{"paper_id":"0ec2cb98-ac89-41e5-9061-9281b150bb47","body_text":"Stress adaptation pathways and HA–CD44 signaling\nmaintain the survival of pancreatic cancer cells with\ncentrosome amplification\nSelahattin Can Ozcan1,2,*, Evrim Goksel3,4, Batuhan Mert Kalkan1,5, Enes Cicek3, Beste\nKanevetci3, and Ceyda Acilan1,6,*\n1Koc ¸ University Research Center for Translational Medicine (KUTTAM), Sariyer, Istanbul, Turkey\n2Current address: Department of Physiology and Cellular Biophysics, Columbia University, New Y ork City, USA\n10032, USA\n3Graduate School of Health Sciences, Koc ¸ University, Sariyer, Istanbul, Turkey\n4Current address: Meyer Cancer Center, Weill Cornell Medicine, New Y ork, USA\n5Current address: Department of Cell Physiology and Metabolism, University of Geneva, Geneva, Switzerland\n6School of Medicine, Koc ¸ University, Sariyer, Istanbul, Turkey\n*Corresponding author, so2716@cumc.columbia.edu, cayhan@ku.edu.tr\nABSTRACT\nCentrosome amplification (CA) is a hallmark of aggressive cancers, including pancreatic ductal adenocarcinoma (PDAC), and\nis linked to genomic instability and poor prognosis. While CA promotes tumor evolution, it also imposes substantial intracellular\nstress that cells must overcome to survive. However, the specific metabolic adaptations that enable cancer cells to tolerate\nstress induced by supernumerary centrosomes remain poorly understood. Here, we show that PDAC cells with CA acquire\ndistinct metabolic dependencies that sustain survival. A metabolism-focused CRISPR-Cas9 screen, coupled with functional\nvalidations, identified critical vulnerabilities in three inter-connected axes: redox homeostasis, nucleotide sugar metabolism,\nand the unfolded protein response (UPR). Specifically, CA elevates intracellular reactive oxygen species (ROS), creating a\nreliance on glutamine metabolism and NRF2-driven antioxidant signaling. CRISPR screen hits in the hexosamine and uronic\nacid pathways revealed dependencies that converge on hyaluronic acid (HA) metabolism, and functional assays demonstrated\nthat the HA–CD44 axis is required for centrosome clustering and mitotic fidelity, with its disruption increasing lethal multipolar\ndivisions. In parallel, CA activated all branches of the UPR, and both hyper-activation and suppression of ER stress proved\ndetrimental, indicating a finely tuned proteostatic equilibrium is essential for adaptation. Together, these findings show that, in\na PLK4-driven model, centrosome-amplified cells rely on coordinated redox control, proteostatic buffering, and extracellular\nmatrix signaling to tolerate CA-induced stress, revealing selective vulnerabilities that could be therapeutically exploited to target\naggressive, therapy-resistant tumor subpopulations.\n1 Introduction\nChromosomal instability (CIN) is a hallmark of cancer that drives tumor evolution while simultaneously imposing cellular\nstress that threatens cell viability1. One major source of CIN is centrosome amplification (CA), the presence of supernumerary\ncentrosomes within a single cell. CA disrupts mitotic spindle organization, leading to merotelic attachments, chromosome mis-\nsegregation, and aneuploidy2, 3. To prevent lethal multipolar divisions, cancer cells rely on centrosome clustering mechanisms to\nrestore the pseudo-bipolar spindle geometry, creating a structural vulnerability that has been proposed as a therapeutic target4, 5.\nCA is highly prevalent across cancers and particularly enriched in pancreatic ductal adenocarcinoma (PDAC), where it\nassociates with advanced disease, metastasis, and poor patient survival 6–10. Mechanistically, CA can arise from centriole\noverduplication (e.g., PLK4 or SAS-6 overexpression), loss of tumor suppressors such as p53 or BRCA1/2, or deregulated\ncell cycle progression11, 12. Indeed, high expression of CA-associated genes including PLK4, STIL, and NEK2 predicts poor\nprognosis in PDAC patients8. These observations highlight CA as both a driver of aggressive tumor biology and a marker of\nlethal disease.\nBeyond its direct impact on mitosis, CA triggers broader cellular stress adaptation programs. Centrosome-amplified cells\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nexhibit a secretory phenotype characterized by increased release of cytokines and growth factors, which can reshape the tumor\nmicroenvironment13, 14, and experience increased oxidative stress14. Supporting such outputs likely imposes proteotoxic and\nbiosynthetic demands, while extra centrosomes themselves may create unique dependencies on cellular structures and signaling\npathways beyond centrosome clustering. Consequently, CA likely necessitates a profound rewiring of cellular metabolism\nto fuel these adaptive responses and maintain survival. However, while the structural adaptations to CA, such as clustering,\nare well studied, the specific metabolic dependencies that enable cancer cells to tolerate the constant stress of supernumerary\ncentrosomes remain a critical unanswered question.\nPDAC represents a particularly relevant context in which to interrogate these adaptations. PDAC is defined by profound\nmetabolic plasticity, including reliance on aerobic glycolysis, rewired glutamine metabolism, and scavenging of extracellular\nnutrients15, 16. Moreover, PDAC cells must tolerate a hypoxic, nutrient-poor, and fibrotic microenvironment17, conditions that\nmay exacerbate the stress imposed by CA. Yet, despite the prevalence of CA in PDAC and its association with poor prognosis,\nthe metabolic requirements that allow cancer cells to tolerate supernumerary centrosomes remain poorly understood.\nHere, we combined a doxycycline-inducible PLK4 model of CA with a metabolism-focused CRISPR-Cas9 screen in PDAC\ncells to systematically identify the survival pathways essential in the context of CA. This approach revealed that centrosome-\namplified cells become critically dependent on specific pathways for redox homeostasis, nucleotide sugar metabolism, and UPR\nsignaling. Furthermore, we discovered that the hyaluronic acid (HA)–CD44 axis is up-regulated and required for maintaining\nboth centrosome clustering and cytokinesis fidelity. Our findings define a suite of targetable metabolic vulnerabilities that are\nessential for centrosome-amplified cell survival under CA-induced stress, revealing a new dimension of cancer cell addiction\nrooted in genomic instability and division disorders.\n2 Results\n2.1 Cells with centrosome amplification requires L-glutamine availability and GLS activity.\nWe first utilized a doxycycline-inducible PLK4 over-expression system18 to explore the metabolic dependencies associated with\nCA in PDAC cells. After three days of doxycycline (dox) treatment, approximately 60% of Panc1-PLK4 and Mia Paca-2-PLK4\ncells exhibited increased centrosome numbers (Fig. 1A, S1A). Upon extended duration of dox treatment, both cell lines\nmaintained this increase in centrosome numbers for up to 20 days (Fig. 1B). While dox-induced CA reduced the proliferation\nrates in Panc1 cells over the course of three weeks, the proliferative decrease in Mia Paca-2 cells was partially rescued by week\n3 (Fig. 1C). Additionally, we examined the cellular response to prolonged CA in U2OS cells, which harbor wild-type p53.\nAlthough the initial CA rate after 3 days was comparable to PDAC cell lines, U2OS cells did not sustain elevated centrosome\nnumbers over time (Fig. S1B), a difference that could be linked to PIDDosome-mediated activation of the p53 pathway, which\nrestricts the persistence of supernumerary centrosomes19. While cell proliferation was significantly affected by dox induction\nduring the first week, this effect was diminished in the following two weeks (Fig. S1C). The ability of both PDAC cell lines,\nwhich carry mutant p53, to survive prolonged CA makes them good models for studying the long-term effects of CA in cancer.\nSince CA is linked to elevated intracellular reactive oxygen species (ROS) levels14, and glutamine metabolism plays a critical\nrole in multiple ROS-eliminating pathways20, 21, we hypothesized that cells with CA would exhibit increased vulnerability to\ndisruptions in glutamine metabolism. Supporting this hypothesis, cells with CA exhibited increased lethality when treated\nwith CB-839 (Telaglenastat), a glutaminase-1 (GLS1) inhibitor 22 (Fig. 1D). Notably, both short-term (3d) and long-term\n(7d) centrosome-amplified cells demonstrated similar sensitivity to CB-839 treatment. Comparable results were observed in\ncolony formation (CF) assays in both cell lines (Fig. 1E, 1F, 1H). Given that mutant KRAS rewires glutamine metabolism in\na non-canonical cytoplasmic NADPH-synthesizing pathway in PDAC cells23, we extended this analysis to KRAS wild-type\nBxPC-3 cells. Similar to Panc1 and Mia Paca-2 cells, BxPC-3 cells with CA displayed increased sensitivity to CB-839 treatment\n(Fig. 1G, 1H), suggesting that the vulnerability is not attributable to the non-canonical glutamine function described. To rule\nout possible drug-specific off-target effects, we also tested another GLS1 inhibitor, BPTES, in Panc1-doxPLK4 cells and\nobserved the reduction in colony formation assays as CB-839 (Fig. S1D). Finally, we evaluated the potential combined effects\nof doxycycline and CB-839 in cells lacking a doxycycline-inducible PLK4 construct. No significant differences were observed\nin colony formation across all three cell lines in the presence or absence of doxycycline (Fig. S1E). Together, these results\nindicate that, independent of KRAS mutation status, PDAC cells with PLK4-induced CA display increased sensitivity to GLS1\ninhibition.\nWe next assessed the dependency for L-glutamine (L-gln) availability on the survival of cells with CA. Reducing L-gln\nconcentrations in the culture medium significantly impaired cell viability in Panc1 and Mia Paca-2 cells. Notably, Panc1 cells\nwith long-term CA (7d) displayed greater sensitivity to L-gln reduction compared to cells with short-term amplification (3d)\n(Fig. S1F). In colony formation assays, when CA was induced at the time of seeding, no colonies formed in the absence of\n2/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nNa-pyruvate (Na-pyr), regardless of L-gln concentration (Fig. S1G). While cells with long-term CA were able to tolerate\nNa-pyr withdrawal and still form colonies when supplemented with 4 mM L-gln, lowering L-gln levels markedly impaired\ncolony formation (Fig. S1H-J). Importantly, reduced L-gln concentrations in the culture media had no significant effect on dox–\ncontrol cells. Together, these results suggest that CA imposes an absolute requirement for Na-pyr at early stages, and that with\nprolonged amplification, cells accumulate further metabolic stress, resulting in a conditional dependency on extracellular L-gln.\nTaken together, these results suggest that L-gln metabolism is essential for the survival of the PDAC cells with CA.\nA) C)\ndox -\ndox +\nMia Paca-2\nE)\nEndogenous\nDay 3 dox-\nDay 10 dox-\nDay 20 dox-\nDay 3 dox+\nDay 10 dox+\nDay 20 dox+\nDay 20 dox+\nEndogenous\nDay 3 dox-\nDay 10 dox-\nDay 20 dox-\nDay 3 dox+\nDay 10 dox+\n0\n20\n40\n60\n80\n100\nCentrosome amplification (%)\nPanc1\n Mia Paca-2\nDay 7 dox+\nDay 14 dox+\nDay 21 dox+\nDay 7 dox+\nDay 14 dox+\nDay 21 dox+\n-60\n-50\n-40\n-30\n-20\n-10\n0\nProliferation reduction (%)\n(compared to dox-)\nPanc1\n Mia Paca-2\nB)\nPanc1Mia Paca-2\nDNA Centrosomes\ndox-\n dox+\nD)\nF)\nG)\nH)\np < 0.0001\nn.s.\np < 0.0001\nn.s.\ndox- dox+\n-2.5\n-2.0\n-1.5\n-1.0\n-0.5\n0.0\nColony formation\nCB-839 / DMSO\n(log2)\nPanc1: p=0.0022\nMia Paca-2: p=0.052\nBxPC-3 p=0.099\nDMSO\n2\n5\n10\n20\n40\n0\n20\n40\n60\n80\n100\n120\nCB-839 (μM)\n% cell viability\nPanc1\ndox-\np = 0.0040 p < 0.0001\ndox+ 5d\ndox+ 3d\nDMSO\n2\n5\n10\n20\n40\n0\n20\n40\n60\n80\n100\n120\nCB-839 (μM)\n% cell viability\nMia Paca-2\ndox-\ndox+ 5d\ndox+ 3d\nGAPDH\nFLAG\n    dox:   -         +          -         +\n      3d              7d\nDMSO\ndox -\ndox +\nPanc1\nCB-839 DMSO\ndox -\ndox +\nBxPC-3\nCB-839\np < 0.0001\np = 0.001\np = 0.0012\np = 0.0013\np = 0.0018\np = 0.0131\nFigure 1. Centrosome amplification increases dependency on L-Glutamine metabolism in PDAC cells.A) Centrosome\namplification in PDAC cell lines Panc1 and Mia Paca-2. Top panel: Confocal microscopy images. Blue: DAPI, nuclei; Red:\nγ\n-tubulin, centrosomes. Bottom panel: Induction of PLK4 expression by doxycycline. GAPDH was used as loading control. B)\nPDAC cells sustain high levels of CA over time. C) Persistent CA reduces cell proliferation rates in PDAC cells. D) PDAC\ncells with CA exhibit increased sensitivity to GLS1 inhibition by CB-839. Left panel: Panc1 cells, Right panel: Mia Paca-2\ncells. E-H) CB-839 treatment significantly decreases the colony-formation ability of PDAC cells with CA. (E) Panc1 cells (F)\nMia Paca-2 cells. (G) BxPC-3 cells. (H) Quantification results of colony formation experiments. Statistical significances were\nmeasured by two-way ANOV A in B and H, by two-tailed t-test on C, by non-linear curve fitting in D. p values were reported on\ngraphs.\n2.2 Disruption of redox homeostasis pathways is lethal in cells with centrosome amplification\nL-glutamine (L-gln) is a central metabolic node in PDAC cells. Following its conversion to L-glutamate (L-glu) by GLS1, it\nfuels multiple essential processes, including TCA cycle anaplerosis and glutathione (GSH) synthesis (Fig. 2A). To disentangle\nthese functions, we examined the dual contributions of L-gln to core metabolism and stress adaptation. In particular, we assessed\nits entry into the TCA cycle and its role in GSH production, focusing on how these pathways intersect with NRF2-driven\nantioxidant responses, since CA elevates ROS and activates NRF2 signaling14 (Fig. 2B). Using the fluorogenic probe DCFDA,\nwe detected a modest increase in intracellular ROS (∼1.5-fold) upon PLK4 induction in Panc1, Mia Paca-2, and BxPC-3 cells\n(Fig. 2C, 2D). Treatment with CB-839 or ML385 (NRF2 inhibitor) had little impact on ROS levels after 48 hours, whereas\ninhibition of glutathione synthesis with buthionine sulfoximine (BSO) caused marked ROS accumulation (Fig. S2A, S2B).\nAmong the tested PDAC models, BxPC-3 cells with CA displayed the highest ROS levels across treatments, indicating greater\n3/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nsensitivity to oxidative stress (Fig. S2B). Consistent with this, PLK4 induction led to a decrease in the GSH:GSSG ratio in all\nthree cell lines, reflecting increased utilization of glutathione to maintain redox balance under centrosome amplification (Fig.\n2E).\nNRF2\nGAPDH\nHistone H3\ndox-\ndox+\ncyto\ndox-\ndox+\nnuc\nA) B)\nE) F)\nD)\nPanc1 or\nMia Paca-2 cells\nPanc1 PLK4  or \nMia Paca-2 PLK4 cells\nco-culture\n+ DMSO or\ninhibitors\ndox- / dox+\n/f_low\ncytometry\nγ-GC\nGSH\nGSS\nGlu Cys\nGCLC\nGCLM\nBSO\nGly\nGln\nGlu\nGLS1\nCB-839\na-KGTCA\ncycle\nGLUD1\nEGCG\nNRF2\nKeap1\nNRF2\nML385\nNRF2\nROS response\nnucleus\nML334\nC)\nG)\nH)\nDMSO\nCB-839\nBSO\nEGCG\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-2\n-1\n0\n1\n% mCherry cells in population\ndox+/dox- (log2) p = 0.0065\np < 0.0001\np = 0.4139\nI)\n1\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-2\n-1\n0\n% mCherry cells in population\ndox+/dox- (log2)\nDMSO\nML385\nML334\np < 0.0001\np = 0.2543\nPanc1 Panc1\nJ)\n10\n0\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n10\n6\nComp-FL2-A :: EGFP-A\n0\n20\n40\n60\n80\n100\nCounts (Normalized To Mode)\nPanc1-PLK4 dox 3d\nPanc1-PLK4 dox -\nBFP + cells\n8xARE eGFP SV40 promoter TagBFP\nDMSO\nCB-839\nBSO\nML385\nMia Paca-2\nK) L)\nDMSO\nmeAIB\nFerrostatin-1\nPanc1\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-1.5\n-1.0\n-0.5\n0.0\n0.5\n1.0\n% mCherry cells in population\ndox+/dox- (log2) p = 0.0096\np = 0.9858\nDay0\nDay3\nDay6\nDay9\nDay12\nDay0\nDay3\nDay6\nDay9\nDay12\nDay0\nDay3\nDay6\nDay9\nDay12\nDay0\nDay3\nDay6\nDay9\nDay12\n-1.0\n-0.5\n0.0\n0.5\n% mCherry cells in population\ndox+/dox- (log2) p < 0.0001\np = 0.8875\np < 0.0001\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\nBL1-H :: H2DCFAD\n0\n20\n40\n60\n80\n100\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n0\n20\n40\n60\n80\n100\nCounts (Normalized To Mode)\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n0\n20\n40\n60\n80\n100\ndox-\ndox+\ndox-\ndox+\ndox-\ndox+\n0\n10\n20\n30\n40\n50high ROS levels (>104)\nPanc1\nMia Paca-2\nBxPC-3\nPanc1\nMia Paca-2\nBxPC-3\n0.0009\n0.0124 0.0128\nPanc1\nMia Paca-2\nBxPC-3\n0.0\n0.5\n1.0\n1.5\n2.0\n2.5\nchange of histogram median\n(fold change)\nPanc1\nMia Paca-2\nBxPC-3\ndox-\ndox+\ndox-\ndox+\ndox-\ndox+\ndox-\ndox+\n0\n2\n4\n6\n8GSH/GSSG ratio\n0.0002\n<0.0001\n<0.0001\np = 0.0242\np = 0.0314\np = 0.0290\nFigure 2. Centrosome amplification increases ROS, creating a vulnerability to ROS elimination pathway inhibition. A)\nSchematic representation of L-glutamine metabolism pathways and enzymes targeted by specific inhibitors in the following\nexperiments. B) Diagram of the NRF2 signaling pathway and inhibitors targeting NRF2 and Keap1. C) CA increases\nintracellular ROS levels in Panc1, Mia Paca-2 and BxPC-3 cells. D) Quantification of ROS measurement results in 2C. Left\npanel: Changes in histogram median values. Right panel: Percentage of cells with high ROS levels. E) Induction of CA\ndecreases GSH:GSSG ratios in PDAC cell lines. F) Induction of CA increases nuclear localization of NRF2 in Panc1 cells.\nGAPDH and Histone H3 blots represent cytoplasmic and nuclear fractionation. G) CA increases the Antioxidant Response\nElement (ARE)-mediated gene expression in Panc1 cells. H) Overview of the competition experiments performed in panels\nH-K. I-J) Treatment with CB-839, BSO and ML385 significantly reduces the viability of Panc1 cells with CA in in-vitro\ncompetition assays. K) CB-839 and ML385 treatments diminish the survival of Mia Paca-2 cells with CA in in vitro\ncompetition assays. L) Inhibition of SNAT1-mediated glutamine uptake reduces the viability of Panc1 cells with CA in in vitro\ncompetition assays. Statistical significances were measured by two-tailed t-test in D (left panel), and by two-way ANOV A in D\n(right panel), E, and I-L. Dots represent individual repeats. p values were reported on graphs.\nAs intracellular ROS accumulation triggers NRF2 activation via dissociation from Keap1 and nuclear translocation (Fig. 2B),\nwe investigated the nuclear localization of NRF2. Western blot analysis showed increased nuclear NRF2 levels in dox-treated\nPanc1-PLK4 cells (Fig. 2F). Furthermore, CA increased antioxidant responsive element (ARE) activation in Panc1 cells, as\n4/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\ndemonstrated by plasmid-based reporter assays 24 (Fig. 2G and Fig. S2C). Notably, doxycycline treatment of Panc1 cells\nlacking the dox-inducible PLK4 construct did not result in elevated ARE activity, confirming that the observed effect was\nspecific to CA (Fig. S2D).\nTo assess the long-term requirement of L-gln metabolism and NRF2 pathway for the survival of PDAC cells with CA, we\nleveraged dual-color competition assays. We combined H2B-GFP expressing Panc1 and Mia Paca-2 cells with H2B-mCherry\nexpressing dox-PLK4 counterparts, and tracked the changes of cell populations on different time points (Fig. 2H). This\nmethod offered two key advantages over traditional cell viability experiments: first, it enabled the assessment of long-term\noutcomes; and second, it eliminated potential confounding effects of drug-dox interactions, as both cell populations were\nexposed to identical concentrations of the drug and doxycycline simultaneously. In all long-term competition experiments, H2B-\nmCherry-expressing dox-PLK4 cells were progressively depleted over time in the DMSO treated control groups, highlighting\nthe anti-proliferative impact of CA (Fig. 2I-L). Treatment of the mixed Panc1 cell populations with CB-839 or BSO led to a\ngreater depletion of mCherry+ cells, whereas EGCG had no significant effect, suggesting that GLUD1-mediated incorporation\nof L-glutamine into the TCA cycle does not hold differential importance for cells with CA (Fig. 2I, Fig. S3A-C). Similarly,\nML385 treatment caused a significant depletion of mCherry+ cells; however, inhibition of Keap1 by ML334 failed to rescue\nthis depletion (Fig. 2J, Fig. S3D), possibly due to already elevated NRF2 nuclear localization in centrosome-amplified cells as\na result of oxidative stress. In Mia Paca-2 cells, CB-839 and ML385 treatment also led to a marked depletion of mCherry+\ncells, while BSO treatment did not cause a significant reduction compared to DMSO (Fig. 2K, Fig. S4A-C). Furthermore,\nBSO treatment impaired colony formation in Panc1 cells with CA, whereas ML385 treatment showed no notable effect on\ncolony formation (Fig. S2E). In contrast, Mia Paca-2 cells were insensitive to BSO but showed marked sensitivity to ML385 in\ncolony formation assays (Fig. S2F). These findings further suggest that distinct mechanisms of ROS elimination may be critical\nin different PDAC models. Specifically, one cell type may rely more heavily on GSH synthesis, while another may depend\npredominantly on NRF2 signaling. Additionally, the consistency between long-term competition assays and independent colony\nformation experiments supports that our dual-color competition strategy faithfully captures the true biological vulnerabilities of\ncentrosome-amplified cells.\nL-gln import in cancer cells predominantly relies on the ASCT2 (SLC1A5), SNAT1 (SLC38A1), and SNAT2 (SLC38A2)\ntransport systems\n25, 26. To evaluate the dependency of cells with CA on L-gln uptake, we used 2-methylamino isobutyrate\n(meAIB), a specific inhibitor of SNAT1/SLC38A1 27, in dual-color competition experiments. Our findings revealed that\nSNAT1-mediated L-gln import is critically required for the survival of Panc1 cells with CA (Fig. 2L, Fig. S5). As glutathione\ndepletion is a hallmark trigger of ferroptosis, we examined whether ferroptotic cell death could account for the CA-associated\nviability reduction. To this end, we treated cells with Ferrostatin-1, a well-characterized inhibitor of ferroptotic cell death28.\nNevertheless, Ferrostatin-1 treatment did not prevent the depletion of mCherry+ cells, indicating that ferroptosis is unlikely\nto play a role (Fig. 2L, Fig. S5). Collectively, these findings underscore the critical dependence of PDAC cells with CA on\nL-glutamine import, glutathione biosynthesis, and NRF2-mediated antioxidant signaling for their survival.\n2.3 Metabolism focused CRISPR screen identifies metabolic dependencies of cells with centrosome\namplification\nTo further delineate the metabolic dependencies of cells with CA, we performed a metabolism-focused CRISPR-Cas9 screen\nin Panc1-PLK4 cells (Fig. 3A). Cells were transduced with a pooled CRISPR library targeting metabolic enzymes at a low\nmultiplicity of infection (MOI = 0.6) and cultured for 21 days under doxycycline-treated and untreated conditions. sgRNA\nabundances in the final populations were compared to the initial library transduced cells to determine gene-level essentiality.\nComparative analysis of beta-scores revealed that while most genes exhibited similar depletion profiles under both conditions,\na distinct subset showed differential depletion, suggesting CA-specific metabolic vulnerabilities (Fig. 3B, 3C). Gene Set\nEnrichment Analysis (GSEA) of the ranked gene list highlighted consistent enrichment of two recurring pathways across\nmultiple terms: (i) nucleotide sugar and N-glycan biosynthesis, and (ii) reactive oxygen species (ROS) detoxification pathways\n(Fig. 3D). Furthermore, by applying a threshold of two median absolute deviations (2-MAD), we identified 109 genes as\nsignificantly depleted in the centrosome-amplified condition, which were used for downstream pathway-level analyses (Fig.\nS6A). Among these genes, 80 have well-characterized intracellular localizations according to the Human Protein Atlas (HPA)\ndatabase29. The majority were localized to the cytoplasm (33) and nucleoplasm (27), with 15 associated with mitochondria,\n15 with the plasma membrane, and 3—CPT1C, CPE, and ENGASE—reported to localize to the centrosome (Fig. S6B).\nPathway enrichment analysis of this gene set revealed significant over-representation of the reactive oxygen species pathway,\nas well as glycosaminoglycan and nucleotide sugar biosynthesis pathways, based on GO-BP, MSigDB, and KEGG analyses\n(Fig. S6C). Furthermore, transcription factor enrichment analysis using the TR-RUST database identified MTF1 and NFE2L2\n(NRF2) as key upstream regulators, suggesting these factors may orchestrate survival-associated transcriptional programs in\ncentrosome-amplified cells (Fig. S6C). Integration of our results with previously identified dependencies from Panc1 CRISPR\n5/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nscreens (DepMap) revealed that while several top-depleted genes such as SOD1, SOD2, and DPAGT1 overlapped with known\ndependencies, many of our differentially depleted hits including GFPT2, TXNRD2, PRDX1, CHST7, SLC5A3, and UGDH\nwere not classified as common dependencies (Fig. 3E). Markov Cluster Algorithm (MCL) clustering of top differentially\ndepleted genes revealed several protein associations, further suggesting pathway-level dependencies (Fig. 3F, 3G).\nTo further investigate the role of metabolic enzymes in specific pathways and cellular functions, we analyzed the CRISPR\nscreen results by filtering for targeted Gene Ontology (GO) terms (Fig. 3H-J & Fig. S6D-G). Among the genes involved in the\ncellular response to superoxide, PRDX1, SOD1, SOD2, and SOD3 were significantly depleted in cells with CA (Fig. 3H).\nSimilarly, within the N-acetylglucosamine metabolic process, GFPT2, DPAGT1, and GFPT1 emerged as top-depleted genes,\nwhile CHST7 and UGDH were identified as key hits in glycosaminoglycan biosynthesis (Fig. 3I, 3J). Additionally, GFPT2\nand GFPT1 were prominent among enzymes that have role in glutamine metabolic processes (Fig. S6D), while GALNT16,\nUGT1A7, and UGT1A8 were top hits among enzymes functioning as UDP-glycosyltransferases (Fig. S6E). PYCR2 was\nthe sole depleted gene identified in the proline biosynthesis pathway (Fig. S6F), whereas GSS, GCLC, and MGST2 were\nhighlighted in the glutathione biosynthesis process (Fig. S6G). To extend our findings, we integrated the top CA-specific\ndifferentially depleted genes from our CRISPR screen with results from two previously conducted unbiased gene expression\nstudies in cells that have CA14, 30. Additionally, we incorporated a curated list of NRF2-regulated genes31 into the analysis,\nmotivated by our observation of increased NRF2 nuclear localization in centrosome-amplified cells (Fig. 2E). This integrative\napproach identified UGDH as a shared hit across all datasets from multiple cell lines (Fig. S6H). Furthermore, filtering the\nscreen results for NRF2-regulated genes highlighted DHFR and UGDH as top differentially depleted hits in cells with CA (Fig.\nS6I).\nAdditionally, we analyzed TCGA patient data to evaluate whether the expression of top CA–specific hits (differential LFC <\n-0.5) from our CRISPR screen correlated with chromosomal instability (CIN25) 32 and centrosome amplification (CA20)33\ntranscriptional signatures. We also included NRF2 (NFE2L2), ATF4, and ATF6 expression, given their central roles in cellular\nstress responses. Unsupervised clustering of pancreatic adenocarcinoma samples (n = 82) revealed that CIN25 and CA20\nexpression profiles were the dominant factors driving patient stratification within this gene set (Fig. S7A). UMAP projection\nyielded a comparable distribution of samples, again primarily structured by CIN25 and CA20 expression (Fig. 3K). Notably,\npatient subsets with high CIN25 and CA20 scores exhibited elevated NRF2 and ATF4 expression, suggesting a potential link\nbetween genomic instability, CA, and activation of stress response pathways (Fig. S7A). Furthermore, several top hits from our\nCRISPR screen—including DHFR, GSTM4, UGDH, SOD1, PRDX1, and DPAGT1—were highly expressed in patients with\nelevated CIN25 and CA20 signatures, highlighting their potential clinical relevance as metabolic requirements in genomically\nunstable pancreatic tumors (Fig. 3K, S7A). Expression of DHFR, DPAGT1, SLC5A3, PRDX1, and UGDH also correlated\nstrongly with PLK4 levels (Fig. S7B, S7C, 3K), further strengthening the connection between these metabolic genes and CA\nin patient tumors. To gain additional insight into the transcriptional programs associated with PLK4, we performed gene set\nenrichment analysis (GSEA) using the GENI platform34. As expected, PLK4 expression was significantly enriched for cell\ncycle–related pathways, including E2F targets, G2/M checkpoint, and mitotic spindle (Fig. S7D). Intriguingly, enrichment\nwas also observed for pathways involved in the unfolded protein response (UPR), protein secretion, and interferon signaling,\nindicating that PLK4 over-expression may engage broader stress-response and immune signaling programs in pancreatic tumors\n(Fig. S7D).\nTo better understand the CA–associated dependencies, we also analyzed the expression changes of a selected gene panel\nfollowing CA in Panc1 and Mia Paca-2 cells. After three days of doxycycline induction, both cell lines showed modest\nincreases in PRDX1, DPAGT1, and GFPT2 expression, accompanied by a slight reduction in HAS2 expression (Fig. S6J). Cell\nline–specific responses were also observed: GLUL was up-regulated only in Mia Paca-2 cells, whereas CHST7, MGST2, and\nGALNT16 showed increased expression exclusively in Panc1 cells (Fig. S6J).\nAltogether, our analyses highlight ROS detoxification and nucleotide sugar/glycan biosynthesis as key metabolic dependencies\nin cells with PLK4-induced CA, suggesting their potential as therapeutic vulnerabilities in cancers characterized by CA and\ngenomic instability.\n2.4 Inhibition of ROS elimination pathways is selectively lethal in cells with centrosome amplification\nGiven that ROS elimination pathways were among the top enriched terms in our list of genes selectively depleted in centrosome-\namplified cells—and that key antioxidant genes such as SOD1, PRDX1, and TXNRD2 were prominently featured—we initially\nfocused our analysis on this axis. The thioredoxin system, which includes thioredoxin and thioredoxin reductases such as\nTXNRD2, plays a critical role in regulating protein redox status by facilitating disulfide bond reduction and maintaining\nproteins in a reduced state35. In parallel, superoxide dismutases (SODs) catalyze the conversion of superoxide anions into\nhydrogen peroxide, thereby mitigating oxidative stress and preventing ROS-mediated cellular damage36 (Fig. S8A). Therefore,\n6/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nwe employed LCS-1, a potent and selective SOD1 inhibitor, and auranofin, a clinically approved thioredoxin reductase inhibitor,\nin competition experiments. Both treatments led to an increased reduction in cell populations with CA in Panc1 cells (Fig. S8B,\nS9A), highlighting the critical role of these antioxidant systems in maintaining the survival of these cells. In contrast, treatment\nwith N-acetyl cysteine (NAC) or apocynin, which broadly scavenges ROS or inhibits NADPH oxidase, had no impact on the cell\nviability of centrosome-amplified cells in long-term competition experiments. These results suggest that centrosome-amplified\ncells depend specifically on enzymatic ROS detoxification mechanisms, rather than general oxidative stress buffering, for their\nsurvival (Fig. S8C, S9B).\nAdditionally, DHFR (dihydrofolate reductase) emerged as one of the top depleted hits in centrosome-amplified cells (Fig. 3C,\n3E, 3F), it ranked highest among NRF2 target genes (Fig. S6I), and showed a high correlation with PLK4 expression in TCGA\nmRNA expression data (Fig. S7A and S7B). DHFR catalyzes the conversion of dihydrofolate to tetrahydrofolate , a key step\nin folate metabolism, and has been previously linked to the regulation of cellular redox balance 37(Fig. S8D). To evaluate\nits functional relevance, we inhibited DHFR using pralatrexate in competition assays, which led to a greater depletion of\ncentrosome-amplified Panc1 cells compared to controls (Fig. S8E, S9C). Furthermore, DHFR inhibition in these cells resulted\nin elevated ROS levels (Fig. S8F and S8G), supporting its requirement in maintaining redox homeostasis under CA-induced\nstress. Competition experiments employing LCS-1, auranofin, and pralatrexate in MiaPaCa-2 cells recapitulated the results\nobserved in Panc1 cells (Fig. 8H, S10). Consistent with this, DHFR inhibition in MiaPaCa-2 cells with CA similarly increased\nintracellular ROS levels (Fig. S8I and S8J), confirming that DHFR contributes to redox homeostasis across multiple CA models.\nTogether, these findings establish ROS detoxification as critical survival dependencies in centrosome-amplified cells.\n2.5 PDAC cells with centrosome amplification have increased dependency for uronic acid and hexosamine\nbiosynthetic pathways\nAnalysis of differentially depleted sgRNAs in centrosome-amplified cells revealed a strong enrichment for genes involved in\nnucleotide sugar metabolism and N-glycan biosynthesis. Among these, UGDH, GFPT1, GFPT2, and DPAGT1 emerged as\ntop hits, highlighting dependencies in the uronic acid and hexosamine biosynthesis pathways (Fig. 3I, J). These pathways\ngenerate essential nucleotide sugars required for protein glycosylation, glycosaminoglycan biosynthesis, and hyaluronic acid\nproduction, processes that may buffer CA-induced proteotoxic and mechanical stress38, 39. To functionally characterize these\ndependencies, we pharmacologically inhibited key enzymes in these pathways using Azaserine, 4-MU, FR054, Tunicamycin,\nand OSMI-1 in the competition experiments (Fig. 4A). Treatment with FR054, 4-MU, and Tunicamycin led to greater selective\ndepletion of centrosome-amplified cells compared to DMSO treatment, providing evidence for increased dependency (Fig. 4B,\nS11). Although Azaserine and OSMI-1 induced substantial cell death, this effect was not specific to the centrosome-amplified\npopulation. These results demonstrate that CA imposes a specific requirement for uronic acid and hexosamine pathway activity\nand for N-linked glycosylation, while no specific dependence for O-linked glycosylation was observed under tested conditions.\nThe functional interpretation of negative selection in metabolic CRISPR screens can be complex; depletion of a metabolic\nenzyme could indicate either a critical dependence on its product for survival or a toxic buildup of its substrate. To distinguish\nbetween these models for the nucleotide sugar pathway hits, we supplemented cells with the products of the pathways: UDP-\nGlcNAc (Uridine diphosphate-N-acetyl-glucosamine), UDP-GalNAc (UDP-N-acetyl-galactosamine), and UDP-glucuronic\nacid. Among these, UDP-glucuronic acid was the only sugar that partially rescued the depletion of centrosome-amplified\ncells (Fig. 4C, S13A). The hexosamine biosynthesis pathway includes well-characterized salvage routes that allow cells to\nre-utilize sugar metabolites to maintain UDP-sugar pools. Free N-acetylglucosamine (GlcNAc), derived from glycoconjugate\ndegradation or extracellular uptake, can re-enter the pathway via phosphorylation by NAGK40. Similarly, glucosamine and\nN-acetylgalactosamine (GalNAc) can be salvaged and funneled into the synthesis of UDP-GlcNAc and UDP-GalNAc (Fig.\nS12A). To test whether salvage pathway metabolites promote the survival of centrosome-amplified cells, we treated cells with\nglucosamine and GlcNAc. Interestingly, only glucosamine supplementation improved the survival of centrosome-amplified\ncells in competition experiments (Fig. S12B, S13B).\nSince tunicamycin-induced DPAGT1 inhibition is a well-established method for studying ER stress-induced activation of\nthe unfolded protein response (UPR), and given that tunicamycin exerted a stronger effect on centrosome-amplified cells\n(Fig. 4B), we examined the cellular response to ER stress and UPR activation in cells with CA. Western blot analysis showed\nincreased ATF4, ATF6, and IRE1α abundance in PLK4-induced CA cells, consistent with engagement of the UPR (Fig. 4D).\nAdditionally, increased nuclear localization of ATF4 was observed (Fig. S12C), further supporting activation of the PERK\nbranch of the UPR. To evaluate the functional importance of UPR signaling, we treated cells with (i) thapsigargin, a SERCA\ninhibitor, (ii) toyocamycin, an inhibitor of IRE1-mediated XBP1 splicing, and (iii) GSK2656156, an ATP-competitive PERK\ninhibitor. In competition experiments, all three treatments led to increased depletion of centrosome-amplified cells compared to\nDMSO controls (Fig. 4E, S14A). These results suggest that all three canonical branches of the UPR; IRE1, PERK, and ATF6,\ncontribute to the adaptive stress response that supports the survival of PLK4-induced centrosome-amplified cells.\n7/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nA) B)\nC)\nMetabolic enzyme focused\nCRISPR library\nNormal growth\nCentrosome\namplification\ndox-\ndox+\nNext-generation sequencing\nfor sgRNA representation\nMageCK analysis\nInitial sample\nD) E)\ndox+ beta score dox- beta score\nKEGG\nAMINO SUGAR AND NUCLEOTIDE SUGAR METABOLISM\nEnrichment profile\nHits\nRanking metric scores\n0\n500\n1,000\n1,500\n2,000\n2,500\n3,000\nRank in Ordered Dataset\n-0.5\n-0.4\n-0.3\n-0.2\n-0.1\n0.0\n-0.5\n0.0\n0.5\n1.0\nZero cross at 1422\n'na_pos' (positively correlated)\n'na_neg' (negatively correlated)\nEnrichment Score (ES)Ranked list metric (PreRanked)\nEnrichment profile\nHits\nRanking metric scores\n0\n500\n1,000\n1,500\n2,000\n2,500\n3,000\nRank in Ordered Dataset\n-0.5\n-0.4\n-0.3\n-0.2\n-0.1\n0.0\n-0.5\n0.0\n0.5\n1.0\nZero cross at 1422\n'na_pos' (positively correlated)\n'na_neg' (negatively correlated)\nREACTOME\nDETOXIFICATION OF REACTIVE OXYGEN SPECIESEnrichment Score (ES)Ranked list metric (PreRanked)\nKEGG_AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM\nREACTOME_DETOXIFICATION_OF_REACTIVE_OXYGEN_SPECIES\nKEGG_OTHER_GLYCAN_DEGRADATION\nREACTOME_BIOSYNTHESIS_OF_THE_N_GLYCAN_PRECURSOR\nREACTOME_SYNTHESIS_OF_SUBSTRATES_IN_N_GLYCAN_BIOSYTHESIS\n0\n1\n2\n3\n−2.0 −1.5 −1.0 −0.5 0.0\nNES\n-log10p value\n-log10p\n0 1 2\nNES\n−1.5\n−1.0\n−0.5\n−2.0\nF)\nG)\ntop 50 differentially depleted genes in dox+\nENO1\nPRDX1\nENTPD7\nSOD1\nDHFR\nALG2\nTXNRD2\nSLC2A1\nNAA50\nATP6V0B\nSLC30A2\nSUCLG1\nCHST7\nCPT1C\nCA6\nPLA2G2C\nJMJD6\nSLC5A9\nSOD2\nRHBG\nCYP1B1\nPYCR2\nALOX15\nFOXRED1\nCLCC1\nKCNQ2\nKCNK17\nCOX4I2\nIAH1\nATP13A1\nGFPT2\nGALNT16\nCOX6A2\nPDE3B\nKCNC4\nSLC10A4\nSLC5A3\nUPP1\nGCK\nFAHD2A\nCPE\nPIP4K2C\nUGDH\nCYB5R4\nACSS2\nSLC38A3\nPLA2G10\nTRPV4\nSLC38A5\nSLC19A3\n−2.0 −1.5 −1.0 −0.5 0.0 0.5\nDPAGT1\nTGDS\nALG2\nPRDX1\nTXNRD2\nSOD1\nSLC5A3\nDHFR\nLTC4S\nPIK3R2\nCYB5R4\nPLCB2\nSLC30A2\nPIP4K2C\nPIK3C3\nTYW5\nGCK\nKCNQ2\nGFPT2\nKCNC4\nENO1\nCACNA1B\nSLC2A1\nSCN1B\nACADL\nCHRNA4\nACBD5\nAIFM3\nTFAM\nGSTM4\nSOD2\nCYP1B1\nACADM\nGSS\nACSS2\nDDOST\nSUCLG1\nKCNK17\nATP6V1B1\nCA6\nPPA2\nDOHH\nATP6V0B\nKDM8\nNAA50\nJMJD6\nPLA2G2C\nALOX15\nUGDH\n0.5\n0.6\n0.7\n0.8\n0.9\n1.0\n1.1\n1.2\nSignal\nSLC-mediated transmembrane transport\nBiosynthesis of the N-glycan precursor\n(dolichol lipid-linked\noligosaccharide, LLO) and transfer to...\nSynthesis of substrates in N-glycan\nbiosythesis\nAsparagine N-linked glycosylation\nFatty acid metabolism\nDetoxification of Reactive Oxygen\nSpecies\nBiological oxidations\nAmino sugar and nucleotide sugar\nmetabolism\nPancreatic secretion\nGroups at similarity 0.5\n1.0e-02\n2.0e-03\n4.0e-04\n7.0e-05\n1.0e-05\n2.0e-06\nFDR\n4\n8\n13\nGene count:\nCHST7\nUGDH\nST3GAL3\nNDST3\nCHST6\nST3GAL2\nSLC10A7\nHAS2\nHYAL1\nCHST13\nNDST1\nB3GNT2\nGCNT2\nHS3ST1\nIL1B\nCHST15\nEXT2\nCHST12\nHS3ST3B1\nCHST1\n−1.2 −0.8 −0.4 0.0 0.4 0.8 1.2\nBeta Score Difference\nGO:0006024 (top 20)\nGlycosaminoglycan biosynthetic process\nGFPT2\nDPAGT1\nGFPT1\nGNPDA1\nAMDHD2\nGNPDA2\nGNPNAT1\nUAP1\nB4GALNT2\nSLC35A3\nPGM3\nUAP1L1\nGNE\nMGAT1\n−0.8 −0.4 0.0 0.4 0.8\nBeta Score Difference\nGO:0006047\nUDP−N−acetylglucosamine metabolic process\nPRDX1\nSOD1\nSOD2\nSOD3\nUCP3\nMPO\nAPOA4\nGLRX2\nATP7A\nNQO1\nUCP2\nPRDX2\nNOS3\n−0.8 −0.4 0.0 0.4 0.8\nBeta Score Difference\nGO:0000303\nResponse to superoxide\nSLC5A3\nPYCR2\nDPAGT1\nUGDH\nGFPT2\nCLCC1\nPRDX1\nSOD1\nTXNRD2\n−2.0\n−1.5\n−1.0\n−0.5\n0.0\n0.5\n1.0\n−2.0 −1.5 −1.0 −0.5 0.0 0.5 1.0\nPanc1 dox− (beta score)\nPanc1 dox+ (beta score)\nTM7SF2\nNUDT21\nCOQ4\nNDOR1\nSLC28A3\nIAH1\nCOX4I2\nENTPD7\nPRDX1\nPLA2G2C\n−1.0\n−0.5\n0.0\n0.5\n1.0\n0 1000 2000 3000\nGene Rank\nBeta score difference (dox+ - dox-)\nN-Glycan synthesis\nNucleotide sugar metabolism\nROS elimination\nNode of Ranvier\nIon channels\nK)\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP1\nUMAP2\nExpression:\n (Z-score)\n −1 0 1 2\nPLK4\nUMAP2\nPRDX1\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP1\nUMAP2\n−1 0 1 2 3\nUGDH\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP1\nUMAP2\nExpression:\n (Z-score)\n −1 0 1 2\nNEK2\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP2\nUMAP1\n−1 0 1 2\nDHFR\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP2\nUMAP1\n−1 0 1 2\nDPAGT1\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP1\n−1 0 1 2 3\nJ)I)H)\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP1\nUMAP2\nScore\n−1.0 −0.5 0.0 0.5 1.0 1.5\nCIN25 Score\nUMAP1\nScore\n−0.4 0.0 0.4\n−4\n−2\n0\n2\n−3 0 3 6\nUMAP2\nCA20 Score\nALG2\nATP6V1A\nCHST7\nCLCC1\nDDOST\nDHFR\nDPAGT1\nENTPD7\nGCK\nGFPT1\nGFPT2\nPRDX1\nRRM1\nSLC5A3\nSOD1\nSOD2\nTXNRD1\nTXNRD2\nUGDH\nHigh common essentiality\nEssential in CA\nIncreased essentiality in CA\n−3\n−2\n−1\n0\n−1.0 −0.5 0.0 0.5 1.0\nDifferential beta score (dox+ − dox−)\nGene effect in Panc1 (DepMap essentiality)\n−log10 Wald p (dox+): 1 2 3 4 5\nFigure 3. Legend on next page.\n8/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nFigure 3. Metabolism targeted CRISPR screen identifies increased dependency for ROS detoxification and nucleotide\nsugar metabolism in cells with PLK4-induced supernumerary centrosomes. A) Schematic representation of metabolic\nenzyme focused CRISPR screen experiment design. B) Top depleted hits in dox+ and dox- cells compared to initial sample.\nLeft panel: Scatterplot of beta scores for dox+ and dox- sample. Pink dots in the scatterplot represent genes with a beta score\nthat increased after CA. Blue dots represent genes with a beta score that decreased after CA. Right panel: Rank plot showing\nthe genes based on differential beta score in which dox- beta score is subtracted from the dox+ beta score. C) Top 50\ndifferentially depleted genes in dox+ samples. Pink dots represent beta score in dox- comparison, blue dots represent beta score\nin dox+ comparison. D) GSEA analysis of CRISPR screen results. E) Comparison of differential beta score values of CRISPR\nscreen with Panc1 DepMap essentialities. F) MCL clustering results of top differentially depleted metabolic genes in cells with\nCA. Genes that were not included in a cluster (singletons) and clusters contain less than three proteins were removed. G)\nEnrichment analysis of protein-protein interaction network. H-J) Pathway-specific differentially depleted genes in cells with\nCA. (H) Response to superoxide (GO:0000303). (I) UDP-N-acetylglucosamine metabolic process (GO:0006047). (J)\nGlycosaminoglycan biosynthetic process (GO:0006024). K) UMAP projection of TCGA PDAC data for selected genes. CIN25\nand CA20 gene expression scores was shown on the left side plots. PLK4 and NEK2: CA20 genes; PRDX1 and DHFR: ROS\nelimination; UGDH and DPAGT1: N-glycan synthesis/nucleotide sugar metabolism. Gene expression Z-scores were used in\nplots.\nWe next reduced ER stress by treating cells with Tauroursodeoxycholate (TUDC, a chemical chaperone that alleviates ER stress)\nand 4-Phenylbutyric acid (4PBA, an ER stress–reducing agent) in competition experiments, which also resulted in depletion of\ncentrosome-amplified cells (Fig. 4F, S14B). Although this outcome may initially seem counterintuitive, it is consistent with\nthe dual role of the UPR, which can either promote survival or trigger apoptosis depending on the intensity and context of\nER stress41. By facilitating the protein folding and reducing misfolded protein burden, TUDC and 4-PBA likely attenuate the\nadaptive, pro-survival arm of the UPR. Because competition experiments could reflect either a true reduction in the proliferation\nof centrosome-amplified cells or a relative effect caused by increased proliferation of non-PLK4 over-expressing cells, we\ntested individually seeded cell populations. This confirmed a genuine reduction, as TUDC and 4-PBA treatments decreased the\nproliferation specifically in centrosome-amplified cells (Fig. 4G). Importantly, similar results were observed in Mia Paca-2\ncompetition experiments (Fig. 4H, S15A), highlighting that centrosome-amplified cells may require a finely tuned level of UPR\nactivity for viability.\nSince the glucuronic acid and hexosamine biosynthesis pathways contribute to hyaluronic acid (HA) synthesis (Fig. 4A),\nand we observed increased depletion with 4-MU (Fig. 4B), we next examined HA production in these cells. Because HA is\nsecreted following synthesis and associates with the cell surface, we measured surface HA levels and observed significantly\nhigher levels in cells with CA (Fig. 4I). Importantly, doxycycline treatment in cells lacking the doxycycline-inducible PLK4\nconstruct did not increase HA levels, confirming that this effect stems from CA rather than doxycycline exposure (Fig. S12E).\nIn competition assays, adding exogenous HA to the cell culture media did not improve the survival of centrosome-amplified\ncells (Fig. S12E, S15B), but it rescued the depletion caused by 4-MU treatment, though not by tunicamycin (Fig. S12F, S16).\nGiven the established link between the unfolded protein response and hexosamine pathway activity42, 43, we next asked whether\nER stress modulates HA production in centrosome-amplified cells. Pharmacological induction of ER stress with tunicamycin\nincreased surface HA, whereas reducing ER stress with 4-PBA or TUDC lowered HA levels in centrosome-amplified Panc1\ncells (Fig. 4J, S12G, S12H). Together, these findings indicate that CA enhances HA synthesis and couples it to ER-stress status,\ncreating a metabolic requirement that supports the survival of centrosome-amplified PDAC cells.\nIn addition, our CRISPR screen identified the myo-inositol transporter SLC5A3 as a selective dependency in CA cells\n(Fig. 3B, C, E, F), consistent with its proposed oncogenic role in other cancers 44, 45. Imported myo-inositol contributes to\nphosphatidylinositol synthesis or can be oxidized to D-glucuronate, a potential entry point into the pentose phosphate pathway\n(Fig. S12I). Although D-glucuronate can be converted to UDP-glucuronate in some species, this pathway is absent in humans\ndue to the lack of UDP-glucuronate dehydrogenase (UGD) 46. Supplementation with either myo-inositol or D-glucuronate\nsignificantly increased the survival of centrosome-amplified cells in competition assays (Fig. S12J, S17), indicating an increased\nrequirement for these metabolites. Expression analysis further revealed strong down-regulation of IMPA1, which encodes a\nkey enzyme for endogenous inositol biosynthesis, in Mia PaCa-2 cells with CA (Fig. S12K). While Panc1 cells did not show\naltered IMPA1 expression, they nevertheless remained dependent on exogenous myo-inositol.\nIn summary, our data show that CA increases dependence on nucleotide-sugar biosynthesis, hyaluronic acid production, and\nextracellular myo-inositol uptake. These metabolic alterations coincide with elevated ER stress, activation of all three UPR\nbranches, and a requirement for adaptive stress signaling to maintain cell survival.\n9/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nOSMI-1\nAzaserine\n4-MU\nUDP-Glucose\nGlucose-6-phosphate\nGlucose-1-phosphate\nPFK1\nPGM1\nGPIHK\nUGPP\nGlucose GLYCOLYSISFructose-6-phosphate\nN-acetylglucosamine-6-phosphate\nGlucosamine-6-phosphate\nGFPT1\nGFPT2\nGNPNAT1\nAzaserineAzaserine\nUDP-Glucuronate\nUGDH\nN-acetylglucosamine-1-phosphate\nOGT1\nUAP1\nPGM3\nO-linked protein glycosylation\nUDP-N-acetylglucosamine\nHexosamine Biosyntesis Pathway\nUronic acid Pathway\nUTP\nPPi\n2 NAD+\n2 NADH\nL-Gln\nL-Glu\nAcetyl-CoA\nCoA\nUTP\nPPi\nGlycosaminoglycan\nsynthesis\nHyaluronic acid\nsynthesis\nHAS1-3\nFR054\nN-linked protein glycosylation\nDPAGT1\nTunicamycin\nA) B)\nF)\nC) D)\nATF4\nATF6\nIRE1α\nGAPDH\ndox-\ndox+\nPanc1-PLK4 Panc1-PLK4\nPanc1-PLK4\nPanc1-PLK4\nPanc1-PLK4\nATF4\nATF6\nIRE1a\n0\n1\n2\n3\n4\nband intensity fold change\n(normalized to GAPDH)\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-2\n-1\n0\n1\n% mCherry cells in population\ndox+/dox- (log2) % mCherry cells in population\nDMSO Tauroursodeoxycholate\n4-Phenylbutyric acid\n<0.0001\n<0.0001\nE)\nG) H)\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-2\n-1\n0\n1\n% mCherry cells in population\ndox+/dox- (log2)\nDMSO Azaserine\nOSMI-14-MU\nFR054\nTunicamycin\n0.0762\n0.2046\n<0.0001\n<0.0001\n0.0010\nDMSO\nToyocamycin\nThapsigargin\nGSK2656157\n<0.0001\n<0.0001\n0.0005\nPanc1-PLK4 Mia Paca-2-PLK4 BxPC3-PLK4\nYL2-H :: Streptavidin-568 (Surface HA)\nCounts (Normalized To Mode)\nUnstained\nStaining\nControl\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n0\n20\n40\n60\n80\n100\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n5\n10\n1\n10\n2\n10\n3\n10\n4\n10\ndox+ 7d\ndox-\nI) J)\nPanc1\nMia Paca-2\nBxPC-3\n0\n1\n2\n3\n4\nchange of histogram median\n(fold change dox+/dox-)\np = 0.0020\np = 0.0057\np = 0.0003\nDMSO\n4-PBA\nTUDC\n-1.0\n-0.5\n0.0\n0.5\n1.0\nCell viability\nDrug/DMSO - log2)\nD5\n0.3062 0.0048\ndox-\ndox+\n0.4629\n0.0265\n0.9838\n0.9829\nDMSO\n4-PBA\nTUDC\n-2\n-1\n0\n1\nD10\n<0.0001\n0.9915\n0.1890\n<0.0001\n<0.0001\n<0.0001\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-4\n-3\n-2\n-1\n0\n1\n% mCherry cells in population\ndox+/dox- (log2)\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-1.5\n-1.0\n-0.5\n0.0\n0.5\n1.0\nDMSO UDP-GlcNac\nUDP-Glucuronic acidUDP-GalNA\n0.9504\n0.0069\n0.2811\nMia Paca-2 PLK4\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\nDay0\nDay3\nDay8\nDay12\n-6\n-4\n-2\n0\n% mCherry cells in population\ndox+/dox- (log2)\nDMSO\nTUDC\n<0.0001\n4-PBA\n0.0180\n10\n0\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n10\n6\nYL2-H ::Streptavidin-568 (Surface HA)\nDMSO\nPanc1-PLK4 dox+ (5d)\nTunicamycin\n4-PBA\nTUDC\nCounts (Normalized To Mode)\ndox+/dox- (log2)\nFigure 4. Legend on next page.\n10/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nFigure 4. Cells with centrosome amplification has increased dependency for uronic acid and hexosamine pathways. A)\nSchematic representation of uronic acid and hexosamine biosynthesis pathways and enzymes targeted by specific inhibitors in\nthe following experiments in Fig. 4B. Green boxes show metabolites that were used in competition experiments in Fig. 4C. B)\n4-MU, FR054 and Tunicamycin treatment reduces viability of the cells with CA more compared to normal counterparts. C)\nSupplementation of UDP-glucuronic acid reduces depletion of cells with CA in competition experiments. D) CA induces ER\nstress / UPR associated protein levels. Left panel: A representative western blot result. GAPDH was used as loading control.\nRight panel: Quantification of the western blot results. Dots represent independent experiment repeats. E) Induction of\nER-stress and disruption of UPR signaling mechanisms increase the depletion of cells with CA in competition experiments. F)\nReduction of ER-stress by TUDC and 4-PBA increase the depletion of cells with CA in competition experiments. G) Reduction\nof ER-stress by TUDC and 4-PBA decrease viability of cells with CA. H) Reduction of ER-stress by TUDC and 4-PBA\nincrease the depletion of cells with CA in competition experiments in Mia Paca-2 cells. I) CA increases cell surface hyaluronic\nacid levels in PDAC cells. Left panel: Cell surface hyaluronic acid levels in control and dox+ PDAC cell lines. Right panel:\nQuantification of histogram median shift fold changes. Statistical significances were measured by two-way ANOV A in B, C, E,\nF, G, and H, by two-tailed t-test in I. Dots represent independent experiment repeats. p values were reported on graphs.\n2.6 Disruption of hyaluronic acid synthesis triggers cytokinesis failure in centrosome-amplified cells\nTo gain mechanistic insight into how sugar metabolism and glycosylation pathways support centrosome-amplified cells,\nwe examined 4-MU (glucuronic acid metabolism), tunicamycin (N-linked protein glycosylation), and FR054 (hexosamine\nbiosynthesis). DNA content analysis revealed shifts in cell-cycle profiles in both centrosome-amplified (dox+) and non-amplified\n(dox–) cells (Fig. 5A). Changes in the G1 peak indicated cell cycle (DNA content per cell) abnormalities (Fig. 5B), while\nthe appearance of sub-G1 peaks, particularly in centrosome-amplified Mia Paca-2 cells (Fig. 5A, S18A), reflected increased\napoptotic cell death. To better understand the specific effects in centrosome-amplified cells, we compared cell viability at\ndifferent time points across three cell lines. Tunicamycin significantly reduced proliferation in Panc1 and Mia Paca-2 cells after\nfive days of treatment, whereas BxPC-3 cells showed no differential effect (Fig. 5C). In contrast, 4-MU selectively impaired\nthe viability in centrosome-amplified cells across all three lines (Fig. 5D), prompting further investigation. After 10 days of\ntreatment with 4-MU, confocal imaging revealed pronounced increases in cell size and the accumulation of multinucleated\ncells, a definitive indicator of cytokinesis failure and impaired proliferative capacity (Fig. 5E, S18B). Quantification confirmed\nthat 4-MU treatment did not alter the frequency of CA itself but significantly increased the proportion of multinucleated cells\n(Fig. 5F). These findings suggest that suppression of glucuronic acid–dependent HA synthesis perturbs not only extracellular\nmatrix interactions but also intracellular processes critical for mitotic fidelity.\nTo directly test whether disruption of HA biosynthesis contributes to the generation of multinucleated cells, we performed\ngenetic perturbation of UGDH using individual sgRNAs. In sgUGDH-transduced cells, we observed increased multinucleation\neven in non-centrosome–amplified cells, with a higher percentage after CA (Fig. 5H, S18C). Consistent with its role in HA\nsynthesis, sgUGDH was also associated with reduced surface HA levels (Fig. S18D). Importantly, supplementation with\nexogenous HA partially rescued the multinucleation phenotype in sgUGDH cells (Fig. 5I), supporting the conclusion that\nimpaired HA production contributes to cytokinesis defects.\nCollectively, our data show that disruption of nucleotide sugar–dependent pathways, including glucuronic acid–mediated HA\nsynthesis, exerts a greater inhibitory effect on PLK4-induced centrosome-amplified pancreatic cancer cells. This metabolic\ninterference not only diminishes proliferative capacity, but also provokes cytokinesis defects, establishing a mechanistic link\nbetween metabolic dependencies, mitotic fidelity, and cell survival that may be leveraged therapeutically.\n2.7 CD44 activation contributes to centrosome clustering in PDAC cells with centrosome amplification.\nSince surface HA levels were elevated in centrosome-amplified PDAC cells, we next examined the expression of major\nHA-binding receptors. Among CD44, RHAMM, LYVE1, and HARE, CD44 is the most abundantly expressed in PDAC and is\nthe main mediator of HA-dependent signaling47–49. RHAMM can cooperate with CD44 by promoting its surface localization\nand stabilizing HA binding, particularly when HA is immobilized50. However, RHAMM lacks a trans-membrane domain and\nalso carries out intracellular functions49, 51. Based on these features, we focused our analysis on CD44 as the primary receptor\nfor HA signaling in PDAC cells.\nTo test whether CA alters CD44 expression, we first analyzed an unbiased gene expression dataset14, which revealed significant\nup-regulation of CD44 in centrosome-amplified cells (Fig. S19A). Consistently, CD44 and RHAMM (HMMR) expression\nlevels were positively correlated in TCGA PDAC samples (Fig. S19B). Flow cytometry further confirmed increased surface\nCD44 levels in centrosome-amplified Panc1, Mia Paca-2, and BxPC-3 cells (Fig. 6A, S19C). Notably, BxPC-3 cells exhibited\n11/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nthe greatest increase, whereas Mia Paca-2 cells showed a more modest elevation among the tested cell lines (Fig. S19C). Finally,\ndoxycycline treatment alone in PDAC cells lacking the doxPLK4 construct did not affect CD44 levels (Fig. S19D, S19E),\nconfirming that CD44 up-regulation is specifically driven by CA. To assess the role of CD44 in the survival of centrosome-\namplified cells, we sorted subpopulations of Panc1-PLK4 and BxPC-3-PLK4 cells with high (top 10%) or low (bottom 10%)\nCD44 surface expression, induced CA, and monitored cell proliferation for 10 days. CD44-low Panc1-PLK4 cells exhibited\nmarkedly reduced survival compared to CD44-high cells (Fig. 6B). In contrast, this effect was not observed in BxPC-3-PLK4\ncells, suggesting that CD44 dependency may be context-specific, potentially influenced by KRAS mutation status. To directly\nevaluate the requirement of CD44, we generated CD44-KO cells by targeting CD44 with three different sgRNAs and isolating\npopulations that lost CD44 surface expression by FACS (Fig. S19F). Consistently, loss of CD44 further compromised the\nsurvival of centrosome-amplified Panc1 cells (Fig. 6C), reinforcing its role in supporting tolerance to CA.\nBecause CD44 function is regulated through alternative splicing, with the standard isoform (CD44s) and variant isoforms\n(CD44v) linked to distinct cancer phenotypes in PDAC 52, we next examined whether CA alters CD44 splicing. RT-PCR\nanalysis revealed that splicing patterns of CD44 were unchanged in centrosome-amplified Panc1 and MiaPaCa-2 cells, whereas\ninduction of variant isoforms was observed in BxPC-3 cells upon CA (Fig. S20A). This BxPC-3–specific shift toward CD44v\nmay underlie their reduced reliance on overall CD44 levels for survival, providing a potential explanation for the context-\ndependent requirement of CD44 in tolerating CA. Since CD44v has been linked to redox regulation through stabilization of the\ncystine/glutamate antiporter xCT and support of glutathione synthesis53, we tested whether this splicing shift affected ROS\nlevels. In Panc1 cells, which predominantly expressed CD44s even after CA, CD44 knockout did not alter ROS levels (Fig.\nS20B). By contrast, in BxPC-3 cells, where CA induced CD44v expression, CD44 loss led to a modest but significant increase\nin ROS (Fig. S20C), consistent with a partial role of CD44v in redox control. This difference may reflect how KRAS genetic\ncontext modulates the cellular response to centrosome amplification, including ROS regulation.\nTo directly assess the functions of CD44 in the context of CA, we performed competition assays by mixing CD44-KO (sg1–3)\nPanc1-PLK4-GFP cells with parental Panc1-PLK4 cells and treating them with inhibitors to evaluate whether they became\nmore sensitive to glutamine utilization and ROS modulation (CB-839, BSO), inhibition of hyaluronic acid synthesis (4-MU),\nincreased ER stress (tunicamycin), or reduced UPR signaling (TUDC) (Fig. 6D). Results revealed that CD44-KO cells were\nassociated with increased sensitivity to 4-MU and TUDC only, suggesting that CD44 contributes to stress adaptation when\nUPR signaling is reduced by TUDC treatment. Notably, the increased 4-MU sensitivity suggests that HA remains functionally\nimportant even in the absence of CD44, consistent with HA signaling through additional receptors (Fig. 6E). As a control,\nsgAA VS1-GFP transduced Panc1-PLK4 cells were not differentially depleted under any treatment, with or without doxycycline\ninduction (Fig. 6E). We also confirmed reduced survival of CD44-KO cells upon TUDC treatment in individually seeded cell\ngroups. Although 4-MU also impaired the survival of CD44-KO cells, TUDC exhibited a stronger differential effect in CA\ncells (Fig. S20D). These results position CD44 as a key node in CA-induced stress tolerance, particularly under conditions of\nUPR suppression.\nSince HA–CD44 interactions can activate diverse downstream pathways depending on receptor interactions47, and CD44 has\nbeen implicated in MAPK signaling48, 51, we examined phosphorylation of p38 MAPK following HA treatment. CA elevated\nbasal phospho-p38 levels in Panc1 cells, consistent with enhanced stress signaling, whereas exogenous HA supplementation\nselectively reduced this phosphorylation (Fig. S20E). Functionally, HA treatment negatively affected the proliferation in control\n(dox–) cells but had no effect in cells with CA (Fig. S20F). Given that p38 is a stress-activated kinase capable of inducing\napoptosis in response to cellular damage54, these findings indicate that HA–CD44 engagement attenuates p38-driven stress\nresponses. In this context, HA may partially buffer centrosome-amplified cells against the detrimental consequences of stress\nsignaling.\nSince CA is associated with aberrant cell divisions and increased chromosomal instability, we next investigated the role of\nCD44 in maintaining mitotic fidelity under these conditions. Extra centrosomes are linked to multipolar spindle formation\nin metaphase, but cancer cells typically rely on centrosome clustering mechanisms to prevent lethal multipolar divisions55, 56\n(Fig. 6F). To assess how CD44 influences centrosome clustering, we used two approaches. First, we induced CA in CD44-KO\nPanc1-PLK4 cells and quantified multipolar spindle formation, observing a significant increase in multipolar metaphases in all\nthree sgRNA transduced groups compared to controls (Fig. 6G). Second, we compared CD44low (bottom 10%) and CD44high\n(top 10%) sorted cells. After 7 days of CA, multipolar spindles were more frequent in CD44 low cells (∼50%) than in CD44high\n(∼30%) or unsorted populations (∼30%) (Fig. 6H). We further tested this in Mia Paca-2-PLK4 cells, which showed a weaker\nCD44 increase upon CA compared to Panc1 (Fig. 6A, S19B). Nevertheless, CD44low Mia Paca-2 cells still exhibited increased\nmultipolar spindle formation, mirroring the pattern observed in Panc1 (Fig. 6I).\nSince centrosome amplification is associated with aggressive, drug-resistant cancers and poor patient outcomes8, 10, we next\nevaluated the prognostic value of the HA-CD44 axis in TCGA patient samples. We first compared overall survival in patients\n12/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nE)\nA) B)\nF)\n<0.0001\nControl\n0\n20\n40\n60Centrosome amplification (%)\nDMSO 4MU\ndox+dox-\n0.0003\n0.1132\nDMSO 4MU\n0\n20\n40\n60\n80Multinucleated giant cells (%)\n0.3346\n0.0100\n0.0324\ndox+dox-\nDMSO4-MU\n3 days 7 days 10 days\nG)\n3d 7d 10d 3d 7d 10d\n0\n1 103\n2 103\n3 103\n4 103\nnucleus area (px2)\np < 0.0001\np < 0.0001\nDMSO 4-MU\n3d 7d 10d 3d 7d 10d\n0\n2 103\n4 103\n6 103\n8 103\n1 104\ncell area (px2) p = 0.0028\np < 0.0001\nDMSO 4-MU\ndox-\ndox+\nPanc1\n5 days 10 days\n-4\n-3\n-2\n-1\n0\nCell viability\n(4MU/DMSO - log2)\n0.046882\n0.012976\nMia Paca-2\n5 days 10 days\n-5\n-4\n-3\n-2\n-1\n0\n0.049632\n0.037474dox-\ndox+\n5 days 10 days\n-4\n-3\n-2\n-1\n0\n0.769333\n0.017188dox-\ndox+\nBxPC-3\nH) I)\n0\n5.0K\n10K\n15K\nMia Paca-2-PLK4\n0\n5.0K\n10K\n15K BxPC-3-PLK4\n0\n5.0K\n10K\n15KPanc1-PLK4\ndox-\ndox+\n4-MU\nFR054\nTunicamycin\n4-MU\nFR054\nTunicamycin\nDMSO\nDMSO\nDNA Content (PI intensity)\nD)\nD3\n D7\n D10\nPanc1-PLK4 (dox+3d)\nNucleus\nCentrosomes and cell boundaries\nNucleus\nCentrosomes and cell boundaries\nPanc1\nMia Paca-2\nBxPC-3\n-5\n-4\n-3\n-2\n-1\n0\nCell viability\nTunicamycin/DMSO - log2)\nD3\n0.0042\n0.0673\n0.1604\nPanc1\nMia Paca-2\nBxPC-3\nD5\n<0.0001 <0.0001\n>0.9999\nDMSO\n0\n1\n2\n3\nPanc1\nMia Paca-2\nBxPC-3\n4-MU\nFR054\nTunicamycin\nDMSO\n4-MU\nFR054\nTunicamycin\nG1 peak intensity (fold change)\nrelative to dox- DMSO\nC)\ndox-\ndox+\ndox- dox+\ndox-\ndox+\ndox-\ndox+\ndox-\ndox+\ndox-\ndox+\n0\n10\n20\n30Multinucleated cells (%)\nsgAAVS1\nsgUGDH-1\nsgUGDH-2\nsgUGDH-3\np = 0.0003\np < 0.0001\np = 0.0012\nVehicle\nHA\nVehicle\nHA\nVehicle\nHA\nVehicle\nHA\n0\n5\n10\n15\n20\n25\n30Multinucleated cells (%)\np = 0.0020\np = 0.0002\np < 0.0001\np < 0.0001\np < 0.0001\np < 0.0001\nsgUGDH samples\nFigure 5. Legend on next page.\n13/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nFigure 5. Chemical and genetic perturbation of hyaluronic acid synthesis induces multinucleation in pancreatic cancer\ncells with centrosome amplification. A) 4-MU, tunicamycin and FR054 treatments increase DNA content of individual cells.\nB) Quantification of the G1-peak intensities in Fig. 5A. C) Tunicamycin treatment significantly reduces proliferation of\ncentrosome-amplified Panc1 and Mia Paca-2 cells compared to control. D) 4-MU treatment significantly reduces proliferation\nof centrosome-amplified Panc1, Mia Paca-2, and BxPC-3 cells compared to control. E) Long-term 4-MU treatment results in\ngeneration of multinucleated cells. Left panel: Inverted confocal images. Purple color shows DNA content of the cells, and\norange color shows centrosomes and cell boundaries. Scale bar: 20 µm. Right panel: Quantification of cell area and nucleus\narea in pixel squares. F) Quantification of multinucleated giant cells in DMSO and 4-MU treated cells with CA. Left panel:\nQuantification of CA. Right panel: Quantification of multinucleated cells. G) CRISPR/Cas9 targeted disruption of UGDH gene\nresults in generation of multinucleated cells. Purple color shows DNA content of the cells, and orange color shows centrosomes\nand cell boundaries. Scale bar: 20 µm. H) Quantification of multinucleated cells in sgAA VS1 and sgUGDH expressing\ncentrosome-amplified and control cells. I) Quantification of multinucleated cells in sgAA VS1 and sgUGDH expressing HA or\nVehicle treated cells with CA. Significance was determined by two-tailed t-test in C, by two-way ANOV A test in D, F, H, and I,\nby one-way ANOV A in E. Dots represent individual repeats. p values were reported on graph.\nstratified by PLK4, CD44, or HMMR expression levels. While PLK4 or HMMR levels alone did not significantly affect survival,\nCD44 expression levels were associated with a marked reduction in overall survival (Fig. S21A-C). We then examined the\ncombined effects and found that patients with both PLK4high/CD44high expression had significantly worse survival compared\nto PLK4high/CD44low (p = 0.0287) and PLK4low/CD44low patients (p = 0.0116). No significant effect was observed for the\ncombined effects of PLK4 and HMMR (Fig. S21D-E). These findings highlight CD44 as a key modifier of the poor prognosis\nassociated with centrosome amplification.\nTogether, these results highlight CD44 as a critical regulator of centrosome clustering and a potential vulnerability in centrosome-\namplified PDAC cells. Importantly, our data indicate that this role extends beyond spindle mechanics: HA–CD44 signaling\nsimultaneously reduces p38-mediated stress responses and cooperates with UPR pathways to buffer proteotoxic stress, as\nreflected by the increased sensitivity of CD44-KO cells to TUDCA-induced UPR inhibition. Thus, CD44 safeguards centrosome-\namplified cells by integrating centrosome clustering with stress adaptation mechanisms.\n3 Discussion\nCentrosome amplification (CA) is a hallmark of many cancers5, including pancreatic ductal adenocarcinoma (PDAC), where it\nis associated with genomic instability and poor outcomes7, 8. While CA promotes tumor evolution, it also imposes significant\nstress that must be managed for cell survival 55, 56. Our study reveals that PDAC cells with CA adopt distinct metabolic\nadaptations, creating specific, targetable dependencies in redox homeostasis, unfolded protein response (UPR) signaling, and\nhyaluronic acid (HA) synthesis (Fig. 7). These adaptations are not correlative but essential, as their disruption selectively\nimpairs the survival of cells with CA.\nA primary consequence of CA is increased intracellular reactive oxygen species (ROS)14, which we confirmed across PDAC\nmodels. This oxidative stress creates a strong reliance on antioxidant defenses. CA cells were highly sensitive to inhibition of\nglutaminase (GLS1), the rate-limiting enzyme in glutamine catabolism, consistent with the role of glutamine as a precursor\nfor glutathione (GSH) synthesis 20. Inhibiting GSH synthesis with BSO was selectively lethal to CA cells. Because GSH\ndepletion is a canonical trigger of ferroptosis, we tested whether ferroptotic death contributed to this phenotype. Ferrostatin-1, a\npotent ferroptosis inhibitor, did not rescue CA cells, suggesting that classical ferroptosis is not the dominant death mechanism.\nNonetheless, given the central role of GSH in ferroptotic regulation and the variability of ferroptosis across tumors, we cannot\nexclude its contribution under conditions not captured here.\nOur metabolism-focused CRISPR screen and subsequent validation experiments further pinpointed antioxidant dependencies,\nidentifying essential roles for the thioredoxin system (e.g., TXNRD2), superoxide dismutases (SOD1/2/3), and peroxiredoxins\n(PRDX1) in CA cell survival. This is consistent with glutamine-derived glutamate fueling GSH synthesis, supporting\nredox balance beyond anaplerosis 21. Dihydrofolate reductase (DHFR), a folate cycle enzyme, also emerged as a novel\nredox vulnerability: its inhibition increased ROS and impaired survival, suggesting a potential role in redox regulation via\ntetrahydrobiopterin and nitric oxide synthase activity37. Together, these results position CA cells as reliant on a multilayered\nantioxidant network, largely fueled by glutamine metabolism, to tolerate oxidative stress. In parallel, CA was associated with\nactivation of NRF2 signaling, as shown by NRF2 nuclear translocation and increased ARE reporter activity, consistent with\nprior reports14. The differential sensitivities observed, greater GSH dependence in Panc1 and a stronger reliance on NRF2\n14/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nA)\nFSC-H :: FSC-H\nYL1-H :: CD44-PE\n0\n200K\n400K\n600K\n800K\n1.0M\n0\n200K\n400K\n600K\n800K\n1.0M\n0\n200K\n400K\n600K\n800K\n1.0M\n10\n0\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n10\n6\n0\n200K\n400K\n600K\n800K\n1.0M\nsgAAVS1\nsgCD44-1\nsgCD44-2\nsgCD44-3\nPanc1-PLK4 cells\n0\n20\n40\n60\n80\n100\n10\n1\n10\n2\n10\n3\n10\n4\n10\n5\n10\n6\nYL1-A :: CD44-PE\n0\n20\n40\n60\n80\n100\n0\n20\n40\n60\n80\n100\nCounts (Normalized To Mode)\nPanc1-PLK4\nMia Paca-2-PLK4\nBxPC3-PLK4\ndox-\ndox+ 5d\nCell counts ( dox+/dox- log2)\nDay 10 Day 5\nC)\nI)G)\nF)\nPanc1 PLK4 cells\nPanc1 PLK4 sgAAVS1-GFP\nPanc1 PLK4 sgCD44-(1,2,3)-GFP\nco-culture\ndox- / dox+\n+inhibitors\n/f_low\ncytometry\nE)\nH)\nD3 D5 D10\n-2.0\n-1.5\n-1.0\n-0.5\n0.0\nCD44 low\nCD44 high\n0.9692 0.4383 <0.0001\nCell counts ( dox+/dox- log2)\n-3\n-2\n-1\n0\np = 0.7509\np =0.2416\np =0.8812\nsgAAVS1\nsgCD44-1\nsgCD44-2\nsgCD44-3\n-3\n-2\n-1\n0\np = 0.0138\np = 0.0032\np = 0.0462\nPanc1-PLK4\nDMSO\nDMSO+dox\nCB-839\nCB839+dox\nBSO\nBSO+dox\nTUDC\nTUDC+dox\n4-MU\n4-MU+dox\nTunicamycin\nTunicamycin+dox\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\n-5\n-4\n-3\n-2\n-1\n0\n%GFP positive cells\nnormalized to D0 (log2)\nPanc1-PLK4 vs Panc1-PLK4-sgCD44-g2-GFP\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\n-5\n-4\n-3\n-2\n-1\n0\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\n-5\n-4\n-3\n-2\n-1\n0\n%GFP positive cells\nnormalized to D0 (log2)\nPanc1-PLK4 vs Panc1-PLK4-GFP\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\nD0\nD5\nD10\nD15\n-5\n-4\n-3\n-2\n-1\n0\n%GFP positive cells\nnormalized to D0 (log2)\nPanc1-PLK4 vs Panc1-PLK4-sgCD44-g1-GFP\n%GFP positive cells\nnormalized to D0 (log2)\nPanc1-PLK4 vs Panc1-PLK4-sgCD44-g3-GFP\nD)\nD3 D5 D10\n-2.5\n-2.0\n-1.5\n-1.0\n-0.5\n0.0\nCD44 low\nCD44 high\n0.7001 0.1417 0.9880\nMetaphase type (%)\nBPC\nMPS\nsgAA\nVS1\nsgCD44-1sgCD44-2sgCD44-3\n0\n20\n40\n60\n80\n100 p = 0.0074\np < 0.0008\np = 0.0039\nNucleus\nCentrosomes and cell boundaries\nMultipolar Spindles\nBipolar Clustered Spindles\nBPC\nMPS\nunsortedCD44-highCD44-low\n0\n20\n40\n60\n80\n100Metaphase type (%)\nBPC\nMPS\np = 0.4860\np = 0.0020\np = 0.0028\nB)\n10\n0\n10\n2\n10\n4\n10\n6\nFL3-A :: Surface CD44 intensity\nFL3-A :: Surface CD44 intensity\n0\n100\n200\n300\nCounts\nlow 10%\nhigh\n10%\nPanc1-PLK4\n10\n0\n10\n2\n10\n4\n10\n6\n0\n100\n200\n300\nlow 10%\nhigh\n10%\nBxPC-3-PLK4\nlow 10%\nhigh\n10%\nMia Paca-2-PLK4\n10\n0\n10\n2\n10\n4\n10\n6\n0\n100\n200\n300\nCounts\nMetaphase type (%)\nunsortedCD44-highCD44-low\n0\n20\n40\n60\n80\n100\np = 0.9907\np = 0.0189\np = 0.0122\nFigure 6. Cell surface CD44 is increased upon centrosome amplification and contributes to centrosome clustering in\nPDAC cells. A) Flow cytometry analysis showing elevated CD44 surface levels in centrosome-amplified PDAC cells. B)\nCD44-low Panc1 cells are more sensitive to CA. Left panel: FACS-sorted CD44-low and CD44-high populations in Panc1 and\nBxPC-3 cells. Right panel: Cell proliferation following different durations (3, 5, and 10 days) of CA. C) CD44-KO Panc1 cells\nare more sensitive to CA. Left panel: Flow cytometry confirming loss of CD44 expression in CD44-KO cells. Right panel: Cell\nproliferation following CA (5 and 10 days). D) Schematic of the competition assay design used in panel E. E) CD44-KO\ngenerates an increased vulnerability for UPR reduction in centrosome-amplified Panc1 cells. F) Representative confocal images\nof metaphase spindle organizations in Panc1 cells with CA. Top panel: bipolar clustered spindles; Bottom panel: multipolar\nspindles. G) Quantification showing reduced centrosome clustering in CD44-KO Panc1-PLK4 cells. H) CD44-low\nPanc1-PLK4 cells have increased multipolar spindle formation in metaphase. I) CD44-low Mia Paca-2-PLK4 cells have\nincreased multipolar spindle formation in metaphase. Significance was determined by two-way ANOV A test in B, by one-way\nANOV A in C, G, H, and I. Dots represent individual repeats. p values were reported on graph.\n15/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nsignaling in MiaPaCa-2, likely reflect likely reflect the redundancy of antioxidant systems and the genetic context of PDAC\nmodels57.\nAlongside redox stress, CA imposed a strong reliance on protein quality-control associated pathways. The depleted hits in\nCRISPR screen revealed enrichment for genes in the hexosamine biosynthesis and uronic acid pathways, which generate UDP-\nsugars for glycosylation and glycosaminoglycan synthesis. Functional validations confirmed that inhibition of HA synthesis\n(4-MU), HBP flux (FR054, targeting PGM3), or N-linked glycosylation (tunicamycin) was selectively toxic to cells with CA,\nwhereas O-linked glycosylation, though important for centrosome-regulated polarity58, was dispensable. Mechanistically, CA\ntriggered robust activation of all three UPR branches, PERK, IRE1α, and ATF6, indicating elevated proteostatic stress. This\nactivation was adaptive: both hyper-activation and suppression (via chemical chaperones TUDC and 4-PBA) were detrimental,\nsuggesting that CA cells maintain a precarious “hyper-equilibrium” of UPR signaling41. Together, these observations raise the\nimportant question of how distinct stress-responsive transcription factors, including NRF2, ATF4, and ATF6, differentially\ncontribute to the survival of centrosome-amplified cells. Determining whether these pathways function redundantly or in a\ncontext-dependent manner to buffer oxidative and proteostatic stress will be an important direction for future investigation.\nThe reliance on hexosamine and uronic acid metabolism is consistent with previous findings that the HBP constitutes a metabolic\nvulnerability in cancers such as lung59 and breast60. However, in our experiments, this vulnerability was not a general feature of\nPDAC cells, but rather emerged specifically in the context of CA-induced stress, indicating that HBP and uronic acid pathway\ndependence are tightly linked to this altered cellular state. This is reminiscent of a recently described conditional essentiality in\nsugar nucleotide metabolism, where cells with high UGDH expression become dependent on UXS1 to detoxify accumulated\nUDP-glucuronic acid and maintain Golgi homeostasis 61. In our system, supplementation with UDP-glucuronic acid—the\nmetabolite produced by UGDH—partially rescued viability, suggesting that CA cells may experience increased utilization of\nthis key metabolite, creating a dependency on its production. Conversely, the failure of N-acetyl glucosamine (GlcNAc) to\nprovide a survival advantage points to a bottleneck in the salvage pathway, potentially at the level of N-acetylglucosamine\nkinase (NAGK) activity or transport. Collectively, our findings suggest that centrosome amplification creates a unique metabolic\nstate characterized by an increased demand for specific sugar nucleotides, unveiling a targetable liability that could be exploited\ntherapeutically.\nThe identification of SLC5A3 as a selective dependency of centrosome-amplified cells, together with the ability of myo-inositol\nor D-glucuronate supplementation to rescue their survival, indicates that CA creates an increased requirement for extracellular\ninositol. This may arise from impaired de novo biosynthesis, as suggested by IMPA1 downregulation in Mia PaCa-2 cells,\nor from functional bottlenecks in inositol metabolism or flux in other models. Because inositol-derived phospholipids play\ncentral roles in ER membrane composition, vesicular trafficking, and protein quality control62, increased reliance on inositol\nuptake may reflect an adaptive response to the proteotoxic and membrane stress imposed by centrosome amplification. In this\ncontext, SLC5A3-mediated inositol import may act as a metabolic buffer that supports ER function and proteostasis under\nchronic CA-induced stress.\nAdditionally, our work highlights the importance of CA, chromosomal instability–associated division abnormalities, and\nrelated genetic backgrounds in shaping metabolic programs. For instance, previous studies have shown that LKB1/KRAS\nmutant lung adenocarcinoma cells display elevated flux through the hexosamine biosynthesis pathway (HBP) and increased\ndependence on GFPT259. Given the established role of LKB1 (STK11) in centrosome biology63, as well as in regulating the\nchromosomal passenger complex (CPC) and maintaining genome stability 64, it would be intriguing to investigate whether\nCA contributes to the enhanced HBP flux observed in LKB1/KRAS mutant cancers. Moreover, a recent aneuploidy-focused\nmetabolic CRISPR screen identified DHODH as a top dependency in aneuploid cells65, highlighting how stable aneuploidy\ncreates distinct metabolic vulnerabilities. By contrast, our findings suggest that CA—potentially together with chromosomal\ninstability—drives reliance on ROS detoxification and nucleotide sugar/glycan biosynthesis. Together, these insights point to\nthe value of integrating genomic instability with metabolic profiling to reveal novel, context-specific therapeutic vulnerabilities\nacross cancers.\nA central discovery of our study is that centrosome amplification (CA) co-opts hyaluronic acid (HA) metabolism to sustain the\nmitotic fidelity. We find that CA up-regulates both HA synthesis and the expression of its receptor, CD44, creating a dependency\non this ligand-receptor axis. Functionally, we demonstrate that the HA-CD44 system is required for two critical and distinct\nprocesses: (i) centrosome clustering to suppress multipolar divisions, and (ii) successful cytokinesis to prevent multinucleation.\nThis reveals a dual mechanism through which extracellular matrix remodeling safeguards cell division under the profound stress\nof CA. We propose that the HA-CD44 axis facilitates this by orchestrating cytoskeletal organization and force distribution, a role\nconsistent with CD44-mediated mechanotransduction48. This is supported by our observation that CA elevates pro-apoptotic\np38-MAPK phosphorylation and that exogenous HA attenuates this signal. Furthermore, HA supplementation partially rescues\nthe cytokinesis defects induced by UGDH knockout, confirming its active role as a regulatory factor, not just a structural\n16/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nscaffold. Notably, the HA-CD44 pathway becomes essential when proteostatic buffering is compromised, as reducing UPR\nactivity with TUDC selectively targets CA cells in a CD44-dependent manner. This suggests that, under extreme stress, cancer\ncells rely on this mechanical signaling network as a critical compensatory survival mechanism. Integrating our findings with the\nestablished role of RHAMM as a mitotic spindle regulator66, 67, we postulate the existence of an HA-CD44-RHAMM network\nthat synchronizes extracellular cues with intracellular machinery to monitor and execute accurate cell division. The strong\ncorrelation between RHAMM and PLK4 expression in PDAC patient tumors further underscores the clinical relevance of this\nmechanism. Thus, we define a critical metabolic-physical circuit that maintains viability under the proteotoxic and mitotic\nstress generated by centrosome amplification, revealing a new vulnerability in aggressive cancers.\nS-S\nS-S\nS-S\n44DC\nS-S\nS-S\nS-S\nROS\nROS \nelimination \nmechanisms\nGlutamine\nmyo-inositol\nNucleotide sugar \nsynthesis\nS-S\nS-S\nS-S\nBSO\nLCS1\nML385\nFR054\n4-MU\nHyaluronic\nacid\nSurvival\nER stress Survival\ncanonical\nUPR\nCD44\nS-S\nS-S\nS-S\nS-S\nS-S\nS-S\nS-S\nS-S\nS-S\nCentrosome clustering Multipolar metaphase\nFigure 7. Proposed model of metabolic dependencies in PDAC cells with PLK4-induced centrosome amplification. CA\nincreases cellular vulnerability to inhibition of ROS detoxification pathways and to suppression of both the glucuronic acid and\nhexosamine biosynthetic pathways (HBP). These cells also exhibit increased dependence on hyaluronic acid synthesis,\nextracellular glutamine and inositol availability. Moreover, disruption of HA–CD44 signaling impairs centrosome clustering,\nthereby compromising mitotic fidelity. Created in BioRender. Ozcan, S. (2026) https://BioRender.com/hrtnym8\nNotably, our findings align with and extend the concept of an extra-centrosome associated secretory phenotype (ECASP)13, 14.\nWhile a prior study demonstrated that CA drives the secretion of pro-inflammatory cytokines and growth factors, such as IL-8,\nwith the potential to remodel the tumor microenvironment, our study reveals an additional dimension to this phenotype: the\nprofound metabolic reprogramming required to sustain both cell-autonomous survival and secretory capacity. The increased\ndependency on hexosamine and uronic acid pathways that we identified likely supports not only intracellular stress management\nbut also the extensive glycosylation requirements for secreted factors that characterize ECASP. This connection suggests that\nCA cells must tightly coordinate their metabolic and secretory activities to thrive in challenging tumor environments.\nWhile our study reveals profound metabolic dependencies in PDAC cells with CA, a limitation is the absence of comprehensive\nmetabolomic profiling to determine whether these vulnerabilities reflect broader metabolic rewiring. Although we identify\ndiscrete dependencies in HA synthesis, UPR signaling, and redox homeostasis, these processes are highly interconnected within\ncellular metabolism. CA may therefore induce metabolic adaptations that extend beyond the pathways directly tested here.\nFor example, increased demand for nucleotide sugars in HA synthesis could alter flux through the hexosamine biosynthesis\npathway, with consequences for both N-glycosylation and glycosaminoglycan synthesis. Indeed, CA cells were selectively\nsensitive to inhibition of N-glycosylation, consistent with an increased need for protein processing and extracellular matrix\nremodeling to tolerate stress. Likewise, redox stress and UPR activation may reshape amino acid and nucleotide metabolism,\nwith alterations in glutathione and thioredoxin systems affecting redox-sensitive signaling pathways beyond oxidative damage\ncontrol. Our findings further suggest that these adaptations are dynamic: under reduced UPR signaling, CA cells become\nincreasingly reliant on the HA–CD44 axis, implying that extracellular matrix–driven mechanotransduction can substitute\n17/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nfor diminished proteostasis in maintaining mitotic fidelity and survival. Definitive assessment of such global changes will\nrequire metabolomics and flux-based analyses, such as stable isotope tracing with 13C-glucose or 15N-glutamine, to map\ncarbon and nitrogen flow, uncover compensatory pathways, and clarify whether the dependencies we observe represent absolute\nrequirements or instead bottlenecks within a reorganized metabolic network.\nA limitation of our study is that centrosome amplification was induced exclusively through PLK4 overexpression. Although\nPLK4 overexpression is a widely used and effective approach to generate supernumerary centrosomes, PLK4 has also\nbeen implicated in regulating cytoskeletal organization and signaling pathways that are intimately connected to centrosome\namplification, chromosomal instability, and mitotic errors68. This interdependence makes it difficult to assign strict causality\nto centrosome number alone in PLK4 overexpression models. Accordingly, the metabolic and stress-response dependencies\nidentified here should be interpreted as vulnerabilities associated with the PLK4-induced CA state. Validation of these\ndependencies using orthogonal approaches to induce centrosome amplification, such as cytokinesis failure or centriole\ndisengagement defects, will be necessary to more fully disentangle centrosome-driven effects from potential kinase-dependent\ncontributions of PLK4.\nTaken together, our results reveal that CA drives non-redundant metabolic dependencies in redox control, proteostasis, and\nglycosaminoglycan synthesis. These pathways converge on the HA–CD44 signaling axis, which safeguards centrosome\nclustering and mitotic fidelity, integrating metabolic and structural adaptations that ensure the survival of genomically unstable\nCA cells. Importantly, these vulnerabilities are targetable: GLS1 inhibitors, UPR modulators, and HA synthesis blockade each\nselectively impaired CA cell fitness, and combined perturbations showed enhanced lethality. Since CA marks aggressive and\ntreatment-resistant PDAC, therapeutic strategies that disrupt these adaptations could selectively eliminate the most dangerous\ntumor cell populations.\n4 Methods\nCell culture\nHuman pancreatic ductal adenocarcinoma cell lines Panc1 (CRL-1469), MiaPaCa-2 (CRL-1420), BxPC-3 (CRL-1687), and\nU2OS osteosarcoma cell line (HTB-96) were obtained from ATCC. All cell lines were tested monthly for mycoplasma\ncontamination. Cells were maintained in DMEM (Sigma, D6429) supplemented with 10% tetracycline-free FBS (biowest,\nS181T) and 1% penicillin-streptomycin at 37°C in 5% CO2. In glutamine depletion experiments, a DMEM without L-gln and\nNa-pyr was used (Sigma, D5671). Centrosome amplification was induced with 2 µg/mL doxycycline for the indicated durations.\nDetailed information about the chemicals and inhibitors used in the study is provided in Supplementary tables 1 and 2.\nPlasmids and lentivirus generation\nDoxycycline-inducible cell lines were generated by lentiviral transduction with pCW57-PLK418 at an MOI (multiplicity of\ninfection) of 5, followed by hygromycin selection (200 µg/mL). For competition assays, lentiviral H2B-GFP (Addgene, 21210)\nand H2B-mCherry (Addgene, 21217) expression plasmids were used. For CRISPR knockout, sgRNAs targeting UGDH and\nCD44 (gRNA sequences are provided in Supplementary table 3) were cloned into lentiCRISPR-v2 (Addgene, 52961). Lentiviral\nparticles were produced in HEK293T cells using psPAX2 (Addgene, 12260) and VSV .G (Addgene, 14888) packaging plasmids.\nTarget cells were infected at an MOI of 2 in the presence of 8 µg/mL polybrene and selected with the appropriate antibiotics. For\nARE-reporter assays, pREP-8xARE-GFP-SV40-BFP (Addgene, 134910) plasmid was transfected to cells with Lipofectamine\n3000 (Thermo Fisher, L3000015).\nMetabolic enzyme targeted CRISPR screen\nThe metabolism-focused CRISPR knockout library 69, targeting 2,981 metabolic genes with 10 sgRNAs per gene (29,790\nsgRNAs total), was obtained from Addgene (110066) and amplified following published protocols 70. Library quality was\nconfirmed by next-generation sequencing to ensure uniform sgRNA representation prior to lentiviral production. Viral titers\nwere determined in Panc1 cells, and infections were carried out at a multiplicity of infection (MOI) of 0.6 to favor single-copy\nintegration while maintaining 300x coverage. After puromycin selection, baseline (day 0) samples were collected, and cells\nwere split into doxycycline-treated and untreated groups for 21 days of culture. At each passage, cell numbers were monitored\nto preserve library representation and minimize dropout. CRISPR screening experiments were conducted in two independent\nrepeats. Genomic DNA was isolated (Macherey-Nagel, NucleoSpin Tissue, 740952), sgRNA cassettes were PCR-amplified,\nand sequencing was performed on an Illumina NovaSeq 6000 platform.\nCRISPR screen data analysis\nSequencing reads were processed and analyzed using MAGeCK71, with normalization performed at the sgRNA level. Gene\nessentiality was quantified by β scores, and differential essentiality was defined as βdox+ − βdox-. MAGeCK-Flute was used\n18/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nfor cell cycle normalization of beta scores and for the visualization of MAGeCK-MLE analysis72. To generate a top depleted\ngene list, a depletion threshold was established by calculating the median of all differential β scores and subtracting twice\nthe median absolute deviation (MAD) from this median. Genes with differential β scores falling below this threshold were\nconsidered significantly depleted. Additionally, Wald p values from the MAGeCK MLE analysis were used in downstream\nanalyzes (Fig. 3E). Gene Set Enrichment Analysis (GSEA) was carried out using MSigDB gene sets, and protein-protein\ninteraction networks were constructed with STRINGdb ( https://string-db.org) and clustered using the Markov\nClustering Algorithm (MCL), with visualization in Cytoscape (v.3.10.3). Gene Ontology annotations were obtained from\nQuickGO (\nhttps://www.ebi.ac.uk/QuickGO/), with corresponding datasets filtered for human genes and used to\nanalyze our screening results. GO-BP, MSigDB, KEGG, and TR-RUST analyzes were performed using ShinyGO (v.0.82,\nhttps://bioinformatics.sdstate.edu/go/).\nTCGA data analysis\nTCGA pancreatic cancer dataset was accessed from the NIH–National Cancer Institute (NCI) web portal (https://portal.\ngdc.cancer.gov/projects/TCGA-PAAD; last accessed 04/30/2025). Patient samples were filtered to include only\nadenocarcinoma cases ( n = 82). Gene Expression Clustering analysis was performed, applying unsupervised Euclidean\nclustering across both genes and patients. Gene lists were restricted to top CRISPR screen hits as well as the CA20 and\nCIN25 gene signatures. All genes included in the analysis are presented in Supplementary Fig. 7A. Uniform Manifold\nApproximation and Projection (UMAP) clustering was applied to the downloaded dataset using the umap package (v.0.2.10) in\nR (v.4.5.1)73. Signature scores for chromosomal instability (CIN25) and centrosome amplification (CA20) were calculated\nas the mean Z-score normalized expression of their respective gene sets. Z-score normalization was performed across\nsamples prior to score calculation. Gene expression correlations in the TCGA dataset were assessed using cBioPortal\n(\nhttps://www.cbioportal.org). Additionally, Gene Expression-based Network Inference (GENI) analysis was\nconducted via the online platform (https://www.shaullab.com/geni).\nFlow cytometry assays\nFor intracellular ROS measurement, cells were stained with 10 µM H2DCFDA (Thermo Fisher, D399) for 20 minutes at 37°C.\nFor cell surface hyaluronic acid detection, cells were stained with biotinylated HA-binding peptide (Anaspec, AS-65199),\nfollowed by Streptavidin-AlexaFluor-568 (Thermo Fisher, S11226), in accordance with a previously published method 74.\nCD44 surface levels were measured using PE-conjugated anti-CD44 antibody (BioLegend, 103007). For cell cycle distribution\nanalysis, cells were fixed in 70% ethanol, treated with RNase A (Thermo Fisher, EN0531), and stained with propidium iodide. In\nall assays, FSC/SSC populations were gated to identify live cell populations, and single cells were selected using SSC-H/SSC-A\ngating strategies. All analyses were performed on single-cell gated populations. For ARE-activation experiments, BFP-positive\ncells were first gated, and GFP intensity in this subpopulation was measured and compared. Single fluorescent expressing cells\nwere used for compensation. Flow cytometry analyses were performed using either an Attune NxT flow cytometer (Thermo\nFisher) or a BD CytoFLEX instrument (BD Biosciences). All flow cytometry data were analyzed and visualized using FlowJo\nsoftware (v10.8.1).\nDual color competition assays\nH2B-GFP and H2B-mCherry labeled cells were mixed at 1:1 ratio and plated in 6-well plates. Cells were treated with\nindicated compounds and doxycycline, with media and compounds refreshed every two days. At indicated time points, cells\nwere trypsinized and analyzed by flow cytometry to determine GFP/mCherry ratios. Relative depletion was calculated as\nlog2(%mCherrydox+ / %mCherrydox-). Representative flow cytometry plots showing non-normalized %mCherry and %GFP\nlevels were presented in supplementary figures.\nGlutathione assays\nIntracellular reduced and oxidized glutathione levels were quantified using the GSH/GSSG-Glo assay (Promega, V6611)\naccording to the manufacturer’s instructions. Control and centrosome-amplified (5d) cells were seeded in white, clear-bottom\n96-well plates and allowed to adhere overnight. Cells were lysed and bioluminescent signals corresponding to total and\noxidized (GSSG) glutathione were measured using a plate reader. GSH/GSSG ratios were calculated for each condition from\nthe respective luminescence values. To correct for differences in cell number, signals were normalized to total protein content\ndetermined from parallel wells using a BCA assay.\nFluorescence activated cell sorting\nFor CD44 high/low and CD44-KO experiments, cells were stained with PE-conjugated anti-CD44 antibody (BioLegend,\n103007) and sorted on a cell sorter (Sony, SH800S). The top and bottom 10% of CD44-expressing populations were collected\n19/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nfor downstream assays. All fluorescence-activated cell sorting (FACS) procedures were carried out using the ultra-purity sorting\nmode. The sorted populations are displayed in the corresponding plots within the manuscript figures.\nImmunofluorescence and confocal microscopy\nCells grown on glass coverslips were fixed in methanol at−20◦C for 10 minutes, washed three times with PBS, and blocked\nwith 5% BSA in PBS. Cells were then stained with anti–γ-tubulin antibody (Sigma, T6557; 1:500 dilution), followed by Alexa\nFluor 488–conjugated secondary antibody (Invitrogen, A-11001; 1:500 dilution). DNA was counterstained with DAPI, and\ncoverslips were mounted on glass slides. Images were acquired using either a Leica DMI8 widefield microscope or a Nikon\nAXR confocal microscope. Maximum intensity projections of Z-stacks were generated. Images were analyzed using ImageJ\nand QuPath. For quantification of cell and nuclear size, cell boundaries were identified using thresholding of the cytoplasmic\nsignal, and nuclei were segmented by automated thresholding of the DAPI channel (Otsu method).\nWestern blotting and antibodies\nCells were lysed in RIPA buffer with protease and phosphatase inhibitors. Nuclear and cytoplasmic fractions were separated\nusing a commercial kit (Thermo Fisher, 78833). Proteins were separated by SDS-PAGE, transferred to PVDF membranes, and\nprobed with antibodies: FLAG (Sigma, F1804, 1:1000), GAPDH (Cell Signaling, 2118, 1:2000), Histone H3 (Cell Signaling,\n4499, 1:2000), NRF2 (Cell Signaling, 12721, 1:1000), ATF4 (Cell Signaling, 11815, 1:1000), ATF6 (Cell Signaling, 65880,\n1:1000), IRE1α (Cell Signaling, 3294, 1:1000), phospho-p38 (Cell Signaling, 4511, 1:1000). Blots were developed with ECL\nreagent (Luminata Forte, Millipore, WBLUF0020) and imaged on the ChemiDoc system (Bio-Rad).\nRT-qPCR and RT-PCR\nTotal RNA was extracted using the NucleoSpin RNA kit (Macherey-Nagel; 740955), and cDNA was synthesized from 1 µg RNA\nwith M-MLV reverse transcriptase (Invitrogen; 28025013). For RT-qPCR, 10 ng of cDNA was amplified with SYBR Green\nMaster Mix (Roche; 04707516001), using GAPDH as the endogenous control. Primer sequences are listed in Supplementary\nTable 4. For CD44 splicing analysis by RT-PCR, 2 µl of synthesized cDNA was used as template. The primers were: forward\n5′-AGTCACAGACCTGCCCAATGCCTTT-3′ and reverse 5′-TTTGCTCCACCTTCTTGACTCCCATG-3′.\nCell viability and colony formation assays\nCell viability in 96-well plates was assessed using sulforhodamine B (SRB) staining. For viability assays in 6-well plates,\ncell numbers were quantified using an automated cell counter (BioRad) with the trypan blue exclusion method. For colony\nformation assays, 300 cells (Panc1) or 500 cells (Mia Paca-2 and BxPC-3) were seeded per well in standard 6-well plates and\ncultured under the indicated conditions. Colonies were fixed with methanol, stained with crystal violet, and imaged. Stain was\nthen solubilized in acetic acid (3%) and quantified using a microplate reader at 590 nm absorbance.\nStatistical analyses\nAll experiments were performed with multiple independent biological replicates, and independent repeats were shown in the\nrelated plots. Data are presented as mean ± SD, unless otherwise stated. Statistical analyzes were performed using GraphPad\nPrism 9 and R. For comparisons between two groups, unpaired two-tailed Student’s t-test was used. For multiple group\ncomparisons, a one-way ANOV A with appropriate post-hoc tests was applied. For competition assays, two-way ANOV A with\nmultiple comparisons was used. A p-value of < 0.05 was considered statistically significant.\n5 Acknowledgements\nThe authors gratefully acknowledge the use of the services and facilities of the Koç University Research Center for Translational\nMedicine (KUTTAM), funded by the Presidency of Turkey, Presidency of Strategy and Budget.\n6 Author contributions statement\nConceptualization: SCO, Investigation: SCO, EG, BMK, EC, and BK, Methodology: SCO, EG, Visualization: SCO, Project\nAdministration: SCO, CAA, Funding Acquisition: SCO, Writing - original draft: SCO, Writing - review & editing: CA.\n7 Additional information\nThis research was funded by TUSEB (23066, SCO) and in part by TUBITAK (120Z830, SCO). The funders had no role in\nstudy design, data collection and analysis, the decision to publish, or preparation of the manuscript.\nData availability : The raw and processed sequencing data from the CRISPR screen are available as Data Table 1. All other\ndata are available from the corresponding authors (S.C.O. and C.A.A.) upon request.\n20/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nCompeting interests The authors declare no conflict of interest.\nReferences\n1. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. cell 144, 646–674 (2011).\n2. Ganem, N. J., Godinho, S. A. & Pellman, D. A mechanism linking extra centrosomes to chromosomal instability. Nature\n460, 278–282 (2009).\n3. Silkworth, W. T., Nardi, I. K., Scholl, L. M. & Cimini, D. Multipolar spindle pole coalescence is a major source of\nkinetochore mis-attachment and chromosome mis-segregation in cancer cells. PLoS One 7, e36501 (2012).\n4. 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Supplementary table - 1: Inhibitors used in this study\nCompound Target Vendor Catalog Number Concentration Range\nCB-839 GLS1 inhibitor MedChemExpress HY-12248 10-20 µM\nBPTES GLS1 inhibitor MedChemExpress HY-12683 10-20 µM\nBSO GCL inhibitor MedChemExpress HY-106376A 100-200 µM\nEGCG GLUD1/2 inhibitor MedChemExpress HY-13653 20-40 µM\nML385 NRF2 inhibitor MedChemExpress HY-100523 5-10 µM\nML334 Keap1-NRF2 disruptor MedChemExpress HY-110258 5-10 µM\nmeAIB SNAT1 inhibitor MedChemExpress HY-134452 5 mM\nFerrostatin-1 Ferroptosis inhibitor MedChemExpress HY-100579 2 µM\nLCS-1 SOD1 inhibitor MedChemExpress HY-115445 0.5 µM\nAuranofin TXNRD inhibitor MedChemExpress HY-B1123 0.5 µM\nPralatrexate DHFR inhibitor TargetMol T6120 1-2 nM\nAzaserine GFPT inhibitor MedChemExpress HY-B0919 5-10 µM\nFR054 PGM3 inhibitor TargetMol T9468 100-200 µM\n4-MU HA synthesis inhibitor TargetMol T1391 500-750 µM\nOSMI-1 OGT inhibitor MedChemExpress HY-119738 20 µM\nTunicamycin N-glycosylation inhibitor TargetMol T13229 0.25-1 µg/mL\nThapsigargin SERCA inhibitor TargetMol TQ0302 10 nM\nToyocamycin XBP1 splicing inhibitor TargetMol T17143 10 nM\nGSK2656157 PERK inhibitor TargetMol T2654 10 µM\nTUDCA Chemical chaperone TargetMol T2532 200-400 µM\n4-PBA Chemical chaperone TargetMol T5886 200-400 µM\n24/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nTable 2. Supplementary table - 2: Chemical compounds used in this study\nCompound Vendor Catalog Number Concentration Range\nDoxycycline MedChemExpress HY-N0565B 1 µg/mL\nL-Glutamine MedChemExpress HY-N0390 2-4 mM\nSodium pyruvate MedChemExpress HY-Y0810 1-2 mM\nL-glutamate MedChemExpress HY-W337739 2-4 mM\nOxaloacetate MedChemExpress HY-W010382 2-4 mM\nUDP-GlcNAc Disodium Salt TargetMol T19596 50-100 µM\nUDP-GalNAc MedChemExpress HY-114365 50-100 µM\nUDP-glucuronic acid trisodium TargetMol T19595 50-100 µM\nD-Glucosamine sulphate MedChemExpress HY-N0487 125-250 µM\nN-Acetyl-D-Glucosamine TargetMol T4514 50 µM\ni-inositol TargetMol T0421 50-100 µM\nD-Glucuronic acid sodium salt monohydrate TargetMol T5068 50-100 µM\nHyaluronic acid sodium (MW 20 kDa) TargetMol T88852 10-15 µM\nHyaluronic acid sodium (MW 40 kDa) TargetMol T88854 10-15 µM\nTable 3. Supplementary table - 3: guide-RNA sequences used in this study\nTarget gRNA Forward Sequence (5’→ 3’) Reverse Sequence (5’→ 3’)\nUGDH 1 caccGAAGTGGTAGAATCCTGTCG aaacCGACAGGATTCTACCACTTC\nUGDH 2 caccgAAGATCTGTTGCATCGGTGC aaacGCACCGATGCAACAGATCTTc\nUGDH 3 caccgTGCCAATAACGAGCTACTTC aaacGAAGTAGCTCGTTATTGGCAc\nCD44 1 caccgCTGTGCAGCAAACAACACAG aaacCTGTGTTGTTTGCTGCACAGc\nCD44 2 caccGCAATATGTGTCATACTGGG aaacCCCAGTATGACACATATTGC\nCD44 3 caccgCGTGGAATACACCTGCAAAG aaacCTTTGCAGGTGTATTCCACGc\n25/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint \n\nTable 4. Supplementary table - 4: RT-qPCR primer sequences used in this study\nTarget Forward Primer (5’→ 3’) Reverse Primer (5’→ 3’)\nCHST6 GACCCTCCCAGTGAAGAGAAAG CATGGGAGATGATTAGAGGTTCC\nCHST7 ACGTAGCCTCCCATCCCTGT TCTGAGAGTGTGACAGATTGCC\nDPAGT1 TGTCTTCAACCTGGTAGAGTTGG GCAAAGGTCATGCCAGCAAA\nGALNT16 TGTGCAACCCTAGAGAAGGC CAGGGCTACCGTCATGTG\nGCLC GTTCTCAAGTGGGGCGATGA TTGGCCTTTGTCCTTTCCCCC\nGCLM CAGACGGGGAACCTGCTG GCATGAGATACAGTGCATTCC\nGFPT1 CAGATTGCCCACCGAAGCTC CTCGTCTCGTTCGAGGAACA\nGFPT2 TCGAAACCCTCATCAAGGGC GAGAGCCTTGACTTTCCCCC\nGLUL GTCTGAGAAAGAGGAGAGGCG AGTGGGAACTTGCTGAGGTG\nGPX2 TGGCTTCCCTTGCAACCAAT ATGCTCGTTCTGCCCATTCA\nGSS TCGCGGAGGAAAGGCGA GGTCCTCAGCAATACTCCCT\nHAS2 CTCGCAACACGTAACGCAAT GGCTGGGTCAAGCATAGTGT\nMGST2 TATTCTCTCGGCCTGTCAGC TCCACACAGTTTTGTTGTGCC\nMTF1 TGAAGGTGCAACCCTCACTC CTCGGTGAGTCTTCTGGTGG\nNFE2L2 AACCAGTGGATCTGCCAACT AAGTGACTGAAACGTAGCCGA\nOGT GCAGCAGGACCAATTACCTCT CCCTTGGAAGGAAAGCATACG\nPFAS CAGTGCTGGCTGGCTTCG TAGACGGGACCTCCAACCTT\nPRDX1 GGTGCGGGAACCTGGTTGAA TGGCATAACAGCTGTGGCTTT\nPYCR2 AGCCAGCTCCCCAGAAATGA CTTCACCGTCTCCTTGTTGC\nSOD1 GTGAAGGTGTGGGGAAGCAT TTTGGCCCACCGTGTTTTCT\nSOD2 CGTTGGCCAAGGGAGATGT AGCAACTCCCCTTTGGGTT\nST3GAL2 CCTGGACCTTCTGTGGATCG GTGATGCTCTGTCCACCTGT\nST3GAL3 ACTCTAGCTCACCCCAGGAG GAGGAAGCCCAACCGATCAT\nTXNRD2 CAGCAGGTCAGCGGGA CCCACCGGGTGCCTTG\nUGDH TATGGAATGGGGAAAGGCCG ACGGATACTTTCTGCTGCCC\nUGT1A7 GTGGTCGTAGTCATGCCAGA ACTTCGCAATGGTGCCGTC\nUGT1A8 GCCCCATTCCCCTATGTGTTTC TTGCCAACTCACCTCTGGC\nSLC5A3 TGATGGTCTTGTGGAGAGTGG GAGCAACACAGCAGGGTCAA\nMIOX TCCGGAACTACACGTCAGGT GGGAAATCTACGTCCGGGTC\nISYNA1 CCAATCGACTGCGTTTGTCC GAGTCAGCGAGCCGTAGTAG\nIMPA1 TCATTGCCGCTGGATTCTGT TACGTGCCAAGGGATAAGGC\n26/26\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted January 26, 2026. ; https://doi.org/10.64898/2026.01.24.701523doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}