Mitochondrial UQCC3 Modulates Hypoxia Adaptation by Orchestrating OXPHOS and Glycolysis in Hepatocellular Carcinoma

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Mitochondrial UQCC3 Modulates Hypoxia Adaptation by Orchestrating OXPHOS and Glycolysis in Hepatocellular Carcinoma Graphical Abstract

Authors Yun Yang, Guimin Zhang, Fengzhu Guo, ..., Jia Gan, Lin Xu, Hanshuo Yang

Correspondence [email protected]

In Brief Yang et al. demonstrate that mitochondrial UQCC3 plays an indispensable role as HCC cells adapt to hypoxia. UQCC3 forms a positive feedback loop with mitochondrial ROS to sustain mitochondrial homeostasis, HIF1a stabilization, and glycolytic activity in hypoxia and thus promotes bioenergetic reprogramming of HCC cells by simultaneously regulating OXPHOS and glycolysis.

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UQCC3 is indispensable for bioenergetic reprogramming of HCC cells in hypoxia

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UQCC3 is required for mitochondrial homeostasis and OXPHOS in hypoxia

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Deficiency of UQCC3 impairs glycolysis and HIF-1a stabilization in hypoxia

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UQCC3 and ROS generate positive feedback in hypoxia

Yang et al., 2020, Cell Reports 33, 108340 November 3, 2020 ª 2020 The Authors. https://doi.org/10.1016/j.celrep.2020.108340

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Mitochondrial UQCC3 Modulates Hypoxia Adaptation by Orchestrating OXPHOS and Glycolysis in Hepatocellular Carcinoma Yun Yang,1 Guimin Zhang,1 Fengzhu Guo,2 Qiqi Li,1 Hui Luo,1 Yang Shu,1 Yuge Shen,1 Jia Gan,1 Lin Xu,1 and Hanshuo Yang1,3,4,* 1State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China 2Lung Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China 3Experimental and Research Animal Institute, Sichuan University, Chengdu, China 4Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.celrep.2020.108340

SUMMARY

Bioenergetic reprogramming during hypoxia adaption is critical to promote hepatocellular carcinoma (HCC) growth and progression. However, the mechanism underlying the orchestration of mitochondrial OXPHOS (oxidative phosphorylation) and glycolysis in hypoxia is not fully understood. Here, we report that mitochondrial UQCC3 (C11orf83) expression increases in hypoxia and correlates with the poor prognosis of HCC patients. Loss of UQCC3 impairs HCC cell proliferation in hypoxia in vitro and in vivo. Mechanistically, UQCC3 forms a positive feedback loop with mitochondrial reactive oxygen species (ROS) to sustain UQCC3 expression and ROS generation in hypoxic HCC cells and subsequently maintains mitochondrial structure and function and stabilizes HIF-1a expression to enhance glycolysis under hypoxia. Thus, UQCC3 plays an indispensable role for bioenergetic reprogramming of HCC cells during hypoxia adaption by simultaneously regulating OXPHOS and glycolysis. The positive feedback between UQCC3 and ROS indicates a self-modulating model within mitochondria that initiates the adaptation of HCC to hypoxic stress.

INTRODUCTION Hepatocellular carcinoma (HCC) is the major type of primary liver cancer and the sixth most common cancer in the world (Bray et al., 2018). Hypoxia is a common phenomenon of HCC, and it can be induced during clinical treatments aiming to restrict tumor growth through the blockade of blood supply, such as transcatheter arterial embolization or chemoembolization or some multi-targeted tyrosine kinase inhibitors (Semenza, 2012). The adaption of HCC to hypoxia is associated with aggressive phenotypes, resistance to chemotherapy and radiotherapy, and poor prognosis (Ba´rcena-Varela et al., 2019). However, the mechanisms underlying this important process are still not clear. Bioenergetic metabolism reprogramming in HCC cells is a vital adaptive response to hypoxic stress (Shi et al., 2009). Alternation between glycolysis and oxidative phosphorylation (OXPHOS) in response to hypoxia provides metabolic flexibility to favor tumor cell survival (Schito and Rey, 2018). Mitochondria are responsible for 70%–90% of total oxygen consumption and rapidly sense and integrate stress signals to coordinate intracellular pathways for the adaptions to environmental stresses such as hypoxia (Chandel, 2010; Chen et al., 2009). Reducing mitochondrial function is therefore an effective way to decrease total oxygen consumption to make cells adapt to hypoxia and result in the

compensatory increase of glycolysis (Hsu and Sabatini, 2008). Glycolysis is indispensable for hypoxic tumor cells, but OXPHOS is a more efficient approach than glycolysis for the generation of ATP. It is not reasonable for tumor cells to abolish this highly efficient process for ATP generation in low-oxygen conditions. Studies have demonstrated that varying degrees of OXPHOS are still maintained in hypoxic tumor cells (Ashton et al., 2018; Molina et al., 2018). The stabilization of hypoxia-inducible factor -1a (HIF-1a), the master regulator of hypoxia adaption, also depends on functional mitochondria in hypoxia (Tormos and Chandel, 2010), so mitochondrial OXPHOS and cytosolic glycolysis are thought to cooperate to maintain the cellular energetic balance. However, how OXPHOS and glycolysis are orchestrated during tumor cells’ adaption to hypoxia is still not clear. Complex III of the mitochondria OXPHOS system couples the oxidation of ubiquinol and reduction of cytochrome c to the translocation of protons across the mitochondrial inner membrane. Most patients with complex III deficiency die in early childhood because of mitochondria-deficiency-related symptoms (Gil Borlado et al., 2010; Wanschers et al., 2014). Ubiquinol-cytochrome c reductase complex assembly factor 3 (UQCC3), also named C11orf83, was reported as a human complex III assembly factor and is involved in the early assembly stages by stabilizing supercomplexes that contain complex III,

Cell Reports 33, 108340, November 3, 2020 ª 2020 The Authors. 1 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Figure 1. Overexpressed UQCC3 in Hypoxia Is Associated with HCC Progression and Poor Survival (A) Immunoblots confirming increased UQCC3 in hypoxia for indicated hours. (B) Representative images and statistics of UQCC3 expression on different regions in the HepG2 xenograft (n = 5). Scale bar: 100 mm. (C) Representative images and statistics showing the overlap between hypo-probe and UQCC3 expression using the HepG2 xenograft (n = 5). Scale bar: 200 mm. (D and E) UQCC3 expression in human HCC based on TCGA and NCBI Wurmbach cohorts. (F and G) Representative images (left) and statistical analyses (right) showing the relevance between UQCC3 expression and pathological (Patho) or clinical (Clini) stages based on 90 HCC patients. Scale bar: 50 mm. (H) Survival curves of HCC patients with higher or lower expression of UQCC3 based on tissue array and TCGA. *p < 0.05; **p < 0.01. Data are presented as the mean ± SD.

especially the IIIn/IV supercomplex (Desmurs et al., 2015). Cells from a patient with a homozygous missense mutation in UQCC3 have reduced complex III activity and impaired OXPHOS (Wanschers et al., 2014). In this study, we revealed that UQCC3 plays a critical role upstream of bioenergetic metabolism reprogramming during HCC adaptation to hypoxia. RESULTS UQCC3 Is Elevated during Hypoxia Adaptation and Correlated with HCC Poor Prognosis To explore the response of UQCC3 to hypoxia in HCC cells, we determined UQCC3 protein expression under hypoxic stress. UQCC3 expression was elevated in hypoxic HCC cells (1% oxygen; Hypo group) in vitro (Figure 1A) and increased gradually

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from the edge to the inner regions in HepG2 xenografts (Figure 1B), in which hypoxic cells had higher expression of UQCC3 (Figure 1C). These data revealed that UQCC3 is an oxygen-sensitive protein that is highly expressed in hypoxic HCC cells in vitro and in vivo. However, knockdown of HIF-1a did not impair UQCC3 expression at mRNA and protein levels in hypoxic HepG2 cells (Figures S1A and S1B), which indicates that UQCC3 was not a target of HIF-1a. We next examined whether UQCC3 was associated with the progression and survival of HCC patients. The analysis using two independent datasets from The Cancer Genome Atlas (TCGA) and NCBI GEO (Wurmbach cohort; GEO: GSE6764) showed that UQCC3 was significantly overexpressed in HCC tumor tissues compared with non-tumor tissues (Figure 1D; TCGA, p = 3E
14, n = 422; Wurmbach, p = 0.027, n = 45). In addition,

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(A) Growth curves of HepG2 cells with UQCC3-KO under normoxia and hypoxia for 4 days (n = 4). (B) Growth curve showing the rescuing effect using UQCC3-OE on HepG2-UQCC3-KO cells in hypoxia (n = 4). (C) Growth curve of xenografts using HepG2UQCC3-KO cells (n = 5). (D) Immunoblots showing Ki67 and PCNA expression in HepG2-UQCC3-KO cells under normoxia and hypoxia for 3 days. (E) Representative immunohistochemistry (IHC) images (left) and statistical analysis (right) of Ki67 expression in UQCC3-KO xenografts. Scale bar: 100 mm. (F) Distribution of different phases during the cell cycle in HepG2-UQCC3-KO cells in normoxia and hypoxia for 72 h (sub-G1, events with <2N DNA content). (G) Statistical analysis of annexin V-positive cells among HepG2-UQCC3-KO cells in normoxia and hypoxia for 3 days by flow cytometry. *p < 0.05; **p < 0.01; NS, no significance. Data are presented as the mean ± SD.

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Figure 2. UQCC3 Deletion Diminishes HCC Cell Proliferation in Hypoxia

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UQCC3 expression increased with gradually advanced stages (Figure 1E), the advanced pathological stage (Figure 1F), or the late clinical stage (Figure 1G). In the overall survival of patients with HCC, higher expression of UQCC3 was significantly associated with poor prognosis (Figure 1H; tissue assay, n = 70, p = 0.001; TCGA, n = 355, p = 2E
4). Altogether, these results highlight the clinical significance of UQCC3 for HCC progression and patient survival. Loss of UQCC3 Impedes HCC Cell Proliferation in Hypoxia We next dissected the impact of UQCC3 on HCC cell growth in hypoxia. UQCC3 was knocked out in human HCC cell lines (HepG2, Hep-3B, and HuH7) using the CRISPR-Cas9 system. The relative growth of UQCC3 knockout (KO) cells was equal to that of knockout control (KO-Con) cells in vitro under normoxia (21% oxygen) but was markedly retarded in hypoxia (Figures 2A,

S2A, and S2B; Norm group). Supplemental expression of UQCC3 effectively rescued the inhibition of UQCC3-KO on HepG2 cell growth in hypoxia (Figure 2B), which showed the specific role of UQCC3 in hypoxia. In subcutaneous xenograft models, HepG2-UQCC3-KO tumors grew more slowly than controls (Figure 2C), which indicates the necessity of UQCC3 for HCC tumor growth in vivo. Growth delay might result from either reduced proliferation or increased cell death such as apoptosis. We found that proliferative markers PCNA and Ki67 were both markedly attenuated in UQCC3-KO cells under hypoxia in vitro (Figure 2D) and in HepG2-UQCC3-KO xenografts in vivo (Figure 2E). Ki67 is an important protein that controls the cell cycle of proliferative cells and is absent from quiescent cells but present during all active phases of the cell cycle (Sobecki et al., 2017). Cellcycle analysis showed that UQCC3-KO resulted in marked G1 phase arrest in hypoxia, but the sub-G1 cell population in all samples was less than 1% (events of sub-G1 with <2N DNA content; Figure 2F), which indicates that cell death was not the major event. These findings were consistent with assays evaluating apoptosis and/or necrosis using annexin V/phosphatidylinositol (PI) double staining, in which PI-positive cells were also less than 1% and annexin V-positive cells were less 10% (Figures 2G and S2C). Altogether, these results indicated that the growth retardation of UQCC3-KO cells under hypoxia resulted from the quiescence of HCC cells, but not cell death. Cyclin-dependent kinase (CDK)/cyclin complexes and retinoblastoma protein (Rb) play critical roles in controlling the

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Figure 3. UQCC3 Is Required for Mitochondrial Homeostasis in HCC Cells under Hypoxia

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cell cycle (Salazar-Roa and Malumbres, 2017). We investigated the effects of UQCC3-KO on the expression of cyclin A/D/E, CDK2/4/6, and the phosphorylation of Rb1 under either normoxic or hypoxic conditions in HepG2, Hep-3B, and HuH7 cells. The results showed that hypoxia caused a general inhibitory effect on the expression of CDK and cyclin proteins; however, loss of UQCC3 specifically and immensely inhibited the expression of CDK2, cyclin E1, and phosphorylated Rb1 under hypoxia (Figures S2D–S2F). These results supported our observations (Figure 2F) that the cell cycle of UQCC3-deficient cells was arrested in the G1 stage under hypoxic conditions. UQCC3 Is Required for Mitochondrial Homeostasis and OXPHOS in Hypoxia UQCC3 is a mitochondrial protein (Desmurs et al., 2015; Wanschers et al., 2014). Transmission electron microscopy

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(A) Representative TEM images showing mitochondrial morphology within HepG2-UQCC3-KO cells 3 days after normoxia and hypoxia. The black triangle indicates a representative mitochondrion. Scale bar: 1 mm. (B and C) Flow cytometry (left) and statistical analysis (right) showing MtMP (upper; Mito potential) and mitochondrial mass (bottom; Mito mass) in HepG2-UQCC3-KO cells 3 days after normoxia and hypoxia. (D and E) OCR (n = 4) analysis using HepG2UQCC3-KO cells under hypoxia. (E) Complex III and V activity (n = 3) in HepG2-UQCC3-KO cells 72 h after normoxia and hypoxia. (F) ATP level in HepG2-UQCC3-KO cells cultured 72 h after normoxia and hypoxia by an ATP determination kit (n = 4). (G) ATP level and the relative cell number in HepG2-UQCC3-KO cells cultured in medium containing glucose (25 mM) or galactose (10 mM) 72 h after normoxia (n = 4). (H) ATP level and ATP/ADP ratio in HepG2UQCC3-KO cells 72 h after normoxia and hypoxia by targeted metabonomics. RMF, relative mean fluorescence. *p < 0.05; **p < 0.01; ***p < 0.001. Data are presented as the mean ± SD.

(TEM) revealed that UQCC3-KO cells had limited mitochondrial abnormalities in normoxia but showed severe internal structure disorders within mitochondria in hypoxia, manifesting as swelling morphology and less recognizable cristae (Figure 3A). The tetramethylrhodamine (TMRM) and MitoTracker assays revealed a significant decrease of mitochondrial membrane potential (MtMP) and mitochondrial mass in UQCC3-KO cells under hypoxia, but not normoxia, in HepG2, Hep-3B, and HuH7 HCC cell lines (Figures 3B, 3C, and S3A–S3D). Consistent with these results, the baseline oxygen consumption rate (OCR) and maximal respiration by addition of trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP) were significantly decreased in the three UQCC3-KO HCC cell lines with hypoxic exposure (Figures 3D, S3E, and S3F). The central function of mitochondria is to produce ATP. Therefore, we examined the contributions of UQCC3 to ATP production in HCC cells. Complex III generates membrane potential for ATP synthesis, and complex V symbolizes mitochondrial ability to generate ATP. The results showed that the activities of these complexes significantly decreased in hypoxic UQCC3-KO cells (Figure 3E). Bioluminescent detection showed the decrease of ATP production by 20% under normoxia but by 50% under hypoxia in UQCC3-KO cells (Figures 3F, S3G, and S3H). Under glucose conditions, UQCC3-

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Figure 4. Loss of UQCC3 Impaired Mitochondrial Activity and HIF Target Gene Expression in Hypoxia (A) ATP level within mitochondria (Mito) and cytoplasm (Cyto) from HepG2-UQCC3-KO cells under different oxygen levels (n = 4). (B) Quantitative analysis of relative ATP level using OXPHOS (2-DG) and glycolysis (Oligo) in HepG2-UQCC3-KO cells (KO-#1) 72 h after normoxia and hypoxia (n = 4). (C) ECAR analysis using HepG2-UQCC3-KO cells under hypoxia (n = 4). (D and E) Immunoblots using HepG2-UQCC3-KO cells or showing the rescuing effect of UQCC3-OE on UQCC3-KO cells 72 h after normoxia and hypoxia. (F) Representative images (left) and correlative analysis (right) by Spearman’s rank correlation test based on HCC tissue assay. Scale bar: 50 mm. (G) qRT-PCR analysis of CA9, GLUT1, and VEGFa (n = 3) using HepG2-KO cells. (H) Heatmap for the expression of HIF-1a target genes using the fragments per kilobase of transcript per million mapped reads (FPKM) value of RNA-seq using HepG2 cells expressing shRNA targeting UQCC3 (n = 3, biological repeats). (I) Cell proliferation determined by CCK-8 in HepG2-UQCC3-OE expressing shRNA targeting HIF-1a. IOD, integrated optical density; CCK-8, cell counting kit-8. *p < 0.05; **p < 0.01; ***p < 0.001. Data are presented as the mean ± SD.

KO reduced ATP levels but had no obvious impact on cell number, which demonstrates that the reduced ATP is not enough to impair cell growth. In galactose media, however, UQCC3-KO not only decreased ATP production but also significantly inhibited cell proliferation (Figure 3G), which suggests the importance of UQCC3 in mitochondrial activity and energy metabolism through the OXPHOS pathway. Furthermore, we quantified the amounts of ATP and ADP using targeted metabolomics to elucidate energy metabolites with ATP production and the ATP/ADP ratio. The results indicated that UQCC3-KO significantly lowered ATP amounts and the ATP/ADP ratio in hypoxic HCC cells (Figure 3H). Altogether, these results indicated the important role of UQCC3 in maintaining mitochondrial homeostasis (structure and function) and OXPHOS activity in hypoxic HCC cells.

Deficiency of UQCC3 Impairs Glycolysis and HIF-1a Stabilization in Hypoxia Mitochondrial OXPHOS and cytosolic glycolysis are two major pathways of ATP production. To discriminate the function of UQCC3 during glycolysis and OXPHOS, we isolated mitochondrial and cytosolic fractions of HepG2 cells by magnetic beads and determined ATP amounts. The results showed that UQCC3-KO decreased the ATP level only in mitochondria from normoxic cells but resulted in dual reduction of ATP production in both mitochondria and cytoplasm in the three HCC cell lines under hypoxia (Figures 4A and S4A). These finding show that the function of UQCC3 in biogenetics was not limited to mitochondria under hypoxia. We then precisely examined the contribution of UQCC3 to ATP production from two energetic pathways under normoxic

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Figure 5. UQCC3-ROS-Positive Feedback Stabilizes HIF-1a Protein (A) Flow analysis showing UQCC3-KO suppressed mROS in HepG2-UQCC3-KO cells 72 h after normoxia and hypoxia. (B and C) Immunoblots demonstrating (B) HIF-1a and (C) UQCC3 expression in HepG2 cells with BHA 24 h after normoxia and hypoxia. (D) Immunoblots of UQCC3 and HIF-1a in HepG2 cells with overexpressed UQCC3 and MitoQ 24 h after hypoxia. (E) Flow cytometry (left) and statistical analysis (right) showing the alteration of UQCC3 and mROS in hypoxic HepG2 cells (top) or HepG2-UQCC3-OE cells (bottom).

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ll Article and hypoxic conditions (Nishikawa et al., 2014) (Figure S4B). In normoxia, only blockage of mitochondrial OXPHOS produced a significant ATP decline to 38.85% (Figure 4B, Norm group, Oligo columns), which was consistent with the effect of 2-deoxy-D-glucose (2-DG) on glycolysis that ATP decreased by 39.58% (Figure 4B, Norm group, 2-DG columns). These results suggest that OXPHOS was the major source of energy production (about 60%) in normoxic HepG2 cells. Under hypoxic conditions, the energetic metabolism in HepG2 cells was reprogrammed. The contribution of OXPHOS to ATP production decreased to about 42% (Figure 4B, Hypo group, 2-DG columns; 27.19/63.99 z 42%) from 60% in normoxia (Figure 4B, Norm group, 2-DG columns), whereas glycolysis conversely increased to about 58% from 40%. ATP production from both OXPHOS and glycolysis decreased significantly because of UQCC3-KO in hypoxia (Figure 4B, Hypo group). Extracellular flux analyses further indicated the essential role of UQCC3 in maintaining glycolysis in hypoxic HCC cells (Figures 4C, S4C, and S4D). These results quantitatively demonstrated the critical role of UQCC3 in regulating OXPHOS under both normoxic and hypoxic conditions, but the regulation of glycolysis only occurred under hypoxia. Given that HIF-1a is a master regulator of glycolysis during hypoxia adaption, we investigated whether HIF-1a was regulated by UQCC3 in hypoxia. The HIF-1a protein accumulated in hypoxic conditions, but HIF-1a was inhibited when UQCC3 was knocked out or interfered with in HCC cells, whereas hydroxylated HIF (HIF-OH) was elevated (Figures 4D and S4E–S4G). When UQCC3 was re-expressed in UQCC3-KO HCC cells, HIF-1a expression was recovered (Figure 4E), which indicates the important role of UQCC3 in HIF-1a stabilization in hypoxia. This was supported by the observation that UQCC3 correlated with the expressions of HIF-1a in consecutive HCC tissue sections (Figure 4F). Transcriptional expressions of carbonic anhydrase 9 (CA9), glucose transport 1 (GLUT1), and vascular endothelial growth factor a (VEGFa) were significantly blunted when UQCC3 was absent from hypoxic HCC cells (Figures 4G, S4H, and S4I). RNA sequencing (RNA-seq) data confirmed that HIF-1 target genes were generally suppressed when UQCC3 was knocked down by short hairpin RNA (shRNA) against UQCC3 in hypoxic HepG2 cells (Figure 4H). We then examined whether HIF-1a is the major downstream mechanism underlying UQCC3 and modulating HCC cell growth in hypoxia. HepG2 cells overexpressing UQCC3 were transfected with shRNA targeting HIF-1a. The results revealed that the increase of cell growth caused by UQCC3 overexpression (UQCC3-OE) was almost abolished once HIF-1a was knocked down (Figure 4I).

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UQCC3-ROS-Positive Feedback Stabilizes UQCC3 and HIF-1a in Hypoxia We uncovered the mechanism underlying how mitochondrial UQCC3 stabilizes cytosolic HIF-1a. It has been reported that mitochondrial reactive oxygen species (mROS) contributed to HIF-1a stabilization in hypoxia by modulating the activities of prolyl hydroxylases (Schieber and Chandel, 2014). Thus, we hypothesized that UQCC3 stabilizes HIF-1a by regulating the generation of mROS in hypoxia. We confirmed that HCC cells produced more mROS when they were exposed to hypoxic stress (Figures 5A, S5A, and S5B, dotted line) and UQCC3-KO lightly deceased mROS in normoxia (Figures 5A, S5A, and S5B, Norm group), but elevated mROS was almost abolished when UQCC3 was deleted in hypoxia (Figures 5A, S5A, and S5B, Hypo group). Butylated hydroxyanisole (BHA) is generally used as the ROS scavenger (Briggs et al., 2016), and HIF-1a was destabilized by BHA in hypoxic HCC cells (Figure 5B). Unexpectedly, BHA also reduced the UQCC3 protein that should be enriched by hypoxic stress (Figure 5C). To reveal the function of mROS in UQCC3 expression, we used another antioxidant, MitoQ, which is structurally unrelated to BHA but specifically targeted to mROS (Mao et al., 2013). MitoQ drastically decreased UQCC3 expression in hypoxia and suppressed the enhancement of HIF-1a accumulation promoted by UQCC3-OE in hypoxia (Figure 5D). Similar results were observed in Hep-3B and HuH7 cells (Figures S5C and S5D). Altogether, these results indicated that UQCC3 and mROS interact reciprocally in hypoxic HCC cells. To identify the initial factor that triggers the positive feedback between UQCC3 and mROS in hypoxia, we detected the earliest time that mROS or UQCC3 increased in hypoxic HCC cells by flow cytometry. In HepG2 cells, the results showed that increased ROS was detected at 10 min, whereas UQCC3 did not increase until 90 min (Figure 5E). Importantly, although UQCC3 had a dramatic increase at 90 min, mROS also had a drastic rise after hypoxic treatment (Figure 5E), which unveiled the leading role of mROS and supported the reciprocal interaction between UQCC3 and mROS in response to hypoxia. This was confirmed in UQCC3-overexpressing HepG2 cells (HepG2-UQCC3-OE). UQCC3 increased early at 20 min in HepG2-UQCC3-OE cells, whereas it occurred at 90 min in HepG2 cells (Figure 5E). mROS had more obvious elevation at 10 min in UQCC3-OE cells than in control cells (Figure 5E). These data revealed that UQCC3-OE promoted the earlier establishment of UQCC3-mROS positive feedback in hypoxic HCC cells. We next studied how mROS supported UQCC3-OE in hypoxia. The mitochondrial metalloprotease OMA1 is a substantial component of the quality control system to remodel cristae and can cleave UQCC3 upon mitochondrial stress (Chan et al., 2011; Desmurs et al., 2015). We thus hypothesized that OMA1

(F) Immunoblots showing the chain reaction in the OMA1-UQCC3-HIF-1a axis under hypoxia (left) and with MitoQ 24 h after hypoxia (right). (G) Immunoblots showing the alteration by OMA1 overexpression 72 h after normoxia and hypoxia. (H) Proposed model underscoring the new mechanism that the positive feedback between increased UQCC3 and elevated mROS modulates HIF-1a in hypoxia. (I) Cell proliferation determined by CCK-8 in HepG2 cells with UQCC3-OE or HIF-1a overexpression or treated with MitoQ. (J) Cell proliferation determined by CCK-8 in HepG2-UQCC3-KO cells in medium containing H2O2 (25 mM). *p < 0.05; **p < 0.01. Data are presented as the mean ± SD.

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ll Article expression may decrease during hypoxia, which results in upregulated UQCC3. Analysis of OMA1 protein expression confirmed that OMA1 expression was decreased by hypoxia (Figure 5F, left panel) and significantly recovered to the normoxic level when mROS was scavenged by MitoQ (Figures 5F, right panel, and S5E), which indicates the contribution of mROS to the downregulation of OMA1 during hypoxia. Moreover, we validated the direct contribution of OMA1 to UQCC3 expression. Overexpressed OMA1 markedly alleviated UQCC3 and HIF-1a in hypoxic HCC cells (Figure 5G), which strengthened the model that UQCC3 regulates HIF-1a stabilization through the regulation in ROS generation (Figure 5H). To address the connection of UQCC3-mROS-HIF-1a in the regulation of HCC cell growth, UQCC3 or HIF-1a was overexpressed in HepG2 cells and the cells were treated with MitoQ. The results showed that UQCC3-OE markedly enhanced HCC cell growth only under hypoxia, but once the scavenger cleared mROS, the increased cell growth induced by UQCC3-OE was almost abolished. The MitoQ treatment effects on growth could be partially but significantly rescued by HIF-1a expression (Figure 5I). HIF-1a knockdown also significantly alleviated the promotion of UQCC3-OE to cell growth in hypoxia, and there was no significant difference between MitoQ treatment and HIF-1a knockdown (Figure S5F). Conversely, exogenous supplementation of H2O2 into the medium of HepG2 cells, which increases ROS level within cells (Chandel et al., 2000), partly but significantly rescued the inhibition of cell growth caused by UQCC3KO in hypoxia (Figure 5J). Altogether, these results indicated that UQCC3 promotion of cell growth in hypoxic HCC cells mostly depends on mROS-HIF-1a. UQCC3 Is Indispensable for Bioenergetic Reprogramming in HCC Cells To obtain a panoramic view of the mechanism underlying the quiescence of UQCC3-deficient HCC cells in hypoxia, we analyzed the transcriptomics of UQCC3-deficient and control cells in hypoxic conditions. The RNA-seq results showed that UQCC3 deficiency resulted in significant gene expression alterations in cells in hypoxia for 72 h (Figure S6A). Biological processes including the cell cycle, ROS metabolic process, and ATP metabolic processes were altered, whereas cell death processes such as apoptosis, autophagy, and necroptosis showed no significant change (Figure 6A, left panel), which was consistent with the previously described results (Figures 2F and 2G). The cellular component analysis revealed that most significant differential genes were closely associated with mitochondria (Figure 6A). Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that OXPHOS, along with several metabolic pathways, was significantly changed (Figure 6B).

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Expression of genes of complex I–V components that constitute to electron transport chain (ETC) and O2-coupled ATP synthesis (OXPHOS) generally decreased in hypoxia at 72 h (Figure 6C), which suggests comprehensive inhibition of mitochondrial activities and biogenesis. These results indicated that the quiescence of UQCC3-deficient HCC cells under hypoxia might result from the insufficient supply of energetic metabolism. Principal-component analysis based on targeted metabolomics data demonstrated a distinct separation of UQCC3-KO and control cells in hypoxia (Figure S6B), which indicates the critical role of UQCC3 in energy metabolism under hypoxia. The detailed analyses revealed that 11 metabolites were altered in UQCC3 null cells, including oxaloacetate, lactate, ATP, AMP, isocitrate, guanosine monophosphate (GMP), malate, thiamine pyrophosphate (TPP), guanosine diphosphate (GDP), fumarate, and NADH (Figure S6C). ATP, the major energy currency, was significantly decreased in UQCC3-deficient cells (Figure 6D), as previously demonstrated (Figure 3H). The NADH/NAD+ ratio was attenuated in HepG2-UQCC3-KO cells (Figure 6E), which indicates severe impairment of metabolic status in hypoxia (Canto´ et al., 2015), and the result of hypoxia-induced alteration was consistent with the literature (Garcia-Bermudez et al., 2018). Given that tumor cells are mostly addicted to glycolysis to obtain energy in hypoxia, we analyzed the effects of UQCC3 deficiency on glycolytic enzyme genes based on RNA-seq data. The results showed that almost all glycolytic enzyme genes decreased to various levels when UQCC3 was absent, with ENO3 and ALDOC genes showing a 40%–50% decrease (Figure 6F). Similar results were observed in Hep-3B and HuH7 cells by qRT-PCR (Figures S6D and S6E), which indicates the general inhibition of glycolysis in HCC cells. We then performed bioenergetic phenotype profiling analysis to reveal the function of UQCC3 in regulating bioenergetic reprogramming of HCC cells during hypoxia adaption. The results showed that hypoxia promoted the energetic metabolism of HCC cells shifting to extracellular acidification rate (ECAR) (glycolysis) and accompanied by the decrease of OCR (OXPHOS). However, when UQCC3 was absent, OCR decreased further in hypoxia and the elevated ECAR decreased to the level comparable to that in normoxia (Figure 6G). These results not only confirmed our view that mitochondria were still functional in hypoxia but also demonstrated the contributions of UQCC3 to energy production in hypoxic HCC cells. UQCC3 Is Critical to Spontaneous Oncogene-Primed Hepatocarcinogenesis and HCC Progression In Vivo Hypdrodynamic transfection (HDT) via the tail-vein injection of oncogenes has been successfully used to establish an endogenous genetic model of liver cancer in mice (Brown et al.,

Figure 6. UQCC3 Contributes to Metabolic Reprogramming in HCC (A and B) Enrichment analysis identifying functional tendency using different genes based on (A) GO and (B) the KEGG pathway. (C) Heatmaps of each complex composed of genes based on FPKM value (n = 3, biological repeats). (D) Heatmaps of energetic metabolites in HepG2-UQCC3-KO cells 72 h after normoxia and hypoxia (n = 3, biological repeats). (E) Before-and-after graphs indicating the alteration of the NADH/NAD+ ratio normalized as 1 by the mean value of the normoxic KO-Con group. (F) Transcriptional levels of glycolysis-associated enzymes based on FPKM value. (G) Energy phenotype test measuring the two major energy-producing pathways in live HepG2-UQCC3-KO cells in normoxia and hypoxia (n = 4). *p < 0.05; **p < 0.01; ***p < 0.001. Data are presented as the mean ± SD.

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E

H

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Figure 7. UQCC3 Is Essential for Oncogene-Primed Hepatocarcinogenesis In Vivo

(A) Schema for the oncogene-primed HCC model by HDT with indicated plasmids into UQCC3Dhep and UQCC3F/F mice with a C57BL/6 background. (B) H&E histology along with corresponding gross morphology using liver tissues from indicated mice six weeks after HDT. Scale bar: 100 mm. (C) Liver-to-body ratio (left) and girth (right) showing statistical significance between the two groups six weeks after HDT. (D) Kaplan-Meier curve of indicated mice subjected with HDT. (E) Representative TEM images showing mitochondrial morphology in liver tissues from indicated mice six weeks after HDT. Scale bar: 0.5 mm. (F) ATP level in liver cancer tissues of indicated mice. (G) Immunoblots showing the HIF-1a protein level in liver tissues of indicated mice. (H) Representative images (left) and statistical analysis (right) of Ki67 expression using liver tissues from indicated mice after HDT. Scale bar: 100 mm. (I) Growth curves of primary hepatoma cells isolated from indicated mice in normoxia and hypoxia for 4 days (n = 4). *p < 0.05; **p < 0.01. Data are presented as the mean ± SD.

2018). Co-expression of activated AKT and N-Ras by HDT provided cooperative evidence in vivo for hepatocarcinogenesis, during which the major effect of the interaction is the increase of cell proliferation, which leads to fast malignant transformation and tumor progression (Ho et al., 2012). This system has been used to study the function of individual genes or genes within signaling that are involved in abnormal cell proliferation (Ho et al., 2012; Moon et al., 2017). Thus, this model is suitable for the study of UQCC3 function in regulating abnormal cell proliferation during hepatic tumorigenesis and progression. We generated UQCC3Dhep mice in which UQCC3 was specifically deleted from liver (Figures S7A–S7C); these mice showed no significantly differences in liver function compared with control mice (UQCC3F/F) (Figure S7D). The two mouse strains were subjected to HDT, along with oncogenes encoding myr-AKT

10 Cell Reports 33, 108340, November 3, 2020

and NRasV12 (Figure 7A). Six weeks post-injection, UQCC3F/F livers showed that excessive nodular lesions occupied most of the liver surface with typically malignant pathomorphology. Conversely, UQCC3Dhep mice barely presented tumor nodules on the liver surface but had normal hepatocytes and stromal cells (Figure 7B). The liver/body ratio and abdominal girth in UQCC3Dhep mice was close to normal, whereas they were significantly elevated in UQCC3F/F mice (Figure 7C). Increased survival of UQCC3Dhep mice was observed in comparison with the UQCC3F/F mice (Figure 7D), which confirms that hepatocarcinogenesis was suppressed by UQCC3 deletion in the genetic context of myr-AKT plus NRasV12. Furthermore, we explored the contribution of UQCC3 deletion to mitochondrial function and cell proliferation in primary HCC from mouse models. The TEM images showed distorted and atypical mitochondria with vacuoles or abnormal cristae in the HCC cells from UQCC3Dhep

ll Article mice, but not in those from UQCC3F/F mice (Figure 7E). ATP determination using liver tumor tissues demonstrated a significant reduction of ATP in UQCC3Dhep mice compared with UQCC3F/F mice (Figure 7F). HIF-1a expression was markedly decreased in UQCC3-KO mice compared with controls (Figure 7G). We observed a significant decrease in Ki67 staining in cancer tissues from UQCC3Dhep mice (Figure 7H). The primary hepatoma cells from UQCC3Dhep mice showed slower growth in hypoxia compared with normoxia ex vivo (Figure 7I), which consolidates the specificity of UQCC3 to HCC cell proliferation in response to hypoxia. DISCUSSION In this study, we have shown that mitochondrial UQCC3 expression sensitively increased in hypoxic conditions, correlating with more progressive outcomes and shortened survival in patients. Different from the canonical reprogramming model in which hypoxia suppresses mitochondrial activities, we found that UQCC3 was indispensable for mitochondrial homeostasis and is required by both OXPHOS and glycolysis in hypoxic HCC cells. Moreover, UQCC3 functions critically in hypoxiainduced elevation of mROS and reciprocally forms a positive feedback loop with ROS. Tumor cells need to produce more ATP than most normal cells for proliferation, migration, biosynthesis of signaling molecules, and other activities (Porporato et al., 2018). Otto Warburg observed decades ago that tumor cells are apt to produce ATP through glycolysis, but not through mitochondrial OXPHOS, even when oxygen is sufficient; this is called the Warburg effect (Warburg et al., 1927). Warburg proposed that the defects of mitochondria were the reason for this metabolic trait. However, we now know that the mitochondrial function in cancer is not as simple as Warburg envisioned. Ongoing research has revisited its initial conclusion and highlighted the central role of mitochondria in cancer (Ashton et al., 2018; Bajzikova et al., 2019; Molina et al., 2018). Although mitochondrial OXPHOS in tumor cells is decreased compared with normal cells, many tumors maintain substantial mitochondrial OXPHOS to produce a significant fraction of ATP, seeing that defection or depletion of the mitochondrial genome impairs tumorigenesis (Bajzikova et al., 2019). Here, we uncover that UQCC3, a component of mitochondrial complex III, plays an essential role in reserving mitochondrial structure and function under hypoxic stress. UQCC3 deficiency generally downregulates expression of complex I–V components that are important to ETC and O2-coupled ATP synthesis (OXPHOS) in hypoxia, indicating the critical role of UQCC3 in mitochondrial activities and biogenesis in hypoxic conditions and suggesting the harmonious modulation of ETC as a monolithic biochemical machine in the face of lowoxygen stress. HIF is a master regulator of hypoxia adaption and glycolysis. HIF-1 accumulation in hypoxia impairs mitochondrial complex IV or I activities by decreasing COX4L1 or increasing NDUFA4L2 (Fukuda et al., 2007; Tello et al., 2011). HIF-1 also promotes high expression of glycolytic enzymes and enhances glucose uptake by upregulating the expression of glucose transporters (Denko, 2008). UQCC3

OPEN ACCESS

expression is required by HIF-1a stabilization in hypoxia and thus is important for ATP production from glycolysis. Collectively, UQCC3 is indispensable for bioenergetic reprogramming by orchestrating OXPHOS and glycolysis in hypoxic HCC cells. Mitochondria are the main oxygen sensors and play a central role in regulating HIF-1a stabilization in hypoxia. ROS produced from the mitochondrial respiratory chain is necessary and sufficient for hypoxia-dependent stabilization of HIF-1a in vitro and in vivo by preventing HIF-1a hydroxylation (Chandel et al., 1998; Ivan and Kaelin, 2017). We reveal that UQCC3-KO almost abolishes hypoxia-induced increase of ROS generation and is essential to HIF-1a stabilization. mROS is generated mostly from complexes I, II, and III of the ETC. ROS from complexes I and II exits in mitochondrial matrix, whereas complex III generates ROS in both the matrix and the intermembrane space (Sena et al., 2013). Only ROS in the intermembrane space can transmit through voltage-dependent anion channels (VDACs) and travel into the cytosol (Muller et al., 2004). Therefore, our results are apt to support the important role of complex III for HIF-1a stabilization. Hypoxia inhibited OXPHOS and mitochondrial functions, but studies have verified that varying degrees of OXPHOS and mitochondria functions are maintained in hypoxic tumor cells (Ashton et al., 2018; Molina et al., 2018; Tormos and Chandel, 2010). This means that there must be a finely intrinsic mechanism of maintaining OXPHOS and mitochondrial function in hypoxia to make cells adapt to hypoxic stress. Our studies showed that UQCC3 was increasingly expressed under hypoxia, which was critical to maintain mitochondrial structure and function under hypoxia and played an indispensable role in bioenergetic reprogramming by simultaneously regulating OXPHOS and glycolysis in hypoxic HCC cells. Moreover, UQCC3 and mROS generate positive feedback in hypoxia. So, we proposed that mitochondrial function was locally enhanced under hypoxia, although mitochondrial function remained inhibited generally by hypoxia. Otherwise, the condition caused by hypoxia would be worse. The local enhancement was not to rescue mitochondrial function to the level in normoxia but rather to maintain mitochondrial function at a necessary level to support tumor cells adapting to hypoxic stress. This is a novel self-modulating mechanism within mitochondria responding to hypoxia. It also provides a reasonable answer to the open question of how ROS generation is elevated paradoxically in repressed mitochondria under hypoxic conditions. Increasing lines of evidence have showed the important role of mROS for tumor cells in metabolic reprogramming and adaptation to hypoxia (Hamanaka et al., 2016; Papandreou et al., 2006). However, it remains elusive how ROS generation is elevated paradoxically from repressed mitochondria by hypoxia. Our findings in this study provide a potential mechanistic explanation to this open question. We uncover that the increase of ROS generation in hypoxia depends on UQCC3 expression. In turn, ROS is indispensable for the regulation of UQCC3 expression. Moreover, we reveal that the regulation of ROS to UQCC3 acts in the protein level by inhibiting the expression of metalloendopeptidase OMA1

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that can cleave UQCC3 protein in hypoxic stress. Thus, a selfenhanced positive feedback loop between UQCC3 and ROS forms locally in mitochondrial complex III, maintaining the homeostasis of mitochondria in the face of hypoxic stress. Then, luxuriant ROS generated from mitochondria diffuses into cytosol, initiating hypoxic adaptive cascades including HIF1a stabilization, tumor angiogenesis, and formation of immune-suppressive microenvironments. Consequently, UQCC3 null cells are more intolerant of hypoxia in vitro and less intolerant of tumorigenesis in vivo. Our findings in the present study provide clues for developing therapeutic approaches for HCC. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d

d

d

d

d

KEY RESOURCES TABLE RESOURCE AVAILABILITY B Lead Contact B Materials Availability B Data and Code Availability EXPERIMENTAL MODEL AND SUBJECT DETAILS B Cell lines and Animals B Xenograft models B Isolation and culture of primary liver tumor cells METHOD DETAILS B Histology B Western blot B Flow cytometry B Mitochondria isolation B Plasmid constructs and Lentivirus packaging B RNA extraction and qRT-PCR B TEM B ATP determination B Extracellular flux Analysis QUANTIFICATION AND STATISTICAL ANALYSIS B RNA-sequencing analysis B Targeted metabolomics analysis B TCGA, GEO and tissue assay data analysis B Statistical analysis MINHAS ET AL., 2019 ADDITIONAL RESOURCES

SUPPLEMENTAL INFORMATION

Article AUTHOR CONTRIBUTIONS H.Y. conceived the initial concept and designed the study. Y.Y. and H.Y. designed and performed the experiments and wrote the manuscript. Y.Y. and Y. Shu generated and analyzed the bioinformatic data. F.G., G.Z., Q.L., H.L., Y.S., J.G., and L.X. assisted with experiments. Y.Y., F.G., and G.Z. analyzed human pathology. Y.Y. and H.Y. performed metabolite analysis and obtained samples and clinical data. DECLARATION OF INTERESTS The authors declare no competing interests. Received: January 12, 2020 Revised: August 7, 2020 Accepted: October 9, 2020 Published: November 3, 2020 REFERENCES Akiel, M., Guo, C., Li, X., Rajasekaran, D., Mendoza, R.G., Robertson, C.L., Jariwala, N., Yuan, F., Subler, M.A., Windle, J., et al. (2017). IGFBP7 Deletion Promotes Hepatocellular Carcinoma. Cancer Res. 77, 4014–4025. Ashton, T.M., McKenna, W.G., Kunz-Schughart, L.A., and Higgins, G.S. (2018). Oxidative Phosphorylation as an Emerging Target in Cancer Therapy. Clin. Cancer Res. 24, 2482–2490. Bajzikova, M., Kovarova, J., Coelho, A.R., Boukalova, S., Oh, S., Rohlenova, K., Svec, D., Hubackova, S., Endaya, B., Judasova, K., et al. (2019). Reactivation of Dihydroorotate Dehydrogenase-Driven Pyrimidine Biosynthesis Restores Tumor Growth of Respiration-Deficient Cancer Cells. Cell Metab. 29, 399–416. Ba´rcena-Varela, M., Caruso, S., Llerena, S., A´lvarez-Sola, G., Uriarte, I., Latasa, M.U., Urtasun, R., Rebouissou, S., Alvarez, L., Jimenez, M., et al. (2019). Dual Targeting of Histone Methyltransferase G9a and DNA-Methyltransferase 1 for the Treatment of Experimental Hepatocellular Carcinoma. Hepatology 69, 587–603. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., and Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424. Briggs, K.J., Koivunen, P., Cao, S., Backus, K.M., Olenchock, B.A., Patel, H., Zhang, Q., Signoretti, S., Gerfen, G.J., Richardson, A.L., et al. (2016). Paracrine Induction of HIF by Glutamate in Breast Cancer: EglN1 Senses Cysteine. Cell 166, 126–139. Brown, Z.J., Heinrich, B., and Greten, T.F. (2018). Mouse models of hepatocellular carcinoma: an overview and highlights for immunotherapy research. Nat. Rev. Gastroenterol. Hepatol. 15, 536–554. Canto´, C., Menzies, K.J., and Auwerx, J. (2015). NAD(+) Metabolism and the Control of Energy Homeostasis: A Balancing Act between Mitochondria and the Nucleus. Cell Metab. 22, 31–53.

Supplemental Information can be found online at https://doi.org/10.1016/j. celrep.2020.108340.

Chan, N.C., Salazar, A.M., Pham, A.H., Sweredoski, M.J., Kolawa, N.J., Graham, R.L., Hess, S., and Chan, D.C. (2011). Broad activation of the ubiquitin-proteasome system by Parkin is critical for mitophagy. Hum. Mol. Genet. 20, 1726–1737.

ACKNOWLEDGMENTS

Chandel, N.S. (2010). Mitochondrial regulation of oxygen sensing. Adv. Exp. Med. Biol. 661, 339–354.

We are grateful to Junyan Liu (Academy of Life Sciences, University of Science and Technology of China) for image processing and quantitative analyses and the Laboratory of Pathology at West China Hospital for technical assistance, as well as assistance from Prof. Hongbo Hu of Sichuan University for plasmid constructs. This project was supported by grants from Natural Science Funding of China (81171956, 81772605, and 81572402) and the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYJC18008).

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Chandel, N.S., Maltepe, E., Goldwasser, E., Mathieu, C.E., Simon, M.C., and Schumacker, P.T. (1998). Mitochondrial reactive oxygen species trigger hypoxia-induced transcription. Proc. Natl. Acad. Sci. USA 95, 11715–11720. Chandel, N.S., McClintock, D.S., Feliciano, C.E., Wood, T.M., Melendez, J.A., Rodriguez, A.M., and Schumacker, P.T. (2000). Reactive oxygen species generated at mitochondrial complex III stabilize hypoxia-inducible factor1alpha during hypoxia: a mechanism of O2 sensing. J. Biol. Chem. 275, 25130–25138.

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Garcia-Bermudez, J., Baudrier, L., La, K., Zhu, X.G., Fidelin, J., Sviderskiy, V.O., Papagiannakopoulos, T., Molina, H., Snuderl, M., Lewis, C.A., et al. (2018). Aspartate is a limiting metabolite for cancer cell proliferation under hypoxia and in tumours. Nat. Cell Biol. 20, 775–781. Gil Borlado, M.C., Moreno Lastres, D., Gonzalez Hoyuela, M., Moran, M., Blazquez, A., Pello, R., Marin Buera, L., Gabaldon, T., Garcia Pen˜as, J.J., Martı´n, M.A., et al. (2010). Impact of the mitochondrial genetic background in complex III deficiency. PLoS ONE 5, e12801. Hamanaka, R.B., Weinberg, S.E., Reczek, C.R., and Chandel, N.S. (2016). The Mitochondrial Respiratory Chain Is Required for Organismal Adaptation to Hypoxia. Cell Rep. 15, 451–459. Ho, C., Wang, C., Mattu, S., Destefanis, G., Ladu, S., Delogu, S., Armbruster, J., Fan, L., Lee, S.A., Jiang, L., et al. (2012). AKT (v-akt murine thymoma viral oncogene homolog 1) and N-Ras (neuroblastoma ras viral oncogene homolog) coactivation in the mouse liver promotes rapid carcinogenesis by way of mTOR (mammalian target of rapamycin complex 1), FOXM1 (forkhead box M1)/SKP2, and c-Myc pathways. Hepatology 55, 833–845. Hsu, P.P., and Sabatini, D.M. (2008). Cancer cell metabolism: Warburg and beyond. Cell 134, 703–707. Ivan, M., and Kaelin, W.G., Jr. (2017). The EGLN-HIF O2-Sensing System: Multiple Inputs and Feedbacks. Mol. Cell 66, 772–779. Khiati, S., Baechler, S.A., Factor, V.M., Zhang, H., Huang, S.Y., Dalla Rosa, I., Sourbier, C., Neckers, L., Thorgeirsson, S.S., and Pommier, Y. (2015). Lack of mitochondrial topoisomerase I (TOP1mt) impairs liver regeneration. Proc. Natl. Acad. Sci. USA 112, 11282–11287. Mao, P., Manczak, M., Shirendeb, U.P., and Reddy, P.H. (2013). MitoQ, a mitochondria-targeted antioxidant, delays disease progression and alleviates pathogenesis in an experimental autoimmune encephalomyelitis mouse model of multiple sclerosis. Biochim. Biophys. Acta 1832, 2322–2331. Minhas, P.S., Liu, L., Moon, P.K., Joshi, A.U., Dove, C., Mhatre, S., Contrepois, K., Wang, Q., Lee, B.A., and Coronado, M. (2019). Macrophage de novo NAD(+) synthesis specifies immune function in aging and inflammation. Nat Immunol 20, 50–63. Molina, J.R., Sun, Y., Protopopova, M., Gera, S., Bandi, M., Bristow, C., McAfoos, T., Morlacchi, P., Ackroyd, J., Agip, A.A., et al. (2018). An inhibitor of oxidative phosphorylation exploits cancer vulnerability. Nat. Med. 24, 1036– 1046. Moon, H., Ju, H.-L., Chung, S.I., Cho, K.J., Eun, J.W., Nam, S.W., Han, K.-H., Calvisi, D.F., and Ro, S.W. (2017). Transforming Growth Factor-b Promotes

Porporato, P.E., Filigheddu, N., Pedro, J.M.B., Kroemer, G., and Galluzzi, L. (2018). Mitochondrial metabolism and cancer. Cell Res. 28, 265–280. Salazar-Roa, M., and Malumbres, M. (2017). Fueling the Cell Division Cycle. Trends Cell Biol. 27, 69–81. Schieber, M., and Chandel, N.S. (2014). ROS function in redox signaling and oxidative stress. Curr. Biol. 24, R453–R462. Schito, L., and Rey, S. (2018). Cell-Autonomous Metabolic Reprogramming in Hypoxia. Trends Cell Biol. 28, 128–142. Semenza, G.L. (2012). Hypoxia-inducible factors in physiology and medicine. Cell 148, 399–408. Sena, L.A., Li, S., Jairaman, A., Prakriya, M., Ezponda, T., Hildeman, D.A., Wang, C.R., Schumacker, P.T., Licht, J.D., Perlman, H., et al. (2013). Mitochondria are required for antigen-specific T cell activation through reactive oxygen species signaling. Immunity 38, 225–236. Shi, D.Y., Xie, F.Z., Zhai, C., Stern, J.S., Liu, Y., and Liu, S.L. (2009). The role of cellular oxidative stress in regulating glycolysis energy metabolism in hepatoma cells. Mol. Cancer 8, 32. Sobecki, M., Mrouj, K., Colinge, J., Gerbe, F., Jay, P., Krasinska, L., Dulic, V., and Fisher, D. (2017). Cell-Cycle Regulation Accounts for Variability in Ki-67 Expression Levels. Cancer Res. 77, 2722–2734. Tello, D., Balsa, E., Acosta-Iborra, B., Fuertes-Yebra, E., Elorza, A., Ordo´n˜ez, A´., Corral-Escariz, M., Soro, I., Lo´pez-Bernardo, E., Perales-Clemente, E., et al. (2011). Induction of the mitochondrial NDUFA4L2 protein by HIF-1a decreases oxygen consumption by inhibiting Complex I activity. Cell Metab. 14, 768–779. Tormos, K.V., and Chandel, N.S. (2010). Inter-connection between mitochondria and HIFs. J. Cell. Mol. Med. 14, 795–804. Wanschers, B.F., Szklarczyk, R., van den Brand, M.A., Jonckheere, A., Suijskens, J., Smeets, R., Rodenburg, R.J., Stephan, K., Helland, I.B., Elkamil, A., et al. (2014). A mutation in the human CBP4 ortholog UQCC3 impairs complex III assembly, activity and cytochrome b stability. Hum. Mol. Genet. 23, 6356– 6365. Warburg, O., Wind, F., and Negelein, E. (1927). The Metabolism of Tumors in the Body. J. Gen. Physiol. 8, 519–530. Yang, Y., Xiong, S., Cai, B., Luo, H., Dong, E., Li, Q., Ji, G., Zhao, C., Wen, Y., Wei, Y., and Yang, H. (2017). Mitochondrial C11orf83 is a potent Antiviral Protein Independent of interferon production. Sci. Rep. 7, 44303.

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STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Anti-human UQCC3

Sigma

Cat# HPA046851

Anti-human UQCC3 (Polyclonal)

Abmart

Customization

Antibodies

Anti-mouse UQCC3 (monoclonal)

Abmart

Customization

Anti-human HIF-1a

BD Biosciences

Cat# 610959 Cat# ab2185

Anti-mouse HIF-1a

Abcam

Anti-human HIF-OH

Cell Signaling Technology

Cat# 3434S

Anti-Ki67

Abcam

Cat# ab16667

Anti-GFP

Abmart

Cat# P30010S

Anti-PCNA

Cell Signaling Technology

Cat# 2586

Anti-Flag Tag

Cell Signaling Technology

Cat# 14793

Anti-OMA1

Novus

Cat# NBP1-56970

Goat Anti-Rabbit IgG (HRP)

Beyotime

Cat# A0208

Goat Anti-Mouse IgG (HRP)

Beyotime

Cat# A0216

pT-myr-AKT-HA

Addgene

Cat# 31789

pT/Caggs-NRasV12

Addgene

Cat# 20205

pCMV(CAT)-T7-SB100

Addgene

Cat# 34879

pT-mouse UQCC3

This paper

N/A

pcDNA3.1-human HIF1a

Hongbo Hu’s Lab

N/A

Recombinant DNA and Virus

pcDNA3.1-human UQCC3

This paper

N/A

Lentiviruses

ShangHai OBiO

N/A

OUTDO BIOTECH CO.LTD

HLiv-HCC180

Mitoquinone (MitoQ)

MedChemExpress (MCE)

Cat# HY-100116

Butylhydroxyanisole (BHA)

MedChemExpress (MCE)

Cat# HY-B1066

Biological Samples Human Liver cancer Samples Chemicals, Peptides, and Recombinant Proteins

Critical Commercial Assays RNA-Seq Systems

Novogene

N/A

Targeted metabolomics

Shanghai Applied Protein Technology (APTBIO)

N/A

PrimeSTAR PCR mix

TAKARA

Cat# RR045

PrimeScript RT reagent Kit

TAKARA

Cat# RR047

SYBR Green qPCR Master Mix

TAKARA

Cat# 638320

Hypoxyprobe Kit

Hypoxyprobe Inc

Cat# HP-001

Dextran

Invitrogen

Cat# D7139

Annexin V Apoptosis Detection

BD Biosciences

Cat# 556547

Mitochondrial Isolation Kit

Miltenyi Biotec

Cat# 130-094-532

Mitochondria Extranction Kit

Miltenyi Biotec

Cat# 130-097-340

Mito Stress Test Kit

Seahorse, Angilent

Cat# 103010

Glycolysis Stress Test Kit

Seahorse, Angilent

Cat# 103017

Energy Phenotype Test Kit

Seahorse, Angilent

Cat# 103275

MitoTracker Red FM

Invitrogen

Cat# M22425

MitoProbeTM TMRM Kit

Invitrogen

Cat#M20036 (Continued on next page)

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Article Continued REAGENT or RESOURCE MitoSOX

TM

Red

SOURCE

IDENTIFIER

Molecular Probes

Cat# M36008

ATP Determination Kit

Molecular Probes

Cat# A22066

Bradford assay

Abcam

Cat# ab102535

DAB+

Agilent

Cat# K346811-2

Lipofectamine 3000 Transfection Reagent

Life Technologies

Cat# L3000015

Human: HepG2

ATCC

HB-8065

Human: HuH7

HongXin Deng’s Lab

N/A

Human: Hep-3B

ATCC

HB-8064

Human: 293T

ATCC

CRL-3216

The Jackson Lab

https://www.jax.org/strain/003574

Beijing View Solid Biotechnology

http://www.v-solid.com

Flowjo vX.0.7

BD Biosciences

https://www.bdbiosciences.com/cn/go/ flowjov10/index.html

GraphPad Prism

N/A

https://www.graphpad.com

SPSS

N/A

https://spssau.com/index.html?102000000

PCR primer Design tool

Integrated DNA Technologies

http://sg.idtdna.com/Primerquest/Home/Index

TCGA

N/A

https://www.cancer.gov/about-nci/organization/ ccg/research/structural-genomics/tcga

UALCAN

N/A

http://ualcan.path.uab.edu/index.html

The Huamn Protein Altas

N/A

https://www.proteinatlas.org

DAVID

N/A

https://david.ncifcrf.gov/summary.jsp

MetaboAnalyst

N/A

https://www.metaboanalyst.ca/faces/home.xhtml

The Cancer Genome Atlas

N/A

https://www.cancer.gov/about-nci/organization/ ccg/research/structural-genomics/tcga

Experimental Models: Cell Lines

Experimental Models: Organisms/Strains Mouse: Alb-Cre Mouse: UQCC3 loxP/loxP (UQCC3

F/F

)

Software and Algorithms

RESOURCE AVAILABILITY Lead Contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Hanshuo Yang ([email protected]). Materials Availability Plasmids and mouse lines generated in this study will be made available on request, but we may require a payment and/or a completed Materials Transfer Agreement if there is potential for commercial application. Data and Code Availability Original/source data for Figures 6A–6C and S6A in the paper is available in Data S1. EXPERIMENTAL MODEL AND SUBJECT DETAILS Cell lines and Animals HepG2 (ATCC), HuH7 (JCRB Cell Bank), SK-Hep1 (ATCC), Hep-3B (ATCC), and 293T (Chen’s Lab in Sichuan University, China) cells were cultured in Dulbecco’s modified Eagle’s medium (GIBCO). The two mediums were supplemented with 10% fetal bovine serum (GIBCO) and antibiotics in a 37 C incubator with 5% CO2. Cell Counting was determined by Countstar Bio Tech system. Cell growth was determined by cell counting and shown as a relative cell growth (Fold Change) using the seeding cell number in the initial day as ‘‘1’’ for normalization. CCK-8 solution (MCE, 10 mL) was added into each 96-well to determine cell proliferation, incubated for 2 hours at 37 C followed by absorbance measurement at 450 nm (OD450).

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All animal experiments were approved by The Committee for Animal Research at the Experimental and Research Animal Institute of Sichuan University. Wild-type C57BL/6 and nude mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. Homozygous UQCC3 loxP (UQCC3F/F) mice were produced by Beijing View Solid Biotechnology Co., Ltd. UQCC3F/F mice were crossed with Alb-Cre mice (Jackson no. 003574) to generate UQCC3Dhep mice. For genotyping the UQCC3 alleles, PCR was done using the following primers: Mouse UQCC3 exon1 F’: 50 -TTC TCT ATG ATT TGA GGC CCG GC-30 ; Mouse UQCC3 exon1 R’: 50 -AAC TGC CAC AAG TGC TTT ACG AG-30 ; Mouse UQCC3 exon2 F’: 50 -GAG AAC GTG GCC TGG AGG AGA AAC-30 ; Mouse UQCC3 exon2 R’: 50 -ACC GCA GTT CCC AGT GAC TTG GAG-30 ; Mouse UQCC3 exon1 and Exon2 F’: 50 -TGT ATG AGA GTG TCA GAT TCC-30 ; Mouse UQCC3 exon1 and Exon2 R’: 50 -TAT TAG TTC CGT TGG TTC CA-30 ; Alb-Cre F’: 50 -GTT AGC ACC GCA GGT GTA GAG30 ; Alb-Cre R’: 50 -TCC AGG TTA CGG ATA TAG TTC ATG-30 . Xenograft models For xenograft models, 5 3 106 HepG2 cells with UQCC3 deletion were xenografted into the subcutaneous region of nude mice. Five days later, tumor volumes were monitored 3 times/week to observe dynamic developments of tumor growth. The tumor volume was calculated with the following formula: 0.52 3 length 3 width2. Three weeks post inoculation the mice were euthanized. Tumors were dissected out, weighed, and fixed in 10% formalin and frozen in liquid nitrogen for further analysis. Isolation and culture of primary liver tumor cells Primary hepatocytes and liver tumor cells from UQCC3Dhep and UQCC3F/F mice six weeks post HDT were isolated and cultured as previously described (Akiel et al., 2017). Briefly, livers from mouse were removed, washed, and chopped into pieces. Collagenase/ dispase solution was added and incubated for 40 minutes at 37 C. Then the liquid was sieved through medical gauze into 50ml tube and spun at 1000rpm for 5 minutes at 4 C. It was resuspended in 10mL PBS/2%FBS and filter through 70 mm filter followed by 45 mm filter. The pellet was resuspended with 5mL PBS/2%FBS and spun at 1000rpm for 2 minutes again. Further it was resuspended in 5mL of 1 3 RBC Lysis butter and incubated on ice for 5 minutes. The step should to be repeated for 2 times. At last the obtained cells were resuspended with TM1 medium and seeded into 6-well plate at 1 million per well. The formulation for buffer or reagent can be seen at URL: https://pharm.ucsf.edu/xinchen/protocols/. METHOD DETAILS Histology The human HCC tissuearrays were purchased from Shanghai Outdo Biotech (Shanghai, China). Human HCC samples, xenograft and spontaneous liver cancer tissues were fixed in 10% formaldehyde solution (Sigma), then processed, embedded in paraffin, and sliced to 4 mM sections. Slides were subjected to H&E or IHC stain in Laboratory of Pathology (West China Hospital, Sichuan University, China). To ensure antibody specificity, PBS replaced primary antibody as a negative control. The density of IHC was determined by Image Pro Plus 6.0 software. Western blot Cells or tissues were harvested and lysed with RIPA (Millipore). Immunoblots were performed in the standard fashion. The following antibodies were used: Human UQCC3 (Abmart, customization), Mouse UQCC3 (Abmart, customization), HIF-1a (BD Biosciences, #610959), PCNA (CST, #2586), Ki67 (Abcam, #ab16667), OMA1 (Novus, #NBp1-56970), HIF-OH (CST, #3434S), GFP (Abmart, #P30010), VEGFa (Abcam, ab52917), Flag-Tag (CST, #14793), CDK2 (Proteintech, #10122-1-AP), Cyclin E1 (Proteintech, #11554-1-AP), Cyclin D1 (Proteintech, #60186-1-lg), CDK4 (Proteintech, #11026-1-AP), Cyclin A2, (Proteintech, #18202-1-AP), CDK6 (Proteintech, #14052-1-AP), Phos Rb1 (S780) (Abcam, #184702), Rb1 (Proteintech, #17218-AP), and a-Tubulin (Beyotime, AF0001). Flow cytometry For cell apoptosis, cell cycle, mitochondrial mass, MtMP and mROS, HepG2 cells with 40%–50% confluence in 6-wells carrying UQCC3 deletion were cultured under normoxia and hypoxia for 72 hours. Then the experiments were carried out according to each manufacturer’s protocol. Each flow analysis was performed on a FACS LSR Fortessa (BD Biosciences), and the data were analyzed with FlowJo 10.7 software. Mitochondria isolation Intact mitochondria were isolated using the MACS Technology (130-094-532, MACS, Miltenyi Biotec) from HepG2 cells carrying UQCC3 deletion, cultured in normoxia and hypoxia for 72 hours. Mitochondria were extracted and stored according to the manufacturers protocol. Plasmid constructs and Lentivirus packaging For knockout or overexpression of human UQCC3 in various cell lines, the method of plasmid construction was described in our previous study (Yang et al., 2017). The construction of other candidate genes, including mouse UQCC3, human OMA1 and GFP, were

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made using standard molecular cloning procedures and PCR-mediated deletion of plasmid sequences. pcDNA3.1-HIF1a plasmid was from Prof. Hongbo Hu as a generous gift. To interfere the expression of human UQCC3 or HIF-1a mRNA in HepG2 cells, interference sequences against either UQCC3 (UQCC3-shRNA, #1: 50 - GGA AGC AGG AAA TGC TAA A-30 and #2: 50 - GGA GGA AGA ACT GGA TGG T-30 ) or sequences against HIF-1a (HIF-1a-shRNA, #1: 50 - GGG TAA AGA ACA AAA CAC A - 30 and #2: 50 - TGT GAG TTC GCA TCT TGA T - 30 ) were designed, synthesized and inserted into plasmid vectors. This work was done independently by Shanghai Genechem Co., LTD. Lentivirus containing the constructs above were generated by co-transfecting with the packaging plasmid psPAX2 and the envelope plasmid pMD2.G into 293T packaging cells. RNA extraction and qRT-PCR Total RNA was isolated with the TRIZOL reagent (Life technology). The cDNA was collected using the PrimeScript RT reagent Kit (TAKARA). The quantitative RT-PCR analysis was performed using a BIO-RAD CFX96 Real-Time PCR System (BIO-RAD) with SYBR Green qPCR Master Mix for all the target genes (TAKARA). Primers for ACTB were used as an internal control. All of the primer sequences were used as follows. Human ACTB F’: 50 -CTT CGC GGG CGA CGA T-30 ; Human ACTB R’: 50 -TCT CCA TGT CGT CCC AGT TG-30 ; Human UQCC3 1F’: 50 -GAT CTC AGT CGC AAT GCT GG-30 ; Human UQCC3 1R’: 50 -CTC TCC CGG GGT CAC GAT AA-30 ; Human CA9 F’: 50 -GGT GTC ATC TGG ACT GTG TTT A-30 ; Human CA9 R’: 50 -CTC AAT CAC TCG CCC ATT CA-30 ; Human Glut1 F’: 50 -TGC CTG AGG GTG GAG ACT AA-30 ; Human Glut1 R’: 50 -GAT GGG AAG GGG CAA ATC CT-30 ; Human VEGFa F’: 50 -TGG AGC GTG TAC GTT GGT G-30 ; Human VEGFa R’: 50 -AAT TCC AAG AGG GAC CGT GC-30 ; Human HIF-1a F’: 50 -CAG ATC TCG GCG AAG TAA AGA A-30 ; Human HIF-1a R’: 50 -GAT GGT AAG CCT CAT CAC AGA G-30 ; Human HK1 F’: 50 -GGA CGG CGT CTG GAG TTT T-30 ; Human HK1 R’: 50 -CTT GGC ACA AAG CAG GTG TG-30 ; Human HK2 F’: 50 -GTG AAT CGG AGA GGT CCC AC-30 ; Human HK2 R’: 50 -CAA GCA GAT GCG AGG CAA TC-30 ; Human GPI F’: 50 -GCG TCT CAC TCA GTG TAC CTT C-30 ; Human GPI R’: 50 -TGG TTG AAG CGG TCC TTG TT-30 ; Human PFKL F’: 50 -AGA GGC GTG GCA CCT CA-30 ; Human PFKL R’: 50 -CCT TGG CAA AGC CAG TTT CC-30 ; Human ALDOC F’: 50 -AGC CTC ATC TGT TTG CGG AT-30 ; Human ALDOC R’: 50 -ATG GTG ACA GCT CCC TGT GC-30 ; Human ALDOA F’: 50 -TCC CCA TCA ATA GGG CCG AC-30 ; Human ALDOA R’: 50 -CCA CAT GTG TCC CCG ATC TT-30 ; Human TPI1 F’: 50 -CCA CTT CGC GGC GCT CTA T-30 ; Human TPI1 R’: 50 -CCA GTT TCC CCC AAC GAA GA-30 ; Human GAPDH F’: 50 -GAA TGG GCA GCC GTT AGG AA-30 ; Human GAPDH R’: 50 -AAA AGC ATC ACC CGG AGG AG-30 ; Human PGK1 F’; 50 -CCA CTG TGG CTT CTG GCA TA-30 ; Human PGK1 R’: 50 -ATG AGA GCT TTG GTT CCC CG-30 ; Human PGAM1 F’: 50 -GGT TAG ACC CCC ATA GTG CC-30 ; Human PGAM1 R’: 50 -GGT TAG ACC CCC ATA GTG CC-30 ; Human PGAM5 F’: 50 -TAA TGG CAG CAT CAC CCA CC-30 ; Human PGAM5 R’: 50 -CGA GTG ATC TTG TCG GGA GG-30 ; Human ENO1 F’: 50 -TGG GGA AGG GTA AGC CTT AGA-30 ; Human ENO1 R’: 50 -AAG TGC CAC CCA GAG AGG AC-30 ; Human ENO2 F’: 50 -CTT GTC CCA CGT GTC TTC CA-30 ; Human ENO2 R’: 50 -CGT GAT CAC ATG CCC AAC AC-30 ; Human ENO3 F’: 50 -ATC TTG GAC TCC AGG GGC AA-30 ; Human ENO3 R’: 50 -GGC CCC AAA CTT GGC TTT CC-30 ; Human PKM F’: 50 -CCT GAT AGC TCG TGA GGC TG-30 ; Human PKM R’: 50 -TTG AGG CTC GCA CAA GTT CT-30 . TEM Cells or HCC tissues were quickly collected in 0.5mL EP tubes and cut into small (1 3 1 mm3) cubes, then fixed in a mix of 2% glutaraldehyde and 4% paraformaldehyde in 0.1M cacodylate buffer. Samples were processed and examined in an electron microscope operated at 80kV (Hitachi H7650, Tokyo by the Electron Microscopy Core at Laboratory of Pathology in West China Hospital as previously described (Khiati et al., 2015). ATP determination The ATP concentrations were analyzed using the targeted metabolomics or the ATP Determination Kit (Molecular Probes). For the kit, a standard curve was drawn and the ATP concentration of all samples to be tested was normalized to the total protein content evaluated by Bradford assay. Extracellular flux Analysis OCR, ECAR and energy phenotype test were all measured using a XF24 Extracellular Flux Analyzer (Seahorse, Agilent). Briefly, each group of cells was seeded in an XF culture dish at 9000 cells per pore one day before the test. For experiments performed under hypoxia, the XF test medium was pretreated into hypoxic incubator on the same day for reserve. On the day of testing, the pretreated medium was used to replace the previous medium quickly and put into the machine for testing. QUANTIFICATION AND STATISTICAL ANALYSIS RNA-sequencing analysis Total RNA was extracted from HepG2 cells stably expressing either shRNA against UQCC3 (sh#1) or empty vector under hypoxia for different time points (n = 3, Biological repeats). Then the 18 cell samples were sent to Beijing Novogene Bioinformation Technology for RNA sample preparation and sequencing. RNA sequencing libraries were constructed using the NEBNext UltraTM RNA Library Prep Kit for Illumina (NEB). Fragmented and randomly primed 125bp paired-end libraries were sequenced using Illumina HiSeq

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2500. The RPKM values were used to evaluate the expression levels of genes. The hierarchical clustering was conducted by Cluster 3.0 and TreeView. In the scatterplot, Log2FC > 0.585 or Log2FC <
0.585, and
1og10p > 1.30 were set for analysis. Gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with web server DAVID 6.8. Targeted metabolomics analysis HepG2 cells stably expressing UQCC3 knockout (KO-#1 and KO-#2) or empty vector in triplicate under normoxia or hypoxia for 72 hours were mixed with cold methanol/acetonitrile/H2O (2:2:1, v/v/v) to remove the protein. Then the 18 cell samples were sent to ShangHai Applied Protein Technology for further sample preparation and mass spectrometry analysis as previously described (Minhas et al., 2019). Except that the NADH/NAD+ ratio was normalized with the mean value of the control groups of normoxia, other graphs using data from targeting metabolomics was normalized with the mean value of each self-control group. TCGA, GEO and tissue assay data analysis The UQCC3 expression datasets and associated survival data in liver cancer were downloaded from The Cancer Genome Atlas (TCGA) and GEO (GSE6764), and further analysis was performed with the assistance of Heng Xu’s Lab in SiChuan University. For survival data, TCGA and tissue assay matrices was divided into 2 groups (High and Low) using hierarchical clustering with Euclidean distance and Ward’s linkage criterion. Non-parametric Dunnet’s Test was used to assess difference in the distribution of genetic characteristics and subtype between High and Low MitSig groups in TCGA data. In case of 2 categories (e.g., High versus Low) Kaplan-Meier curves were drawn and Logrank test was used. Statistical analysis Box-and-Whisker plots showing the expression of UQCC3 were used to display the first quartile, median, and third quartile, and the lower and upper error bars, indicating the 1.5 3 interquartile range, respectively. Outliers are excluded from the plot. Statistics were analyzed using a nonparametric Kruskal-Wallis with Dunn’s multiple comparison. Experimental data were presented as the mean ± standard deviation. Two samples t test was used to compare the values between the two groups. Comparisons of the values obtained with 3 groups even more were analyzed using two-way analysis of variance, and Bonferroni post hoc test was used to determine these differences using the SPSS software 19.0. Survival analyses were performed via drawing Kaplan-Meier curves, and the differences between subgroups were analyzed using the log-rank test. p < 0.05 was considered to indicate a statistically significant difference. MINHAS ET AL., 2019 ADDITIONAL RESOURCES The FPKMs of the RNA-Seq results from 18 samples are listed as a ZIP file named Data S1. This data is related to Figures 6A–6C and S6A.

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