Metabolic clogging of mannose triggers dNTP loss and genomic instability in human cancer cells

  1. Yoichiro Harada  Is a corresponding author
  2. Yu Mizote
  3. Takehiro Suzuki
  4. Akiyoshi Hirayama
  5. Satsuki Ikeda
  6. Mikako Nishida
  7. Toru Hiratsuka
  8. Ayaka Ueda
  9. Yusuke Imagawa
  10. Kento Maeda
  11. Yuki Ohkawa
  12. Junko Murai
  13. Hudson H Freeze
  14. Eiji Miyoshi
  15. Shigeki Higashiyama
  16. Heiichiro Udono
  17. Naoshi Dohmae
  18. Hideaki Tahara
  19. Naoyuki Taniguchi
  1. Department of Glyco-Oncology and Medical Biochemistry, Research Institute, Osaka International Cancer Institute, Japan
  2. Department of Cancer Drug Discovery and Development, Research Institute, Osaka International Cancer Institute, Japan
  3. Biomolecular Characterization Unit, RIKEN Center for Sustainable Resource Science, Japan
  4. Institute for Advanced Biosciences, Keio University, Japan
  5. Systems Biology Program, Graduate School of Media and Governance, Keio University, Japan
  6. Department of Immunology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Japan
  7. Department of Oncogenesis and Growth Regulation, Research Institute, Osaka International Cancer Institute, Japan
  8. Department of Molecular Biochemistry and Clinical Investigation, Graduate School of Medicine, Osaka University, Japan
  9. Division of Cell Growth and Tumor Regulation, Proteo-Science Center, Ehime University, Japan
  10. Department of Biochemistry and Molecular Genetics, Graduate School of Medicine, Ehime University, Japan
  11. Human Genetics Program, Sanford Burnham Prebys Medical Discovery Institute, United States
  12. Project Division of Cancer Biomolecular Therapy, Institute of Medical Science, The University of Tokyo, Japan

Abstract

Mannose has anticancer activity that inhibits cell proliferation and enhances the efficacy of chemotherapy. How mannose exerts its anticancer activity, however, remains poorly understood. Here, using genetically engineered human cancer cells that permit the precise control of mannose metabolic flux, we demonstrate that the large influx of mannose exceeding its metabolic capacity induced metabolic remodeling, leading to the generation of slow-cycling cells with limited deoxyribonucleoside triphosphates (dNTPs). This metabolic remodeling impaired dormant origin firing required to rescue stalled forks by cisplatin, thus exacerbating replication stress. Importantly, pharmacological inhibition of de novo dNTP biosynthesis was sufficient to retard cell cycle progression, sensitize cells to cisplatin, and inhibit dormant origin firing, suggesting dNTP loss-induced genomic instability as a central mechanism for the anticancer activity of mannose.

Editor's evaluation

Mannose is toxic to honeybees and some cancer cells that lack sufficient capacity to metabolize this sugar. However, the precise metabolic consequences of impaired mannose metabolism require further understanding. In this important study, Harada et al. provide convincing evidence that an inability to metabolize mannose leads to impaired synthesis of deoxynucleotide triphosphates and DNA, which can be leveraged to sensitize cancer cells to chemotherapy.

https://doi.org/10.7554/eLife.83870.sa0

eLife digest

In order to grow and divide, cells require a variety of sugars. Breaking down sugars provides energy for cells to proliferate and allows them to make more complex molecules, such as DNA. Although this principle also applies to cancer cells, a specific sugar called mannose not only inhibits cancer cell division but also makes them more sensitive to chemotherapy. These anticancer effects of mannose are particularly strong in cells lacking a protein known as MPI, which breaks down mannose.

Evidence from honeybees suggests that a combination of mannose and low levels of MPI leads to a build-up of a modified form of mannose, called mannose-6-phosphate, within cells. As a result, pathways required to release energy from glucose become disrupted, proving lethal to these insects. However, it was not clear whether the same processes were responsible for the anticancer effects of mannose.

To investigate, Harada et al. removed the gene that encodes the MPI protein in two types of human cancer cells. The experiments showed that mannose treatment was not lethal to these cells but overall slowed the cell cycle – a fundamental process for cell growth and division. More detailed biochemical experiments showed that cancer cells with excess mannose-6-phosphate could not produce the molecules required to make DNA. This prevented them from doubling their DNA – a necessary step for cell division – and responding to stress caused by chemotherapy.

Harada et al. also noticed that cancer cells lacking MPI did not all react to mannose treatment in exactly the same way. Therefore, future work will address these diverse reactions, potentially providing an opportunity to use the mannose pathway to search for new cancer treatments.

Introduction

In mammals, mannose is a monosaccharide that is essential for life and is synthesized de novo from glucose through a glycolysis branch (Figure 1—figure supplement 1A). This process requires the action of mannose phosphate isomerase (MPI), the enzyme that catalyzes the interconversion between fructose-6-phosphate (Fruc-6-P) and mannose-6-phosphate (Man-6-P) (Alton et al., 1998). Man-6-P is further converted to GDP-mannose for the biosynthesis of asparagine-linked glycans (N-glycans) in the endoplasmic reticulum (Harada et al., 2013). In the secretory pathway, extensive trimming of N-glycans by mannosidases generates free mannose, which is secreted from cells and contributes to the extracellular pool of mannose (30–130 μM in blood) (Sharma and Freeze, 2011). The extracellular mannose can be taken up by cells and the salvaged mannose is directly converted to Man-6-P, the majority of which is efficiently directed into glycolysis by the action of MPI for unknown reasons (Ichikawa et al., 2014; Sharma and Freeze, 2011).

The large influx of mannose is known to suppress cell proliferation and enhance the efficacy of chemotherapy, particularly in cancer cells that express low levels of MPI (Gonzalez et al., 2018), although the underlying mechanisms remain poorly understood. It has been known for nearly a century that feeding mannose to honeybees, which are believed to express negligible amounts of MPI (Sols et al., 1960), is lethal to these insects (Staudenmayer, 1939). This is because the intracellular levels of Man-6-P exceed the capacity to metabolize it, and the excess Man-6-P inhibits glucose metabolism and decreases the intracellular ATP pool (DeRossi et al., 2006; Sols et al., 1960). This metabolic deficiency is called honeybee syndrome; however, it is unknown whether this syndrome plays a key role in the anticancer activity of mannose, and if so, what metabolic checkpoints are targeted by mannose to trigger its anticancer activity. Moreover, it is enigmatic how mannose sensitizes poorly proliferating cancer cells to chemotherapy that is designed to target actively proliferating cells. In this study, we addressed these two fundamental questions by using MPI-knockout (MPI-KO) human cancer cells as a model system for honeybee syndrome.

Results

Induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity

To establish MPI-KO human cancer cells using the CRISPR–Cas9 system, we exploited the mannose auxotrophy and sensitivity observed in MPI-KO mouse embryonic fibroblasts (MPI-KO MEFs) (DeRossi et al., 2006). The addition of a physiological concentration of mannose (50 μM, unchallenged) to culture medium supported the proliferation of MPI-KO MEFs (Figure 1—figure supplement 1B). In contrast, mannose starvation or the addition of a supraphysiological concentration of mannose (5 mM, challenged) suppressed the proliferation of MPI-KO MEFs, but not that of wild-type MEFs (Figure 1—figure supplement 1C). On these bases, we knocked out the MPI gene in human fibrosarcoma HT1080 cells and screened the gene-edited clones under mannose-unchallenged conditions. Three MPI-KO HT1080 clones were obtained (Figure 1A). In one clone (#1), cell division stopped under mannose-unchallenged conditions, but the other two clones (#2 and #3) could proliferate. These two clones exhibited mannose auxotrophy and sensitivity as expected (Figure 1B and Figure 1—figure supplement 2A and B), while the parental HT1080 cells showed marginal defects in cell proliferation at mannose concentrations higher than 15 mM (Figure 1C). The effects of mannose starvation and mannose challenge on the proliferation of MPI-KO cells were almost fully rescued by reintroduction of the human MPI gene (Figure 1D–F and Figure 1—figure supplement 2C–E), ruling out the potential off-target effects of gene editing. As expected, mannose challenge to MPI-KO HT1080 cells caused the dramatic accumulation of hexose-6-phosphate (the sum of glucose-6-phosphate and Man-6-P) compared with that under mannose-unchallenged conditions (Figure 1G). Consistent with the essential role of exogenous mannose in the production of Man-6-P and therefore that of GDP-mannose for N-glycan biosynthesis in MPI-KO MEFs (Harada et al., 2013), mannose starvation severely decreased N-glycosylation in MPI-KO HT1080 cells (Figure 1H). However, mannose challenge showed negligible effects on N-glycosylation, indicating that mannose challenge suppresses cell proliferation through a mechanism distinct from N-glycosylation defects.

Figure 1 with 3 supplements see all
The induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity.

(A) Western blot analysis of HT1080 (parent) and mannose phosphate isomerase knockout (MPI-KO) HT1080 (KO, clone #1–3) cells. The blots have been vertically flipped for presentation purpose. (B, C) The cell numbers of MPI-KO HT1080 (#3, B) and the parental HT1080 (C) cells after 48 hr incubation in culture medium supplemented with mannose at the indicated concentrations. A dashed line indicates the cell numbers at seeding. (D) Western blot analysis of MPI-KO HT1080 (#3) cells retrovirally transduced with empty vector (pMXs) or human MPI gene (pMXs-MPI). (E, F) The relative cell numbers of MPI-KO HT1080 (#3) cells retrovirally transduced with empty vector (pMXs, E) or human MPI gene (pMXs-MPI, F) after 48 hr incubation under mannose-starved (0 mM), mannose-unchallenged (50 μM), or mannose-challenged (5 mM) conditions. (G) The quantification of hexose-6-P in MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose. (H) Lectin blot and western blot analyses of MPI-KO HT1080 (#3) cells cultured as in (B). Con A, concanavalin A lectin. (I) Cell viability assay in MPI-KO HT1080 (#3) cells co-treated with mannose (50 μM or 5 mM) and DNA replication inhibitors (cisplatin or doxorubicin) for 24 hr (I, without preconditioning), or preconditioned with mannose (50 μM or 5 mM) for 24 hr, followed by incubation with the DNA replication inhibitors for an additional 24 hr in the presence of the same concentrations of mannose used for preconditioning (J, with preconditioning). Data represent the mean ± SD; n = 3 independent experiments. *p<0.05, **p<0.01, and ***p<0.001, NS, not significant, one-way ANOVA with post hoc Dunnett’s test (B, C, E, F) Welch’s t-test (G), or two-way ANOVA with post hoc Bonferroni’s test (I, J).

Mannose challenge increased the sensitivity of MPI-KO HT1080 cells to DNA replication inhibitors (i.e., cisplatin and doxorubicin) when the cells had been preconditioned with excess mannose prior to the drug treatment (Figure 1I and J and Figure 1—figure supplement 2F and G). We also generated MPI-KO HeLa cells as another cell model (Figure 1—figure supplement 3A–I) and found that mannose challenge also increased the sensitivity of these cells to cisplatin and doxorubicin (Figure 1—figure supplement 3J and K). All of these results demonstrate that the induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity in our MPI-KO human cancer cell models. We mostly used MPI-KO HT1080 (#3) cells for subsequent study, while the other cell models showed similar results.

Mannose challenge generates slow-cycling cells

Cell proliferation is tightly controlled by cell cycle progression. To explore the mechanism behind the antiproliferative activity of mannose, we compared the cell cycle progression between mannose-challenged and -unchallenged MPI-KO HT1080 cells by using a two-color fluorescent ubiquitination-based cell cycle indicator [Fucci(CA)] (Sakaue-Sawano et al., 2017). In this reporter system, G1 phase was defined by the exclusive expression of mCherry-hCdt1(1/100)Cy(−), which was rapidly turned off upon the onset of S phase where mVenus-hGem(1/110) gradually accumulated (Figure 2A and B and Video 1). The mCherry-hCdt1(1/100)Cy(−) was re-expressed upon the onset of G2 phase (the double-positive phase), followed by the termination of M phase where mVenus-hGem(1/110) was abruptly turned off. Under mannose-unchallenged conditions, MPI-KO HT1080 cells showed exponential growth (Figure 2C) and a typical Fucci(CA) signal profile (Figure 2B and D), as reported in HeLa cells (Sakaue-Sawano et al., 2017). In contrast, mannose challenge almost completely suppressed cell proliferation (Figure 2C) and significantly prolonged the cell cycle, showing a variety of atypical Fucci(CA) signal profiles (Figure 2E and F and Figure 2—figure supplement 1A). We classified these profiles based on the order of expression of Fucci(CA) reporters [mCherry-hCdt1(1/100)Cy(−) as R, mVenus-hGem(1/110) as G, and the double-negative phase as Dn, Figure 2—figure supplement 1A]. The classification analysis revealed that a small proportion of the mannose-challenged cells showed normal-like but strikingly extended Fucci(CA) signal profiles (Figure 2E and F and Figure 2—figure supplement 1A, normal-like, 12% of the total). Notably, these normal-like cell populations frequently failed to undergo cytokinesis in M phase and re-entered G1 phase without generating two equivalent daughter cells (Video 2). Other fractions included cells that were arrested in G1 phase (Figure 2E and F and Figure 2—figure supplement 1A, RRR, 26.7%), that showed little to no double-positive phase (i.e., the G2-to-M phase) (Figure 2E and F and Figure 2—figure supplement 1A, RGR, 15.8% and GRG, 9.9%, GDn, 5.9%, RGG, 5.0%, RGDn, 4.0%; 40.6% of total), or that remained in the double-negative phase (Figure 2F and Figure 2—figure supplement 1A, Dn, 8.9%), which is normally seen only at the G1/S transition (Sakaue-Sawano et al., 2017). These results suggest that mannose challenge severely impairs the entry of the cells into S phase and its progression to mitotic phase. Strikingly, however, switching of the mannose-challenge medium to the mannose-unchallenged medium after long-term mannose challenge (6 d) resulted in robust cell proliferation (Figure 2G) with cell cycle progression indistinguishable from that of mannose-unchallenged cells (Figure 2—figure supplement 2A–D), suggesting that some fraction of the mannose-challenged cells had entered a quiescent state upon mannose challenge. Supporting this assumption, mannose challenge induced early accumulation of the cell cycle inhibitors p21 and p27 (Abukhdeir and Park, 2008; Figure 2H). To directly measure DNA synthesis, mannose-challenged and -unchallenged cells were pulse-labeled with 5-bromo-2′-deoxyuridine (BrdU) and stained with Hoechst 33342 for DNA. BrdU was incorporated into DNA in both mannose-challenged and -unchallenged cells, but mannose challenge gradually decreased cell populations that were actively synthesizing DNA over the incubation time (Figure 2I and J). Small proportions of the mannose-challenged cells exhibited DNA content greater than 4n after 2-day mannose challenge, which also exhibited BrdU incorporation, suggesting that these cell populations underwent endoreplication (Edgar and Orr-Weaver, 2001). Collectively, these results indicate that mannose challenge suppresses cell proliferation through a complex mechanism involving extremely slow cell cycle progression.

Figure 2 with 2 supplements see all
Mannose challenge generates slow-cycling cells.

(A) Schematic representation of cell cycle progression (G1, S, G2, and M phases) visualized by Fucci(CA). Magenta, green, and yellow indicate the expression of mCherry-hCdt1(1/100)Cy(−), mVenus-hGem(1/110), and both, respectively. (B) A representative Fucci(CA) signal profile in mannose phosphate isomerase knockout (MPI-KO) HT1080 cells under the mannose-unchallenged conditions (50 μM). Fluorescent intensity (F.I.) for mCherry (left y-axis) and mVenus (right y-axis) is shown by magenta and green, respectively. Individual phases of the cell cycle (G1, S, G2, and M) are demarcated by dashed lines. (C) The number of MPI-KO HT1080 cells during the time-lapse imaging under mannose-unchallenged (50 μM, blue) and mannose-challenged (5 mM, orange) culture conditions. (D) The durations of M–G1 (the sum of both phases), S, and G2 phases (n=1109 cells) in MPI-KO HT1080 cells under mannose-unchallenged conditions (50 μM). (E) Representative Fucci(CA) signal profiles of MPI-KO HT1080 cells cultured under mannose-challenged conditions for 76 hr. The order of expression of mCherry-hCdt1(1/100)Cy(−) (denoted by R) and mVenus- hGem(1/110) (denoted by G) was used to classify the Fucci profiles and the classification is indicated in parentheses. See also Figure 2—figure supplement 1. (F) The proportion of Fucci(CA) signal profiles of the mannose-challenged MPI-KO HT1080 cells. R, mCherry-hCdt1(1/100)Cy(−) positive; G, mVenus-hGem(1/100) positive; Dn, double negative for Fucci indicators. One hundred cells were visually inspected for the classification and the cells that could not be classified were categorized as ‘not clear.’ See also Figure 2—figure supplement 1. (G) The number of MPI-KO HT1080 (#3) cells. The cells were cultured in the presence of 5 mM mannose for 6 d, and they were further cultured in the presence of 50 μM or 5 mM mannose for 5 d. A dashed line indicates the cell numbers at seeding. (H) Western blot analysis of MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. (I) Flow cytometry for BrdU and DNA content (Hoechst33342) in MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. The cells were labeled with 10 μM BrdU for 1 hr before harvest. The highly replicating cells (BrdUhigh) were annotated with black boxes. (J) The percentage of BrdUhigh cells in (I) (in the gated populations). Total numbers of single cells were set to 100%. Data represent the mean ± SD; n = 4 independent fields (C) and n = 3 independent experiments (G, J). ***p<0.001, one-way ANOVA with post hoc Tukey’s test (J).

Figure 2—source data 1

Original blot images depicting cropped regions for Figure 2H.

https://cdn.elifesciences.org/articles/83870/elife-83870-fig2-data1-v1.zip
Video 1
Time-lapse imaging of cell cycles in mannose phosphate isomerase knockout (MPI-KO) HT1080 cells.

MPI-KO HT1080 cells that expressed Fucci(CA) reporters were observed for 76 hr under the mannose-unchallenged (left) or the mannose-challenged (right) culture conditions. Red, mCherry-hCdt1(1/100)Cy(-); green, mVenus-hGem(1/110). Images were acquired every 15 min. Image size: 253.44 μm × 190.48 μm for each panel.

Video 2
Abnormal cytokinesis during M phase in the mannose-challenged mannose phosphate isomerase knockout (MPI-KO) HT1080 cells with normal-like Fucci(CA) signal profiles.

Time-lapse movies of three representative Fucci(CA)-expressing MPI-KO HT1080 cells that were cultured under the mannose challenge conditions. Red, mCherry-hCdt1(1/100)Cy(-); green, mVenus-hGem(1/110). Note that the cells in G2-M phase (yellow) show abnormal cytokinesis. Images were acquired every 15 min. Image size: 52.8 μm × 52.8 μm for each panel.

Mannose challenge limits DNA synthesis at ongoing replication forks

We adopted a proteomic approach to dissect the molecular mechanism by which mannose challenge generated slow-cycling cells in MPI-KO HT1080 cells. Of over 7000 proteins identified in the proteomic datasets (Source data 1), proteins that were significantly up- and downregulated relative to those in the cells cultured for 1 d under mannose-unchallenged conditions were extracted at each time point (Source data 2). Functional annotation analysis of the proteomic data using DAVID bioinformatic resources (https://david.ncifcrf.gov) revealed the downregulation of proteins related to the cell cycle and DNA replication in mannose-challenged cells (Figure 3A), which were enriched with the mini-chromosome maintenance 2-7 (MCM2-7) complex (Figure 3B). Western blot analysis and quantitative polymerase chain reaction (qPCR) confirmed the decrease in the expression levels of MCM2-7 proteins (Figure 3C) and their genes (Figure 3D) during mannose challenge over 6 d. The MCM2-7 complex is a core component of DNA helicase that unwinds the DNA duplex at replication forks in S phase (Jones et al., 2021; Rzechorzek et al., 2020), and the complex also plays a central role in the licensing of replication origins in G1 phase by forming a pre-replicative complex with the six-subunit origin recognition complex (ORC1-6), CDC6, and CDT1 on chromatin (Frigola et al., 2017; Remus et al., 2009; Zhai et al., 2017). CDC6 and CDT1, which were not detected in our proteomic analysis, were depleted after 2-day mannose challenge, whereas the ORC2 subunit was expressed at relatively constant levels (Figure 3C). These results indicate that mannose challenge induces slow proteomic alterations in origin licensing factors, while this cannot be an essential trigger for the generation of slow-cycling cells as these cells appeared immediately after mannose challenge.

Mannose challenge limits DNA synthesis at ongoing replication forks.

(A) Functional annotation analysis of the proteomic data in mannose phosphate isomerase knockout (MPI-KO) HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. (B) Heatmap representation of the relative amounts of MCM2-7 proteins in MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. (C) Western blot analysis of whole-cell lysates of MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. (D) Heatmap representation of the relative expression levels of MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, CDT1, and CDC6 genes in MPI-KO HT1080 (#3) cells cultured in the presence of 50 μM or 5 mM mannose for the indicated time. (E) Chromatin flow cytometry for MCM2 and DNA content (Hoechst33342) in MPI-KO HT1080 (#3) or MPI-KO HeLa (#2) cells cultured in the presence of 50 μM or 5 mM mannose for 24 hr. The binding of MCM2 to chromatins (origin licensing) is detected in G1 phase (step i) and it peaks at G1/S boundary (step ii). As S phase progresses, MCM2 is dissociated from chromatins (step iii). G1/S’ denotes the G1/S boundary of cell populations that underwent endoreplication.

We performed chromatin flow cytometry for MCM2 and DNA content to elucidate the impact of mannose challenge on the chromatin-bound states of the MCM complex at the cell cycle level. Under the mannose-unchallenged conditions, the binding of MCM2 to chromatin took place in G1 phase (Figure 3E, step i) and peaked at the G1/S boundary (Figure 3E, step ii) in MPI-KO HT1080 cells and MPI-KO HeLa cells, indicating that replication origins were fully licensed (Matson et al., 2017). The cells with the fully licensed origins entered S phase as a tight population, and MCM2 dissociated from chromatin as two replication forks converged and terminated (Figure 3E, step iii; Low et al., 2020). Mannose challenge did not severely impair origin licensing (Figure 3E, step i), but the same treatment caused the accumulation of MCM2-positive chromatin in S phase (Figure 3E, step iii). Notably, the mannose-challenged cells were not actively incorporating BrdU into DNA (Figure 2I and J and Figure 4—figure supplement 1A), suggesting that ongoing replication forks were stuck on chromatin with little DNA synthesis under the mannose-challenged conditions. Taking these findings together, the limited DNA synthesis at ongoing replication forks likely contributes to the abnormally extended progression of S phase in the mannose-challenged cells.

Mannose challenge disengages dormant origins from DNA synthesis during replication stress

Although our findings indicated that mannose challenge limits DNA synthesis at ongoing replication forks, its relevance to the increased chemosensitivity was still unclear. In humans, replication origins are licensed far more than actually used for DNA replication (Langley et al., 2016). The excess origins remain dormant under physiological conditions, while the dormant origins are activated for DNA replication when nearby replication forks stall upon encountering DNA lesions, thus preventing cells from the permanent replication arrest that leads to cell death (Blow et al., 2011; Ge et al., 2007; Kawabata et al., 2011; Shima et al., 2007). To test whether mannose challenge may also impair DNA synthesis from dormant origins during replication stress, we compared BrdU incorporation between the cells that were first pulsed with cisplatin to induce replication stress, followed by being left untreated or being treated with an inhibitor of the ataxia telangiectasia and Rad3-related protein (ATR) to forcibly activate dormant origins (Moiseeva and Bakkenist, 2019; Moiseeva et al., 2019; Figure 4A). In the mannose-unchallenged MPI-KO HT1080 cells (Figure 4B and C) and MPI-KO HeLa cells (Figure 4—figure supplement 1A and B), the cisplatin treatment alone partially suppressed the incorporation of BrdU, which was greatly recovered by ATR inhibition with VE-821 (ATRi) (Charrier et al., 2011; Prevo et al., 2012; Reaper et al., 2011), indicating that dormant origins are present in excess and their activation can engage in DNA synthesis during replication stress. In the mannose-challenged cells, however, the cisplatin treatment more severely reduced BrdU incorporation, which was barely restored by ATRi treatment (Figure 4B and C and Figure 4—figure supplement 1A and B). Forced activation of dormant origins during replication stress is known to cause the uncoupling of DNA unwinding and synthesis (Murai et al., 2018), leading to the accumulation of single-stranded DNA on chromatin marked by phosphorylation of the replication protein A2 (RPA2) (Figure 4D), confirming the activation of dormant origins by ATRi treatment in both mannose-challenged and -unchallenged cells. These results indicate that mannose challenge limits DNA synthesis from dormant origins during replication stress.

Figure 4 with 4 supplements see all
Mannose challenge disengages dormant origins from DNA synthesis during replication stress.

(A) Schematic representation of drug treatments. Man, 50 μM or 5 mM mannose; C, 100 μM cisplatin; W, wash; V, 1 μM VE-821; BrdU, 10 μM 5-bromo-2′-deoxyuridine. (B) Flow cytometry for BrdU and DNA content (Hoechst33342) in mannose phosphate isomerase knockout (MPI-KO) HT1080 (#3) cells treated as indicated in (A). The BrdU-positive cells were gated in black boxes for quantification of the geometric mean fluorescent intensity. (C) Relative geometric mean fluorescent intensity (gMFI) of BrdU in (B) (in the gated populations). The gMFI of BrdU in cells treated with 50 μM mannose in the absence of cisplatin (None) was set to 1.0. (D) Western blot analysis of whole-cell lysates and chromatin fractions of MPI-KO HT1080 (#3) cells treated as indicated in (A), except that BrdU labeling was omitted. (E, F) Chromatin flow cytometry for γH2AX, MCM2, and DNA content (Hoechst33342) in MPI-KO HT1080 (#3) cells treated as in (A), except that VE-821 treatment and BrdU labeling were omitted. The binding of MCM2 to chromatins (origin licensing) is detected in G1 phase (step i) and it peaks at G1/S boundary (step ii). As S phase progresses, MCM2 is dissociated from chromatins (step iii). G1/S’ denotes the G1/S boundary of cell populations that underwent endoreplication. Data represent the mean ± SD; n = 3 independent experiments. ***p<0.001, one-way ANOVA with post hoc Tukey’s test (C).

The replication initiation step requires chromatin loading of cell division cycle 45 (CDC45), one of the essential and limiting factors for replication initiation (Moyer et al., 2006), while this process was not severely impaired by mannose challenge (Figure 4D and Figure 4—figure supplement 2A and B). Under mannose-unchallenged conditions, cisplatin treatment induced the unloading of CDC45 from chromatin, which was restored by the forced activation of dormant origins (Figure 4D and Figure 4—figure supplement 2A and B). However, mannose challenge abrogated the CDC45 unloading/reloading dynamics under replication stress conditions (Figure 4D and Figure 4—figure supplement 2A and B).

The deficiency in the DNA synthesis from dormant origins and the CDC45 dynamics was associated with the increase in cisplatin-induced γH2AX (Figure 4D and E and Figure 4—figure supplement 2C), the phosphorylated form of the histone H2AX that marks DNA double-strand breaks (Kuo and Yang, 2008). Notably, mannose challenge did not enhance cisplatin-induced γH2AX in human cancer cell lines that express varying levels of MPI, although mannose challenge significantly suppressed cell proliferation in MPIlow cancer cells (Figure 4—figure supplement 3A–F) as reported previously (Gonzalez et al., 2018). To identify which phase(s) of the cell cycle is associated with γH2AX positivity (γH2AX+), we performed triple-staining chromatin flow cytometry for MCM2, γH2AX, and DNA content (Figure 4—figure supplement 4A). Total single cells were used to plot total chromatin-bound MCM2 against DNA content, while γH2AX+ populations were gated from single cells and used to plot the chromatin-bound MCM2 profiles against DNA content. These two plots were overlaid to visualize γH2AX+ cells in each phase of the cell cycle. The endogenous levels of γH2AX were detected in S phase of both mannose-challenged and -unchallenged MPI-KO HT1080 and MPI-KO HeLa cells (Figure 4F and Figure 4—figure supplement 4B), and cisplatin strongly induced γH2AX throughout S phase even in these mannose-challenged MPI-KO cancer cells (Figure 4F and Figure 4—figure supplement 4B). Together, these results indicate that mannose challenge disengages dormant origins from DNA synthesis during replication stress, thus exacerbating DNA damage in MPI-KO cancer cells.

Mannose challenge causes bioenergetic imbalance and ATP insufficiency

Although our data showed that mannose challenge generated slow-cycling cells that failed to engage dormant origins in DNA synthesis during replication stress, the underlying mechanism that functionally links these two phenotypes was still unclear. Since cell cycle progression and DNA replication are metabolically demanding processes (Zylstra and Heinemann, 2022), we hypothesized that the metabolic checkpoints activated by honeybee syndrome (DeRossi et al., 2006; Sols et al., 1960) may play a key role in the anticancer activity of mannose. To test this hypothesis, we first examined the impact of mannose challenge on the cellular bioenergetics in MPI-KO HT1080 cells by monitoring the real-time changes in glycolytic flux in the form of extracellular acidification rate (ECAR) and the activity of oxidative phosphorylation (OXPHOS) in the form of oxygen consumption rate (OCR). Mannose challenge caused a steep drop of ECAR with a faint increase in OCR (Figure 5A and B), resulting in a marked reduction in ATP production rates (Figure 5C). The remaining ECAR further decreased after oligomycin A treatment in mannose-challenged cells, while the same treatment increased ECAR in mannose-unchallenged cells (Figure 5A), indicating that mannose challenge ablates glycolytic capacity, which is required to buffer the defects in OXPHOS. Consistent with these bioenergetic estimations, mannose challenge decreased the ATP pool in MPI-KO HT1080 cells, MPI-KO HeLa cells, and MPI-KO MEFs (Figure 5D and E and Figure 5—figure supplement 1A and B), and the remaining pool was almost completely depleted by co-treatment with IACS-010759, a preclinical small-molecule inhibitor of complex I of the mitochondrial respiratory chain (Molina et al., 2018; Figure 5D and E and Figure 5—figure supplement 1A and B), which in turn increased necrosis (Figure 5F). These results indicate that mannose-challenged cells highly depend on OXPHOS for cellular bioenergetics.

Figure 5 with 2 supplements see all
Mannose challenge impairs bioenergetic balance and generates a distinct metabolic landscape.

(A, B) Extracellular acidification rates (ECAR, A) and oxygen consumption rates (OCR, B) in mannose phosphate isomerase knockout (MPI-KO) HT1080 (#3) cells. The cells were first cultured in 50 μM mannose, and then the medium alone or mannose (5 mM final, M) was added to the cells. They were incubated for 120 min and further treated with 1.5 μM oligomycin A (O) for 18 min, followed by 0.5 μM each of rotenone and antimycin A (R/A) for 18 min. (C) ATP production rates (APR) estimated from the data in (A, B). See also the ‘Materials and methods.’ (D) Relative ATP levels in MPI-KO HT1080 (#3) cells treated for 6 hr with or without 30 nM IACS-010759 (IACS) in the presence of 50 μM or 5 mM mannose. (E) Relative lactate dehydrogenase (LDH) levels in MPI-KO HT1080 (#3) cells treated as in (D). (F) LDH release from MPI-KO HT1080 (#3) cells treated for 24 hr with or without 30 nM IACS-010759 (IACS) in the presence of 50 μM or 5 mM mannose. (G–L) Metabolomic profiling of glycolysis (G), the pentose phosphate pathway (H), ribonucleotides (I–K), and deoxyribonucleoside triphosphates (L) in MPI-KO HT1080 (#3) cells cultured for 24 hr in the presence of 50 μM or 5 mM mannose. P, phosphate; PRPP, 5-phosphoribosyl-1-pyrophosphate. Data represent the mean ± SD; n = 3 independent experiments. *p<0.05, **p<0.01, and ***p<0.001, one-way ANOVA with post hoc Tukey’s test (D–F) or Welch’s t-test (G–L).

Mannose challenge generates a distinct metabolic landscape

To provide deeper insights into the metabolic landscape generated by honeybee syndrome, we compared the metabolome between mannose-challenged and -unchallenged MPI-KO HT1080 cells (Figure 5—figure supplement 2A, Source data 3). One-day mannose challenge greatly increased the pool of Fruc-6-P, while that of the three-carbon metabolites in the lower glycolysis chain, including lactate, were substantially decreased (Figure 5G), supporting our earlier findings that mannose challenge decreased ECAR to the basal level. Despite this glycolytic alteration, the steady state levels of metabolites in oxidative and non-oxidative arms of the pentose phosphate pathway (PPP) remained relatively unchanged in the mannose-challenged cells, except for the large accumulation of 6-phosphogluconoate and the slight decrease in sedoheptulose-7-phosphate (Figure 5H). In contrast, mannose challenge severely decreased tricarboxylic acid (TCA) cycle intermediates (isocitrate, 2-oxoglutarate, succinate, fumarate, and malate) that are used to generate NADH and FADH2 as electron donors for the mitochondrial respiratory chain (Figure 5—figure supplement 2B). Strikingly, the amounts of ribonucleoside diphosphates and triphosphates, but not ribonucleoside monophosphates, were proportionally and moderately decreased (Figure 5I–K), whereas deoxyribonucleoside triphosphates (dNTPs) were substantially decreased in the mannose-challenged cells (Figure 5L). Collectively, these results indicate that mannose challenge generates a distinct metabolic landscape in MPI-KO HT1080 cells, which ultimately lead to the depletion of dNTP pools.

Mannose challenge severely reduces the capacity for biosynthesizing nucleotides

dNTPs are the essential donor substrates for DNA synthesis, raising the possibility that the dNTP loss caused by mannose challenge may be a major mechanism linking the generation of slow-cycling cells and the failure to engage dormant origins in DNA synthesis during replication stress. To further elucidate the impacts of mannose challenge on the biosynthesis of dNTPs, we performed [13C6]-glucose tracer experiments that, unlike steady-state metabolomics, allow to estimate the activity of glucose-related metabolic pathways by analyzing the fractional enrichment of the metabolites in a dynamic labeling phase (Lorkiewicz et al., 2019). The MPI-KO HT1080 cells were preconditioned under mannose-challenged or -unchallenged conditions before [13C6]-glucose labeling. The 13C-labeling of most metabolites detected in glycolysis, the PPP, and the TCA cycle (Figure 6—figure supplement 1A) already reached isotopic steady states within 30 min in both mannose-challenged and -unchallenged cells (Figure 6—figure supplement 1B), most likely due to their high metabolic activity (Jang et al., 2018; Lorkiewicz et al., 2019). In contrast to these central metabolic pathways, metabolites in purine and pyrimidine metabolism showed a dynamic or sub-dynamic labeling in mannose-challenged cells. Phosphoribosyl pyrophosphate (PRPP) is an essential ribose donor substrate for the biosynthesis of nucleotides in both purine and pyrimidine metabolism (Figure 6A and B and Figure 6—figure supplement 2A). Fractional enrichment of 13C-PRPP (M+5) was saturated at 30 min in unchallenged cells, while it was still in a sub-dynamic labeling phase in mannose-challenged cells (Figure 6C). Despite this slower fractional enrichment, the pool size of 13C-PRPP (M+5) in mannose-challenged cells was similar to that in unchallenged cells (Figure 6D), implying that mannose challenge reduced the utilization of PRPP for nucleotide biosynthesis. Consistent with this assumption, we found a marked reduction in both the fractional enrichment and the pool size of 13C-purine metabolic intermediates (M+5) in mannose-challenged cells, which included inosine-5′-monophosphate (IMP; Figure 6E and F), adenosine-5′-monophosphate (AMP; Figure 6G and H), and guanosine-5′-monophosphate (GMP; Figure 6I and J).

Figure 6 with 4 supplements see all
Mannose challenge severely impairs purine metabolism.

(A) A schematic representation of [13C6]-glucose tracing in purine metabolism. Blue and white circles denote 13C and 12C, respectively. Phosphoribosyl pyrophosphate (PRPP) is produced from glucose 6-phosphate (G6P) through glycolysis and the pentose phosphate pathway, while serine (Ser), glycine (Gly), 5,10-methylenetetrahydrofolate (me-THF), and 10-fomyltetrahydrofolate (CHO-THF) are produced through the glycolysis-serine biosynthesis-folate cycle axis. In salvage pathway of purine metabolism, PRPP is conjugated with hypoxanthine (X), adenine (A), or guanine (G) to form inosine 5’-monophosphate (IMP), adenosine 5’-monophosphate (AMP), or guanosine 5’-monophosphate (GMP), respectively. In the de novo pathway, IMP is formed from PRPP via multiple steps and used to produce AMP and GMP. Possible labeling patterns with 12C and 13C were indicated. GAR, 5'-phosphoribosylglycinamide; FGAR, 5'-phosphoribosyl-N-formylglycinamide; AICAR, 5'-phosphoribosyl-5-amino-4-imidazolecarboxamide; FAICAR, 5'-phosphoribosyl-5-formamido-4-imidazolecarboxamide. (B) A molecular structure of IMP. Red, green, blue and orange circles indicate carbon atoms originating from PRPP, Gly, CHO-THF and CO2, respectively. (C−L) Fractional enrichment (fraction labeled; C, E, G, I, K) and pool size (D, F, H, J, L) of the indicated metabolites. The mannose phosphate isomerase knockout (MPI-KO) HT1080 (#3) cells were preconditioned in mannose-challenged (5 mM) or -unchallenged (50 μM) culture medium for 24 hr and then labeled with [13C6]-glucose for the indicated periods before harvest. The amounts of AMP were estimated as a mixture with 3’-AMP [(3’)-AMP] due to their insufficient separation. Data represent the mean ± SD; n = 3 independent experiments. *p<0.05, **p<0.01, ***p<0.001, Welch’s t-test.

PRPP is utilized for both de novo and salvage synthesis of purine nucleotides (Figure 6A), and therefore the M+5 fraction of 13C-IMP, 13C-AMP, and 13C-GMP can be accounted for the sum of their de novo and salvage pools. In contrast, purine nucleotides with the labeled fractions greater than M+5 can originate from de novo synthesis (Figure 6A). We found a progressive increase in the M+6 fraction of 13C-IMP, 13C-AMP, and 13C-GMP in unchallenged cells (Figure 6E, G and I), suggesting that 13C-10-formyl-tetrahydrofolate (CHO-THF; M+1), which is produced de novo via the glycolysis-serine biosynthesis-folate cycle (GSF) axis (Figure 6A; Yang and Vousden, 2016), contributed to the de novo synthesis of purine nucleotides. Although we could not directly detect 13C-labeling in serine and CHO-THF in our metabolomic analysis, the fractional enrichment and the pool size of 13C-glycine (M+2), which is a signature metabolite produced in coupled with 5,10-methylenetetrahydrofolate via the GSF axis, progressively increased in unchallenged cells (Figure 6K and L). However, the fractional enrichment and the pool size of 13C-glycine (M+2) largely decreased in mannose-challenged cells (Figure 6K and L), suggesting that the GSF axis is compromised in these cells. These results may partly explain why the de novo synthesis of purine nucleotides is limited in honeybee syndrome.

In the early stage of pyrimidine metabolism, aspartate (Asp) is transferred to carbamoyl phosphate, giving rise to N-carbamoyl aspartate (carbamoyl Asp) (Figure 6—figure supplement 2A). We found a progressive increase in the M+2, M+3, and M+4 fractions of both 13C-Asp (Figure 6—figure supplement 2B and C) and 13C-carbamoyl Asp (Figure 6—figure supplement 2D and E) at similar labeling rates in unchallenged cells. The 13C-Asp (M+2, M+3, and M+4) could originate from [13C6]-glucose-derived oxaloacetate that is formed in the first, second, and third rounds of the TCA cycle (Figure 6—figure supplement 2F), as indicated by the presence of the M+2, M+3, and M+4 fractions of 13C-malate (Figure 6—figure supplement 2G and H). However, mannose challenge severely decreased the fractional enrichment of both 13C-Asp (M+2, M+3, and M+4) and 13C-carbamoyl Asp (M+2, M+3, and M+4) (Figure 6—figure supplement 2B and D). In contrast, the pool size of 13C-Asp (M+2) remained relatively unchanged between mannose-challenged and -unchallenged cells (Figure 6—figure supplement 2C), while the pool size of 13C-carbamoyl Asp (M+2) greatly decreased in mannose-challenged cells (Figure 6—figure supplement 2E), indicating that mannose challenge reduced the utilization of Asp in pyrimidine metabolism. In the immediate downstream metabolites of carbamoyl Asp, we could detect significant amounts of uridine-5′-monophosphate (UMP) and found that a large majority of 13C-UMP formed in unchallenged cells was consisted of the M+5 fraction (Figure 6—figure supplement 2I and J). This fraction was most likely to originate from unlabeled carbamoyl Asp (Figure 6—figure supplement 2K) and 13C-PRPP (M+5). Moreover, we identified the M+6, M+7, and M+8 fractions of 13C-UMP in unchallenged cells (Figure 6—figure supplement 2I), indicating that both 13C-carbamoyl Asp (M+2, M+3, and M+4) and 13C-PRPP (M+5) contributed to forming 13C-orotidine-5′-monophosphate (M+7, M+8, and M+9), which is decarboxylated to give 13C-UMP (M+6, M+7, and M+8). However, mannose-challenged cells showed a substantial reduction of 13C-UMP in both the fractional enrichment (M+5, M+6, M+7, and M+8) and the pool size (M+5) (Figure 6—figure supplement 2I and J), clearly indicating that mannose challenge impaired the biosynthesis of pyrimidine nucleotides. As expected, dNTPs showed little 13C enrichment (dATP and dGTP, Figure 6—figure supplement 3A–E) and a very low 13C enrichment (dTTP and dCTP, Figure 6—figure supplement 3F–J) in mannose-challenged cells compared with those in unchallenged cells. Taking all these findings together, mannose challenge impairs both purine and pyrimidine metabolism at the early stage, thereby potentially limiting the de novo synthesis of dNTPs.

Pharmacological inhibition of de novo dNTP biosynthesis retards cell cycle progression, increases chemosensitivity, and inhibits DNA synthesis from dormant origins

To examine whether the loss of dNTPs plays a key role in the anticancer activity of mannose, we inhibited de novo synthesis of dNTPs with hydroxyurea (HU), a highly potent inhibitor of ribonucleotide reductase (RNR) (Elford, 1968). We found that the combination of HU and cisplatin more severely reduced cell viability than cisplatin treatment alone, independently of the presence or absence of the MPI gene (Figure 7A). This chemosensitizing effect of HU was associated with the strong induction of γH2AX (Figure 7B). HU is widely used as an agent to arrest cells in S phase by depleting dNTP (Davis et al., 2001). Profiling of the chromatin-bound MCM2 showed that HU treatment at a moderate concentration (0.25 mM) resulted in the arrest of cells in the early to late S phase, while a higher concentration of HU (1 mM) almost completely arrested the cells at the G1/S boundary (Figure 7C), partly recapitulating the inhibitory effects of mannose challenge on cell cycle progression. As with the case of mannose challenge, the forced activation of dormant origins by ATRi failed to increase BrdU uptake in the presence of HU (Figure 7D–G), indicating that the DNA synthesis from dormant origins is highly sensitive to the pool size of dNTPs. Unlike mannose challenge, however, HU treatment did not severely impair the CDC45 unloading/reloading dynamics during replication stress (Figure 7G), indicating that the insufficient dNTP pool is a major cause of the disengagement of dormant origins from DNA synthesis.

Pharmacological inhibition of de novo deoxyribonucleoside triphosphate (dNTP) biosynthesis retards cell cycle progression, increases chemosensitivity, and inhibits DNA synthesis from dormant origins.

(A) Cell viability assay in mannose phosphate isomerase knockout (MPI-KO) HT1080 (#3), the MPI-rescued cells (MPI-rescued), the parental HT1080, and mouse fibrosarcoma MCA205 cells that were preconditioned with hydroxyurea (HU) for 24 hr, followed by incubation with cisplatin for an additional 24 hr in the presence of the same concentrations of HU used for preconditioning. (B) Western blot analysis of whole-cell lysates from the MPI-rescued MPI-KO HT1080 (#3) cells preconditioned with 0.25 mM HU for 24 hr and pulsed with 100 μM cisplatin for 1 hr, followed by a 5 hr chase. Numbers indicate the relative amounts of γH2AX (the average of two independent experiments each from the MPI-rescued MPI-KO HT1080 cells and the parental HT1080 cells). (C) Chromatin flow cytometry for MCM2 and DNA content (Hoechst33342) in the MPI-rescued MPI-KO HT1080 (#3) cells treated with or without HU for 24 hr at the indicated concentrations. The binding of MCM2 to chromatin is detected in G1 phase (step i) and it peaks at G1/S boundary (step ii). As S phase progresses, MCM2 is dissociated from chromatin (step iii). (D) Schematic representation of drug treatment. HU, 0.25 mM hydroxyurea; C, 100 μM cisplatin; W, wash; V, 1 μM VE-821; 10 μM BrdU, 5-bromo-2′-deoxyuridine. (E) Flow cytometry for BrdU and DNA content (Hoechst33342) in the MPI-rescued MPI-KO HT1080 (#3) cells that were treated as indicated in (D). The BrdU-positive cells were gated in black boxes for quantification of the geometric mean fluorescent intensity. (F) Relative geometric mean fluorescent intensity (gMFI) of BrdU in (E) (in the gated populations). The gMFI of BrdU in cells treated without HU (None) was set to 1.0. (G) Western blot analysis of whole-cell lysates and chromatin fractions of the MPI-rescued MPI-KO HT1080 (#3) cells treated as indicated in (D), except that BrdU labeling was omitted. Numbers indicate the relative amounts of chromatin-bound CDC45 (the average of three independent experiments). Data represent the mean ± SD; n = 3 independent experiments. *p<0.05, **p<0.01, and ***p<0.001, two-way ANOVA with post hoc Bonferroni’s test (A) or one-way ANOVA (F) with post hoc Tukey’s test.

Discussion

MPI is the sole enzyme that catalyzes the interconversion between Fruc-6-P and Man-6-P in mammals. The conversion of Fruc-6-P to Man-6-P mediated by MPI is central to the synthesis of GDP-mannose from abundant glucose for normal glycosylation. In contrast, the conversion of Man-6-P to Fruc-6-P mediated by the same enzyme is critical to directing the excess Man-6-P to glycolysis, while the cellular function of this seemingly wasteful metabolic pathway has long remained unknown. In this study, we employed MPI-KO human cancer cells to explore the key mechanism behind the anticancer activity of mannose and demonstrated that the large influx of mannose exceeding the capacity to metabolize it, that is, the onset of honeybee syndrome, induced dramatic metabolic remodeling that led to ATP insufficiency and dNTP loss (Figure 8). These cells were cycling extremely slowly, and upon encountering replication stress, they were unable to rescue stalled forks via dormant origins, thus exacerbating replication stress (Figure 8). These findings shed light on the conversion of Man-6-P to Fruc-6-P mediated by MPI as a genome guard through the maintenance of metabolic integrity, which could be threatened by dietary mannose intake.

Proposed model for the mechanism underlying anticancer activity of mannose in mannose phosphate isomerase knockout (MPI-KO) human cancer cells.

A large influx of mannose into MPI-KO human cancer cells induces metabolic remodeling, leading to the loss of deoxyribonucleoside triphosphates (dNTPs). This metabolic deficiency impairs DNA replication at ongoing replication forks and slows cell cycle progression in the absence of cisplatin (-Cisplatin). On the other hand, mannose-caused dNTP loss disengages dormant origins from DNA replication in the presence of cisplatin, thereby exacerbating replication stress (+Cisplatin).

The induction of ATP insufficiency by honeybee syndrome coincided with that of abnormal cell cycle progression, indicating the critical role of normal mannose catabolism in balancing bioenergetic states and cell cycle progression. This finding is supported by the accumulating evidence indicating that excess mannose inhibits glycolysis and retards cell proliferation in MPI-KO MEFs (DeRossi et al., 2006) and MPIlow cancer cells (Gonzalez et al., 2018), and that the temporal regulation of glycolysis and OXPHOS drives the G1/S transition and chromosome segregation (Icard et al., 2019; Salazar-Roa and Malumbres, 2017). Moreover, the fact that HU treatment inhibits the progression of replication forks suggests that dNTP loss in honeybee syndrome impairs S phase progression. Taking these findings together, the massive changes in crosstalk between metabolic networks and cell cycle machinery in honeybee syndrome potentially contribute to cell cycle dysregulation, which may further complicate metabolic states (Fajas, 2013).

An examination of human cancer cell lines that express varying levels of MPI clearly indicated that the antiproliferative effect of mannose is largely dependent on the expression levels of MPI, while mannose-driven genomic instability is a unique phenotype observed in the absence of MPI. For this reason, the potential clinical use of mannose to induce genomic instability in cancer cells is currently unlikely. Here, we provide experimental evidence showing that pharmacological inhibition of de novo dNTP synthesis, which is critical for the recovery from replication stress (Håkansson et al., 2006; Tanaka et al., 2000), partly mimics the anticancer activity of mannose and chemosensitizes cells to cisplatin independently of the MPI expression and mannose dosing. Our finding that DNA synthesis from dormant origins during replication stress is highly sensitive to the dNTP pool size is in good agreement with the therapeutic advantages of RNR inhibition in enhancing the efficacy of radiochemotherapy (Kunos and Ivy, 2018). These findings strongly suggest dNTP loss as an important mechanism underlying the chemosensitizing effect of mannose in MPI-KO cancer cells.

What causes the loss of dNTPs in honeybee syndrome? The pool size of dNTPs is tightly regulated in order to meet and not to exceed cellular demands by cell cycle-dependent and DNA damage-dependent expression of enzymes involved in the biosynthesis and degradation of dNTPs (Franzolin et al., 2013), as well as their allosteric regulation (Aye et al., 2015; Ji et al., 2013). This tight regulation of dNTP supply is vital to prevent cells from suffering the dNTP pool imbalance, which causes genomic instability (Kumar et al., 2010; Pajalunga et al., 2017). Our proteomic analysis revealed that mannose challenge rapidly increases the expression of RRM2B (Source data 1), which is a rate-limiting and TP53-inducible subunit of RNR (Håkansson et al., 2006; Tanaka et al., 2000), indicating that the equilibrium of dNTP metabolism shifted toward biosynthesis at the protein level. However, mannose challenge caused substantial reductions in the pool of ATP, which is a key allosteric regulator for the catalysis of RNR to occur (Brignole et al., 2018). Moreover, a recent in-depth metabolomic study revealed that a high concentration of mannose impairs glucose metabolism in MPIlow cancer cells (Gonzalez et al., 2018), while its impact on the downstream nucleotide metabolism was not clear. Our [13C6]-glucose tracer experiments clearly indicated that mannose challenge severely impairs the early stages of de novo synthesis of purine and pyrimidine nucleotides. Fueling the salvage pathway of purine and pyrimidine metabolism with hypoxanthine and thymidine (Figure 6—figure supplement 4A) or with deoxyribonucleoside mixture (Figure 6—figure supplement 4B) was not sufficient to improve the defects in proliferation of mannose-challenged MPI-KO HT1080 cells. Thus, the inadequate de novo synthesis of nucleotides in honeybee syndrome may limit dNTP pools in this cell model. Notably, although dNTPs are essential for DNA repair, the chemosensitizing effect of mannose was rather modest when comparing it with that of deficiency in genes for DNA repair processes (e.g., BRCA2, FANCA, and FANCD2) (Bruno et al., 2017; Sakai et al., 2008). This apparent discrepancy most likely arises from the fact that the suppression of de novo nucleotide biosynthesis in honeybee syndrome is incomplete. Taken together, our findings indicate that the insufficient metabolic activity of purine and pyrimidine metabolism in honeybee syndrome causes the loss of dNTPs.

Our proteomic analysis revealed the functional pathways affected by honeybee syndrome. The downregulation of MCM2-7 proteins in mannose-challenged cells supports our hypothesis that honeybee syndrome impairs dormant origins and causes genomic instability (Ibarra et al., 2008; Kawabata et al., 2011; Shima et al., 2007). Mannose challenge also decreased the expression of several large ribosomal subunit proteins. Ribosome biogenesis stress has been identified as a major mechanism by which oxaliplatin kills cancer cells in a DNA damage response-independent manner (Bruno et al., 2017). Moreover, mannose challenge upregulated proteins involved in ferroptosis and necroptosis. These two programmed cell death mechanisms are considered as an effective approach to overcome chemoresistance (Zhang et al., 2022) or to eradicate apoptosis-resistant cancer cells (Su et al., 2016). In addition to metabolic impacts, honeybee syndrome may also enhance cancer cell vulnerability at the proteomic level.

In conclusion, we demonstrate that honeybee syndrome triggers dNTP loss and genomic instability in MPI-KO human cancer cells. Ensuring appropriate amounts of dNTPs is essential for normal DNA replication, as well as for the recovery from replication stress, thus providing a plausible mechanism for how mannose sensitizes poorly proliferating cancer cells to chemotherapy. Elucidation of the precise molecular mechanism underlying dNTP loss in honeybee syndrome will be necessary to unambiguously identify the direct cause(s) of the chemosensitizing effects of mannose. Our findings also pointed to there being significant phenotypic diversity in honeybee syndrome. More extensive biochemical characterization of the metabolomic and proteomic alterations in this syndrome will be needed to further dissect the impact of mannose metabolic alterations in cancer cell homeostasis, which may provide an opportunity for mannose-based drug discovery and development for cancer therapy.

Materials and methods

Experimental models

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The human fibrosarcoma cell line HT1080 used in this study was our laboratory stock and validated by short tandem repeat profiling (Promega). The human cervix adenocarcinoma cell line HeLa and human pancreatic ductal cell carcinoma cell line KP-4 were obtained from RIKEN BioResource Research Center. The human non-small cell lung carcinoma cell line A549 and human ovarian adenocarcinoma cell line SK-OV-3 were obtained from American Type Culture Collection. The mouse fibrosarcoma cell line MCA205 was a kind gift from Dr. S. A. Rosenberg (National Cancer Institute, Bethesda, MD). These cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM; 045-30285, FUJIFILM Wako) supplemented with 4 mM glutamine (073-05391, FUJIFILM Wako) and 10% fetal bovine serum (FBS) (complete DMEM) at 37°C in a 5% CO2 atmosphere. Immortalized wild-type MEFs and MPI-KO MEFs were prepared in a previous study (DeRossi et al., 2006). MPI-KO HT1080 cells and MPI-KO HeLa cells were established in this study (see below). All MPI-KO cell lines were maintained in complete DMEM supplemented with mannose (Man; 130–00872, FUJIFILM Wako) at a concentration of 20 μM at 37°C in a 5% (for MPI-KO HT1080 cells and MPI-KO HeLa cells) and 10% (for MEFs) CO2 atmosphere. Trypan blue dye (0.4%, w/v, FUJIFILM Wako) was mixed with equal volumes of cell suspension for cell counting. All cell lines were routinely tested for Mycoplasma and confirmed to have no contamination using TaKaRa PCR Mycoplasma Detection Set (6601, Takara).

Cell treatments

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Cells were treated with 0–100 μM cisplatin (D3371, Tokyo Chemical Industry), 0–10 μM doxorubicin (040-21521, FUJIFILM Wako), 30 nM IACS-010759 (S8731, Selleckchem), 1 μM VE-821 (SML1415, Sigma-Aldrich), 1× HT supplement (11067030, Thermo Fisher Scientific), the deoxyribonucleoside mix (deoxyadenosine, thymidine, deoxyguanosine, and deoxycytidine; 10 μM each), or 0–1 mM HU (085-06653, FUJIFILM Wako) for the indicated time. As a negative control, cells were treated with vehicle alone.

Gene editing

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MPI gene was knocked out by CRISPR–Cas9 gene editing using the Edit-R system (Dharmacon), in accordance with the manufacturer’s instructions. Briefly, HT1080 cells or HeLa cells were seeded and cultured for 24 hr prior to transfection. Synthetic CRISPR RNAs (CM-011729-01 and CM-011729-03, Dharmacon; 10 μM, 2 μL each) targeting the MPI gene were mixed with 4 μL of 10 μM trans-activating CRISPR RNA (tracrRNA; U-002005-05, Dharmacon) and transfected with 1 μg of Edit-R SMART Cas9_mCMV_(PuroR) expression plasmid (U-005200-120, Dharmacon) using 5 μL of Lipofectamine 2000 (11668027, Thermo Fisher Scientific) in 400 μL of Opti-MEM (31985062, Thermo Fisher Scientific). Transfectants were selected in complete medium supplemented with 50 μM Man and 1 μg/mL puromycin, and then cloned by limiting dilution. For HT1080 cells, MPI-KO clones were screened by polymerase chain reaction (PCR) using genomic DNA as a template and two primer sets (Supplementary file 1) for amplifying the wild-type allele and for the KO allele of the MPI locus. The PCR products for the KO allele were subjected to Sanger sequencing. For HeLa cells, MPI-KO clones were screened by mannose auxotrophy and sensitivity, as well as western blotting using anti-MPI antibody (see below).

Preparation of cDNA

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Total RNA was prepared using TRIzol Reagent (15596026, Thermo Fisher Scientific) or RNeasy Mini Kit (74106, QIAGEN), in accordance with the manufacturer’s instructions. cDNA was prepared by reverse transcription using total RNA (4 μg), oligo dT primers, and SuperScript IV Reverse Transcriptase (18090010, Thermo Fisher Scientific), in accordance with the manufacturer’s instructions.

Preparation of plasmids

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The plasmids used in this study are listed in Supplementary file 2. The coding sequence of the human MPI gene was amplified by PCR using Phusion High-Fidelity DNA polymerase (M0530, New England BioLabs), a primer set (Supplementary file 1), and cDNA from HuH-7 cells as a template. The PCR products were purified from agarose gels using NucleoSpin Gel and PCR Clean-Up Kit (740609, Clontech) and subcloned into the pENTR/D-TOPO vector (K240020, Thermo Fisher Scientific), in accordance with the manufacturer’s instructions. The yielded plasmid (pENTR-hMPI) was used as a template to amplify the MPI gene by PCR using a primer set (Supplementary file 1) and the PCR products were cloned into pMXs-Neo retroviral expression vector (RTV-011, Cell Biolabs) to yield pMXs-Neo-hMPI by using In-Fusion HD Cloning Kit (639648, Clontech), in accordance with the manufacturer’s instructions. mCherry-hCdt1(1/100)Cy(−)/pcDNA3 was purchased from RIKEN BioResource Research Center. The coding sequence of mCherry-hCdt1(1/100)Cy(−) was amplified by PCR using a primer set (Supplementary file 1) and cloned into pMXs-Neo to yield pMXs-Neo-mCherry-hCdt1(1/100)Cy(−) using the In-Fusion HD Cloning Kit. pMMLV-mVenus-hGem(1/110):IRES:Bsd was constructed and purchased from Vector Builder.

Retroviral packaging and transduction

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The Plat-A retroviral packaging cell line (1 × 106 cells, Cell Biolabs) was seeded on collagen I-coated six-well plates (4810–-010, IWAKI) and cultured in complete medium for 24 hr. After the culture medium had been replaced with 1 mL of fresh complete medium, 1 mL of Opti-MEM containing 3 μg of plasmids (Supplementary file 2), 9 μL of Lipofectamine 3000, and 6 μL of P3000 reagent (L3000015, Thermo Fisher Scientific) was added to the cells, followed by culture for 48 hr. The culture medium was then passed through a 0.80 μm syringe filter (SLAA033SS, Merck Millipore). The filtrate, which contained retroviral particles, was mixed with hexadimethrine bromide (8 μg/mL; 17736-44, Nacalai Tesque) and Man (20 μM), and the retroviral solution (1 mL) was added to MPI-KO HT1080 cells or MPI-KO HeLa cells (5 × 104 cells/six-well plates) that were cultured for 24 hr in complete medium supplemented with 20 μM Man. The transduced cells were selected for 14 d in complete medium supplemented with [for pMXs-Neo, pMXs-Neo-mCherry-hCdt1(1/100)Cy(−), and pMMLV-mVenus-hGem(1/110):IRES:Bsd] or without (pMXs-Neo-hMPI) 20 μM Man, and 600 μg/mL G418 [09380-44, Nacalai Tesque; for pMXs-Neo, pMXs-Neo-hMPI, and pMXs-Neo-mCherry-hCdt1(1/100)Cy(−)] or 13 μg/mL blasticidin S [A1113903, Thermo Fisher Scientific; for pMMLV-mVenus-hGem(1/110):IRES:Bsd].

Real-time polymerase chain reaction

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Real-time polymerase chain reaction (PCR) was performed with the 7500 Real-Time PCR System (Applied Biosystems). cDNA (0.5 μL) was mixed in 20 μL of a reaction mixture that contained THUNDERBIRD Next SYBR qPCR Mix (10 μL, QPX-201, TOYOBO) and a primer set (0.06 μL each, Supplementary file 1). cDNA was amplified by an initial denaturation step at 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 55°C for 35 s. The samples were analyzed in duplicate, and the mean number of cycles required to reach the threshold level of fluorescent detection was calculated for each sample. ACTB expression was used to normalize the amounts of cDNA in each sample.

Preparation of whole-cell lysates

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Cells were rinsed once with PBS, scraped in the same buffer, and pelleted by centrifugation at 200 × g for 5 min at 4°C. The wet cell weight was measured after the supernatant had been completely removed. The cell pellet was resuspended at a concentration of 10 mg wet cells/100 μL of PBS. The cell suspension was mixed with an equal volume of 2× Laemmli sample buffer containing 10% β-mercaptoethanol, 1× cOmplete Protease Inhibitor Cocktail (11836170001, Sigma-Aldrich), and 1× PhosSTOP (4906845001, Sigma-Aldrich), and homogenized by two cycles of 10 s sonication at 0°C using a probe-type sonicator (VP-5S, TAITEC). The cell homogenates were denatured by heating at 100°C for 3 min and stored at −80°C until use.

Chromatin extraction

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Cells were washed once with PBS, scraped in the same buffer, and pelleted by centrifugation at 200 × g for 5 min at 4°C. The wet cell weight was measured after the supernatant had been completely removed. The cell pellet was extracted by incubating for 5 min at 0°C at a concentration of 10 mg wet cells/100 μL of CSK buffer (10 mM HEPES-KOH, pH 7.4, 340 mM sucrose, 10% [v/v] glycerol, 10 mM KCl, 1.5 mM MgCl2, 0.1% [v/v] Triton X-100, 1 mM ATP, 1 mM DTT, 1× cOmplete Protease Inhibitor Cocktail, and 1× PhosSTOP). The homogenate was centrifuged at 1390 × g for 5 min at 4°C and the pellet was resuspended in 100 μL of CSK buffer. The homogenate was centrifuged again at 1390 × g for 5 min at 4°C. The pellet was resuspended in 100 μL of CSK buffer and mixed with 100 μL of 2× Laemmli sample buffer containing 10% β-mercaptoethanol. The samples were homogenized by two cycles of 10 s sonication at 0°C using a probe-type sonicator (VP-5S, TAITEC) and the homogenates were denatured by heating at 100°C for 3 min.

Western and lectin blotting

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Whole-cell lysates or chromatin fractions (250 μg of wet cells) were separated by sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) and analyzed by lectin blotting using biotinylated concanavalin A (Con A) lectin (1:10,000, J203, Cosmo Bio) and VECTASTAIN ABC-HRP kit (PK-4000, Vector Laboratories) or by western blotting using primary antibodies and secondary antibodies. The primary antibodies were as follows: anti-MPI (1:5000, GTX103682, GeneTex), anti-β-actin (1:10,000, 010-27841, FUJIFILM Wako), anti-H2AX (1:2000, 938CT5.1.1, Santa Cruz), anti-phospho-H2AX (Ser139) (γH2AX; 1:5000, JBW301, Millipore), anti-RPA2 (1:5000, 9H8, Santa Cruz), anti-Phospho-RPA2 (S33) (1:10,000, A300-246A, Bethyl), anti-MCM2 (1:10,000, D7G11, Cell Signaling Technology), anti-MCM3 (1:10,000, E-8, Santa Cruz), anti-MCM4 (1:5000, GTX109740, GeneTex), anti-MCM5 (1:5000, GTX33310, GeneTex), anti-MCM6 (1:20,000, H-8, Santa Cruz), anti-MCM7 (1:5000, 141.2, Santa Cruz), anti-CDT1 (1:10,000, ab202067, Abcam), anti-CDC6 (1:1000, ab109315, Abcam), anti-ORC2 (1:10,000, 3G6, Santa Cruz), and anti-CDC45 (1:1000, D7G6, Cell Signaling Technology). The secondary antibodies were as follows: horseradish peroxidase (HRP)-conjugated goat anti-mouse immunoglobulin (IgG) (1:10,000, 7076, Cell Signaling Technology), HRP-conjugated goat anti-rabbit IgG (1:10,000, 7074, Cell Signaling Technology), and HRP-conjugated goat anti-rat IgG (1:5000, 7077, Cell Signaling Technology).

Cell cycle analysis

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Cells were labeled with 10 μM BrdU (B1575, Tokyo Chemical Industry) for 1 hr before harvest. Trypsinized cells (1 × 106 cells) were washed two times with ice-cold PBS and fixed in 1 mL of 70% ethanol for no less than 16 hr at 4°C. After washing two times with PBS, fixed cells were denatured by incubating in 500 μL of 2.0 M HCl for 30 min at 25°C. Denatured cells were washed two times with PBS and once with 1 mL of sodium borate buffer, pH 8.5. The cells were permeabilized in 500 μL of PBS containing 0.1% Triton X-100 (PBSTx) for 10 min at 25°C. The permeabilized cells were incubated for 1 hr at 25°C in 500 μL of PBS containing 1% bovine serum albumin (BSA), Alexa Fluor 488-conjugated anti-BrdU antibody (1:100, 3D4, BioLegend), and Cellstain Hoechst 33342 solution (1:100, H342, Dojindo). The cells were then washed once with 1 mL of PBS containing 1% BSA (PBS/BSA) and resuspended in 500 μL of the same buffer. The stained samples were analyzed by BD LSRFortessa X-20 and FlowJo v10.6 (Becton Dickinson).

Chromatin flow cytometry

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Trypsinized cells (1 × 106 cells) were extracted by incubating for 5 min at 0°C in 500 μL of CSK buffer. The cells were washed by adding 1 mL of ice-cold PBS/BSA, pelleted by centrifugation at 1390 × g for 5 min at 4°C, and fixed for 15 min at 25°C in 500 μL of 4% paraformaldehyde (161-20141, FUJIFILM Wako). After quenching the reaction by adding 1 mL of PBS/BSA, the cells were pelleted by centrifugation at 2000 × g for 7 min at 4°C and washed again with 1 mL of PBS/BSA. The washed cells were then resuspended in 200 μL of PBS containing 1% BSA and 0.1% Triton X-100 (PBSTx/BSA) containing primary antibodies (MCM2 [1:200, D7G11, Cell Signaling Technology] and γH2AX [1:200, JBW301, Millipore]) and incubated for 1 hr at 25°C. After washing the cells once in 1 mL of PBSTx/BSA, they were incubated for 1 hr at 25°C in the dark with 200 μL of PBSTx/BSA containing secondary antibodies (R37118, donkey anti-rabbit IgG-Alexa Fluor 488 [1:1,000, Thermo Fisher Scientific]; A-31571, donkey anti-mouse IgG-Alexa Fluor 647 [1:1000, Thermo Fisher Scientific]) and Cellstain Hoechst 33342 solution (1:200). The cells were washed once in 1 mL of PBSTx/BSA, resuspended in 500 μL of the same buffer, and subjected to flow cytometry.

Cell viability (ATP) assay

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Cells (1 × 103 cells/96-well plate) were seeded and cultured for 24 hr in 100 μL of complete medium supplemented with 20 μM Man. For co-treatment assay, the cells were incubated with drugs or vehicle alone for 24 hr in complete medium supplemented with 50 μM or 5 mM Man. For a preconditioning assay, cells were incubated for 24 hr in complete medium supplemented with 50 μM or 5 mM Man, and the preconditioned cells were further incubated with drugs or vehicle alone for 24 hr. After the drug treatment, CellTiter-Glo 2.0 (100 μL/well, G924B, Promega) was added to the cells and incubated for 10 min at 25°C before measuring luminescence with an integration time of 1000 ms using an Infinite 200 Pro microplate reader (Tecan).

LDH assay

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For Figure 5E, cells (1 × 103 cells/96-well plate) that were cultured for 24 hr in 100 μL of complete medium supplemented with 20 μM Man were treated with 30 nM IACS-010759 or dimethylsulfoxide (DMSO) alone for an additional 6 hr in complete medium supplemented with unchallenged or challenged concentrations of Man. Total LDH activity was measured using Cytotoxicity LDH Assay Kit-WST (CK12, Dojindo), in accordance with the manufacturer’s instructions. For Figure 5F (necrosis assay), cells (3.3 × 104 cells/24-well plate) that were cultured for 24 hr in 500 μL of complete medium supplemented with 20 μM Man were treated with 30 nM IACS-010759 or DMSO alone for an additional 24 hr in 500 μL of complete medium supplemented with unchallenged or challenged concentrations of Man. After centrifugation at 250 × g for 5 min at 4°C, the culture supernatant (50 μL, extracellular LDH) was transferred to a 96-well plate. The cells were solubilized by adding 20 μL of 10% (w/v) Tween 20 for 30 min at 37°C with gentle agitation. The homogenates (50 μL, total LDH) were transferred to the same 96-well plate prepared as above. LDH activity was measured using Cytotoxicity LDH Assay Kit-WST, in accordance with the manufacturer’s instructions.

Seahorse real-time cell metabolic analysis

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The ATP rate assay was performed using an XFe96 Extracellular Flux analyzer, in accordance with the manufacturer’s instructions (Seahorse Bioscience). MPI-KO HT1080 cells (#3, 1 × 104 cells/96-well assay plate) were seeded and incubated for 18 hr in complete DMEM. Prior to the assay, the cells were preincubated for 60 min at 37°C in XF DMEM medium containing 10% FBS, 10 mM glucose, 4 mM L-glutamine, and 50 μM Man. The cells were then treated with 5 mM Man or medium alone for 120 min, followed by incubation with 1.5 μM oligomycin for 18 min and then 0.5 μM each of rotenone and antimycin A for 18 min. Data analysis was performed using the XF Real-Time ATP Rate Assay Report Generator (Seahorse Bioscience).

Label-free proteomics

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Cells (5 × 105 cells/10 cm dish) were cultured for 24 hr in 10 mL of complete medium supplemented with 20 μM Man. The cells were further incubated for 1, 2, and 6 d in 10 mL of complete medium supplemented with 50 μM Man (1 and 2 d) or 5 mM Man (1, 2, and 6 d). For 6-day incubation, the medium was replaced every other day. The cells were washed two times with PBS and scraped in 1 mL of PBS. After the cells had been pelleted by centrifugation at 200 × g for 5 min at 4°C, the cell pellets were flash-frozen in liquid nitrogen and stored at −80°C until use. Protein precipitates were obtained by adding 10% trichloroacetic acid to freeze-thawed cells and centrifuging at 12,000 × g for 20 min. After washing the precipitates three times with acetone, they were dissolved with 7 M guanidine, 1 M Tris-HCl (pH 8.5), 10 mM EDTA, and 50 mM dithiothreitol. After alkylation with iodoacetic acid, samples were desalted using PAGE Clean Up Kit (06441-50, Nacalai Tesque). The resultant precipitates were dissolved with 20 mM Tris-HCl (pH 8.0), 0.03% (w/v) n-dodecyl-β-D-maltoside, and digested with trypsin (tosyl phenylalanyl chloromethyl ketone-treated; Worthington Biochemical) at 37°C for 18 hr. The concentration of the peptide mixture was quantified by amino acid analysis (Masuda and Dohmae, 2011). 1 μg of each peptide mixture was subjected to liquid chromatography (LC)-tandem mass spectrometry (MS/MS). Solvent A (0.1% formic acid) and solvent B (80% acetonitrile with 0.1% formic acid) were used as eluents. Peptides were separated using an Easy nLC 1200 (Thermo Fisher Scientific) equipped with a nano-ESI spray column (NTCC-360, 0.075 mm internal diameter × 105 mm length, 3 μm, Nikkyo Technos) at a flow rate of 300 nL/min under linear gradient conditions over 250 min. The separated peptides were analyzed with an online coupled Q Exactive HF-X Mass Spectrometer (Thermo Fisher Scientific) using the data-dependent Top 10 method. The acquired data were processed using MASCOT 2.8 (Matrix Science) and Proteome Discoverer 2.4 (Thermo Fisher Scientific). The MASCOT search was conducted as follows: Database, NCBIprot; taxonomy, Homo sapiens (human) (438,061 sequences); type of search, MS/MS ion; enzyme, trypsin; fixed modification, none; variable modifications, acetyl (protein N-term), Gln->pyro-Glu (N-term Q), oxidation (M), carboxymethyl (C); mass values, monoisotopic; peptide mass tolerance, ±15 ppm; fragment mass tolerance,±30 mmu; max. missed cleavages, 3; and instrument type, ESI-TRAP. Label-free quantification was performed using the quantification method based on the ion intensity of peptides in Proteome Discoverer 2.4.

Metabolomics

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Approximately 8 × 106 cells were washed two times with 5 mL of 5% (w/v) mannitol (133-00845, FUJIFILM Wako) and scraped in 1.3 mL of methanol (138-14521, FUJIFILM Wako) that was spiked with 10 μM external standards (Human Metabolome Technologies). The cell homogenates were spun at 15,000 × g for 5 min at 4°C, and the wet cell weights were measured. The supernatant was analyzed by capillary electrophoresis (CE) time-of-flight (TOF) mass spectrometry (MS) using an Agilent CE-TOFMS system (Agilent Technologies) at Human Metabolome Technologies.

[13C6]-Glucose-tracing experiments

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Approximately 6 × 106 cells were washed two times with 5 mL of PBS and incubated in 10 mL of glucose-free DMEM (042-32255, FUJIFILM Wako) containing 5 mM [13C6]-glucose (CLM-1396-1, Cambridge Isotope Laboratories), 50 μM or 5 mM Man, and 10% dialyzed FBS (SH30079.02, Cytiva) for the indicated time. The labeled cells were harvested at the indicated time as described in ‘Metabolomics.’ These samples were analyzed using capillary ion chromatography-mass spectrometry and liquid chromatography-mass spectrometry as previously described (Hirayama et al., 2020; Suzuki et al., 2022).

Time-lapse imaging

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Fucci MPI-KO HT1080 cells (#3 subclone 9-5, 4 × 104 cells/six-well dish) were cultured for 24 hr in 2.5 mL of complete medium supplemented with 20 μM Man. The cells were washed once with complete DMEM with no phenol red (040-30095, FUJIFILM Wako), 4 mM glutamine, and 10% FBS. The washed cells were cultured in 2.5 mL of the same medium supplemented with 50 μM Man or 5 mM Man in a humidified chamber (Tokai Hit) at 37°C with 5% CO2. Fluorescence and differential interference contrast images were obtained every 15 min using KEYENCE BZ-X800 with a PlanFluor ×20 objective lens (NA = 0.45, WD = 8.80–7.50 mm, Ph1; KEYENCE) or EVIDENT FLUOVIEW FV10i. In KEYENCE BZ-X810, Fucci mCherry and mVenus signals were detected using a TRITC filter (Ex, 545/25 nm; Em, 605/70 nm; KEYENCE) and mVenus filter (Ex, 500/20; Em, 535/30 nm; M SQUARE), respectively. In EVIDENT FLUOVIEW FV10i, Fucci mCherry and mVenus signals were detected using mCherry (Ex, 559; Em, 570-620) and EYFP (Ex, 473; Em, 490-540) dye settings. Images were acquired after a 30 min equilibration period.

Quantification

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For western blots, the X-ray films were scanned using an EPSON GT-7600UF scanner. ImageJ (Schneider et al., 2012) was used for the quantification of band intensity. For image processing of Fucci time-lapse imaging, a Fiji/ImageJ (Schindelin et al., 2012) plugin, Trackmate (Tinevez et al., 2017), was used to track single cells. MATLAB (MathWorks, Natick, MA) was used to quantify the duration of each cell cycle phase. The Fucci profiles in the mannose-challenged cells (Figure 2E, Figure S4) were visually inspected for classification.

Statistical analysis

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R version 3.3.3 and Prism 9 were used for statistical analysis. Statistical analysis was performed by applying an unpaired two-sided Welch’s t-test for comparison of the means between two groups. Comparisons of the means among more than two groups were performed with one-way or two-way analysis of variance (ANOVA) followed by post hoc testing with Dunnett’s test, Tukey’s or Bonferroni’s test. Data are reported as the mean ± standard deviation (SD). p-Values<0.05 were considered to be statistically significant.

Appendix 1

Appendix 1—key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Homo sapiens)MPIGenBankGene ID: 4351
Genetic reagent (H. sapiens)MPI-KO HT1080This paperClone #2, #3, which can be obtained from the Department of Glyco-Oncology and Medical Biochemistry, Osaka International Cancer Institute
Genetic reagent (H. sapiens)Fucci MPI-KO HT1080This paperClone #3 subclone (9-5), which can be obtained from the Department of Glyco-Oncology and Medical Biochemistry, Osaka International Cancer Institute
Genetic reagent (H. sapiens)MPI-KO HeLaThis paperClone #1, #2, which can be obtained from the Department of Glyco-Oncology and Medical Biochemistry, Osaka International Cancer Institute
Genetic reagent (Mus musculus)Wild-type mouse embryonic fibroblastsDeRossi et al., 2006
Genetic reagent (M. musculus)MPI-KO mouse embryonic fibroblastsDeRossi et al., 2006
Cell line (H. sapiens)HT1080Our laboratory stockValidated by short tandem repeat profiling
Cell line (H. sapiens)HeLaRIKEN BioResource Research CenterRCB0007
Cell line (H. sapiens)KP-4RIKEN BioResource Research CenterRCB1005
Cell line (H. sapiens)SK-OV-3American Type Culture Collection (ATCC)HTB-77
Cell line (H. sapiens)A549ATCCCRM-CCL-185
Cell line (M. musculus)MCA205A gift from Rosenberg lab
Cell line (H. sapiens)The Plat-A retroviral packaging cell lineCell BiolabsRV-102
AntibodyAnti-MPI (rabbit polyclonal)GeneTexGTX103682WB (1:5000)
AntibodyAnti-β-actin (mouse monoclonal)FUJIFILM Wako010-27841WB (1:10,000)
AntibodyAnti-H2AX (mouse monoclonal)Santa Cruz938CT5.1.1WB (1:2000)
AntibodyAnti-phospho-H2AX (Ser139) (mouse monoclonal)MilliporeJBW301WB (1:5000)
Chromatin FACS (1:200)
AntibodyAnti-RPA2 (mouse monoclonal)Santa Cruz9H8WB (1:5000)
AntibodyAnti-Phospho-RPA2 (S33) (rabbit polyclonal)BethylA300-246AWB (1:10,000)
AntibodyAnti-MCM2 (rabbit monoclonal)Cell Signaling TechnologyD7G11WB (1:10,000)
Chromatin FACS (1:200)
AntibodyAnti-MCM3 (mouse monoclonal)Santa CruzE-8WB (1:10,000)
AntibodyAnti-MCM4 (rabbit polyclonal)GeneTexGTX109740WB (1:5000)
AntibodyAnti-MCM5 (rabbit polyclonal)GeneTexGTX33310WB (1:5000)
AntibodyAnti-MCM6 (mousemonoclonal)Santa CruzH-8WB (1:20,000)
AntibodyAnti-MCM7 (mouse monoclonal)Santa Cruz141.2WB (1:5000)
AntibodyAnti-CDT1 (rabbit monoclonal)Abcamab202067WB (1:10,000)
AntibodyAnti-CDC6 (rabbit monoclonal)Abcamab109315WB (1:1000)
AntibodyAnti-ORC2 (rat monoclonal)Santa Cruz3G6WB (1:10,000)
AntibodyAnti-CDC45 (rabbit monoclonal)Cell Signaling TechnologyD7G6WB (1:10,000)
AntibodyAlexa Fluor 488-conjugated anti-BrdU antibody (mouse monoclonal)BioLegend3D4FACS (1:100)
AntibodyAnti-rabbit IgG-Alexa Fluor 488 (donkey polyclonal)Thermo Fisher ScientificR37118Chromatin FACS (1:1000)
AntibodyAnti-mouse IgG-Alexa Fluor 647 (donkey polyclonal)Thermo Fisher ScientificA-31571Chromatin FACS (1:1000)
Recombinant DNA reagentpENTR-hMPIThis paperThis reagent can be obtained from the Department of Glyco-Oncology and Medical Biochemistry, Osaka International Cancer Institute
Recombinant DNA reagentpMXs-Neo-hMPIThis paperThis reagent can be obtained from the Department of Cancer Drug Discovery and Development, Osaka International Cancer Institute
Recombinant DNA reagentmCherry-hCdt1(1/100)Cy(−)/pcDNA3RIKEN BioResource Research CenterRDB15459
Recombinant DNA reagentpMXs-Neo to yield pMXs-Neo-mCherry-hCdt1(1/100)Cy(−)This paperThis reagent can be obtained from the Department of Cancer Drug Discovery and Development, Osaka International Cancer Institute
Recombinant DNA reagentpMMLV-mVenus-hGem(1/110):IRES:BsdVector Builder
Recombinant DNA reagentEdit-R SMART Cas9_mCMV_(PuroR) expression plasmidDharmaconU-005200-120
Recombinant DNA reagentpENTR/D-TOPO vectorThermo Fisher ScientificK240020
Recombinant DNA reagentpMXs-Neo retroviral expression vectorCell BiolabsRTV-011
Sequence-based reagentSynthetic CRISPR RNAs for human MPIDharmaconCM-011729-01
CM-011729-03
Sequence-based reagenttrans-activating CRISPR RNADharmaconU-002005-05
Commercial assay or kitVECTASTAIN ABC-HRP kitVector LaboratoriesPK-4000
Commercial assay or kitCellTiter-Glo 2.0PromegaG924B
Commercial assay or kitCytotoxicity LDH Assay Kit-WSTDojindoCK12
Commercial assay or kitPAGE Clean Up KitNacalai06441-50
Commercial assay or kitIn-Fusion HD Cloning KitClontech639648
Commercial assay or kitRNeasy Mini KitQIAGEN74106
Commercial assay or kitNucleoSpin Gel and PCR Clean-Up KitClontech740609
Commercial assay or kitTHUNDERBIRD Next SYBR qPCR MixTOYOBOQPX-201
Chemical compound, drugCellstain Hoechst 33342 solutionDojindoH342FACS (1:100)
Chromatin FACS (1:200)
Chemical compound, drug5-Bromo-2’-deoxyuridineTokyo Chemical IndustryB1575
Chemical compound, drug2’-DeoxyadenosineWako046-18693
Chemical compound, drugThymidineWako203-19423
Chemical compound, drug2’-DeoxyguanosineTokyo Chemical IndustryD0052
Chemical compound, drug2’-DeoxycytidineTokyo Chemical IndustryD3583
Chemical compound, drugDialyzed FBSCytivaSH30079.02
Chemical compound, drugCisplatinTokyo Chemical IndustryD3371
Chemical compound, drugDoxorubicinFUJIFILM Wako040-21521
Chemical compound, drugIACS-010759SelleckchemS8731
Chemical compound, drugVE-821Sigma-AldrichSML1415
Chemical compound, drugHydroxyureaFUJIFILM Wako085-06653
Chemical compound, drug[13C6]-GlucoseCambridge Isotope LaboratoriesCLM-1396-1
Chemical compound, drugcOmplete Protease Inhibitor CocktailSigma-Aldrich11836170001
Chemical compound, drugPhosSTOPSigma-Aldrich4906845001
Chemical compound, drugBiotinylated concanavalin ACosmo BioJ203Lectin blot (1:10,000)
Chemical compound, drugTRIzol ReagentThermo Fisher Scientific15596026
Chemical compound, drugLipofectamine 3000Thermo Fisher ScientificL3000015
Chemical compound, drugLipofectamine 2000Thermo Fisher Scientific11668027
Software, algorithmMASCOT 2.8Matrix Science
Software, algorithmProteome Discoverer 2.4Thermo Fisher Scientific
Software, algorithmImageJSchneider et al., 2012
Software, algorithmTrackmateTinevez et al., 2017
Software, algorithmMATLABMathWorks

Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD036449 and https://doi.org/10.6019/PXD036449.

The following data sets were generated
    1. Suzuki T
    2. Dohmae N
    (2022) PRIDE
    ID PXD036449. Metabolic clogging of mannose in human cancer cells triggers genome instability via dNTP loss.

References

    1. Kuo LJ
    2. Yang LX
    (2008)
    Gamma-H2Ax - a novel biomarker for DNA double-strand breaks
    In Vivo 22:305–309.

Decision letter

  1. Gina M DeNicola
    Reviewing Editor; Moffitt Cancer Center, United States
  2. Richard M White
    Senior Editor; Ludwig Institute for Cancer Research, University of Oxford, United Kingdom
  3. John A Hanover
    Reviewer; National Institute of Diabetes and Digestive and Kidney Diseases, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Metabolic clogging of mannose triggers genomic instability via dNTP loss in human cancer cells" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Richard White as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: John A Hanover (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The authors should extend their observations to a larger panel of cell lines, including MPI high and low cell lines.

2) The authors should perform stable isotope tracing experiments to examine the activity of key metabolic pathways (e.g. the pentose phosphate pathway) instead of inferring activity from metabolite pool sizes.

3) The authors should further develop their metabolic characterization of the consequences of the inability to metabolize mannose, including the effects on the TCA cycle and oxidative phosphorylation and dNTP depletion as requested by reviewers #1 and #2.

4) The authors should demonstrate causality for dNTP depletion in the reduction of proliferation and cell cycle perturbation.

5) The authors should improve data presentation, including showing individual data points and improving the description of the variables as suggested by the reviewers.

6) When giving context to their work, the authors should more thoroughly discuss prior work on cancer cells (not just honeybees), including very in-depth metabolic phenotyping of mannose phosphate isomerase low and high cells that was recently reported.

Reviewer #1 (Recommendations for the authors):

While the manuscript is well-written and advances our understanding of mannose toxicity based on new findings that dNTP depletion occurs, several concerns remain that could be addressed by the authors:

1. The metabolic findings from Gonzalez et al. are insufficiently discussed in the text. Their study already established, using sophisticated stable isotope tracing approaches, that mannose decreases the contribution of glucose to G6P and lactate, and results in lower levels of glycolytic and TCA cycle intermediates, and the PPP intermediate ribose-5P. Although the authors cite this study in the context of suppression of proliferation and increased chemotherapeutic efficacy, what is already known about mannose metabolism in mammalian cells is understated in the manuscript and the focus on honeybees is overemphasized.

2. Gonzalez et al., found that mannose decreased ribose-5P but in this study no difference in ribose-5P was seen upon mannose treatment. Total measurement of PPP metabolites is not indicative of pathway flux and the authors should instead use 1,2-13C-glucose tracing to determine whether mannose influences PPP flux. Moreover, U-13C-glucose can be used to look at dNTP labeling.

3. The mechanistic connection between mannose metabolism and dNTP depletion is not established. If there is no effect of mannose on the PPP as claimed, how does mannose induce dNTP depletion to induce replication stress?

4. It is unclear whether dNTP depletion is a cause or consequence of reduced proliferation and cell cycle perturbation. Direct evidence is lacking. Can nucleosides rescue mannose toxicity, cell cycle progression, chemosensitivity and DNA synthesis?

Reviewer #2 (Recommendations for the authors):

1. Identification of cancer cell lines that may be susceptible to high mannose-induced toxicity, such as those with endogenous low expression levels of MPI, and demonstration of the same effects in those cell lines would strengthen the study.

2. Utilization of labeled isotope tracers such as 13C-glucose, 13C-mannose, or 13C-glutamine to trace metabolic flux would provide confidence in the conclusions about the changes to the metabolic landscape. Pool sizes decreased for both lower glycolysis and TCA cycle intermediates; this was interpreted as decreased activity for glycolysis but increased activity for the TCA cycle. How is the TCA cycle fueled if lower glycolytic intermediates are not feeding the TCA cycle? Is excess glutamine fueling the TCA cycle? Why are nucleoside monophosphates not depleted?

3. Evaluating the NAD/NADH ratio between mannose challenged and unchallenged cells would also strengthen the claims about increased oxidative phosphorylation.

4. For Seahorse experiments, data following oligomycin, rotenone, and antimycin A addition should be included.

5. Figure 1G: would removal of mannose further reduce hexose-6-phosphate levels?

6. When cell growth is restored after re-introduction of physiologic mannose as shown in Figure 2G, will these cells revert back to a normal cell cycle progression as shown in Figure 2B, or do they retain aberrant cell cycle progression patterns?

7. Figure 2I and J do not show BRdU incorporation into MPI-KO HT1080 cells under 50 μM mannose on day 6. Similarly, Figure 2H does not include p21 and p27 expression for day 6 in the 50 μM mannose condition. Please justify.

8. Inclusion of individual data points for each replicate within bar graphs would improve the reader's ability to interpret results.

Reviewer #3 (Recommendations for the authors):

Specific Points:

The data in Figure 1. The induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity. Shows only modest changes in chemosensitivity and the discussion does not adequately express this. The authors should further discuss this.

Figure 3. These results are discussed almost exclusively in the context of DNA replication and cell cycle for the down regulated pathways. What about ribosomes?

The data are clear but it is hard to assess the variability in some of the assays since there is scant description of the variables particularly the cell sorting experiments to establish DNA replication and cell cycle.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Metabolic clogging of mannose triggers dNTP loss and genomic instability in human cancer cells" for further consideration by eLife. Your revised article has been evaluated by Richard White (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

The stable isotope tracing experiments have problems that leave this point unaddressed. Specifically:

1. It is very difficult to distinguish the isotopologues because of the colors used and the legend, which has too many possibilities beyond the masses possible. The authors should simplify these figures and use colors that are more easily distinguishable.

2. U-13C6-glucose is not suitable to assay the non-oxidative and oxidative branches of the PPP and thus the authors cannot conclude that mannose decreases flux into this pathway. The authors should use 1,2-C2-glucose tracing to draw these conclusions and also perform labeling with additional tracers (e.g. serine, glutamine, mannose) to parse where the defects in nucleotide synthesis are occurring.

3. The authors should address other issues raised in the comments below.

Reviewer #1 (Recommendations for the authors):

The authors have done a good job addressing concerns about (1) extending their observations to a larger panel of MPI high and low cell lines, (2) further develop their characterization of the effects of mannose on the TCA cycle and dNTPs, and (3) examine the causality for dNTP depletion in genomic instability. However, the stable isotope tracing experiments have problems that leave this point unaddressed. The authors chose U-13C6-glucose instead of 1,2-C2-glucose, and this is suitable to assay the TCA cycle. However, it is unsuitable to assay the non-oxidative and oxidative branches of the PPP because these rapidly saturate and labeling in R5P from either branch cannot be distinguished. This is very evident in Figure 6—figure supplement 4, where both 6PG and S7P are completely labeled at all time points and both conditions, with only the total levels different. It is unclear why mannose decreases the fraction labeling in Ru5P + R5P specifically. Therefore, the authors cannot conclude that mannose decreases flux into the oxidative and non-oxidative branches of the PPP. Labeling in other pathways have problems. It is very difficult to distinguish the isotopologues because of the colors used and the legend, which has too many possibilities beyond the masses possible. G6P and F6P are not labeled downstream of 13C-glucose (even in low mannose conditions), but are labeled starting at the F1,6P stage, which is highly unlikely since downstream metabolite labeling cannot exceed upstream metabolite labeling. The tracing also does not provide insight into how mannose is impairing nucleotide synthesis – PRPP labeling is similar but IMP levels (but maybe not fraction labeling, it's unclear) dramatically decrease. Labeling with additional tracers (e.g. serine, glutamine, mannose) are really needed to parse where the defects in nucleotide synthesis are occurring, as suggested in the prior round of review.

Reviewer #2 (Recommendations for the authors):

The authors have somewhat addressed previous concerns. They evaluated additional cancer cell lines with high or low MPI expression, and the results are consistent with their overall conclusions. The authors have now shown that glutamine depletion reduces viability. However, it is surprising that the high mannose-challenged cells seem more resistant to glutamine starvation than unchallenged cells, given that mannose challenge severely blocks glucose flux into the TCA cycle. This needs to be resolved. Stable isotope tracing studies have not been performed in a satisfactory manner and do not support the manuscript's conclusions. The authors need to properly repeat tracing studies using 1,2-13C-glucose to determine glucose flux into oxidative vs. non-oxidative PPP as well as 13C-gluamine and 13C-mannose. Statistical analysis should be provided for Figure 6. Finally, the authors report that OCR is increased in mannose challenged cells, but there is no significant change in the NAD/NADH ratio. This should be resolved.

https://doi.org/10.7554/eLife.83870.sa1

Author response

Essential revisions:

1) The authors should extend their observations to a larger panel of cell lines, including MPI high and low cell lines.

Thank you very much for this insightful suggestion. With additional experiments carried out, we were able to highlight the phenotypic differences in mannose-caused genomic instability between MPI-KO and MPIlow cancer cells.

We extended our observations regarding mannose-caused genomic instability to three human cancer cell lines expressing varying levels of MPI (new Figure 4—figure supplement 3A). These cell lines include one mannose-resistant MPIhigh cancer cell line (human ovarian cancer SK-OV-3 cells) and two mannose-sensitive MPIlow cancer cell lines (human pancreatic cancer KP-4 cells and human non-small-cell lung cancer A549 cells), which were previously characterized by Gonzalez et al. (Gonzalez et al., 2018). Consistent with the previous study, we found that mannose challenge (25 mM) significantly suppressed the proliferation of KP-4 and A549 cells, but not that of SK-OV-3 cells (new Figure 4—figure supplement 3B). Having confirmed mannose sensitivity in these cell lines, we analyzed the effects of mannose on the enhancement of cisplatin-induced genomic instability as the expression levels of γH2AX (new Figure 4—figure supplement 3C-F), revealing little enhancement of cisplatin-induced γH2AX by mannose challenge in all three cell lines. This indicates that mannose-caused genomic instability is a unique phenotype observed in the absence of MPI. According to these new findings, we have revised the text in the Result section (from page 12, lines 19-22) and the Discussion section (page 20, lines 4-8).

2) The authors should perform stable isotope tracing experiments to examine the activity of key metabolic pathways (e.g. the pentose phosphate pathway) instead of inferring activity from metabolite pool sizes.

We appreciate the reviewers for raising this important issue. As requested by reviewers 1 and 2, we performed stable isotope labeling experiments using [13C6]-glucose to examine the activity of key metabolic pathways in MPI-KO HT1080 cells. Reviewer 1 suggested the use of [13C1,2]-glucose for tracing the pentose phosphate pathway (PPP) flux, but we decided to use [13C6]-glucose to unambiguously identify the labeled metabolites in both PPP and the downstream nucleotide metabolic pathways. Reviewer 2 suggested the use of stable isotope-labeled mannose and glutamine to strengthen our conclusion on mannose-mediated metabolic changes. However, we focused on the metabolic flux of [13C6]-glucose in this study because glucose metabolism is a primary target of mannose (Gonzalez et al., 2018). We will further dissect metabolic deficiency in honeybee syndrome by using [13C]-mannose and [13C,15N]-glutamine in a future study.

We analyzed the metabolic flux of [13C6]-glucose into glycolysis, the tricarboxylic acid (TCA) cycle, the mannose biosynthetic pathway (MBP), PPP and the purine and pyrimidine metabolic pathways. The MPI-KO HT1080 cells were preconditioned under mannose-challenged or unchallenged conditions before [13C6]-glucose labeling. In mannose-unchallenged cells, [13C6]-glucose was efficiently metabolized to [13C3]-lactate and [13C3]-pyruvate (Figure 6—figure supplement 1 and Data S4), which progressively fueled the TCA cycle over the period of metabolic labeling (Figure 6—figure supplement 2 and Data S4). In contrast, mannose challenge caused an incomplete, but substantial reduction in the flux of [13C6]-glucose into glycolysis (Figure 6—figure supplement 1 and Data S4), and this glycolytic deficiency reduced the contribution of glucose to the TCA cycle (Figure 6—figure supplement 2 and Data S4), as reported previously (Gonzalez et al., 2018). This result further suggested the importance of glutamine in fueling the TCA cycle in honeybee syndrome.

In MBP, GDP-mannose is produced from GTP and mannose-1-phosphate. In both mannose-challenged and unchallenged cells, GDP-mannose was progressively labeled with [13C5], which was most likely derived from [13C5]-ribose (Figure 6—figure supplement 3 and Data S4). Interestingly, we found a significant increase in the steady-state pool of GDP-mannose in mannose-challenged cells, while this increase was not proportional to the increase in the pools of mannose-6-phosphate and mannose-1-phosphate (as the sum with glucose-1-phosphate) (Figure 6—figure supplement 3 and Data S4), implying that the formation of GDP-mannose is a rate-limiting step in MBP.

In PPP, mannose challenge reduced the flux of [13C6]-glucose to both oxidative and non-oxidative arms in MPI-KO HT1080 cells (Figure 6—figure supplement 4 and Data S4), as reported previously in MPIlow cancer cells (Gonzalez et al., 2018). Unexpectedly, however, the levels of [13C5]-5-phosphoribosyl-1-diphosphate (PRPP), which is synthesized from ribose-5-phosphate, were almost comparable between mannose-challenged and unchallenged cells (Figure 6 and Data S4). More surprisingly, mannose-challenged cells showed very little flux of [13C5]-PRPP into inosine-5’-phosphate and xanthosine-5’-phosphate in purine metabolism (Figure 6 and Data S4) and orotidine-5’-phosphate in pyrimidine metabolism (Figure 6—figure supplement 5 and Data S4), accompanying nearly the complete loss of newly synthesized pools of (deoxy)ribonucleotides (Figure 6 and Figure 6—figure supplement 5 and Data S4). In accordance with these new findings, we have revised the text in the Result section (from page 15, line 19 to page 17, line 7).

3) The authors should further develop their metabolic characterization of the consequences of the inability to metabolize mannose, including the effects on the TCA cycle and oxidative phosphorylation and dNTP depletion as requested by reviewers #1 and #2.

This request is closely related to the issue raised in Essential revision 2. With additional experiments carried out, we have better characterized the impact of honeybee syndrome on the TCA cycle, oxidative phosphorylation (OXPHOS) and dNTP depletion as follows.

The impact of honeybee syndrome on the TCA cycle and OXPHOS

Our stable isotope labeling experiments clearly demonstrate that mannose challenge substantially reduces the contribution of glucose to fueling the TCA cycle in MPI-KO HT1080 cells (new Figure 6—figure supplement 2). However, the intracellular NAD+/NADH ratio remained relatively constant between mannose-challenged and unchallenged cells (new Figure 5—figure supplement 2C, D). Glutaminolysis can contribute to the TCA cycle and thus OXPHOS in cancer cells (Yang et al., 2017). As expected, glutamine depletion greatly reduced the cell viability of both mannose-challenged and mannose-unchallenged cells (new Figure 5—figure supplement 2E, F), suggesting an important role of glutamine in fueling the TCA cycle and OXPHOS in honeybee syndrome. In accordance with these new findings, we have revised the text in the Result section (page 15, lines 6-11).

Although we could not show a clear correlation between the activation states of OXPHOS and NAD+/NADH ratio in honeybee syndrome, our seahorse experiments demonstrated that honeybee syndrome increases OCR (Figure 5A, B), which could arise from compensatory activation of glutaminolysis for decreased glucose contribution to the TCA cycle. We will further investigate the impact of honeybee syndrome on mitochondrial functions in a future study. Irrespective of the mechanisms involved, we believe that this limitation will not affect the conclusion of this study, and hope that the reviewers will agree with our view.

The impact of honeybee syndrome on dNTP depletion

The pool size of dNTPs is tightly regulated in order to meet and not to exceed cellular demands by cell cycle-dependent and DNA damage-dependent expression of enzymes involved in the biosynthesis and degradation of dNTPs (Franzolin et al., 2013), as well as their allosteric regulation (Aye et al., 2015; Ji et al., 2013). This tight regulation of dNTP supply is vital to prevent cells from suffering the dNTP pool imbalance, which causes genomic instability (Kumar et al., 2010; Pajalunga et al., 2017). Our proteomic analysis revealed that mannose challenge rapidly increases the expression of RRM2B (Data S1), which is a rate-limiting and TP53-inducible subunit of RNR (Hakansson et al., 2006; Tanaka et al., 2000), indicating that the equilibrium of dNTP metabolism shifted toward biosynthesis at the protein level. However, mannose challenge caused substantial reductions in the pool of ATP, which is a key allosteric regulator for the catalysis of RNR to occur (Brignole et al., 2018). Moreover, mannose challenge impaired the early steps of purine and pyrimidine metabolism, which in turn limited the de novo pool of ribonucleoside diphosphates essential for RNR reaction. Little metabolic flux of PRPP into purine and pyrimidine metabolism in honeybee syndrome can explain why the pool of newly synthesized PRPP was preserved even in the absence of maximal metabolic activity of PPP. A recent in-depth metabolomic study revealed the mannose-mediated downregulation of metabolic activity of glycolysis and PPP in MPIlow cancer cells (Gonzalez et al., 2018), although its impact on the downstream nucleotide metabolism remains to be explored. Fueling the salvage pathway of purine and pyrimidine metabolism with hypoxanthine and thymidine (new Figure 6—figure supplement 6A) or with deoxyribonucleoside mixture (new Figure 6—figure supplement 6B) was not sufficient to improve the defects in proliferation of mannose-challenged MPI-KO HT1080 cells, suggesting the critical role of de novo nucleotide biosynthesis in supporting cell proliferation in this cell model. Notably, although dNTPs are essential for DNA repair, the chemosensitizing effect of mannose was rather modest when comparing it with that of deficiency in genes for DNA repair processes (e.g., BRCA2, FANCA and FANCD2) (Bruno et al., 2017; Sakai et al., 2008). This apparent discrepancy most likely arises from the fact that the suppression of de novo nucleotide biosynthesis by honeybee syndrome is incomplete. Taken together, our findings indicate that the insufficient metabolic activity of purine and pyrimidine metabolism in honeybee syndrome causes the loss of dNTPs. Accordingly, we have revised the text in the Discussion section (from page 20, line 17 to page 21, line 22).

4) The authors should demonstrate causality for dNTP depletion in the reduction of proliferation and cell cycle perturbation.

We fully agree with the reviewer’s comment. As discussed in our response to Essential revision 3, fueling the salvage pathway of purine and pyrimidine metabolism with hypoxanthine and thymidine (new Figure 6—figure supplement 6A) or with deoxyribonucleoside (dN) mixture (new Figure 6—figure supplement 6B) was not sufficient to improve the defects in proliferation of mannose-challenged cells, suggesting the critical role of de novo nucleotide biosynthesis in supporting cell proliferation in this cell model. Consistent with this, the supplementation of mannose-challenged or unchallenged MPI-KO HT1080 cells with dN mixture could not improve the incorporation of 5-bromo-2’-deoxyuridine (BrdU) (Author response image 1A) and chemosensitivity (Figure R1B, C), except for a slight improvement of the chemosensitivity by dN supplementation in unchallenged cells (Author response image 1B). Although we found that honeybee syndrome reduces the biosynthetic capacity of nucleotides, we do not currently understand the precise molecular mechanism behind this metabolic deficiency, making it difficult for us to further investigate the causality of dNTP depletion in the anti-cancer activity of mannose.

Author response image 1
The effects of deoxyribonucleoside supplementation on cell proliferation, DNA synthesis and chemosensitivity in MPI-KO HT1080 cells.

(A) The percentage of MPI-KO HT1080 (#3) cells that were actively incorporating 2-bromo-5-deoxyuridine (BrdU+). Before 1-h pulse labelling with BrdU, the cells were cultured for 24 h in culture medium supplemented with (+) or without (-) a mixture of dNs in the presence of mannose (Man) at the indicated concentrations. (B and C) Cell viability in the MPI-KO HT1080 (#3) cells that were precondition for 24 h with (+) or without (-) dN mixture in the presence of mannose at 50 µM (B) or 5 mM (C), followed by the incubation with cisplatin for additional 24 h under the same culture conditions. Data represent the mean ± SD, n=3 independent experiments. **p<0.01, and ***p<0.001, NS, not significant, Welch’s t-test (A), two-way ANOVA with post hoc Bonferroni’s (B and C).

For this reason, we toned down our original title “Metabolic clogging of mannose triggers genomic instability via dNTP loss in human cancer cells” to the new title “Metabolic clogging of mannose triggers dNTP loss and genomic instability in human cancer cells,” and described the limitation of our study in the Discussion section (page 22, lines 14-16) as follows: “Elucidation of the precise molecular mechanism underlying dNTP loss in honeybee syndrome will be necessary to unambiguously identify the direct cause(s) of the chemosensitizing effects of mannose”. However, our findings on the metabolic flux of glucose (new Figure 6) and hydroxyurea treatment (now Figure 7) strongly support our conclusion that dNTP loss is an important mechanism for anti-cancer activity of mannose in our cell models. We hope that the reviewers will agree with our view.

5) The authors should improve data presentation, including showing individual data points and improving the description of the variables as suggested by the reviewers.

We included individual data points where appropriate. We decided not to do so in new Figure 6 and Figure 6—figure supplements 1-5, due to the complexity of the metabolomic data. Instead, we provided the raw data as new Data S4.

We apologize for the poor descriptions on the variables in flow cytometry analysis. We have revised the text in the Results and Figure Legends sections to improve the descriptions on the variables as follows.

– Results section (page 9, lines 2-6)

– Results section (page 10, lines 13-20)

– Results section (page 12, line 22 to page 13, line 5)

– Figure Legends for Figure 2I (DNA replication: page 49, lines 1-3)

– Figure Legends for Figure 3E, 4F and 7C (Chromatin flow cytometry: page 51, lines 11-13; page 53, lines 12-15; page 59, lines 13-14)

– Figure Legends for Figure 4B and 7E (Dormant origin assay: page 53, lines 5-8; page 59, lines 18-21)

6) When giving context to their work, the authors should more thoroughly discuss prior work on cancer cells (not just honeybees), including very in-depth metabolic phenotyping of mannose phosphate isomerase low and high cells that was recently reported.

We fully agree with the reviewer’s comment. We have improved the text by more thoroughly discussing our findings on honeybee syndrome and what is already known about the mannose-mediated metabolic changes in cancer cells.

– Results section (page 12, lines 19-22)

– Results section (page 16, lines 7-11)

– Results section (page 16, lines 21-23)

– Discussion section (page 19, lines 18-20)

– Discussion section (page 21, lines 8-11)

This revision has highlighted a phenotypic similarity between MPI-KO and MPIlow cancer cells in mannose-mediated metabolic changes in glycolysis, the TCA cycle and PPP.

Reviewer #1 (Recommendations for the authors):

While the manuscript is well-written and advances our understanding of mannose toxicity based on new findings that dNTP depletion occurs, several concerns remain that could be addressed by the authors:

1. The metabolic findings from Gonzalez et al. are insufficiently discussed in the text. Their study already established, using sophisticated stable isotope tracing approaches, that mannose decreases the contribution of glucose to G6P and lactate, and results in lower levels of glycolytic and TCA cycle intermediates, and the PPP intermediate ribose-5P. Although the authors cite this study in the context of suppression of proliferation and increased chemotherapeutic efficacy, what is already known about mannose metabolism in mammalian cells is understated in the manuscript and the focus on honeybees is overemphasized.

We fully agree with the reviewer’s comment. We have addressed this issue in our response to Essential revision 6.

2. Gonzalez et al., found that mannose decreased ribose-5P but in this study no difference in ribose-5P was seen upon mannose treatment. Total measurement of PPP metabolites is not indicative of pathway flux and the authors should instead use 1,2-13C-glucose tracing to determine whether mannose influences PPP flux. Moreover, U-13C-glucose can be used to look at dNTP labeling.

We have addressed this issue in our response to Essential revision 2.

3. The mechanistic connection between mannose metabolism and dNTP depletion is not established. If there is no effect of mannose on the PPP as claimed, how does mannose induce dNTP depletion to induce replication stress?

We have addressed this issue in our response to Essential revisions 2 and 3.

4. It is unclear whether dNTP depletion is a cause or consequence of reduced proliferation and cell cycle perturbation. Direct evidence is lacking. Can nucleosides rescue mannose toxicity, cell cycle progression, chemosensitivity and DNA synthesis?

We have addressed this issue in our response to Essential revision 4.

Reviewer #2 (Recommendations for the authors):

1. Identification of cancer cell lines that may be susceptible to high mannose-induced toxicity, such as those with endogenous low expression levels of MPI, and demonstration of the same effects in those cell lines would strengthen the study.

We have addressed this issue in our response to Essential revision 1.

2. Utilization of labeled isotope tracers such as 13C-glucose, 13C-mannose, or 13C-glutamine to trace metabolic flux would provide confidence in the conclusions about the changes to the metabolic landscape. Pool sizes decreased for both lower glycolysis and TCA cycle intermediates; this was interpreted as decreased activity for glycolysis but increased activity for the TCA cycle. How is the TCA cycle fueled if lower glycolytic intermediates are not feeding the TCA cycle? Is excess glutamine fueling the TCA cycle?

We have addressed this issue in our response to Essential revisions 2 and 3.

Why are nucleoside monophosphates not depleted?

Despite our extensive metabolic characterization of honeybee syndrome by using [13C6]-glucose, the mechanism by which honeybee syndrome hardly depletes the steady state pool of ribonucleoside monophosphates remained unknown. One plausible explanation for this phenomenon is that honeybee syndrome severely reduces the biosynthesis of nucleoside di/triphosphates from both newly synthesized and pre-existing pools of ribonucleoside monophosphates. Irrespective of the mechanisms involved, we believe that this limitation will not affect our conclusion, and hope that the reviewers will agree with our view.

3. Evaluating the NAD/NADH ratio between mannose challenged and unchallenged cells would also strengthen the claims about increased oxidative phosphorylation.

We addressed this issue in our response to Essential revision 3.

4. For Seahorse experiments, data following oligomycin, rotenone, and antimycin A addition should be included.

We apologize for our poor presentation of the data regarding seahorse experiments. We have included the data following the administration of oligomycin A and a mixture of rotenone and antimycin A (Figure 5A, B) and revised the Results as follows (from page 13, line 22 to page 14, line 4):

“Mannose challenge increased OCR in response to a steep drop of ECAR (Figure 5A, B), indicating that mannose challenge rapidly inhibited glycolysis, which in turn activated OXPHOS. The remaining ECAR further decreased after oligomycin A treatment in mannose-challenged cells, while the same treatment increased ECAR in mannose-unchallenged cells (Figure 5A), indicating that mannose challenge ablates glycolytic capacity, which is required to buffer the defects in OXPHOS.”

5. Figure 1G: would removal of mannose further reduce hexose-6-phosphate levels?

Exogenous mannose is required for the production of mannose-6-phosphate and the downstream metabolites (i.e., mannose-1-phosphate and GDP-mannose) in the absence of MPI, and therefore the removal of mannose from culture medium should reduce all of these mannose-related metabolites and impair protein N-glycosylation (Harada et al., 2013; Harada et al., 2021). Although we did not directly measure the levels of mannose-6-phosphate, the fact that mannose-starved MPI-KO HT1080 cells showed poor protein N-glycosylation (Figure 1H) supports our idea that mannose starvation reduces hexose-6-phosphate levels.

6. When cell growth is restored after re-introduction of physiologic mannose as shown in Figure 2G, will these cells revert back to a normal cell cycle progression as shown in Figure 2B, or do they retain aberrant cell cycle progression patterns?

Thank you very much for this insightful suggestion. We employed Fucci(CA) to compare cell cycle progression between the MPI-KO HT1080 cells that were (i) maintained under mannose-unchallenged conditions or (ii) recovered from mannose challenge as in Figure 2G. The results indicate that the recovered cells show cell cycle progression that is indistinguishable from that of the cells maintained under mannose-unchallenged conditions (new Figure 2—figure supplement 2A–D). In accordance with this new finding, we have revised the text in the Result section (page 8, lines 20-24).

7. Figure 2I and J do not show BRdU incorporation into MPI-KO HT1080 cells under 50 μM mannose on day 6. Similarly, Figure 2H does not include p21 and p27 expression for day 6 in the 50 μM mannose condition. Please justify.

We could not analyze the 6-day culture of mannose-unchallenged cells due to their overgrowth.

8. Inclusion of individual data points for each replicate within bar graphs would improve the reader's ability to interpret results.

We have addressed this issue in our response to Essential revision 5, which, we believe, significantly improved the reader’s ability to interpret the results.

Reviewer #3 (Recommendations for the authors):

Specific Points:

The data in Figure 1. The induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity. Shows only modest changes in chemosensitivity and the discussion does not adequately express this. The authors should further discuss this.

We thank the reviewer for raising this important issue. Although dNTPs are essential for DNA repair, the chemosensitizing effect of mannose was rather modest when comparing it with that of deficiency in genes for DNA repair processes (e.g., BRCA2, FANCA and FANCD2) (Bruno et al., 2017; Sakai et al., 2008). This apparent discrepancy most likely arises from the fact that the suppression of de novo nucleotide biosynthesis in honeybee syndrome is incomplete. We revised the text in the Discussion section accordingly (page 21, lines 16-20).

Figure 3. These results are discussed almost exclusively in the context of DNA replication and cell cycle for the down regulated pathways. What about ribosomes?

We have revised the text in the Discussion section (page 21, line 23 to page 22 line 9) to more thoroughly discuss our proteomic data, including those regarding ribosomes, as follows:

“Our proteomic analysis revealed the functional pathways affected by honeybee syndrome. The downregulation of MCM2-7 proteins in mannose-challenged cells supports our hypothesis that honeybee syndrome impairs dormant origins and causes genomic instability (Ibarra et al., 2008; Kawabata et al., 2011; Shima et al., 2007). Mannose challenge also decreased the expression of several large ribosomal subunit proteins. Ribosome biogenesis stress has been identified as a major mechanism by which oxaliplatin kills cancer cells in a DNA damage response-independent manner (Bruno et al., 2017). Moreover, mannose challenge upregulated proteins involved in ferroptosis and necroptosis. These two programmed cell death mechanisms are considered as an effective approach to overcome chemoresistance (Zhang et al., 2022) or to eradicate apoptosis-resistant cancer cells (Su et al., 2016). In addition to metabolic impacts, honeybee syndrome may also enhance cancer cell vulnerability at the proteomic level.”

We hope that the reviewer will find our revision sufficient for discussing our findings on ribosomes.

The data are clear but it is hard to assess the variability in some of the assays since there is scant description of the variables particularly the cell sorting experiments to establish DNA replication and cell cycle.

We apologize for our poor descriptions on the variables in cell sorting experiments. We have addressed this issue in our response to Essential revision 5.

References

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Brignole, E.J., Tsai, K.L., Chittuluru, J., Li, H., Aye, Y., Penczek, P.A., Stubbe, J., Drennan, C.L., and Asturias, F. (2018). 3.3-A resolution cryo-EM structure of human ribonucleotide reductase with substrate and allosteric regulators bound. eLife 7.

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[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

The stable isotope tracing experiments have problems that leave this point unaddressed. Specifically:

1. It is very difficult to distinguish the isotopologues because of the colors used and the legend, which has too many possibilities beyond the masses possible. The authors should simplify these figures and use colors that are more easily distinguishable.

We apologize for the poor data presentation, which made our data uninterpretable. We have revised the figures by estimating fractional enrichment, by showing the possible isotopologues for a metabolite, and by using colors that are more easily distinguishable (revised Figure 6 and Figure 6—figure supplements 1-3).

2. U-13C6-glucose is not suitable to assay the non-oxidative and oxidative branches of the PPP and thus the authors cannot conclude that mannose decreases flux into this pathway. The authors should use 1,2-C2-glucose tracing to draw these conclusions and also perform labeling with additional tracers (e.g. serine, glutamine, mannose) to parse where the defects in nucleotide synthesis are occurring.

It is technically difficult to accurately estimate the dynamic labeling for PPP intermediates, due to their very rapid saturation as pointed by reviewers and literatures (please see also our response to the Reviewer 1’s comment). For this reason, we have removed our claim for the PPP flux from manuscript.

To substantiate our claim that mannose impairs nucleotide biosynthesis, we re-analyzed data obtained from our U-13C6-glucose tracing experiments. The data have clearly indicated that mannose causes sever defects in the early stages of purine and pyrimidine metabolism (revised Figure 6 and Figure 6—figure supplements 1-3). We hope that the reviewers will find these revisions sufficient for drawing our conclusion.

3. The authors should address other issues raised in the comments below.

Please see our responses to the reviewers’ comments below.

Reviewer #1 (Recommendations for the authors):

The authors have done a good job addressing concerns about (1) extending their observations to a larger panel of MPI high and low cell lines, (2) further develop their characterization of the effects of mannose on the TCA cycle and dNTPs, and (3) examine the causality for dNTP depletion in genomic instability.

Thank you for finding our manuscript being improved.

However, the stable isotope tracing experiments have problems that leave this point unaddressed. The authors chose U-13C6-glucose instead of 1,2-C2-glucose, and this is suitable to assay the TCA cycle. However, it is unsuitable to assay the non-oxidative and oxidative branches of the PPP because these rapidly saturate and labeling in R5P from either branch cannot be distinguished. This is very evident in Figure 6—figure supplement 4, where both 6PG and S7P are completely labeled at all time points and both conditions, with only the total levels different. It is unclear why mannose decreases the fraction labeling in Ru5P + R5P specifically. Therefore, the authors cannot conclude that mannose decreases flux into the oxidative and non-oxidative branches of the PPP.

The reviewer is correct that 1,2-13C2-glucose is suitable for analyzing oxidative and non-oxidative arms of the PPP. However, as explained in our response to Essential revision 2, it is technically difficult to accurately estimate the dynamic labeling for metabolic intermediates of the PPP, as well as those of glycolysis, due to their very rapid saturation. This has also been evident in many excellent literatures, showing that isotopic steady state of glycolysis can be reached within minutes of 13C6-glucose administration (Lorkiewicz et al., 2019), and that glycolysis and the PPP often label at similar rates (Jang et al., 2018). Accordingly, we have revised the text by removing our claims for the flux of glycolysis and the PPP, but leaving descriptions for their rapid saturation (page 15, lines 19-22).

Labeling in other pathways have problems. It is very difficult to distinguish the isotopologues because of the colors used and the legend, which has too many possibilities beyond the masses possible.

We have addressed this issue in our response to Essential revision 1.

G6P and F6P are not labeled downstream of 13C-glucose (even in low mannose conditions), but are labeled starting at the F1,6P stage, which is highly unlikely since downstream metabolite labeling cannot exceed upstream metabolite labeling.

The reviewer raised a concern about precursor-product relationships. However, as mentioned above, isotopic steady state of glycolytic intermediates can be reached within minutes after the addition of 13C6-glucose (Lorkiewicz et al., 2019), making it technically difficult to address this issue in a satisfactory manner.

The tracing also does not provide insight into how mannose is impairing nucleotide synthesis – PRPP labeling is similar but IMP levels (but maybe not fraction labeling, it's unclear) dramatically decrease.

To substantiate our claim that mannose impairs nucleotide biosynthesis, we reanalyzed data obtained from 13C6-glucose tracing experiments by estimating fractional enrichment of metabolites in a dynamic or sub-dynamic labeling phase. The results clearly indicate that mannose causes sever defects in the early stages of purine and pyrimidine metabolism as follows.

To further elucidate the impacts of mannose challenge on the biosynthesis of dNTPs, we performed [13C6]-glucose tracer experiments that, unlike steady-state metabolomics, allow to estimate the activity of glucose-related metabolic pathways by analyzing the fractional enrichment of the metabolites in a dynamic labeling phase (Lorkiewicz et al., 2019). The MPI-KO HT1080 cells were preconditioned under mannose-challenged or -unchallenged conditions before [13C6]-glucose labeling. The 13C-labeling of most metabolites detected in glycolysis, the PPP and the TCA cycle (Figure 6—figure supplement 1A) already reached isotopic steady states within 30 min in both mannose-challenged and unchallenged cells (Figure 6—figure supplement 1B), most likely due to their high metabolic activity (Jang et al., 2018; Lorkiewicz et al., 2019). In contrast to these central metabolic pathways, metabolites in purine and pyrimidine metabolism showed a dynamic or sub-dynamic labeling in mannose-challenged cells.

Phosphoribosyl pyrophosphate (PRPP) is an essential ribose donor substrate for the biosynthesis of nucleotides in both purine and pyrimidine metabolism (Figure 6A, B and Figure 6—figure supplement 2A). Fractional enrichment of 13C-PRPP (M+5) was saturated at 30 min in unchallenged cells, while it was still in a sub-dynamic labeling phase in mannose-challenged cells (Figure 6C). Despite this slower fractional enrichment, the pool size of 13C-PRPP (M+5) in mannose-challenged cells was similar to that in unchallenged cells (Figure 6D), implying that mannose challenge reduced the utilization of PRPP for nucleotide biosynthesis. Consistent with this assumption, we found a marked reduction in both the fractional enrichment and the pool size of 13C-purine metabolic intermediates (M+5) in mannose-challenged cells, which included inosine-5’-monophosphate (IMP; Figure 6 E, F), adenosine-5’-monophosphate (AMP; Figure 6 G, H) and guanosine-5’-monophosphate (GMP; Figure 6 I, J).

PRPP is utilized for both de novo and salvage synthesis of purine nucleotides (Figure 6A), and therefore the M+5 fraction of 13C-IMP, 13C-AMP and 13C-GMP can be accounted for the sum of their de novo and salvage pools. In contrast, purine nucleotides with the labeled fractions greater than M+5 can originate from de novo synthesis (Figure 6A). We found a progressive increase in the M+6 fraction of 13C-IMP, 13C-AMP and 13C-GMP in unchallenged cells (Figure 6E, G and I), suggesting that 13C-10-formyl-tetrahydrofolate (CHO-THF; M+1), which is produced de novo via the glycolysis-serine biosynthesis-folate cycle (GSF) axis (Figure 6A) (Yang and Vousden, 2016), contributed to the de novo synthesis of purine nucleotides. Although we could not directly detect 13C-labeling in serine and CHO-THF in our metabolomic analysis, the fractional enrichment and the pool size of 13C-glycine (M+2), which is a signature metabolite produced in coupled with 5,10-methylenetetrahydrofolate via the GSF axis, progressively increased in unchallenged cells (Figure 6K, L). However, the fractional enrichment and the pool size of 13C-glycine (M+2) largely decreased in mannose-challenged cells (Figure 6K, L), suggesting that the GSF axis is compromised in these cells. These results may partly explain why the de novo synthesis of purine nucleotides is limited in honeybee syndrome.

In the early stage of pyrimidine metabolism, aspartate (Asp) is transferred to carbamoyl phosphate, giving rise to N-carbamoyl aspartate (carbamoyl Asp) (Figure 6—figure supplement 2A). We found a progressive increase in the M+2, M+3 and M+4 fractions of both 13C-Asp (Figure 6—figure supplement 2B, C) and 13C-carbamoyl Asp (Figure 6—figure supplement 2D, E) at similar labeling rates in unchallenged cells. The 13C-Asp (M+2, M+3 and M+4) could originate from [13C6]-glucose-derived oxaloacetate that is formed in the first, second and third rounds of the TCA cycle (Figure 6—figure supplement 2F), as indicated by the presence of the M+2, M+3 and M+4 fractions of 13C-malate (Figure 6—figure supplement 2G, H). However, mannose challenge severely decreased the fractional enrichment of both 13C-Asp (M+2, M+3 and M+4) and 13C-carbamoyl Asp (M+2, M+3 and M+4) (Figure 6—figure supplement 2B, D). In contrast, the pool size of 13C-Asp (M+2) remained relatively unchanged between mannose-challenged and -unchallenged cells (Figure 6—figure supplement 2C), while the pool size of 13C-carbamoyl Asp (M+2) greatly decreased in mannose-challenged cells (Figure 6—figure supplement 2E), indicating that mannose challenge reduced the utilization of Asp in pyrimidine metabolism. In the immediate downstream metabolites of carbamoyl Asp, we could detect significant amounts of uridine-5’-monophosphate (UMP), and found that a large majority of 13C-UMP formed in unchallenged cells was consisted of the M+5 fraction (Figure 6—figure supplement 2I, J). This fraction was most likely to originate from unlabeled carbamoyl Asp (Figure 6—figure supplement 2K) and 13C-PRPP (M+5). Moreover, we identified the M+6, M+7 and M+8 fractions of 13C-UMP in unchallenged cells (Figure 6—figure supplement 2I), indicating that both 13C-carbamoyl Asp (M+2, M+3 and M+4) and 13C-PRPP (M+5) contributed to forming 13C-orotidine-5’-monophosphate (M+7, M+8 and M+9), which is decarboxylated to give 13C-UMP (M+6, M+7 and M+8). However, mannose-challenged cells showed a substantial reduction of 13C-UMP in both the fractional enrichment (M+5, M+6, M+7 and M+8) and the pool size (M+5) (Figure 6—figure supplement 2I, J), clearly indicating that mannose challenge impaired the biosynthesis of pyrimidine nucleotides. As expected, dNTPs showed little 13C enrichment (dATP and dGTP, Figure 6—figure supplement 3A−E) and a very low 13C enrichment (dTTP and dCTP, Figure 6—figure supplement 3F−J) in mannose-challenged cells as compared with those in unchallenged cells. Taken all these findings together, mannose challenge impairs both purine and pyrimidine metabolism at the early stage, thereby potentially limiting the de novo synthesis of dNTPs.

Accordingly, we revised the Results section (from page 15, line 18 to page 18, line 17) and the Discussion section (page 22, lines 4-8 and page 22, lines 12-13).

Labeling with additional tracers (e.g. serine, glutamine, mannose) are really needed to parse where the defects in nucleotide synthesis are occurring, as suggested in the prior round of review.

The results obtained from our [13C6]-glucose tracing experiments have now indicated that mannose challenge impairs the early stages of the de novo synthesis of purine and pyrimidine nucleotides. For this reason, we believe that the additional tracer experiments are not essential to draw our conclusion.

Reviewer #2 (Recommendations for the authors):

The authors have somewhat addressed previous concerns. They evaluated additional cancer cell lines with high or low MPI expression, and the results are consistent with their overall conclusions. The authors have now shown that glutamine depletion reduces viability. However, it is surprising that the high mannose-challenged cells seem more resistant to glutamine starvation than unchallenged cells, given that mannose challenge severely blocks glucose flux into the TCA cycle. This needs to be resolved.

It is beyond the scope for this study to further demonstrate how mannose-challenged cells can be more tolerated with glutamine depletion than unchallenged cells. For this reason, we have removed the uninterpretable data (Figure 5—figure supplements 2E, F) and the related statements from manuscript.

Stable isotope tracing studies have not been performed in a satisfactory manner and do not support the manuscript's conclusions. The authors need to properly repeat tracing studies using 1,2-13C-glucose to determine glucose flux into oxidative vs. non-oxidative PPP as well as 13C-gluamine and 13C-mannose.

Please see our responses to Essential revision 2 and the Reviewer 1’s comment.

Statistical analysis should be provided for Figure 6.

We have performed statistical analysis on data presented in revised Figure 6 and revised Figure 6—figure supplements 1-3.

Finally, the authors report that OCR is increased in mannose challenged cells, but there is no significant change in the NAD/NADH ratio. This should be resolved.

It is beyond the scope for this study to further demonstrate why the NAD/NADH ratio did not change in mannose-challenged cells. For this reason, we have removed the uninterpretable data (Figure 5—figure supplements 2C, D) and the related descriptions from manuscript. We feel appropriate to tone down our claim for the activation of OXPHOS by mannose challenge, as a slight increase in OCR is the only evidence supporting this hypothesis. We have revised the text accordingly (page 13, line 22 to page 14, line 1).

References

Jang, C., Chen, L., and Rabinowitz, J.D. (2018). Metabolomics and Isotope Tracing. Cell 173, 822-837.

Lorkiewicz, P.K., Gibb, A.A., Rood, B.R., He, L., Zheng, Y., Clem, B.F., Zhang, X., and Hill, B.G. (2019). Integration of flux measurements and pharmacological controls to optimize stable isotope-resolved metabolomics workflows and interpretation. Sci Rep 9, 13705.

Yang, M., and Vousden, K.H. (2016). Serine and one-carbon metabolism in cancer. Nat Rev Cancer 16, 650-662.

https://doi.org/10.7554/eLife.83870.sa2

Article and author information

Author details

  1. Yoichiro Harada

    Department of Glyco-Oncology and Medical Biochemistry, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    yoharada3@oici.jp
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1818-9633
  2. Yu Mizote

    Department of Cancer Drug Discovery and Development, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Takehiro Suzuki

    Biomolecular Characterization Unit, RIKEN Center for Sustainable Resource Science, Saitama, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Akiyoshi Hirayama

    1. Institute for Advanced Biosciences, Keio University, Yamagata, Japan
    2. Systems Biology Program, Graduate School of Media and Governance, Keio University, Kanagawa, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  5. Satsuki Ikeda

    Institute for Advanced Biosciences, Keio University, Yamagata, Japan
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Mikako Nishida

    Department of Immunology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Toru Hiratsuka

    Department of Oncogenesis and Growth Regulation, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Software, Formal analysis, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Ayaka Ueda

    Department of Molecular Biochemistry and Clinical Investigation, Graduate School of Medicine, Osaka University, Osaka, Japan
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Yusuke Imagawa

    Department of Oncogenesis and Growth Regulation, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Kento Maeda

    Department of Glyco-Oncology and Medical Biochemistry, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Yuki Ohkawa

    Department of Glyco-Oncology and Medical Biochemistry, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Junko Murai

    1. Institute for Advanced Biosciences, Keio University, Yamagata, Japan
    2. Division of Cell Growth and Tumor Regulation, Proteo-Science Center, Ehime University, Ehime, Japan
    3. Department of Biochemistry and Molecular Genetics, Graduate School of Medicine, Ehime University, Ehime, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Hudson H Freeze

    Human Genetics Program, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared
  14. Eiji Miyoshi

    Department of Molecular Biochemistry and Clinical Investigation, Graduate School of Medicine, Osaka University, Osaka, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Shigeki Higashiyama

    1. Department of Oncogenesis and Growth Regulation, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    2. Division of Cell Growth and Tumor Regulation, Proteo-Science Center, Ehime University, Ehime, Japan
    3. Department of Biochemistry and Molecular Genetics, Graduate School of Medicine, Ehime University, Ehime, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  16. Heiichiro Udono

    Department of Immunology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  17. Naoshi Dohmae

    Biomolecular Characterization Unit, RIKEN Center for Sustainable Resource Science, Saitama, Japan
    Contribution
    Data curation, Formal analysis, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5242-9410
  18. Hideaki Tahara

    1. Department of Cancer Drug Discovery and Development, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    2. Project Division of Cancer Biomolecular Therapy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  19. Naoyuki Taniguchi

    Department of Glyco-Oncology and Medical Biochemistry, Research Institute, Osaka International Cancer Institute, Osaka, Japan
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared

Funding

The Takeda Science Foundation

  • Yoichiro Harada

The Rocket Fund

  • Hudson H Freeze

National Institutes of Health (R01DK99551)

  • Hudson H Freeze

KAKENHI (JP23K06645)

  • Yoichiro Harada

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We wish to thank our laboratory members, Drs. Takashi Akazawa and Yosuke Matsuoka (Osaka International Cancer Institute), for fruitful discussions, Dr. Steven A Rosenberg for MCA205 cells, and Drs. Kazuki Nakajima (Gifu University), Takuro Tojima (RIKEN), Michiko Kodama (Osaka University) and Natsuki Osaka (Keio University) for technical advice and support. We thank Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. This study was funded by The Takeda Science Foundation (YH), JSPS KAKENHI Grant Number JP23K06645 (YH), The Rocket Fund (HHF), and R01DK99551 (HHF).

Senior Editor

  1. Richard M White, Ludwig Institute for Cancer Research, University of Oxford, United Kingdom

Reviewing Editor

  1. Gina M DeNicola, Moffitt Cancer Center, United States

Reviewer

  1. John A Hanover, National Institute of Diabetes and Digestive and Kidney Diseases, United States

Version history

  1. Received: September 30, 2022
  2. Preprint posted: October 17, 2022 (view preprint)
  3. Accepted: June 12, 2023
  4. Version of Record published: July 18, 2023 (version 1)

Copyright

© 2023, Harada et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yoichiro Harada
  2. Yu Mizote
  3. Takehiro Suzuki
  4. Akiyoshi Hirayama
  5. Satsuki Ikeda
  6. Mikako Nishida
  7. Toru Hiratsuka
  8. Ayaka Ueda
  9. Yusuke Imagawa
  10. Kento Maeda
  11. Yuki Ohkawa
  12. Junko Murai
  13. Hudson H Freeze
  14. Eiji Miyoshi
  15. Shigeki Higashiyama
  16. Heiichiro Udono
  17. Naoshi Dohmae
  18. Hideaki Tahara
  19. Naoyuki Taniguchi
(2023)
Metabolic clogging of mannose triggers dNTP loss and genomic instability in human cancer cells
eLife 12:e83870.
https://doi.org/10.7554/eLife.83870

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https://doi.org/10.7554/eLife.83870

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