P3H1-deficienct cells, promoted β-catenin nuclear location and its target gene transcription, including C-MYC, CCND1, and CXCL1 (Figure 6, B-D). In line with this, P3H1-deficiency-induced inhibition of c-Myc and Cyclin D1 expression was reversed by the overexpression of P3H1 or PLK1 (Figure 6, E and F). In addition, total expression and secreted CXCL1 and CXCL5 were restored by re-expression of P3H1 or PLK1 (Figure 6, E-H). Consistent with these findings, re-expression of P3H1 or PLK1 in P3H1 knockdown cells increased pancreatic cancer cell proliferation and macrophages recruitment (Figure 6, I-K). We further validated these findings in vivo. NSG mice were orthotopically injected with an equal number of control or P3H1 knockdown MIA PaCa-2 cells expressing full-length P3H1 or PLK1 (Figure 7A). Similar to what we observed in vitro, nude mice injected with P3H1 knockdown cells showed less tumor burden compared to mice injected with control MIA PaCa-2 cells, while re-expression of P3H1 or PLK1 led to a significant increase of the weight and volumes of tumor (Figure 7, A-C). Moreover, overexpression of PLK1, like that of P3H1, increased tumor cell proliferation (Figure 7D) and induced β-catenin-mediated target gene expression, including c-Myc, Cyclin D1, CXCL1 and CXCL5 (Figure 7E). Taken together, these in vitro and in vivo data demonstrate that the effects of P3H1 on PDAC progression are mediated, at least in part, through control of PLK1 protein expression.

P3H1-PLK1 axis inhibition enhances the responsiveness of PDAC to chemotherapy
Gemcitabine (Gem) is a clinical used drug for the treatment of PDAC patients, either alone or in combination with other drugs 86. However, the treatment efficacy is quite low because of Gem resistance 87. As previously acknowledged, activation of β-catenin signaling and increased TAMs infiltration heavily contribute to the development of Gem resistance 11, 88, 89. Because our results suggested that the P3H1-PLK1 axis is crucial for β-catenin activation and TAMs infiltration in PDAC, we postulated that combining P3H1 or PLK1 inhibitor with Gem may improve the efficacy of PDAC therapy. Since no P3H1 inhibitor is available, we first tested the efficacy of combining PLK1 inhibitor BI2536 with Gem in the orthotopic xenograft pancreatic tumor mouse model used in Figure 7A (Figure 7F). Considering that BI2536 has been reported to induce dose-dependent toxicity and efficacy when combined with other drugs90, 91, we evaluated the toxicity of various doses of BI2536 in combined with Gem in this study. Orthotopic pancreatic tumor-bearing mice were treated with Gem combined with varying doses of BI2536 at 5, 15, and 25 mg/kg. All mice treated with Gem combined with low-dose (5 mg/kg) or middle-dose (15 mg/kg) BI2536 remained viable (Supplementary Figure 17A). However, mice treated with high-dose BI2536 (25 mg/kg) plus Gem (10 mg/kg) died starting from day 5 onward (Supplementary Figure 17A). Moreover, mice treated with low-dose and middle-dose BI2536 (5 and 15 mg/kg) combined with Gem showed no adverse effects on body weight or main organ structure (Supplementary Figure 17, B and C). In contrast, high-dose BI2536 (25 mg/kg) combined with Gem caused a significant reduction in mouse body weight and severe histopathological damage to major organs such as the liver and lungs, indicating that high-dose of BI2536 exhibits notable organ toxicity in mice (Supplementary Figure 17, A-C). Next, we evaluated the therapeutic efficacy of low-dose and middle-dose BI2536 combined with Gem in inhibiting PDAC progression. The results showed that combining Gem with middle-dose (15 mg/kg) of PLK1 inhibitor significantly increased the therapeutic efficacy compared to that of the single Gem treatment or Gem with low-dose (5 mg/kg) of BI2536 (Figure 7, G-I and Supplementary Figure 17, D and E). Considering both drug toxicity and efficacy, we selected the middle dose (15 mg/kg) of BI2536 for subsequent studies and will refer to it as BI2536 hereafter. (Figure 7, G-K). Staining of mouse tumor tissues with Ki67 indicated largely reduced cancer cell proliferation in the Gem/BI2536-treated mice (Figure 7, J and K). In addition, the combination treatment of Gem and BI2536 remarkably inhibited TAMs infiltration in mouse tumor sections compared to that of the single drug treatment (Figure 7, J and K). To enhance the translational relevance of our findings, we evaluated whether PLK1 inhibition could improve the efficacy of other standard PDAC chemotherapies, such as FOLFIRINOX or Gem/nab-paclitaxel. Similar to Gem therapy, combined treatment of BI2536 with FOLFIRINOX or Gem/nab-paclitaxel resulted in significantly greater reductions in tumor volume and weight compared to monotherapies (Supplementary Figure 18). To further validate the potential synergistic effect of this combination therapy in human patients, we assessed the response of three distinct patient-derived PDAC organoids (PDOs) to the combination of BI2536 with Gem/nab-paclitaxel. PDOs displayed histological features consistent with the original patient tumor samples (Figure 7L). Similar to the observations in the orthotopic xenograft mouse model, the drug combination demonstrated superior efficacy in eliminating all three PDAC organoids compared to either agent alone (Figure 7, M and N).

More importantly, to determine whether the additive effect of Gem/nab-paclitaxel and BI2536 depended on P3H1, we assessed the therapeutic response of Gem/nab-paclitaxel in P3H1-depleted MIA PaCa-2 cell lines. The treatment led to a greater reduction in tumor burden in mice xenografted with P3H1-knockdown MIA PaCa-2 cells compared to control cells. (Figure 8, A-C). Pancreatic cancer cell proliferation and TAMs infiltration were significantly reduced in mice engrafted with P3H1-knockdown cells following Gem/nab-paclitaxel treatment (Figure 8D). To validate the findings from PDAC cell lines and to recapitulate PDAC heterogeneity, we then evaluated the impact of P3H1 on cancer cell proliferation in three PDOs. Consistent with the results in MIA PaCa-2 cells, P3H1 depletion in PDOs markedly suppressed organoid growth (Figure 8, E and F). Furthermore, P3H1 knockdown caused a marked decrease in the expression of the proliferation marker Ki67 and the downstream effector PLK1 (Figure 8G). Importantly, Gem/nab-paclitaxel therapy exhibited enhanced effectiveness in eliminating PDOs with P3H1 deficiency (Figure 8H). Collectively, these results support the notion that targeting P3H1-PLK1 axis might serve as an effective therapeutic strategy for treatment of PDAC and enhance the efficacy of chemotherapy.

Methods
Ethics statement
All animal procedures were performed in accordance with the policies and guidelines and approved by the Institutional Animal Care and Use Committee (IACUC) at the Southern University of Science and Technology (SUSTech-JY202308002). The maximal tumor size/burden permitted by IACUC ethics committee was 1500 mm3. We confirm that the maximal tumor size/burden was never exceeded in this study. The experiment using patients’ samples was approved by the by the Clinical Research Ethics Committee of The First Affiliated Hospital, Zhejiang University School of Medicine (Approval number: IIT20230001C-R1).

Animal studies
The P3H1fl/fl mice (C57BL/6J background) were from Cyagen Biosciences. LSL-KrasG12D/+; Trp53fl/fl; Pdx1-Cre (KAPC) mice were obtained from Jackson laboratory and crossed with P3H1fl/fl mice to generate P3H1fl/+; LSL-KrasG12D/+; Trp53fl/fl and P3H1fl/+; Trp53fl/fl; Pdx1-Cre mice. Offspring were then interbred to produce P3H1fl/fl; LSL-KrasG12D/+; Trp53fl/fl; Pdx1-Cre (KPC; P3H1 cKO) and P3H1+/+; LSL-KrasG12D/+; Trp53fl/fl; Pdx1-Cre (KPC; P3H1 WT) littermates. Both female and male mice in 7-week-old were utilized in the described experiments. All experimental mice were fed in the standard animal cages and provided with regular water and food, without any special treatment except for the indicated experimental procedures. CO2 inhalation was used to euthanize all experimental mice.

Tail genotyping was performed by standard PCR protocol. PCR primers were used as follows: (1) P3H1-Forward: 5’-CTCATACACAGTACACACGCTAAG-3’; P3H1-Reverse: 5’-ATCTCCCTTGAAGCAATGTCTCAC-3’. (2) Kras-Forward: 5’-CGTAGGGTCATACTCATCCACA-3’; Kras-Reverse: 5’-CCATGGCTTGAGTAAGTCTGC-3’. (3) p53-Forward: 5’-GCTAACACTCTTGGGATGCTG-3’; p53-Reverse: 5’-TTGTCCCAACTCTAAGAAGCC-3’. (4) Cre-Forward: 5’-CCTGGACTACATCTTGAGTTGC-3’; Cre-Reverse: 5’-AGGCAAATTTTGGTGTACGG-3’.

Orthotopic transplantation and drug treatment
The NOD scid gamma (NSG) mice (Cat. NO. NNM-NSG-001), aged at 4 ~ 5 weeks, were purchased from Shanghai Model Organisms Center, Inc. 2 × 106 MIA PaCa-2 cells were orthotopically injected into the pancreas of anesthetized NSG mice. Mice were monitored for one-week post-surgery for signs of abnormal behaviors. Then NSG mice were randomized to the various treatment groups. Gemcitabine (Tocris Bioscience, 122111-03-9) was dissolved in ddH2O at 2 mg/mL, BI2536 (Selleck, S1109) in corn oil (5% DMSO) at 1 mg/mL, 3 mg/mL or 5 mg/mL, nab-Paclitaxel (Selleck, E1068) in ddH2O at 1 mg/mL, irinotecan (Selleck, S1198) in corn oil (7% DMSO) at 4 mg/mL, oxaliplatin (Selleck, S1224) in 5% glucose solution at 0.4 mg/mL, 5-fluorouracil (Selleck, S1209) in saline (10% DMSO) at 3 mg/mL, leucovorin (Selleck, S5136) in ddH2O at 4.8 mg/mL for injection. Tumor-bearing mice were injected by intraperitoneal injection with Gemcitabine at 10 mg/kg, BI2536 at 5 mg/kg, 15 mg/kg or 25 mg/kg, irinotecan at 20 mg/kg, oxaliplatin at 2 mg/kg, 5-fluorouracil at 15 mg/kg and leucovorin at 24 mg/kg. Nab-Paclitaxel was administered with intravenous injection at 5 mg/kg. At the end of the treatment period, NSG mice were sacrificed, and pancreatic tumor weight and volume were measured. Tumor volumes were calculated using the following formula: volume = (length × width2)/2.

Cell culture
The HEK 293T (CRL-3216), THP-1 (TIB-202), MIA PaCa-2 (CRL-1420) and PANC-1 (CRL-1469) cell lines were purchased from American Type Culture Collection (ATCC). All cell lines were authenticated and tested for mycoplasma contamination. HEK 293T and PANC-1 cells were routinely maintained in DMEM (Invitrogen, 11995-500) supplemented with 10% fetal bovine serum (FBS) (Invitrogen, 10099-141) and 100 U/mL penicillin and streptomycin (Invitrogen, 15140-122). THP-1 cells were cultured in RPMI-1640 (Gibco, C11875500BT), supplemented with 10% FBS and 100 U/mL penicillin and streptomycin. MIA PaCa-2 cells were cultured in DMEM containing 10% FBS, 2.5% horse serum (Gibco, 26050088) as well as 100 U/mL penicillin and streptomycin. All cells were incubated under 5% CO2 humidified atmosphere at 37 °C.

Tumor microarrays of pancreatic cancer patients
Human pancreatic cancer tissue microarrays (TMA) were purchased from the Shanghai Outdo Biotech Company. The correlation between P3H1 expression and clinical variables, including gender, age, TNM stage, and lymph node count, was assessed using TMA (OD-CT-DgPan01-006) with corresponding demographic and clinical data. The samples were selected based on the availability of clinical and pathological data (e.g., gender: 72 cases, age: 71 cases, TNM stage: 68 cases, lymph node count: 65 cases). Nine PDAC Samples were excluded due to insufficient tissue available. The correlation between P3H1 expression and PDAC patients’ survival rates was assessed by TMA (HPan-Ade180Sur-01, OD-CT-DgPan01-006, 119 cases in total) and determined by Kaplan-Meier analysis. The expression of P3H1 and PLK1 was detected in TMA (HPanA060CS04, 59 cases) and measured by the IHC tool box of ImageJ software (NIH, MD, USA).

Organoid culture
Tumor samples were collected from pancreatic cancer patients who received surgical resection at the First Affiliated Hospital, Zhejiang University School of Medicine. All anatomy donors gave their informed and written consent to use their clinical data and surgical specimens for research. Sex was not considered in this study because no reports indicated that P3H1 function is sex-biased and the study was not designed to investigate sex-based research. This study was conducted in accordance with the national guidelines and complied with all relevant ethical regulations. This study was approved by the Clinical Research Ethics Committee of The First Affiliated Hospital, Zhejiang University School of Medicine (Approval number: IIT20230001C-R1). The organoid culture protocol was described previously 49. In brief, tissues from patients were washed three times with PBS containing 1 % penicillin-streptomycin. Then, pancreatic cancer tissues were minced into 1 mm3-fragments using surgical scissors and digested with Collagenase Type 1 (1.5 mg/ml, Worthington, WBC-LS004197) at 37°C for a maximum of 2 hours. The supernatant cells were collected and digested with TrypLE Express (Thermo Fisher Scientific) at 37°C for 10 minutes. For inactivating digestive enzymes, cells were washed with 1640 basic medium with 10% fetal bovine serum (FBS). Isolated cells were resuspended with Matrigel (Corning, 356231) and human PDAC medium for PDAC organoids. Organoid culture medium: basic media (advanced DMEM/F12, 10 mM HEPES, 1× GlutaMAX-I, 100 µg/mL Primocin, 1×penicillin/streptomycin solution) and complete medium (advanced DMEM/F12, 10 mM HEPES, 1× GlutaMAX-I, 100 µg/mL Primocin, 1× penicillin/streptomycin solution, 30% Wnt3A conditioned medium, 2% R-spondin conditioned medium, 4% Noggin conditioned medium, 500 nM A83-01, 10 µM Y-27632, 1.56 mM N-acetylcysteine, 10 mM nicotinamide, 10 ng/mL FGF10, 1× B27 supplement, 10 µM forskolin). The established organoids were routinely tested for mycoplasma contamination.

Drug treatment assay on organoids
For the drug treatment assay, the organoids were digested with Tryp-LE (GIBCO), which was subsequently inactivated with 1640 basic medium containing 10% (v/v) FBS. The cells were washed with 1× PBS and then resuspended in 50 % Matrigel medium and seeded into 96-well plates (10,000 cells per well). When organoids reached approximately 100 ~ 150 µm, Then, Gemcitabine, nab-paclitaxel and BI2536 with different concentration gradients and 0.1% DMSO as the vehicle control were added using the Mosquito workstation. After 3 days of drug treatment, cell viability was detected CellTiter-Glo 3D (CTG) assay. Calculate IC50 curves with GraphPad Prism (100 % and 0 % viability constraints) by adding 25 μL of Cell Counting-Lite 2.0 (Vazyme), gently shaking for 10 minutes at room temperature before luminescence signal detection using the Envision plate reader. The average inhibition rates were calculated from three independent experiments.

Histology and immunohistochemistry staining
Mouse pancreatic tumor tissues were fixed in 10% formalin at 4 °C overnight and then embedded in paraffin. Pancreatic formalin fixed paraffin embedded (FFPSE) blocks were sectioned at 4.5 μm intervals and stained with pancreatic Hematoxylin and eosin (H&E) staining according to the standard procedures. The analyses of histological and histopathological were categorized as 4 grades as the standard described previously, including normal pancreas, early PanIN (ADM and PanIN-1), late PanIN (PanIN-2 and PanIN-3) and PDAC 99. Five independent tumor samples in each group were analyzed.

Immunohistochemistry staining was perform by the MaxVisionTM HRP-Polymer anti-Mouse/Rabbit IHC kit (Maxim, Fuzhou, China) as described previously 100. Primary antibodies were listed as follows (Detailed in Supplementary Dataset 5): rabbit anti-P3H1 (Abcam, ab244326, 1:1,000), rabbit anti-P3H1 (Novus, NBP2-30424, 1:200), rabbit anti-PLK1 (HUABIO, HA722118, 1:100), rabbit anti-Cytokeratin19 (CK19, Abcam, ab52625, 1:10,000), rabbit anti-PCNA (Abclonal, A12427, 1:500), mouse anti-α-smooth muscle actin (α-SMA, sigma, A2547, 1:1,000), rabbit anti-Collagen I (Abcam, ab260043, 1:1,000), rabbit anti-F4/80 (CST, 70076S, 1:500), rabbit anti-Ly6G (CST, 87048s, 1:200), rabbit anti-CD68 (Proteintech, 28058-1-AP, 1:1,000), rabbit anti-ARG1 (CST, 93668T, 1:200), rabbit anti-CXCL1 (Abcam, ab86436, 1:500), rabbit anti-CXCL5 (HUABIO, HA722561, 1:500), rabbit anti-c-Myc (Abcam, ab32072,1:100), rabbit anti-CyclinD1 (HUABIO, ET1601-31, 1:1,000), mouse anti-Ki-67 (Maxim, MAB-0672), rabbit anti-EpCAM (Cell signaling, 93790S, 1:500), rabbit anti-FGF2 (HUABIO, ET1703-18, 1:200), rabbit anti-PDGF-B (HUABIO, HA722003, 1:100). Targeting proteins were detected with peroxidase staining (DAB) and counterstained with hematoxylin. For IHC analysis, whole-tissue slides were scanned by NanoZoomer S60 (Hamamatsu) at 40× magnification and viewed by NDP.Viewer software (version 2.9.25). At least ten section fields for each slide were randomly selected for further analysis. Image analysis was performed by thresholding brown pixels for positive staining and normalizing to intra-field tissue area using the IHC tool box of ImageJ software (NIH, MD, USA) 101.

Lentiviral infection
Plasmids psPAX2 (#12260) and pMD2.G (#12259) were acquired from Addgene. Plasmid pLVX-Flag-Hyg was modified from pLVX-IRES-Hyg (Clontech, 632185). HEK 293T cells were transfected with pLVX-P3H1-Hyg or pLVX-Flag-PLK1-Hyg together with psPAX2 packing plasmid and pMD2.G envelope plasmid by Lipofectamine 3000 kit (Invitrogen, L3000015) to generate P3H1 or PLK1 overexpression cell lines. Lentivirus-containing medium was collected from the HEK 293T after 48 hours transfection. Lentivirus was precipitated by filtration (pore size 0.45 μm) and ultracentrifugation (100,000 × g, 2 hours). MIA PaCa-2 cells were cultured in 6-well plate to around 50% confluency and then transduced with lentivirus at a multiplicity of infection (MOI) of 100 mixed in the presence of 8 μg/mL polybrene. Transduction of MIA PaCa-2 cell line was confirmed by immunoblotting. For organoid lentiviral infection, organoids were dissociated with TrypLE (2 minutes, 37 °C), resuspended in lentivirus (MOI 50 ~ 100) in the presence of 8 μg/mL polybrene, incubated overnight and then re-embedded in Matrigel. The infection efficiency was confirmed by qPCR.

RNAi
For the loss-of-function experiments in this study, transient transfection of small interfering RNA was performed using Lipofectamine RNAiMAX (Invitrogen, 13778150) according to the manufacturer’s instructions. siRNAs were purchased from Gene Pharma, Suzhou. The sequences were as follows: (1) Control siRNA: sense, 5’-ACGCAUGCAUGCUUGCUUUTT-3’; antisense, 5’-AAAGCAAGCAUGCAUGC GUTT-3’; 2) P3H1 siRNA#1: sense, 5’-GGAGAAGAACACACCAGTAUTT-3’; antisense, 5’- AUCUGGUGUGUUCUUCU CCTT-3’; (3) P3H1 siRNA#2: sense, 5’- GCCCAAGGAUGAGCUAUGATT-3’; antisense, 5’-UCAUAGCUCAUCCUUGG GCTT-3’; (4) PLK1 siRNA#1: sense, 5’- CAACCAAAGUCGAAUAUGATT-3’; antisense, 5’-UCAUAUUCGACUUUGGUUGTT-3’; (5) PLK1 siRNA#2: sense, 5’- CAAGAAGAAUGAAUACAGUTT-3’; antisense, 5’-ACUGUAUUCAUUCUUCUU GTT-3’; (6) HIST1H1C siRNA#1: sense, 5’- GAAGAAGAGCGCUAAGAAATT-3’; antisense, 5’-UUUCUUAGCGCUCUUCUUCTT-3’; (7) HIST1H1C siRNA#2: sense, 5’- CCAAGGUUGUCAAGCCUAATT-3’; antisense, 5’-UUAGGCUUGACAACCU UGGTT-3’; (8) SLCO4A1 siRNA#1: sense, 5’- GAAAUGCACUUCAACUUGUTT-3’; antisense, 5’-ACAAGUUGAAGUGCAUUUCTT-3’; (9) SLCO4A1 siRNA#2: sense, 5’-GGAGGUGCCCUGCUGAAUATT-3’; antisense, 5’-UAUUCAGCAGGG CACCUCC-3’.

Cell viability and colony formation assay
Cell viability was measured with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. MIA PaCa-2 cells were seeded as 2,000 cells per well in a 96-well plate. At the indicated incubation time points, the culture medium was removed and 200 μL media containing 10 μL MTT solution (5 mg/mL) was added to each well. After additional 3 hours incubation, the culture media was aspirated and 200 μL dimethylsulfoxide (DMSO) was added to each well. Absorbance at 490 nm was measured with microplate reader (BioTek, Epoch2).

For colony formation assay, 500 cells were seeded in a 6-well plate. After continued incubation for 7 days, the cell colonies were washed with PBS, fixed with 4% paraformaldehyde (PFA) for 10 minutes at room temperature, stained with Modified Giemsa Staining Solution (Beyotime, C0131) for 45 minutes. Digital images of the cell colonies in each well were obtained and counted.

Quantitative real-time PCR analyses
Total RNA was extracted from MIA PaCa-2 cells by TriPure™ Isolation Reagent (Roche, 11667157001). cDNA was synthesized using StarScript III All-in-one RT Mix (GenStar, A230). qPCR was carried out utilizing LightCycler 480 SYBR® Green I Master (Roche, 04887352001) and Applied Biosystems 7500 Real-Time PCR system. Housekeeping gene GAPDH was used as endogenous control. The relative quantification of gene expression was analyzed by the 2-△△Ct method and the control group was normalized. The primer sequences used for the qPCR experiment were listed as follows (Detailed in Supplementary Dataset 5):
GAPDH-Forward: 5’-GTGAAGGTCGGAGTCAACGG-3’; GAPDH-Reverse: 5’-TCCTGGAAGATGGTGATGGG-3’. P3H1-Forward: 5’-GTCACTGGATGTGAGCAGACTG-3’; P3H1-Reverse: 5’-ACTCGTGGTCAGAGATTACGCC-3’. c-Myc- Forward: 5’-GGTTTGCCTCTTCTCCACAGAC-3’; c-Myc- Reverse: 5’-CAGACTCTGACCTTTTGCCAGG-3’. Cyclin-D1-Forward: 5’-TCTACACCGACAACTCCATCCG-3’; Cyclin-D1-Reverse: 5’-TCTGGCATTTTGGAGAGGAGTG-3’. CXCL1-Forward: 5’-AGCTTGCCTCAATCCTGCATCC-3’; CXCL1-Reverse:5’-TCCTTCAGGAACAGCCACCAGT-3’. CXCL5-Forward: 5’- CAGACCACGCAAGGAGTTCATC-3’; CXCL5-Reverse: 5’- TTCCTTCCCGT TCTTCAGGGAG-3’. CXCL8-Forward: 5’- GAGAGTGATTGAGAGTGGACCAC-3’; CXCL8-Reverse: 5’- CACAACCCTCTGCACCCAGTTT-3’. FGF2-Forward: 5’-AGAAGATGCGACCCTCACATCA-3’; FGF2-Reverse: 5’-CGGTTAGCACACACTCCTTTG-3’. PDAGF-B-Forward: 5’-GAGATGCTGAGTGACCACTCGA-3’; PDGF-B-Reverse: 5’- GTCATGTTCAGGTCCAACTCGG-3’. SLCO4A1-Forward: 5’-GCGGAAATGCACCAGTTGAAGG-3’; SLCO4A1-Reverse: 5’- GGTTCTTCAGCAGGAGCCAGAT-3’.

Immunofluorescence staining
MIA PaCa-2 cells were seeded onto fibronectin-coated cover slides placed in 6-well plates and cultured overnight. Cells were fixed in 4% PFA for 30 minutes, followed by permeabilizing with 0.1% Triton X-100 for 10 minutes. Then cells were blocked with 5% BSA for 1 hour and incubated with a primary antibody against rabbit anti-β-catenin (Abcam, ab6302,1:2,000) 4°C overnight. slides were washed with 5% BSA three times and then incubated with goat-anti-rabbit IgG (H+L) secondary antibody Alexa Fluor 488 ((Invitrogen, A11008, 1:500) for 30 minutes at room temperature in the dark. Slides were mounted with ProLongTM Gold antifade reagent with DAPI (Invitrogen, P36941) for microscopic analysis.

Immunoblotting
Cells were lysed with 1% sodium dodecyl sulfate (SDS) supplemented with 1% protease inhibitor cocktail (MCE, HY-K0010) and 1 mM Phenylmethanesulfonyl fluoride (PMSF, Sigma-Aldrich, 32998-6). Protein concentration was measured by the PierceTM BCA Protein Assay Kit (Thermo fisher, 23227) and subjected to SDS-PAGE . Immunoblots was performed as described previously 102. The following primary antibodies were used in this study (Detailed in Supplementary Dataset 5): rabbit anti-P3H1 (Abcam, ab244326, 1:1,000), rabbit anti-PLK1 (CST, 4513S, 1:1,000), rabbit anti-c-Myc (CST, 5605AS, 1:1,000), rabbit anti-CyclinD1 (HUABIO, ET1601-31, 1:1,000), rabbit anti-CXCL1 (Abcam, ab86436, 1:500), mouse anti-CXCL5 (R&D Systems, MAB254, 1:500), rabbit anti-CXCL8 (HUABIO, HA723264, 1:500), rabbit anti-TGF-β1 (HUABIO, HA721143, 1:1,000), mouse anti-GAPDH (ABclonal, AC035, 1:5,000) , rabbit anti-HIST1H1C (HUABIO, ET1706-26 , 1:1,000), rabbit anti-CDC20 (HUABIO, ER62950,1:1,000).

Cycloheximide chase assay
Cycloheximide (CHX) chase assay was conducted to assess protein stability. Briefly, cells were plated in 6-well plates and allowed to adhere overnight. The culture medium was then removed, and the cells were treated with 2 mL of DMEM containing 10 μM CHX (dissolved in DMSO). At indicated time points (0, 3, 6, 9, and 12 hours) after CHX treatment, cells were harvested for protein extraction and subsequent immunoblotting analysis.

Co-immunoprecipitation
Co-immunoprecipitation (Co-IP) analysis was performed as the following procedure. Briefly, MIA PaCa-2 cells were harvested and lysed in cell lysis buffer (Beyotime, P0013) containing 1% proteinase inhibitor cocktail and 1 mM PMSF for 30 minutes at 4℃. Lysates were centrifuged at 12,000 g for 20 minutes and the supernatant was collected. After centrifugation, the supernatant was pre-cleared with Protein A/G Plus-Agarose (Santa Cruz, sc-2003) for 1 hour at 4 °C. Then, equal amounts of cell lysates (2 mg) were incubated with 2 μg rabbit anti-P3H1 antibody (Abcam, ab244326), rabbit anti-PLK1 antibody (CST, 4513S), or control rabbit IgG antibody (Invitrogen, MA5-56524) overnight at 4℃, followed by incubating with Protein A/G Plus-Agarose for another 2 hours. Beads were rinsed three times in pre-cold 1× PBS buffer with 1 mM PMSF. Proteins were then eluted from beads by 1 × SDS PAGE loading buffer containing 10% β-mercaptoethanol and were subsequently subjected to immunoblotting.

ELISA
ELISA assay was performed according to the manufacturer’s protocols. The concentration of released CXCL1, CXCL5, CXCL8 or TGF-β1 in culture medium of MIA PaCa-2 cells was determined using the human CXCL1 ELISA kit (HUABIO, EH0054), human CXCL5 ELISA kit (UpingBio, SYP-H4127), CXCL8 ELISA kit (HUABIO, EH0014) or TGF-β1 ELISA kit (Bioswamp, HM10060). In brief, the culture medium was collected and centrifuged to remove the cell debris (300 × g, 5 minutes). The concentration of CXCL1, CXCL5, CXCL8 or TGF-β1 was measured according to the manufacturer’s protocol. Absorbance at 450 nm was measured with microplate reader (BioTek, Epoch2). The absolute concentration of CXCL1, CXCL5, CXCL8 or TGF-β1 in the MIA PaCa-2 culture medium was calculated from the standard curve.

Chemotaxis assays
A chemotaxis assay was performed to detect the macrophage recruitment capacity. An 8-μm pore diameter chamber (Corning, 3422) was used in this transwell assay. THP-1 cells were pretreated with 100 ng/mL Phorbol 12-myristate 13-acetate (PMA, MCE, HY-18739) for 48 hours. Then THP-1 cells were seeded in the upper chamber, and the lower chamber contained medium supplemented with the indicated treatment. After 2 days, cells were fixed using 4% PFA for 15 minutes at room temperature and subsequently stained with crystal violet solution. ImageJ software was used to quantify the number of migrated cells.

Flow cytometry
Analysis of macrophage surface markers on THP-1 cells was performed by flow cytometry. THP-1 cells were stimulated by CM from MIA PaCa-2 cells, together with or without recombinant chemokines, for 72 hours. After that, the cells were collected and washed with PBS for two times at 4 °C (300 × g, 5 minutes). Then the cells were stained with PE anti-human CD14, Pacific Blue™ anti-human CD86, and APC anti-human CD163 antibodies (301850, 305417, and 333609 respectively, BioLegend, 1:100) for 30 minutes in the dark, followed by two washes with ice-cold PBS. Flow cytometry analysis was performed on Cytoflex Analyzer (Beckman) and analyzed using FlowJo (V.10.9.0).

RNA sequencing
MIA PaCa-2 cells were extracted by TriPure™ Isolation Reagent (Roche, 11667157001) according to the manufacturer's instructions. The quality and purity of RNA were examined by a NanoDrop™ One/OneC spectrophotometer and Life Invitrogen Qubit RNA BR (Broad-Range) Assay Kit. RNA integrity was analyzed using Agilent 4200 TapeStation system. The extracted RNA was reverse-transcribed to create a mRNA-Sequencing library using the ABclonal Poly(A)mRNA Capture Module and ABclonal Fast RNA-seq Lib Prep Kit V2. After verification of the library quality by Qubit dsDNA HS Assay Kit, high-throughput transcriptome sequencing was performed on an Illumina NovaSeq X platform (Illumina, Inc, CA, USA) according to the manufacturer’ s instructions. In brief, for the NovaSeq X, 56 µL of the pooled libraries with a concentration of 1.05 nM were loaded into a lane of a NovaSeq X Series 25B Reagent Kit (300 Cycle) flow cell for a final loading concentration of 125 pM. The pools were sequenced paired-end 150 on the NovaSeq X. Raw sequencing data in FASTQ format was processed using Fastp (version 0.23.0), an ultra-fast FASTQ preprocessor with efficient quality control and data-filtering features 103. The clean reads were mapped to hg38 human reference genome by HISAT2 software (version 2.1.0) 104. A sequencing depth of approximately 40 million mapped reads was sufficient to achieve 90% transcriptome coverage. Differentially expressed genes (DEG) analysis was conducted using DESeq2 (version 1.18.1), which performs internal normalization of raw counts with filtering criteria of |log2(fold change)| > 1 and false discovery rate (FDR) < 0.05 105.

Gene Ontology (GO) analysis was performed via the R package clusterProfiler (version 4.8.2) in R version 4.2.2. Genes used for the analysis were filtered by the criteria of |log2(fold change)| > 1 and FDR < 0.05 and analyzed by the “enricher” function with the following size filters: minGSSize = 10 and maxGSSize = 500. The other parameters were set at their default settings. Enrichment results were visualized via the “ggplot2” R package. Gene Set Enrichment Analysis (GSEA) was performed using the R package clusterProfiler (version 4.6.2) in R version 4.2.3. Genes were ranked according to their log2(fold change) values and analyzed using the “gseGO” function with the following size filters: minGSSize = 3 and maxGSSize = 2000. The other parameters were maintained at their default settings. Enrichment results were visualized using the “gseaplot2” function.

Proteomic analysis
Proteomics samples were prepared with SISPROT kit (BayOmics, cat. no. BO-006) 106. The kit consists of solid-phase extraction pipette tips packed in tandem with C18 membrane layers and a mixed-bed of strong-cation/strong-anion exchange (SCX + SAX) beads, enabling a fully integrated workflow for proteomic sample processing from protein preconcentration, reduction, and alkylation to digestion, reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation. Firstly, MIA PaCa-2 and PANC-1 cells were lysed in Extract buffer with sonication (5 cycles at 4 °C with 30 seconds on and 30 seconds off) and collected by centrifuging at 14,000 × g for 10 minutes at 4 °C. Protein concentration was determined by the PierceTM BCA Protein Assay Kit (Thermo fisher, 23227). 5 μg cell lysates were collected and acidified by adding Acidize buffer. Before sample loading, SISPROT tip was assembled and equilibrated according to manufacturer's instructions. Then acidified sample was loaded onto SISPROT tip by centrifugation, reduced by adding Reduce buffer and digested with Enzymatic Digest Solution. The obtained peptides were transferred to C18 disk and desalted by adding Desalt buffer before elution. Finally, the peptides were eluted by adding Elute buffer, lyophilized using a SpeedVac machine (Thermo Fisher) and resuspended by adding Dissolve B buffer for following LC-MS/MS analysis.

Secretome profiling
Secretome analysis was performed as described previously 49. MIA PaCa-2 cells were seeded in 10 cm dishes at approximately 80% confluence. Cells were washed three times with PBS and then starved in culture medium without fetal bovine serum (FBS) for 24 hours. Conditioned medium (CM) was collected and centrifuged at 12,000 × g for 15 minutes to remove the cell debris and then concentrated with a 3 kDa cut-off filter (Millipore). The concentrated medium was replaced with PBS using the same filter and subsequently dried using a Speed-Vac. Proteins were reconstituted in 8 M urea lysis buffer containing 50 mM ABC, reduced, and alkylated. Subsequently, the urea concentration was diluted to 1 M with 50 mM ABC, and the proteins were subjected to overnight digestion with trypsin. Peptides were desalted on home-made C18 StageTip for following LC-MS/MS analysis.

Phosphoproteomics analysis
Phosphoproteomics analysis was performed as previously described 107. MIA PaCa-2 and PANC-1 cells underwent lysis in a buffer containing 50 mM Tris-HCl (pH 8.5), 7 M Urea, 1% Triton X-100, 10 nM NaF, 1 Mm Na3VO4, protease inhibitor mixtures (Roche, 4693132001) and phosphatase mixtures (Roche, 4906837001). Protein concentration was determined by the PierceTM BCA Protein Assay Kit (Thermo fisher, 23227). 1 mg cell lysates were used to perform chloroform-methanol precipitation. Collected protein precipitates were resuspended in urea buffer (8 M urea, 50 mM Tris-HCl, pH 8.5) with mild sonication. The soluble proteins were reduced using dithiothreitol (DTT), alkylated using iodoacetamide (IAA) and digested with trypsin. After SPE column desalting, the collected peptides were enriched by the Ti4+-immobilized-metal affinity chromatography (IMAC) tip 108, 109. Then the obtained peptides were further desalted on home-made C18 StageTip for following LC-MS/MS analysis.

LC–MS/MS analysis
Global proteomics analysis was performed on an Orbitrap Exploris 480 mass spectrometer coupled with an UltiMate 3000 HPLC system (Thermo Fisher Scientific). Secretome and phosphoproteomics analyses were conducted on a Q Exactive HF-X mass spectrometer coupled with an Easy-nLC 1200 system (Thermo Fisher Scientific). For all analyses, peptides were dissolved in solvent A (0.1% (v/v) formic acid in water) and separated on a 100 μm i.d. × 20 cm capillary column in-house packed with C18 beads (1.9 μm, 120 Å, Dr. Maisch GmbH) at a constant flow rate of 250 nL/minute. The effective gradient increased linearly from 8% to 25% mobile phase B (0.1% (v/v) formic acid in 90% (v/v) ACN) over 50 minutes (for phosphopeptides) or over 100 minutes (for secretome samples), and then increased to 40% over 10 minutes (for phosphopeptides) or 20 minutes (for secretome samples), respectively. MS/MS scans were performed in a data-dependent mode, parameters were set as previously described 49. Full MS scans covering m/z of 350 ~ 1,550 were acquired using an Orbitrap resolution of 120,000, an AGC target of 3 × 106, and a maximum injection time (MIT) of 60 milliseconds. The top 50 (15 for phosphopeptides) most abundant precursors from each full MS1 scan were selected and isolated through a 1.2 Da window (0.7 Da for phosphopeptides), and fragmented by higher-energy collisional dissociation (HCD) at normalized collision energy (NCE) of 27% (32% for phosphopeptides). MS/MS spectra were scanned with a resolution of 7,500 (45,000 for phosphopeptides), an AGC target of 1 × 105 and an MIT of 25 milliseconds (86 milliseconds for phosphopeptides). A

The raw files were searched by the MaxQuant software (Version 1.6.17) and searched against the Uniprot human fasta database 110. The carbamidomethylation of cysteine was set as the fixed modification. Oxidation (M), deamidation (N/Q) and phospho (STY) were set as the dynamic modifications for the phosphopeptide identification; A maximum of two missed cleavages was permitted for trypsin and Lys-C digestions. The results were filtered to a 1% FDR. The “match between runs (MBR)” function was enabled to reduce missing values. Then label-free quantification (LFQ) data was used to evaluate protein abundances and analyzed by the Perseus software (Version 1.5.5.3) as previously described 111.

Statistical analysis
Statistical analysis and related visualization were performed using Perseus software (version 1.5.5.3), GraphPad Prism 10 and R studio (version 4.1.0). Quantitative data are presented as mean ± SEM. 2-tailed Student’s t test was performed for comparison between two groups. One-way ANOVA was performed for multiple comparison test. The log rank test was performed for survival analysis. P value < 0.05 was considered statistically significant. For proteomics data, significance was calculated by two-tailed Student’s t-test with P-value < 0.05 and fold change > 2.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work, Bai P et al., show that the expression of P3H1 in PDAC models is correlated with therapeutic response through the regulation of the PLK1-βcat axis. They state also that this is based on a TME remodelling. The paper is well-written, and the experiments are clearly explained. However, some points need to be addressed, and more data are needed to support their conclusions.\nLine 131. Please provide a clearer explanation for the selection of P3H1 over the other candidate genes. Have you explored potential correlations between their expression and PDAC development? P3H1 expression does not appear to be strongly associated with most clinicopathological parameters, with the exception of TNM staging. It would be important to clarify why P3H1 was prioritized for analysis—particularly at the early stages of the study, when data may have been limited. Was this choice primarily informed by existing literature, or were there already some preliminary indications, such as survival data, that guided the decision?\nLine 137. Table 1 presents only information on the PDAC patients. The information about the non-tumor tissues should be added. For example, are they derived from non-tumor tissue of the PDCA patients?\nLine 139. Please include the complete TMA IHC results in a Supplementary Figure for a more comprehensive view.\nLine 170: Figure 2B: some information is missing. The three columns refer to 3 different biological replicates? Please explain better, in the main text or in the figure legend, or in the figure.\nLine 174–177: In the KPC WT mice, the median survival is 64 days, while in the KPC P3H1 cKO mice it's 76 days. The authors describe this as a significantly improved survival (statistically significant). Have this also a biological relevance? Are there any reference values they could use to confirm/validate this?\nLine 244. Among the cytokines associated with TAM infiltration, IL-8 and TGF-β (among others) were also found to be reduced in P3H1-depleted cells. Have the authors explored the involvement of additional cytokines beyond CXCL1 and CXCL5?\nLine 257–259: The different contribution of TAM when modulating CXCL1-5 should be investigated also by FACS quantitative analysis. Then, when modulating CXCL1-5 expression and consequently TAMs infiltration in the two lines (WT and cKO), do they observe any changes in tumor proliferation or prognosis? This could support the role of the tumor microenvironment (TME) and how its remodeling may be associated with disease outcome.\nLine 271–272: Among the various up/down-regulated proteins (lines 267–268), why did they choose to focus only on PLK1? Even in Figure 4C, in the overlapping region, there are other upregulated proteins. Why was only PLK1 selected? If PLK1 was the most strongly upregulated, did they exclude other candidates for a specific reason?\nLine 298–307: In this section, they identify 7 proteins with known roles in cell cycle and proliferation. Why did they decide to focus only on β-catenin (CTNNB1)? Was it because it's the only one also associated with the TME?\nLine 356–358: Why did they choose only gemcitabine (GEM)? Did they also test other chemotherapeutic agents used in PDAC, such as FOLFIRINOX, to see if they produce similar results?\nLine 359. Is not clearly specified which of the “above-mentioned” PDAC models are used for the final and conclusive in vivo experiment. To finally prove the importance of the P3H1-PLK1 axis in the final biological effect, both the control and the P3H1 ko MIA PaCa2 cells must be used. PLK1inhibitors are well-known antitumor agents, having anticancer effects on different cancer types. The combination of gemcitabine with BI2536 showed in the paper could be the final additive effect of the two drugs, non-dependent on the presence/absence of the PH31 gene.", "reviewer_2": "The manuscript by Panzhu Bai et al presents an extensive analysis of (pancreatic ductal adenocarcinoma) PDAC. The finding that Prolyl 1 3-hydroxylase 1 (P3H1) possibly is involved in pancreatic cancer progression is potentially highly interesting and warrants further investigation, and an impressive number of different experiments and validation steps are included. However, I have a number of concerns with the present manuscript. My main concern is that the study is very focused on the association between P3H1 and Polo-like kinase 1 (PLK1). The authors demonstrate that P3H1 inhibition has a vast impact on many cellular processes and likely is involved in PDAC, as evident by the high number of changed proteins and RNA transcript levels (including many central cellular regulators with broad impact such as cyclin D1). However, part from a GO enrichment analysis and GSEA, no full analysis and interpretation is included of the extensive number changes, and of the hundreds of changes, the study quickly focuses on PLK1 effectively omitting analysis on the reminder of changing features. In my view, the authors have therefore, at most, indicated an association between P3H1 and PDAC, but not demonstrated the direct association between P3H1 and PLK1, as PLK1 may be changing as a result of the vast cellular perturbation. This is especially when considering the role of PLK1 in the synthesis of the structurally important protein collagen. This is a central issue given the current construction of the manuscript. Another central issue is the methods section which severely lacks several fundamental and essential elements, hindering reproduction of the study findings. As an example, it is currently not clear what data was newly generated for the present study, and what was reused. I also have a number of concerns with the applied method. In addition, the manuscript is very condensed and it is in many places difficult to follow the line of argumentation, especially in the Results section. Many figures are included but not described and discussed. I hope that my comments can help guide the authors to get some of the important findings published.\nPlease find below my comments, in no particular order but divided into minor and major concerns.\nMajor concerns\n1. Currently, it is not clear to this reviewer what data was reused and what was generated. This is a critical and a fundamental academic issue.\n2. The method section is lacking a number of elements needed to evaluate the work and reproduce the findings. Below are some of the central points mentioned.\na. The method section is missing details on how the LC-MS/MS analysis was performed (instrument, gradient, solvents,…).\nb. The method section is lacking a clear description of how the IHC scoring was performed, which is a central element in the manuscript. E.g. was the entire picture used or only the outline, was a program used, and what were the criteria.\nc. Missing information on the primary antibody (e.g. animal and dilution factor) and secondary antibody (method line 560) in the “Immunofluorescence staining”. The “Immunoblotting” after is much more detailed and as needed to reproduce the experiment.\nd. Additional information regarding the Illumina NovaSeq sequencing should be provided.\ne. The description of the RNA seq data analysis is missing. Please include a description of how the data was treated (FASTQ files etc as used).\nf. Lacking information on the Human pancreatic cancer tissue microarrays in the methods. This includes the number of samples and any inclusion criteria of the “Human pancreatic cancer tissue microarrays” in the methods (line 468).\ng. Throughout the manuscript, information regarding the correlation analysis is missing. Please clarify which type of correlation analysis was performed, what was correlated, and used cutoffs (e.g. line 136, line 145).\nh. How was the gene ontology enrichment analysis and GSEA performed?\n3. The Results section is dense and difficult to follow. As a general example, the authors state that, “we identified 34 proteins whose expressions were significantly and consistently elevated in both human and mouse samples...” (line 127). While a potentially interesting finding, it would be more prudent to present the datasets in more detail, and separately. E.g. describe a) how many proteins were monitored in each dataset (it is listed in Figure 1B, but would be nice to have in the text), b) were any proteins less abundant and if yes, c) why were only more abundant proteins considered? It is also not clear if this represents newly generated data or a re-analysis of existing data. Throughout the results section, the authors should consider including more argumentation for the included experiments.\n4. It is mentioned that the GSEA (Figure 3E) identified downregulated pathways (line 238), including “cytokine-mediated signalling pathways”. Please provide additional support for the conclusion of a downregulation, as opposed to a upregulation.\n5. Supplementary figure 3. A few outlying datapoints seem to be present on the extreme highs of the infiltration levels (few measurements are very high). This is especially true for panel A, B and C. Please provide the analysis omitting the few very high measurements which could be outliers and significantly affect the results.\n6. It is stated that “P value < 0.05 was considered statistically”. However, given the OMICS-based nature of the analyses, the number of reported significantly regulated proteins is inflated. Correction of all p-values must therefore be included for all datasets.\n7. It would be beneficial to have a volcanoplot of the data in Figure 1B.\n8. Full image of the western blots should be included to allow assessment of the antibody specificity, in addition to the missing negative controls.\nMinor concerns\n1. Consider rephrasing “our study was to identify” (line 122). The phrase is understandable, but not the most natural.\n2. Please provide a reference or additional justification to the statement that “Therefore, targeting TAMs offers a promising strategy for enhancing immune-based therapies and potentially impeding tumor progression.”.\n3. The introduction is good and interesting to read. At times it can seem slightly fragmented. Particularly the sections on P3H1 (line 74), PLK1 (line 85), and Wnt/beta-catenin (line 94) could probably be improved by including a sentence or two providing a more clear link to increase the readability.\n4. Introduction (line 52): Any numbers on annual deaths?\n5. Introduction line 99, consider adding a “the” to “After entering into nucleus…”\n6. Missing reference to Image J in the methods (line 475).\n7. Please add centrifugation details (line 582).\n8. Please clearly state in the proteomics analysis method description where the SISPROT kit sample preparation steps begin and end. As an example, it is difficult to asses whether the C18 disk is part of the kit (line 617).\n9. Supplementary figure 3D is missing correlation indications.\n10. On the correlation figure legend, a description of the gray area is missing.\n11. The description of the included dynamic modifications should be checked. E.g. was oxidation of any residue included or is it the default setting in MaxQuant with Methionine (line 652)? Same for deamidation. Also, please include the FDR filtering threshold for identifications (defaults to FDR<1%).\n12. Did the authors consider investigating the levels of hydroxylation of proline residues of collagen chains in the proteomics data? This is one in the primary post-translational modifications by P3H1 and altered levels could have an impact on the collagen proline hydroxylation levels, thereby providing additional validation for the existing proteomics data for an altered activities of P3H1.\n13. What is meant by a “heatmap analysis” (line 302)?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The study identifies P3H1 as a novel factor upregulated in PDAC, correlating with poor prognosis in both patients and mouse models. Genetic ablation of P3H1 in KPC mice significantly attenuates tumor progression, decreases immunosuppressive macrophage infiltration, and improves survival. Mechanistically, P3H1 depletion downregulates PLK1 protein levels, thereby reducing β-catenin phosphorylation at S191, impairing its nuclear translocation, and suppressing oncogenic transcription (ex. c-Myc, Cyclin D1, CXCL1, CXCL5). Restoration of PLK1 in P3H1-deficient cells rescues these defects. Furthermore, pharmacologic inhibition of PLK1 synergizes with gemcitabine to reduce tumor burden in vivo. This work is noteworthy for defining a previously unknown P3H1-PLK1-β-catenin axis and offering a new combination therapeutic strategy in PDAC, which remains a formidable malignancy with limited treatment options. The integration of transcriptomic, proteomic, and functional data is commendable and adds mechanistic rigor.\nMajor Weaknesses\n1. The authors suggest that P3H1 may stabilize PLK1 via direct hydroxylation or through indirect regulatory proteins. However, no biochemical evidence (ex- co-IP, in vitro hydroxylation assays, PLK1 stability assays) is provided to support this claim.\n2. While S191 phosphorylation is analyzed, the broader phosphoproteomic context and potential functional impact of other phosphorylation sites (S605) mentioned in the introduction are not explored.\n3. Although F4/80+, CD68+, and ARG1+ TAMs are decreased upon P3H1 deletion, functional characterization of these macrophages is lacking.\n4. The PLK1 inhibitor BI2536 is combined with gemcitabine in vivo, but no dose-response curves or toxicity evaluation (body weight etc) are shown for translational ability.\n5. Adding current standard chemotherapy regimen would improve translational value – Gemcitabine and paclitaxel\n6. Given the translational emphasis, testing in patient-derived organoids or xenografts would better support the therapeutic claims.\n7. Many in vitro results are derived from MIA PaCa-2 cells, which do not fully recapitulate PDAC heterogeneity. Validation in additional PDAC models like organoids would enhance their claim.\nMinor Weaknesses\nMethods Section -Specifics on RNA-seq depth, MS parameters, and normalization techniques are insufficiently detailed."}, "author_response": "Reviewers’ Comments and Our Point-to-point Responses:\nReviewer#1\nComments to the Author\nIn this work, Bai P et al., show that the expression of P3H1 in PDAC models is\ncorrelated with therapeutic response through the regulation of the PLK1-βcat axis. They\nstate also that this is based on a TME remodeling. The paper is well-written, and the\nexperiments are clearly explained. However, some points need to be addressed, and\nmore data are needed to support their conclusions.\n1. Line 131. Please provide a clearer explanation for the selection of P3H1 over the\nother candidate genes. Have you explored potential correlations between their\nexpression and PDAC development? P3H1 expression does not appear to be strongly\nassociated with most clinicopathological parameters, with the exception of TNM\nstaging. It would be important to clarify why P3H1 was prioritized for analysis—\nparticularly at the early stages of the study, when data may have been limited. Was this\nchoice primarily informed by existing literature, or were there already some preliminary\nindications, such as survival data, that guided the decision?\nResponse: We thank the reviewer for the expert comments. We have discussed more\nclearly why P3H1 was prioritized for analysis over the other candidates in the revised\nmanuscript (pages 8-9). P3H1 was chosen for further investigation due to its consistent\ninducible expression patterns observed throughout the development of human and\nmouse pancreatic ductal adenocarcinoma (PDAC) (revised Figure 1, B and C).\nAdditionally, P3H1 was prioritized for further investigation because it displayed higher\ndifferential expression in human PDAC versus normal tissues, as evidenced by the\nhuman S-PM protein dataset1 (P = 3.96e-13) and the Clinical Proteomic Tumor\nAnalysis Consortium (CPTAC) cohort RNA-seq dataset2 (P = 9.0e-17). It also showed\na stronger positive correlation with stromal abundance (ρ = 0.42, P = 1.96e-8) and a\nhigher hazard ratio for disease-free survival (DFS) (2.07; 95% CI 1.39 ~ 3.09)\n1\ncompared to other candidates (revised Figure 1B, 1D and Supplementary Figure 2-5).\nWe have added these new results and explanations in the revised manuscript (revised\nFigure 1, B-D, Supplementary Figure 2-5 and pages 8-9).\n2. Line 137. Table 1 presents only information on the PDAC patients. The information\nabout the non-tumor tissues should be added. For example, are they derived from non-\ntumor tissue of the PDCA patients?\nResponse: We have added the information about the non-tumor tissues in revised\nSupplementary Table 3 as suggested. Yes, the non-tumor tissues are derived from the\nPDAC patients.\n3. Line 139. Please include the complete TMA IHC results in a Supplementary Figure\nfor a more comprehensive view.\nResponse: We have provided the complete TMA IHC results in the revised\nSupplementary Figure 6 and revised Supplementary Table 4 as suggested.\n4. Line 170: Figure 2B: some information is missing. The three columns refer to 3\ndifferent biological replicates? Please explain better, in the main text or in the figure\nlegend, or in the figure.\nResponse: We thank the reviewer for pointing out this. We have provided the\ninformation of the three columns in the revised Figure 2B and Figure 2B legend. The\nthree columns refer to the genotyping results for KPC; P3H1 heterogenous mice (line\n1), KPC; P3H1 cKO mice (line 2) and KPC; P3H1 wild-type mice (line 3).\n5. Line 174–177: In the KPC WT mice, the median survival is 64 days, while in the\nKPC P3H1 cKO mice it's 76 days. The authors describe this as a significantly improved\nsurvival (statistically significant). Have this also a biological relevance? Are there any\nreference values they could use to confirm/validate this?\n12-day median survival increase (64 d → 76 d) in KPC P3H1 cKO mice as suggested\nby the reviewer (page 11). Previous studies indicated exceeding 15% survival extension\nare considered significant and clinically relevant in aggressive PDAC mouse models 3,\n4, 5. The observed 12-day median survival increase (from 64 days to 76 days) in KPC\nP3H1 cKO mice represented approximately a 20% extension in lifespan and should be\nconsidered a marked survival benefit. We have added this new explanation to the\nrevised manuscript (page 11).\n6. Line 244. Among the cytokines associated with TAM infiltration, IL-8 and TGF-β\n(among others) were also found to be reduced in P3H1-depleted cells. Have the authors\nexplored the involvement of additional cytokines beyond CXCL1 and CXCL5?\nResponse: We have analyzed whether IL-8 and TGF-β were involved in P3H1-\nmediated TAMs infiltration as suggested. The results showed that similar to CXCL1\nand CXCL5, depletion of P3H1 remarkably inhibited the production of the total and\nsecreted CXCL8 protein (revised Figure 3, O and P). However, P3H1 knockdown did\nnot significantly alter total TGF-β protein level and only modestly decreased secreted\nTGF-β (revised Supplementary Figure 12), indicating that TGF-β may not be the\nprimary cytokine involved in P3H1-mediated regulation of TAMs abundance. Next, to\nexamine whether P3H1-mediated TAMs recruitment was dependent on CXCL8, we\nelevated CXCL8 levels by introducing the recombinant cytokines in the culture media\nfrom P3H1-deficient cells. Similar to CXCL1 and CXCL5, the addition of CXCL8\neffectively reversed the P3H1-deficiency-induced inhibition of macrophage migration\n(revised Figure 3S). Together, these data strongly indicated that P3H1 loss reshapes\nthe TIME by inhibiting CXCL1/5/8 dependent TAMs infiltration in PDAC. These\nresults have been added to the revised manuscript (revised Figure 3 and revised\nSupplementary Figure 12).\n7. Line 257–259: The different contribution of TAM when modulating CXCL1-\n3\n5should be investigated also by FACS quantitative analysis. Then, when modulating\nCXCL1-5 expression and consequently TAMs infiltration in the two lines (WT and\ncKO), do they observe any changes in tumor proliferation or prognosis? This could\nsupport the role of the tumor microenvironment (TME) and how its remodeling may be\nassociated with disease outcome.\nResponse: TAMs are known to polarize to either M1, which represents anti-tumor\nactivity, or M2, which leads to tumor promotion. To investigate the role of CXCL1,\nCXCL5 and CXCL8 on TAMs polarization, we have performed FACS quantitative\nanalysisas suggested. The results showed that knockdown of P3H1 in tumor cells\naltered TAMs polarization. Specifically, P3H1 knockdown in tumor cells decreased the\nproportion of M2-like TAMs (CD14+CD163+ TAMs), while M1-like TAMs\n(CD14+CD86+) remained unaffected, consistent with our previous in vivo observations\n(revised Figure 3C, 3D, 3T, and Supplementary Figure 14). Importantly, reintroducing\nrecombinant CXCL1, CXCL5 or CXCL8 into the culture media from P3H1-depleted\ncancer cells restored M2-like TAMs polarization (revised Figure 3T and Supplementary\nFigure 14). These data indicated P3H1 loss reshapes the TIME by inhibiting CXCL1,\nCXCL5 or CXCL8-dependent M2-like TAMs polarization in PDAC (revised Figure 3T\nand Supplementary Figure 14).\nNext, we have modulated CXCL1/5/8 expression to explore the changes in tumor\nproliferation as suggested. The results indicated that tumor proliferation was suppressed\nupon treatment with the culture media from P3H1-depleted MIA PaCa-2 cells (revised\nFigure 3U). Importantly, elevating CXCL1, CXCL5, or CXCL8 level in the culture\nmedia from P3H1-depleted cells restored the inhibition of tumor proliferation (revised\nFigure 3U). These results have been added to the revised manuscript (revised Figure 3T,\n3U and Supplementary Figure 14), which have strengthened the conclusion of this\nmanuscript.\n8. Line 271–272: Among the various up/down-regulated proteins (lines 267–268), why\ndid they choose to focus only on PLK1? Even in Figure 4C, in the overlapping region,\n4\nthere are other upregulated proteins. Why was only PLK1 selected? If PLK1 was the\nmost strongly upregulated, did they exclude other candidates for a specific reason?\nResponse: PLK1, but not other three candidates, was selected for further investigation\nbecause of a series of reasons. First, PLK1 was one of the most significantly down-\nregulated proteins in P3H1 knockdown cells (revised Figure 4D and Supplementary\nFigure 15A). Second, loss of PLK1 had a more pronounced inhibitory effect on\npancreatic cancer cell growth compared to other candidates (revised Supplementary\nFigure 15, B-D), implying that PLK1 plays a critical role in P3H1-mediated PDAC\nprogression. Third, our RNA-seq data indicated that the differentially expressed genes\nin P3H1 knockdown cells were highly related to those involved in regulation of protein\nserine/threonine kinase activity (revised Figure 3G). Compare to other candidates,\nPLK1 is a serine/threonine kinase that phosphorylates a number of substrates to\nfacilitate cell proliferation6. Taken together, we believe that P3H1-PLK1 axis is most\nlikely involved in the regulation of PDAC progression although we do not completely\nrule out the possibility that other P3H1-mediated potential downstream candidates may\nalso contribute to this process. We have added these new results to the revised\nmanuscript (revised Figure 4D and revised Supplementary Figure 15).\n9. Line 298–307: In this section, they identify 7 proteins with known roles in cell cycle\nand proliferation.\nWhy did they decide to focus only on β-catenin (CTNNB1)? Was it because it's the only\none also associated with the TME?\nResponse: Yes, we chose to focus on β-catenin (CTNNB1) in this study because it is it\nis the only protein known to be involved in both tumor cell proliferation and cytokine\nsecretion modulation, thereby contributing to TME reshaping 7, 8, 9. Given that the\nfunctional significance of other candidates in regulating the tumor microenvironment\nremains uncharacterized10, 11, 12, 13, 14, 15, 16, 17, 18, we have prioritized β-catenin for further\ninvestigation in this study. We have added these explanations in the revised manuscript\n(page 19).\n5\n10. Line 356–358: Why did they choose only gemcitabine (GEM)? Did they also test\nother chemotherapeutic agents used in PDAC, such as FOLFIRINOX, to see if they\nproduce similar results?\nResponse: To enhance the translational relevance of our findings, we evaluated whether\nPLK1 inhibition could improve the efficacy of other standard PDAC chemotherapies,\nsuch as FOLFIRINOX or Gem/nab-paclitaxel. Similar to Gem therapy, combined\ntreatment of BI2536 with FOLFIRINOX or Gem/nab-paclitaxel resulted in\nsignificantly greater reductions in tumor volume and weight compared to\nmonotherapies (revised Supplementary Figure 18). Thus, targeting P3H1-PLK1 axis\nmight offer a novel and promising therapy to improve PDAC chemotherapy. These\nresults have been added to the revised manuscript (revised Supplementary Figure 18),\nwhich have strengthened the clinical significance of this manuscript.\n11. Line 359. Is not clearly specified which of the “above-mentioned” PDAC models\nare used for the final and conclusive in vivo experiment. To finally prove the importance\nof the P3H1-PLK1 axis in the final biological effect, both the control and the P3H1 ko\nMIA PaCa2 cells must be used. PLK1 inhibitors are well-known antitumor agents,\nhaving anticancer effects on different cancer types. The combination of gemcitabine\nwith BI2536 showed in the paper could be the final additive effect of the two drugs,\nnon-dependent on the presence/absence of the PH31 gene.\nResponse: We have clarified the PDAC mouse model (orthotopic xenograft pancreatic\ntumor mouse model) used for the final and conclusive in vivo experiment in the revised\nmanuscript as suggested (page 22).\nTo prove the final additive effect of Gem/nab-paclitaxel with BI2536 depends on the\npresence of the P3H1, we assessed the therapeutic response of Gem/nab-paclitaxel in\nP3H1-depleted MIA PaCa-2 cell xenografted mouse model. The Gem/nab-paclitaxel\ntreatment led to a greater reduction in tumor burden in mice xenografted with P3H1-\nknockdown MIA PaCa-2 cells compared to control cells (revised Figure 8, A-C).\nmice engrafted with P3H1-knockdown cells following Gem/nab-paclitaxel treatment\n(revised Figure 8D). These findings underscore the potential therapeutic implications\nof targeting the P3H1-PLK1 axis in enhancing the efficacy of Gem/nab-paclitaxel\ntreatment in PDAC. These results have been added to the revised manuscript (revised\nFigure 8).\n-------------------------------------------------\nReviewer#2\nComments to the Author\nThe manuscript by Panzhu Bai et al presents an extensive analysis of (pancreatic ductal\nadenocarcinoma) PDAC. The finding that Prolyl 1 3-hydroxylase 1 (P3H1) possibly is\ninvolved in pancreatic cancer progression is potentially highly interesting and warrants\nfurther investigation, and an impressive number of different experiments and validation\nsteps are included. However, I have a number of concerns with the present manuscript.\nMy main concern is that the study is very focused on the association between P3H1 and\nPolo-like kinase 1 (PLK1). The authors demonstrate that P3H1 inhibition has a vast\nimpact on many cellular processes and likely is involved in PDAC, as evident by the\nhigh number of changed proteins and RNA transcript levels (including many central\ncellular regulators with broad impact such as cyclin D1). However, part from a GO\nenrichment analysis and GSEA, no full analysis and interpretation is included of the\nextensive number changes, and of the hundreds of changes, the study quickly focuses\non PLK1 effectively omitting analysis on the reminder of changing features. In my view,\nthe authors have therefore, at most, indicated an association between P3H1 and PDAC,\nbut not demonstrated the direct association between P3H1 and PLK1, as PLK1 may be\nchanging as a result of the vast cellular perturbation. This is especially when\nconsidering the role of PLK1 in the synthesis of the structurally important protein\ncollagen. This is a central issue given the current construction of the manuscript.\nAnother central issue is the methods section which severely lacks several fundamental\n7\nand essential elements, hindering reproduction of the study findings. As an example, it\nis currently not clear what data was newly generated for the present study, and what\nwas reused. I also have a number of concerns with the applied method. In addition, the\nmanuscript is very condensed and it is in many places difficult to follow the line of\nargumentation, especially in the Results section. Many figures are included but not\ndescribed and discussed. I hope that my comments can help guide the authors to get\nsome of the important findings published.\nPlease find below my comments, in no particular order but divided into minor and major\nconcerns.\nMajor concerns\n1. Currently, it is not clear to this reviewer what data was reused and what was generated.\nThis is a critical and a fundamental academic issue.\nResponse: We thank the reviewer for the comments and suggestions. We have clarified\nwhat data were reused and what data were newly generated in the revised manuscript\nas suggested (page 8). In details, on the basis of our recent published the glycosylated\nsecreted and membrane protein (S-PM) database resource1, we conducted a re-analysis\nwith a more stringent filtering criterion (Log₂FC > 0, FDR < 0.05) to identify high-\nconfidence subsets of 226 upregulated secreted proteins in the human S-PM protein\nPDAC dataset and 233 upregulated secreted proteins in the LSL-KrasG12D/+; p53fl/fl;\nPdx1-Cre (KPC) mouse S-PM protein dataset (revised Figure 1A and Supplementary\nFigure 1). Additionally, we curated a focused screening set of 227 ECM-annotated\nproteins from the human S-PM protein PDAC dataset (revised Figure 1B). The\nintersection of these three independent subsets yielded a consensus set of 34 candidate\nproteins whose expressions were significantly and consistently elevated in both human\nand mouse samples. These re-analyses are now being reported for the first time. We\nhave added these new analyzes and explanations to the revised manuscript (revised\nFigure 1 and Supplementary Figure 1 and page 8).\n8\n2. The method section is lacking a number of elements needed to evaluate the work and\nreproduce the findings. Below are some of the central points mentioned.\na. The method section is missing details on how the LC-MS/MS analysis was performed\n(instrument, gradient, solvents,…).\nResponse: We have added the missing details on how the LC-MS/MS analysis was\nperformed (instrument, gradient, solvents…) in the revised Methods section as\nsuggested (pages 43-46).\nb. The method section is lacking a clear description of how the IHC scoring was performed,\nwhich is a central element in the manuscript. E.g. was the entire picture used or only the outline,\nwas a program used, and what were the criteria.\nResponse: We have added the clear description of how the IHC scoring was performed\nin the revised Methods section “Histology and immunohistochemistry staining” as\nsuggested by the reviewer (page 34).\nc. Missing information on the primary antibody (e.g. animal and dilution factor) and\nsecondary antibody (method line 560) in the “Immunofluorescence staining”. The\n“Immunoblotting” after is much more detailed and as needed to reproduce the\nexperiment.\nResponse: We have provided the missing information on the primary antibody (e.g.\nanimal and dilution factor) and secondary antibody (method line 560) in the revised\nMethods section “Immunofluorescence staining” as suggested (page 38).\nd. Additional information regarding the Illumina NovaSeq sequencing should be provided.\nResponse: We have provided additional information regarding the Illumina NovaSeq\nsequencing in the revised Methods section as suggested (page 42).\ne. The description of the RNA seq data analysis is missing. Please include a description\nof how the data was treated (FASTQ files etc as used).\nResponse: We have added the description of how the RNA seq data was treated\n(FASTQ files as used) and were analyzed in the revised Methods section “RNA\nsequencing” (pages 41-42).\nf. Lacking information on the Human pancreatic cancer tissue microarrays in the\nmethods. This includes the number of samples and any inclusion criteria of the “Human\npancreatic cancer tissue microarrays” in the methods (line 468).\nResponse: We have added the number of samples and the inclusion criteria of the\n“Tumor microarrays of pancreatic cancer patients” in the revised Methods section (page\n30) as suggested.\ng. Throughout the manuscript, information regarding the correlation analysis is missing.\nPlease clarify which type of correlation analysis was performed, what was correlated,\nand used cutoffs (e.g. line 136, line 145).\nResponse: As suggested by reviewer, we have clarified which type of correlation\nanalysis was performed, what was correlated, and used cutoffs in the main text and\nFigure legends of revised Figure 1G and Figure 4G. In revised Figure 1G, Chi-square\ntest were performed to examine the association between P3H1 protein expression and\nother pathologic parameters (such as TNM staging, gender, age…) in PDAC. The\ncutoffs values are based on the threshold criteria of P < 0.05. The results showed that a\nsignificant correlation between P3H1 expression and TNM stage (P = 0.0216), but no\nsignificant associations observed with other clinical parameters. In revised Figure 4G,\nPearson’s correlation analyses were performed to assess the association between P3H1\nand PLK1 protein expression. The cutoffs values are based on the threshold criteria of\nPearson’s coefficient |R| > 0.5 andP < 0.05. The results indicated a strong positive\ncorrelation (R = 0.53, P = 1.4*e-5) between P3H1 and PLK1 expression. We have added\nthe results to the revised Results section and Figure legends of revised Figures 1G and\n4G (page 10,18,59,65).\n10\nh. How was the gene ontology enrichment analysis and GSEA performed?\nResponse: We have described how the gene ontology enrichment analysis and GSEA\nwere performed in details in the revised Methods section as suggested (page 42).\n3. The Results section is dense and difficult to follow. As a general example, the authors\nstate that, “we identified 34 proteins whose expressions were significantly and\nconsistently elevated in both human and mouse samples...” (line 127). While a\npotentially interesting finding, it would be more prudent to present the datasets in more\ndetail, and separately. E.g. describe\na) how many proteins were monitored in each dataset (it is listed in Figure 1B, but\nwould be nice to have in the text),\nResponse: We have provided the information on how many proteins were monitored in\neach dataset of Figure 1B in the main text of the revised manuscript as suggested (page\n8).\nb) were any proteins less abundant and if yes, c) why were only more abundant proteins\nconsidered? It is also not clear if this represents newly generated data or a re-analysis\nof existing data. Throughout the results section, the authors should consider including\nmore argumentation for the included experiments.\nResponse: We thank the reviewer for pointing out this. We chose candidates based on\nthe differential expression between normal and PDAC tumor samples rather than on the\nprotein abundance. Therefore, we didn’t only consider more abundant proteins to do\nfurther investigation.\nAs suggested, we have clarified the data presented in Figure 1B were a re-analysis of\nexisting data published by our lab1. We have included more argumentation and\nexplanation for the included experiments in the main text and revised Supplementary\nFigure 1 of the revised manuscript as suggested (page 8 and revised Supplementary\nFigure 1).\n11\n4. It is mentioned that the GSEA (Figure 3E) identified downregulated pathways (line\n238), including “cytokine-mediated signaling pathways”. Please provide additional\nsupport for the conclusion of a downregulation, as opposed to a upregulation.\nResponse: We have provided additional support for the conclusion of a downregulation,\nas opposed to an upregulation, for “cytokine-mediated signaling pathways” as\nsuggested. This new analysis is included in revised Supplemental Figure 11 of the\nrevised manuscript.\n5. Supplementary figure 3. A few outlying datapoints seem to be present on the extreme\nhighs of the infiltration levels (few measurements are very high). This is especially true\nfor panel A, B and C. Please provide the analysis omitting the few very high\nmeasurements which could be outliers and significantly affect the results.\nResponse: We have provided the analysis omitting the few very high measurements in\nthe revised Supplementary Figure 9 as suggested. The results from the new analyses do\nnot significantly affect the results and are highly consistent with our previous\nconclusion that P3H1 expression exhibited positively correlated with the infiltration of\ntumor-associated macrophages (TAMs) and tumor-associated neutrophils (TANs), but\nwas not correlated with other immunosuppressive cells, such as myeloid-derived\nsuppressorcells (MDSCs) and regulatory T cells in PDAC. These results are included\nin revised Supplemental Figure 9 of the revised manuscript.\n6. It is stated that “P value < 0.05 was considered statistically”. However, given the\nOMICS-based nature of the analyses, the number of reported significantly regulated\nproteins is inflated. Correction of all p-values must therefore be included for all datasets.\nResponse: We thank the reviewer for pointing out this. We have re-analyzed our\nOMICS-related datasets and have corrected the p-values (revised Figure 3J, 4A, 4B,\n5A) as suggested. Wehave been updated all p-values to revised Figures (revised Figure\n3J, 4A, 4B, 5A) and the Methods section “Statistical analysis” in the revised manuscript\n12\n(page 47), which have strengthened the reliability of our findings.\n7. It would be beneficial to have a volcano plot of the data in Figure 1B.\nResponse: We thank the reviewer for pointing out this. In Figure 1B, we required a\nmethod to illustrate the logical relationships (overlaps and differences) between the\nitems in three independent subsets. A Venn diagram is a powerful statistical tool to\nrepresent logical relationshipsbetween two or more datasets, illustrating their\ndifferences and overlaps to help data analysis and understanding. In contrast, a volcano\nplot is a statistical toolto displaystatistical significanceversus magnitude of change in\nlarge datasets, which aids in identifying key genes or molecules that are significantly\ndifferentially expressed in the datasets. Thus, it is more appropriateto choose a Venn\ndiagram instead of a volcano plot in Figure B to visualize dataset relationships.\n8. Full image of the western blots should be included to allow assessment of the\nantibody specificity, in addition to the missing negative controls.\nResponse: We have provided the full images of the western blots in the Supplementary\nFiles (see Source Data) as suggested.\nMinor concerns\n1. Consider rephrasing “our study was to identify” (line 122). The phrase is\nunderstandable, but not the most natural.\nResponse: We have revised the phrase and it now reads “our study aimed to identify…”\n(page 8 of the revised manuscript).\n2. Please provide a reference or additional justification to the statement that “Therefore,\ntargeting TAMs offers a promising strategy for enhancing immune-based therapies and\npotentially impeding tumor progression.”.\nResponse: We have provided the references to support our statement that “Therefore,\ntargeting TAMs offers a promising strategy for enhancing immune-based therapies and\n13\npotentially impeding tumor progression” in the revised manuscript as suggested\n(references 14 and 15, page 5 of the revised manuscript).\n3. The introduction is good and interesting to read. At times it can seem slightly\nfragmented. Particularly the sections on P3H1 (line 74), PLK1 (line 85), and Wnt/beta-\ncatenin (line 94) could probably be improved by including a sentence or two providing\na more clear link to increase the readability.\nResponse: We have revised these sentences in the Introduction section and have added\nclearer links to increase the readability in the revised manuscript as suggested (page 5\nof the revised manuscript).\n4. Introduction (line 52): Any numbers on annual deaths?\nResponse: We have provided the numbers on annual deaths in the Introduction section\nas suggested (page 4).\n5. Introduction line 99, consider adding a “the” to “After entering into nucleus…”\nResponse: We have added “the” to “After entering into nucleus…” in the revised\nmanuscript as suggested (page 6, line 106).\n6. Missing reference to Image J in the methods (line 475).\nResponse: We have added the reference to Image J in the revised manuscript (reference\n99 and page 34 of the revised manuscript).\n7. Please add centrifugation details (line 582).\nResponse: We have provided the centrifugation details in the revised Methods section\nas suggested (page 40, line 833 of the revised manuscript).\n8. Please clearly state in the proteomics analysis method description where the\nSISPROT kit sample preparation steps begin and end. As an example, it is difficult to\n14\nassess whether the C18 disk is part of the kit (line 617).\nResponse: We have clearly described the SISPROT kit sample preparation steps in\ndetails in the revised Methods section as suggested (pages 43-44).\n9. Supplementary figure 3D is missing correlation indications.\nResponse: We have added the correlation indications in the revised Supplementary\nFigure 9D as suggested. The Spearman'scorrelation analysis was used to clarify\nthecorrelationof P3H1 expression with immune cells infiltration in PDAC.\n10. On the correlation figure legend, a description of the gray area is missing.\nResponse: We have provided a description of the gray area in the figure legend of\nrevised Supplementary Figure 9. The gray area represents the 95% confidence interval.\n11. The description of the included dynamic modifications should be checked. E.g. was\noxidation of any residue included or is it the default setting in MaxQuant with\nMethionine (line 652)? Same for deamidation. Also, please include the FDR filtering\nthreshold for identifications (defaults to FDR<1%).\nResponse: We have clearly described the included dynamic modifications as suggested\n(pages 45-46). We have also added the FDR filtering threshold in the revised Methods\nsection (page 46 of the revised manuscript).\n12. Did the authors consider investigating the levels of hydroxylation of proline\nresidues of collagen chains in the proteomics data? This is one in the primary post-\ntranslational modifications by P3H1 and altered levels could have an impact on the\ncollagen proline hydroxylation levels, thereby providing additional validation for the\nexisting proteomics data for an altered activities of P3H1.\nResponse: We appreciate the reviewer's thoughtful question. Regrettably, we were\nunable to detect the total protein of type I collagen, a known substrate catalyzed by\nP3H1, in our current proteomics data (revised Supplementary Table 6). This is likely\n15\ndue to the fact that type I collagen is predominantly produced by stromal cells rather\nthan cancer cells, making its protein expression levels fall below the detection threshold\nof our mass spectrometry analysis 19, 20. As a result, we were unable to evaluate the\nlevels of hydroxylation of proline residues of type I collagen in our proteomics dataset.\n13. What is meant by a “heatmap analysis” (line 302)?\nResponse: In this study, heatmap analysis is used to visualize the differences in protein\nphosphorylation levels between control and P3H1 knockdown MIA PaCa-2 cells. The\nx-axis represents the samples, the y axis representsup-regulated (red color) and down-\nregulated(blue color)phosphorylated proteins. To state more clearly, we have revised\nthe phrase (line 380) and it now reads “The heatmap confirmed the increased\nphosphorylation levels of seven candidates in control MIA PaCa-2 cells” (page 19 of\nthe revised manuscript).\n-------------------------------------------------\nReviewer#3\nComments to the Author\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nResponse: We thank the reviewer for the comments and suggestions.\n-------------------------------------------------\nReviewer#4\nComments to the Author\nThe study identifies P3H1 as a novel factor upregulated in PDAC, correlating with poor\n16\nprognosis in both patients and mouse models. Genetic ablation of P3H1 in KPC mice\nsignificantly attenuates tumor progression, decreases immunosuppressive macrophage\ninfiltration, and improves survival. Mechanistically, P3H1 depletion downregulates\nPLK1 protein levels, thereby reducing β-catenin phosphorylation at S191, impairing its\nnuclear translocation, and suppressing oncogenic transcription (ex. c-Myc, Cyclin D1,\nCXCL1, CXCL5). Restoration of PLK1 in P3H1-deficient cells rescues these defects.\nFurthermore, pharmacologic inhibition of PLK1 synergizes with gemcitabine to reduce\ntumor burden in vivo.\nThis work is noteworthy for defining a previously unknown P3H1-PLK1-β-catenin axis\nand offering a new combination therapeutic strategy in PDAC, which remains a\nformidable malignancy with limited treatment options. The integration of\ntranscriptomic, proteomic, and functional data is commendable and adds mechanistic\nrigor.\nMajor Weaknesses\n1. The authors suggest that P3H1 may stabilize PLK1 via direct hydroxylation or\nthrough indirect regulatory proteins. However, no biochemical evidence (ex- co-IP,\nin vitro hydroxylation assays, PLK1 stability assays) is provided to support this claim.\nResponse: We thank the reviewer for the comments and suggestions. As suggested, we\nhave performed a series of experiments to test whether P3H1 stabilizes PLK1 via direct\nhydroxylation or through indirect regulatory proteins. First, we have analyzed the\ninteraction of P3H1 and PLK1 in MIA PaCa-2cells by co-IP assay. The results showed\nthat PLK1 were not co-IPed with P3H1 (revised Supplementary Figure 16A).\nReciprocally, P3H1 were not co-IPed with PLK1 (revised Supplementary Figure 16A),\nindicating that P3H1 did not regulate PLK1 stability via direct binding. Second, we\nhave analyzed whether P3H1 could hydroxylate PLK1 proline residues because P3H1\ncould modify specific prolinesin the Gly-Pro-Hyp repeating sequence into (3S)-\nhydroxyproline(3-Hyp) in target proteins 21, 22. However, PLK1 sequence alignment\n17\nindicated that PLK1 lacked the specific proline residues within the Gly-Pro-Hyp\nrepeating motif, indicating that P3H1 is unlikely to hydroxylate PLK1 directly (revised\nSupplementary Figure 16B). Third, to assess whether P3H1 indirectly affected PLK1\nprotein stability, a cycloheximide (CHX) chase assay was conducted. The results\nindicated no significant difference in the degradation rate of PLK1 between P3H1\nknockdown cells and wild-type cells after CHX treatment (revised Supplementary\nFigure 16, C and D), suggesting that P3H1 did not influence PLK1 protein stability.\nFinally, we hypothesized that P3H1 regulated PLK1 expression at the transcriptional\nlevel rather than by affecting protein stability. The results showed that P3H1 depletion\nresulted in a significant decrease in PLK1 mRNA levels, supporting a potential role for\nP3H1 in modulating PLK1 transcription (revised Supplementary Figure 16E). Although\nour studies have confirmed the function of P3H1 in regulating PLK1 expression at the\ntranscriptional level (revised Supplementary Figure 16E), how P3H1 affects PLK1\ntranscription remains elusive. Further studies are required to clarify the precise\nmolecular mechanisms by which P3H1 regulates PLK1 transcription. We have added\nthese new results and discussion in the revised manuscript (revised Supplementary\nFigure 16 and Discussion Section, pages 18-19 and page 26).\n2. While S191 phosphorylation is analyzed, the broader phosphoproteomic context and\npotential functional impact of other phosphorylation sites (S605) mentioned in the\nintroduction are not explored.\nResponse: We thank the reviewer for pointing out this. Previous studies indicated that\nthe phosphorylation of β-catenin at S191 and S605 controls its nuclear translocation23.\nWe have analyzed the functional impact of S191 phosphorylation of β-catenin in this\nstudy. However, the role of S605 phosphorylation was not further explored because the\nβ-catenin S605 phosphorylation site was not detected in either P3H1 knockdown or\nwild-type MIA PaCa-2 cells in our phosphor-proteomic dataset (revised Supplementary\nTable 7). Additionally, validation of S605 phosphorylation was not possible due to the\nlack of a specific commercial antibody against this site. Therefore, our data strongly\n18\nindicate that the P3H1-PLK1 axis primarily regulates β-catenin nuclear localization\nthrough phosphorylation at S191. However, we cannot rule out the possibility that this\naxis may also affect β-catenin nuclear expression by modulating phosphorylation at\nother sites, such as S605 23. These explanations have been added to the Discussion\nsection of the revised manuscript (page 26).\n3. Although F4/80+, CD68+, and ARG1+ TAMs are decreased upon P3H1 deletion,\nfunctional characterization of these macrophages is lacking.\nResponse: We have tested the functional characterization of M2-like TAMs (CD68+\nand ARG1+ TAMs) upon P3H1 deletion as suggested. Given that M2-like TAMs\nfacilitate cancer cell proliferation by secreting tumor-promoting factors such as PDGF\nand FGF224, 25, we investigated whether P3H1 deletion could modulate the functional\nstate of M2-like TAMs and thereby impact tumor growth. Notably, tumor sections from\nKPC;P3H1 cKO mice demonstrated a significant reduction in PDGF-B and FGF2\nexpression compared to KPC;WT controls (revised Figure 3, E and F). To validate this,\nmacrophages treated in vitro with conditioned media from P3H1-deficient MIA PaCa-\n2 cells showed decreased transcriptional levels of PDGF-B and FGF2 (revised\nSupplementary Figure 10). These findings suggest that P3H1 depletion not only\ndecreased the abundance of M2-like TAMs but also diminished their pro-tumorigenic\nactivity. These results have been added to the revised manuscript (revised Figure 3, E\nand F and Supplementary Figure 10).\n4. The PLK1 inhibitor BI2536 is combined with gemcitabine in vivo, but no dose-\nresponse curves or toxicity evaluation (body weight etc) are shown for translational\nability.\nResponse: Considering that BI2536 has been reported to induce dose-dependent\ntoxicity and efficacy when combined with other drugs26, 27, we evaluated the toxicity of\nvarious doses of BI2536 in combined with Gem in this study. Orthotopic pancreatic\ntumor-bearing mice were treated with Gem combined with varying doses of BI2536 at\n19\n5, 15, and 25 mg/kg. All mice treated with Gem combined with low-dose (5 mg/kg) or\nmiddle-dose (15 mg/ kg) BI2536 remained viable (revised Supplementary Figure 17A).\nHowever, mice treated with high-dose BI2536 plus Gem (25 mg/mL) began to die at 5\ndays (revised Supplementary Figure 17A). Moreover, mice treated with low-dose and\nmiddle-dose BI2536 (5 and 15 mg/kg) combined with Gem showed no adverse effects\non body weight or main organ structure (revised Supplementary Figure 17, B and C).\nIn contrast, high-dose BI2536 (25 mg/kg) combined with Gem caused a significant\nreduction in mouse body weight and severe histopathological damage to major organs\nsuch as the liver and lungs, indicating that high-dose of BI2536 exhibits notable organ\ntoxicity in mice (revised Supplementary Figure 17, A-C). Next, we evaluated the\ntherapeutic efficacy of low-dose and middle-dose BI2536 combined with Gem in\ninhibiting PDAC progression. The results showed that combining Gem with middle-\ndose (15 mg/kg) of PLK1 inhibitor significantly increased the therapeutic efficacy\ncompared to that of the single Gem treatment or Gem with low-dose (5 mg/kg) of\nBI2536 (revised Figure 7, G-I and Supplementary Figure 17, D and E). Collectively,\nthese data indicate that middle-dose of PLK1 inhibitor BI2536 enhances the\nresponsiveness of PDAC to chemotherapy. These results have been added to the revised\nmanuscript (revised Figure 7, G-I and Supplementary Figure 17, page 22).\n5. Adding current standard chemotherapy regimen would improve translational value –\nGemcitabine and paclitaxel.\nResponse: To enhance the translational relevance of our findings, we evaluated whether\nPLK1 inhibition could improve the efficacy of other standard PDAC chemotherapies,\nsuch as FOLFIRINOX or Gem/nab-paclitaxel. Similar to Gem therapy, combined\ntreatment of BI2536 with FOLFIRINOX or Gem/nab-paclitaxel resulted in\nsignificantly greater reductions in tumor volume and weight compared to\nmonotherapies (revised Supplementary Figure 18). Overall, targeting P3H1-PLK1\naxis might offer a novel and promising therapy to improve PDAC chemotherapy. These\nresults have been added to the revised manuscript (revised Supplementary Figure 18,\n20\npage 23), which have strengthened the clinical significance of this manuscript.\n6. Given the translational emphasis, testing in patient-derived organoids or xenografts\nwould better support the therapeutic claims.\nResponse: We thank the reviewer for this expert suggestion. To improve our study\ntranslational potentials, we have analyzed the response of three distinct patient-derived\nPDAC organoids (PDOs) to the combination of BI2536 with Gem/nab-paclitaxel.\nPDOs displayed histological features consistent with the original patient tumor samples\n(revised Figure 7L). Similar to the observations in the orthotopic xenograft mouse\nmodel, the drug combination demonstrated superior efficacy in eliminating all three\nPDAC organoids compared to either agent alone (revised Figure 7, M and N). These\nresults have been added to the revised manuscript (revised Figure 7, L-N), which have\nstrengthened the therapeutic claims of our study.\n7. Many in vitro results are derived from MIA PaCa-2 cells, which do not fully\nrecapitulate PDAC heterogeneity. Validation in additional PDAC models like organoids\nwould enhance their claim.\nResponse: To validate the findings from MIA PaCa-2 cell lines and to recapitulate\nPDAC heterogeneity, we evaluated the impact of P3H1 on cancer cell proliferation in\nthree distinct patient-derived PDAC organoids (PDOs). Consistent with the results in\nMIA PaCa-2 cells, P3H1 depletion in PDOs markedly suppressed organoid growth\n(revised Figure 8, E and F). Furthermore, P3H1 knockdown caused a marked decrease\nin the expression of the proliferation marker Ki67 and the downstream effector PLK1\n(revised Figure 8G). Importantly, Gem/nab-paclitaxel therapy exhibited enhanced\neffectiveness in eliminating PDOs with P3H1 deficiency (revised Figure 8H).\nCollectively, these results enhance our findings that targeting P3H1-PLK1 axis is a\nnovel and effective therapeutic strategy for PDAC. We have added these results to the\nrevised manuscript (revised Figure 8, E-H, page 24).\nMethods Section -Specifics on RNA-seq depth, MS parameters, and normalization\ntechniques are insufficiently detailed.\nResponse: As suggested, we have provided the information of RNA-seq depth, MS\nparameters, and normalization techniques in the Methods section of the revised\nmanuscript (pages 41-45).\nReferences\n1. Huang PW, et al. Clinical functional proteomics of intercellular signalling in pancreatic\ncancer. Nature637, (2025).\n2. Rudnick PA, et al. A Description of the Clinical Proteomic Tumor Analysis Consortium\n(CPTAC) Common Data Analysis Pipeline. J Proteome Res15, 1023–1032 (2016).\n3. Peran I, et al. Cadherin 11 Promotes Immunosuppression and Extracellular Matrix\nDeposition to Support Growth of Pancreatic Tumors and Resistance to Gemcitabine in\nMice. Gastroenterology160, (2020).\n4. Renz BW, et al. β2 Adrenergic-Neurotrophin Feedforward Loop Promotes Pancreatic\nCancer. Cancer Cell33, (2017).\n5. Yang D, et al. TREM2 Depletion in Pancreatic Cancer Elicits Pathogenic Inflammation and\nAccelerates Tumor Progression via Enriching IL-1β+ Macrophages. Gastroenterology168,\n1153–1169 (2025).\n6. Strebhardt K. Multifaceted polo-like kinases: drug targets and antitargets for cancer\ntherapy. Nature reviews Drug discovery9, 643–660 (2010).\n7. Luke JJ, Bao R, Sweis RF, Spranger S, Gajewski TF. WNT/β-catenin pathway activation\ncorrelates with immune exclusion across human cancers. Clinical Cancer Research25,\n3074–3083 (2019).\n8. Raghavan S, Mehta P, Xie Y, Lei YL, Mehta G. Ovarian cancer stem cells and macrophages\nreciprocally interact through the WNT pathway to promote pro-tumoral and malignant\nphenotypes in 3D engineered microenvironments. Journal for immunotherapy of cancer\n7, 190 (2019).\n9. Zhang F, et al. β-Catenin-CCL2 feedback loop mediates crosstalk between cancer cells\nand macrophages that regulates breast cancer stem cells. Oncogene40, 5854–5865\n(2021).\n10. Lee H-J, et al. Phosphorylation of Stim1 at serine 575 via netrin-2/Cdo–activated ERK1/2\nis critical for the promyogenic function of Stim1. Molecular biology of the cell23, 1376–\n1387 (2012).\n11. Pozo-Guisado E, et al. Phosphorylation of STIM1 at ERK1/2 target sites modulates store-\noperated calcium entry. Journal of cell science123, 3084–3093 (2010).\n12. Montagnoli A, et al. Identification of Mcm2 phosphorylation sites by S-phase-regulating\nkinases. Journal of Biological Chemistry281, 10281–10290 (2006).\n22\n13. Fei L, Xu H. Role of MCM2–7 protein phosphorylation in human cancer cells. Cell &\nbioscience8, 43 (2018).\n14. Thakur BL, et al. Convergence of SIRT1 and ATR signaling to modulate replication origin\ndormancy. Nucleic acids research50, 5111–5128 (2022).\n15. Suren D, et al. Lack of relation of AKAP12 with p53 and Bcl-2 in colorectal carcinoma.\nAsian Pacific Journal of Cancer Prevention15, 3415–3418 (2014).\n16. Akakura S, Gelman IH. Pivotal role of AKAP12 in the regulation of cellular adhesion\ndynamics: control of cytoskeletal architecture, cell migration, and mitogenic signaling.\nJournal of signal transduction2012, 529179 (2012).\n17. Akakura S, Nochajski P, Gao L, Sotomayor P, Matsui S-i, Gelman IH. Rb-dependent cellular\nsenescence, multinucleation and susceptibility to oncogenic transformation through PKC\nscaffolding by SSeCKS/AKAP12. Cell cycle (Georgetown, Tex)9, 4656–4665 (2010).\n18. Zhang J, et al. LINC00163 inhibits the invasion and metastasis of gastric cancer cells as a\nceRNA by sponging miR-183 to regulate the expression of AKAP12. International Journal\nof Clinical Oncology25, 570–583 (2020).\n19. Ene–Obong A, et al. Activated pancreatic stellate cells sequester CD8+ T cells to reduce\ntheir infiltration of the juxtatumoral compartment of pancreatic ductal adenocarcinoma.\nGastroenterology145, 1121–1132 (2013).\n20. Whittle MC, Hingorani SR. Fibroblasts in pancreatic ductal adenocarcinoma: biological\nmechanisms and therapeutic targets. Gastroenterology156, 2085–2096 (2019).\n21. Vranka JA, Sakai LY, Bachinger HP. Prolyl 3-hydroxylase 1, enzyme characterization and\nidentification of a novel family of enzymes. J Biol Chem279, 23615–23621 (2004).\n22. Cabral WA, et al. Prolyl 3-hydroxylase 1 deficiency causes a recessive metabolic bone\ndisorder resembling lethal/severe osteogenesis imperfecta. Nat Genet39, 359–365 (2007).\n23. Wu X, Tu X, Joeng KS, Hilton MJ, Williams DA, Long F. Rac1 activation controls nuclear\nlocalization of β-catenin during canonical Wnt signaling. Cell133, 340–353 (2008).\n24. Larionova I, Kazakova E, Gerashchenko T, Kzhyshkowska J. New angiogenic regulators\nproduced by TAMs: perspective for targeting tumor angiogenesis. Cancers13, 3253\n(2021).\n25. Xu J, et al. Dual roles and therapeutic targeting of tumor-associated macrophages in\ntumor microenvironments. Signal Transduct Target Ther10, 268 (2025).\n26. Zhang J, et al. BRCA1 orchestrates the response to BI-2536 and its combination with\nalisertib in MYC-driven small cell lung cancer. Cell Death & Disease15, 551 (2024).\n27. Kumar BP, Rajput S, Bharti R, Parida S, Mandal M. BI2536–A PLK inhibitor augments\npaclitaxel efficacy in suppressing tamoxifen induced senescence and resistance in breast\ncancer cells. Biomedicine & Pharmacotherapy74, 124–132 (2015).\n23"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed to the requested revisions.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "Appreciate the authors thorough and detailed responses to the previously raised concerns. The newly added experimental data—including co-IP, CHX chase, phosphoproteomics, in vivo efficacy, and PDO validation—strengthen both the mechanistic understanding and the translational relevance of the manuscript. Below are a few minor suggestions for further refinement and clarity:\n1. The authors now demonstrate that P3H1 regulates PLK1 at the mRNA level. Although the mechanistic details remain unresolved, it would be helpful to briefly outline plausible hypotheses based on the known or proposed roles of P3H1 transcriptionally.\n2. To provide better context for the PDO findings, include molecular or clinical annotations—such as KRAS or P53 mutation status, treatment-naive or therapy status, and histological subtype.\n3. In the revised response to Major Weakness #1, please revise “PLK1 were not co-IPed” to “PLK1 was not co-IPd”; similarly, “P3H1 were not co-IPd” should be corrected. Also, ensure consistent gene and protein nomenclature and formatting throughout the manuscript and figures.\nOverall, the revised manuscript now presents a well-supported model in which P3H1 regulates β-catenin nuclear localization and PDAC progression via a PLK1-dependent axis. The newly added in vivo efficacy data and PDO validation significantly enhance the clinical relevance."}, "author_response": "Reviewers’ Comments and Our Point-to-point Responses:\nReviewer#4\nComments to the Author\nAppreciate the authors thorough and detailed responses to the previously raised\nconcerns. The newly added experimental data—including co-IP, CHX chase,\nphosphoproteomics, in vivo efficacy, and PDO validation—strengthen both the\nmechanistic understanding and the translational relevance of the manuscript. Below are\na few minor suggestions for further refinement and clarity:\n1. The authors now demonstrate that P3H1 regulates PLK1 at the mRNA level.\nAlthough the mechanistic details remain unresolved, it would be helpful to briefly\noutline plausible hypotheses based on the known or proposed roles of P3H1\ntranscriptionally.\nResponse: We thank the reviewer for the comments. We have discussed the plausible\nhypotheses based on the known or proposed roles of P3H1 transcriptionally in the\nrevised manuscript (page 27). Although our studies have confirmed the function of\nP3H1 in regulating PLK1 expression at the transcriptional level, how P3H1 affects\nPLK1 transcription remains elusive. Potentially, P3H1 facilitates collagen maturation\nand remodels ECM assembly, activating integrin–FAK/Src–Akt/ERK signaling and\nthereby enhancing FOXM1/E2F-driven PLK1 transcription. Alternatively, P3H1 may\nperturb cellular oxygen/ROS homeostasis or collagen–HIF crosstalk, indirectly\nmodulating HIF–Myc programs that regulates PLK1 transcription 1-5. We have added\nthis new discussion in the revised manuscript (revised Discussion Section, page 27).\n2. To provide better context for the PDO findings, include molecular or clinical\nannotations—such as KRAS or P53 mutation status, treatment-naive or therapy status, and\nhistological subtype.\nResponse: We have provided the better context for the PDO findings, include molecular\nor clinical annotations—such as KRAS or P53 mutation status, treatment-naive or therapy\nstatus, and histological subtype in revised Supplementary Table 4 as suggested.\n3. In the revised response to Major Weakness #1, please revise “PLK1 were not co-IPed”\nto “PLK1 was not co-IPd”; similarly, “P3H1 were not co-IPd” should be corrected. Also,\nensure consistent gene and protein nomenclature and formatting throughout the\nmanuscript and figures.\nOverall, the revised manuscript now presents a well-supported model in which P3H1\nregulates β-catenin nuclear localization and PDAC progression via a PLK1-dependent axis.\nThe newly added in vivo efficacy data and PDO validation significantly enhance the\nclinical relevance.\nResponse: We thank the reviewer for pointing out this. We have corrected the errors\nand re-written the sentences in the previous version of revised response to Major\nWeakness#1(e.g. we have revised “PLK1 were not co-IPed” to “PLK1 was not co-\nimmunoprecipitated” and “P3H1 were not co-IPd” to “P3H1 was not co-\nimmunoprecipitated”). We wrote the similar sentence “P3H1 did not physically interact\nwith PLK1” in the revised manuscript (page19). In addition, we have ensured\nconsistent gene and protein nomenclature and formatting throughout the manuscript\nand figures (pages 20-21).\n1. Pokidysheva, E. et al. Biological role of prolyl 3-hydroxylation in type IV collagen. Proceedings\nof the National Academy of Sciences 111, 161-166 (2014).\n2. Kowalski-Chauvel, A. et al. Alpha6-integrin regulates FGFR1 expression through the\nZEB1/YAP1 transcription complex in glioblastoma stem cells resulting in enhanced\nproliferation and stemness. Cancers 11, 406 (2019).\n3. Fu, Z. et al. Plk1-dependent phosphorylation of FoxM1 regulates a transcriptional programme\nrequired for mitotic progression. Nature cell biology 10, 1076-1082 (2008).\n4. Wang, D. et al. A regulatory loop of FBXW7-MYC-PLK1 controls tumorigenesis of MYC-\ndriven medulloblastoma. Cancers 13, 387 (2021).\n5. Dufies, M. et al. Plk1, upregulated by HIF-2, mediates metastasis and drug resistance of clear\ncell renal cell carcinoma. Communications biology 4, 166 (2021)."}]

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