Metabolomics (2018) 14:136 https://doi.org/10.1007/s11306-018-1430-0

ORIGINAL ARTICLE

Mitochondrial is a key regulator of energy production for growth and expression in Chinese hamster ovary cells

Neha Dhami1 · Drupad K. Trivedi2 · Royston Goodacre2 · David Mainwaring1,3 · David P. Humphreys1

Received: 5 March 2018 / Accepted: 21 September 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract Introduction Mammalian cells like Chinese hamster ovary (CHO) cells are routinely used for production of recombinant therapeutic . Cells require a continuous supply of energy and nutrients to sustain high cell densities whilst expressing high titres of recombinant proteins. Cultured mammalian cells are primarily dependent on and for energy production. Objectives The TCA cycle is the main source of energy production and its continuous flow is essential for cell survival. Modulated regulation of TCA cycle can affect ATP production and influence CHO cell productivity. Methods To determine the key metabolic reactions of the cycle associated with cell growth in CHO cells, we transiently silenced each of the TCA cycle using RNAi. Results Silencing of at least four TCA cycle was detrimental to CHO cell growth. With an exception of mitochondrial aconitase (or Aco2), all other genes were associated with ATP production reactions of the TCA cycle and their resulting substrates can be supplied by other anaplerotic and cataplerotic reactions. This study is the first of its kind to have estab- lished key role of aconitase gene in CHO cells. We further investigated the temporal effects of aconitase silencing on energy production, CHO cell metabolism, oxidative stress and recombinant protein production. Conclusion Transient silencing of mitochondrial aconitase inhibited cell growth, reduced ATP production, increased produc- tion of reactive oxygen species and reduced cell specific productivity of a recombinant CHO cell line by at least twofold.

Keywords TCA cycle · m-Aconitase · CHO cells · Oxidative stress · Glucose metabolism

1 Introduction secrete correctly folded glycosylated proteins. For large- scale recombinant protein production, cultivation of cell Chinese hamster ovary (CHO) cells are the most com- cultures with high cell density is a prerequisite. The total monly used mammalian cell line for production of ther- amount of recombinant protein expressed by the cells is apeutic recombinant proteins owing to their ability to dependent on time integral of viable cell density and the cell specific productivity (Lai et al. 2013). Mammalian cells in culture require a continuous supply of carbon, Electronic supplementary material The online version of this article (https​://doi.org/10.1007/s1130​6-018-1430-0) contains nitrogen and energy to sustain cellular growth (Dietmair supplementary material, which is available to authorized users. et al. 2012b; Vander Heiden et al. 2009; Young 2013). The energetic yield of mammalian cells in culture in a * Neha Dhami typical oxygen consumption environment was measured to [email protected] be around 2.5–4.5 pmol ATP/cell/h. Cells with a doubling 1 Protein Sciences, UCB, 216 Bath Road, Slough, time of approximately 24 h like CHO cells would use at Berkshire SL1 3WE, UK least 1.2–1.5 pmol ATP/h of the total ATP for generation 2 Manchester Institute of Biotechnology and School of cellular components. The production of proteins, osmo- of Chemistry, University of Manchester, 131 Princess Street, sis and transport of solutes further add to the expenditure Manchester M1 7DN, UK of energy. Furthermore, growing cells in culture required 3 Present Address: Pall Europe Limited, 5 Harbourgate ~ 30% more energy than quiescent cells (Mulukutla et al. Business Park, Southampton Road, Portsmouth, 2010). To add to the energy requirement for maintaining Hampshire PO6 4BQ, UK

Vol.:(0123456789)1 3 136 Page 2 of 16 N. Dhami et al. cell growth and proliferation, cells also require energy to CHO cells in culture typically utilise glucose and glu- produce recombinant proteins. At least three ATP mol- tamine as major sources for energy production (Quek et al. ecules are used to synthesise one peptide bond (Seth et al. 2010). The TCA cycle (or , Krebs cycle) is the 2006). A typical IgG composed of two heavy chains with main pathway associated with ATP production (Altamirano 451 amino acids and two light chains of 213 amino acids et al. 2013; Vander Heiden et al. 2009; Young 2013) (Fig. 1). has a metabolic cost of 5518 and 3073 high-energy phos- Previous studies have demonstrated high activity of TCA phate bonds per heavy and light chain respectively as per cycle and oxidative pentose phosphate pathway during peak the metabolic cost of amino acid biosynthesis calculated antibody production (Templeton et al. 2013). The intermedi- by Akashi and Gojobori (2002). Therefore, to generate a ates of the TCA cycle are also used as precursors for syn- single copy of IgG antibody, cells require approximately thesis of many other biomolecules. However, during high ≥ 17,000 ATP molecules. Furthermore, recombinant CHO energy consumption to sustain normal functioning of the cell lines have at an average cell specific productivity of cell, there is an influx of intermediates into the TCA cycle over 20–40 pg/cell/day (Li et al. 2010). The molecular via anaplerosis and exit via cataplerosis. To cope with high weight of a typical IgG is 150 kDa therefore at the cell rates of energy production and maintaining cellular growth specific productivity of 20 pg/cell/day, cells express during recombinant protein production, CHO cells require 8.03 × 107 moles of IgG which require 1.36 × 1012 mol- a continuous and efficient functioning of the TCA cycle. ecules of ATP or 2.3 pmol ATP/cell/day or 0.1 pmol ATP/ With an aim to identify the TCA cycle genes which regu- cell/h. This leads to a significant metabolic burden on cel- late cellular growth and energy production in CHO cells, lular machinery during recombinant protein production. we transiently knocked-down expression of each gene of

Fig. 1 TCA cycle in mammalian cells. Mitochondrial genes identi- drogenase, Dlst dihydrolipoamide succinyltransferase, Fh fumarate fied from Cricetulus griseus (Chinese hamster) KEGG pathway. The hydratase, Idh , FADH2 flavin adenine dinu- genes in ‘green’ independently catalyse the TCA reactions. The genes cleotide (hydroquinone form), Mdh , NADH in ‘blue’ in the boxes are either isozymes part of the complex nicotinamide adenine dinucleotide reduced form, Ogdh2 oxoglutarate or are sub-units catalysing the TCA reaction. The genes in ‘red’ in the dehydrogenase, PDC complex, Pdha1 pyru- boxes are splice variants. Aco2 Aconitase 2, Cs , Dlat vate dehydrogenase alpha and Sdh succinyl dehydrogenase dihydrolipoamide S-acetyl , Dld dihydrolipoamide dehy-

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TCA cycle using siRNA duplexes in host and recombinant GlutaMAX™ contained a stabilised l-alanyl-l-glutamine CHO cell lines. Transiently silencing expression of targeted dipeptide. The stable recombinant CHO cell line express- genes using RNAi (Agrawal et al. 2003; Elbashir et al. 2001; ing mouse monoclonal antibody was cultured in CD CHO Harborth et al. 2001) allowed us to observe temporal effects medium without the supplement. The suspension CHO cells on cell growth, ATP production and cellular metabolism for were sub-cultured every 3–4 days with a seeding density CHO cells. Since TCA cycle is the key energy generating of 2 × 105 cells per mL in 1 L Erlenmeyer shake flasks in pathway in mammalian cells, we hypothesised that the ATP 200 mL culture volume and were grown in a 37 °C shaking generating reactions of the cycle would be the main regu- incubator with 8% ­CO2 at 140 rpm shaking speed. lator of energy production and cellular growth. However, For siRNA experiments, first a siRNA pool comprising our study highlighted the TCA cycle reaction catalysed by of three different siRNA duplexes (Aco2.1, Aco2.2 and the aconitase 2 (aconitate hydratase, ACO2, EC: 4.2.1.3) Aco2.3) targeting different binding sites along the length enzyme is one of the key focal points of the TCA cycle in of the sequence of the Aco2 gene was used (supplementary CHO cells. text), followed by identification of a single siRNA duplex ACO2 catalyses the reversible conversion of citrate to (Aco2.3) with the maximal effect on growth of CHO cells. cis-aconitate followed by conversion of cis-aconitate to Similar approach of siRNA design and experiments was isocitrate (Breusch 1937; Krebs and Holzach 1952; Mar- used for silencing of all other TCA cycle genes. The siRNA tius 1937). In mammalian cells, ACO2 activity rate is duplexes were screened for GC% (30–50%) to reduce effect higher compared to the other TCA cycle reactions and it of GC content on transfection efficiency (Chan et al. 2009; maintains equilibrium between citrate, cis-aconitate and Reynolds et al. 2004). isocitrate (Costello and Franklin 2013). Another important The siRNA transfections were performed using the Lipo- role of ACO2 is as a biosensor for oxidative stress (Cantu fectamine® RNAiMAX transfection reagent (Life Technolo- et al. 2011). Aconitase is an iron–sulphur protein [4Fe–4S] gies) in accordance to the manufacturer’s instructions. The (Beinert et al. 1996) and is prone to inactivation in an oxi- reagents and cells were proportionally scaled up for 20 mL dative environment (Cantu et al. 2009; Gardner et al. 1995; transfection volumes in static Corning® (Flintshire, UK) Murakami and Yoshino 1997; Yan et al. 1997). Mitochon- T-75 vented flasks. The static cultures were grown in 37 °C 2+ drial aconitase contains an active [4Fe–4S] cluster in its humidified incubator with 8% ­CO2. The cells were incubated with three iron atoms bound to cysteinyl group for 24–72 h for assessment of effects of siRNA-mediated and inorganic sulphur atoms. The fourth iron atom is labile knock-down of the targeted gene. A siRNA duplex (GFP2.1) (Fe-α) and is bound to the hydroxyl group of the substrate targeting de-stabilised GFP sequence (C-terminal PEST and water. On oxidation, especially in presence of superox- sequence fused to the fluorescent marker; Chevaillier 1993) ides, the [4Fe–4S]2+ cluster group loses its labile Fe-α atom, was used as a negative control for siRNA transfections. leading to formation of an inactive [3Fe–4S]1+ enzyme. The The cell growth was assessed by an automated cell coun- ­Fe2+ atom is released with subsequent production of hydro- ter Vi-CELL XR (Beckman Coulter, High Wycombe, UK) gen peroxide (H­ 2O2) (Cantu et al. 2009; Vásquez-Vivar et al. using Trypan Blue. 2+ 2000). The Fe­ catalysed homolysis of H­ 2O2 generates free hydroxyl radicals via Fenton reaction which further oxidises 2.2 Measurement of adenylate and pyridine mitochondrial proteins, DNA and lipids (Ott et al. 2007; metabolites Winterbourn 1995). We further investigated the effect of mitochondrial aco- Cell lysates were generated 72 h post-siRNA transfec- nitase in regulating cellular growth, ATP production and tion using the manufacturer’s recommended protocol for the rate of other metabolic pathways in mammalian cells Cell Culture Lysis Reagent (CCLR) (Promega, South- in culture. ampton, UK). The adenylate metabolites: ATP, ADP and AMP were measured in cell lysates using an ATP 2 Materials and methods Bioluminescence Assay Kit CLS II (Roche, Burgess Hill, UK) per the manufacturer’s instructions. ADP and 2.1 Cell culture and siRNA transfections AMP metabolites were converted to ATP for quantifica- tion as described by Sellick et al. (2009). The adenylate energy charge (AEC) was calculated using the formula, The CHO-K1 cell line was obtained from ATCC® (CCL- 1 ([ATP]+ ([ADP]))/([ATP]+[ADP]+[AMP]) 61™) and adapted to chemically-defined animal compo- 2 (Atkinson nent-free media. The cells were cultured in Gibco® CD 1968). AEC determines the energetic state of a biological CHO medium (1X) (Life Technologies, Paisley, UK) with system by measuring the concentrations of adenylate metab- 6 mM GlutaMAX™ supplement (Life Technologies). olites. The value of adenylate energy charge varies between

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0 and 1 with higher value corresponding to cells in prolifera- The GC–MS data deconvolution was carried out using tive state (Atkinson 1968). Leco ChromaTOF (v2.3) as described in Begley et al. The pyridine nucleotides: NAD­ + and NADH were meas- (2009). Data pre-processing was performed using MAT- ured in cell lysates using the colorimetric NAD/NADH LAB (MathWorks). Missing values were replaced using Assay Kit (Abcam) using manufacturer’s recommended k-nearest neighbour (k-NN) prior to the statistical analysis. protocol. For NADH measurement, all the ­NAD+ was 100 metabolite peaks were identified by deconvolution of decomposed by incubation at 60 °C for 1 h (Sellick et al. the QC samples and cross-matching with libraries. Once all 2009). The catabolic reduction charge was calculated using peaks were assigned, peaks that were absent in 50% of QCs the formula, ([NADH])/([NADH]+[NAD+]) (Andersen and were removed. The peak intensities were normalised with von Meyenburg 1977). In the catabolic reactions, ­NAD+ is the peak of the internal standard metabolite (succinic acid- a substrate and NADH is the product therefore the catabolic d4, Sigma Aldrich, UK) for each injection, and the viable reduction charge is always at low levels (0.03–0.07) in the cell concentrations at the time of metabolic extractions. Sta- growing cells (Villadsen et al. 2011). tistical tests including principal component analysis (PCA), discriminant function analysis (DFA), univariate t tests and 2.3 Measurement of reactive oxygen species analysis of variance (ANOVA) were performed on the data.

Reactive oxygen species were measured using the ROS- 2.5 Gene expression analysis Glo™ ­H2O2 Assay (Promega) as per manufacturer’s instruc- tions. The luminescence was measured using a Biotek Syn- The ­RT2 Profiler PCR Arrays (Chinese hamster arrays ergy2 (Swindon, UK) plate reader. The luminescent signal (PAJJ); Qiagen, Manchester, UK) were used for estimating was directly proportional to H­ 2O2 concentration. The H­ 2O2 the expression profile of a panel of 84 genes in the glucose concentration was further normalized to viable cell number. and amino-acid metabolism along with five housekeeping genes (Actb, Actr5, B2m, Gapdh and Hgprt). Additionally, 2.4 Metabolite extractions and GC–MS analysis each array included a panel of controls to monitor genomic DNA contamination (GDC); the first strand synthesis (RTC) The intracellular metabolites were extracted from CHO cells and real-time PCR efficiency (PPC). The expression analysis using the quenching and metabolite extraction method devel- was performed per manufacturer’s recommended protocol. oped by Sellick et al. (2011). To reduce leakage of metabolites The data analysis was undertaken using the web portal at during quenching, 60% (v/v) methanol and 0.85% (w/v) ammo- http://www.qiagen.com/geneg​ lobe​ using the ΔΔCT method. nium bicarbonate (AMBIC) pH 7.4 quenching solution was The ­CT cut-off was set to 35 cycles. The fold-regulation rep- used. As non-viable cells have lost membrane integrity, result- resented a positive-/up-regulation or negative-/down-regu- ing in leakage of their metabolites into the culture media (Bor- lation fold-change between the normalised gene expression dag et al. 2016; León et al. 2013), the cells were pelleted post of the test samples compared to the controls. quenching by gentle centrifugation. All the supernatant which contained extracellular metabolites, cell culture media, metabo- 2.6 Measurement of antibody expression lites leaked from non-viable cells and the quenching solution, was removed from the samples prior to intracellular metabolite For measurement of antibody concentration of the recom- extraction from viable cells. The extraction steps were modified binant CHO cell line post-siRNA transfections, 200 µL of from Sellick et al. (2011) method i.e. instead of two extractions cell culture was centrifuged at 15,000×g for 5 min at room with methanol and one extraction with water, three extractions temperature and the supernatant was transferred to a 96-well were performed with 80% (v/v) methanol/water extraction solu- black, flat bottom, microplate. The concentration was deter- tion. The metabolite extract was dried using a centrifugal evap- mined by using the Anti-Murine IgG Quantitation biosen- orator at room temperature for 4.5–5 h. The dried metabolite sors (Pall ForteBio Europe, Portsmouth, UK) in a 96-well pellets were stored at − 80 °C till required for GC–MS analysis. format on the Octet instrument (ForteBio). The standard The intracellular metabolitle extracts lyophilised for curve was generated using a purified mouse IgG antibody. GC–MS analysis were derivatized using a two-step derivati- The cell specific rate of production, Qp (pg/cell/day) (Brez- zation process as described by Wedge et al. (2011). The QC insky et al. 2003; Mason et al. 2014) was calculated using samples were generated by mixing 100 µL of the derivatized the following equation: CHO cell extractions from all samples. GC–TOF–MS analysis ln nt ΔP was performed using an Agilent 6890 GC instrument (Agi- n0  Qp = lent, Cheshire, UK) coupled to a LECO Pegasus III TOF mass nt − n0 t spectrometer (Leco, Stockport, UK) as previously described   (Begley et al. 2009; Dunn et al. 2011; Wedge et al. 2011).

1 3 Mitochondrial aconitase is a key regulator of energy production for growth and protein expression… Page 5 of 16 136 where ∆P is the total antibody concentration (mg/L), t is the reactions of TCA cycle in conjunction with other ; elapsed culture time, nt is the viable cell density at time t, n0 secondly, they generate ATP directly; and third, their sub- is the viable cell density at start of the culture. sequent substrates (α-ketoglutarate and succinyl-CoA) can All statistical analysis was performed using the GraphPad be replenished by other metabolic reactions for continuation Prism 6 (GraphPad Software, California, USA). The two- of the TCA cycle. These results were in accordance with our way ANOVA analysis was corrected for multiple compari- hypothesis. Although Aco2 is not part of a complex catalys- sons using Bonferroni’s correction. ing a TCA cycle reaction and does not generate ATP directly, silencing of the aconitase gene still significantly reduced growth of CHO cells. This surprising result prompted us to 3 Results investigate the role of Aco2 further in regulating CHO cell growth. The TCA cycle is the main source of energy production. To validate that the recorded effects were due to reduc- Each molecule of pyruvate that enters the TCA cycle includ- tion in targeted gene expression and not an artefact of using ing oxidative decarboxylation of pyruvate to acetyl-CoA pooled siRNA duplexes, we subsequently targeted the Aco2 generates one GTP, four molecules of NADH and one mol- gene using individual siRNA duplexes (Fig. S1). The single ecule of FADH­ 2 (Cooper 2000) generating in total 15 ATP. siRNA duplex, Aco2.3 demonstrated the greatest effect on Mammalian cells use this energy to support growth, cell viability post-transfections. All the experiments described division and other cellular processes (Locasale and Cantley henceforth illustrate results from knock-down of Aco2 gene 2011; Vander Heiden et al. 2009). To determine if reduction using a single siRNA duplex, Aco2.3. in TCA cycle enzymes affected cellular growth in mam- At 48 h post-transfection, approx. 30% reduction in via- malian cells, we transiently silenced each gene of the cycle bility and twofold decline in VCD was measured for the independently in CHO cells using high throughput pooled Aco2 siRNA transfections compared to the controls which approach (mixture of at least three siRNA duplexes target- was further reduced by ~ 60% and sixfold respectively 72 h ing different positions along the length of the targeted gene post-transfection (Fig. 2a, b). sequences) to maximise chance for effective evaluation of A significant increase in AMP production (p ≤ 0.0001) siRNA mediated knock-down of TCA cycle genes. All the and a decline in ATP concentration (p ≤ 0.0001) were results were compared with negative control transfections recorded for the Aco2 knock-down samples compared to using a siRNA duplex targeting a GFP sequence. The control the negative controls. Only a small reduction in ADP con- siRNA sequence was not homologous to any known gene in centration (p ≤ 0.05) was measured. The adenylate energy the CHO genome. The transfection conditions were opti- charge (AEC) of 0.1 was calculated for the Aco2 knock- mised (data not shown) to confirm the transfection complex down samples in comparison to 0.7 for the control samples on its own did not affect the growth of CHO cells. (p ≤ 0.0001) (Fig. 2c). Reduction in ­NAD+ (p ≤ 0.0001) and NADH (p ≤ 0.05) concentrations were recorded for the Aco2 3.1 Effect of siRNA mediated silencing of TCA cycle samples leading to an increase in the catabolic reduction genes on phenotype and energy metabolism charge (p ≤ 0.0001) (Fig. 2d). In comparison to other TCA cycle genes (using the pooled approach), the Aco2 samples Cell growth was measured by calculating the viable cell den- demonstrated largest reduction in AEC and increase in cata- sity (VCD) and viability % of CHO cells 0–72 h post-trans- bolic reduction charge (Table 1). fection owing to the transient nature of siRNA transfections. On comparison of effects on cell growth, highest reduction 3.2 Production of reactive oxygen species in viability (≥ 40%) using the pooled approach was measured on aconitase silencing for at least three genes (Aco2, Idh3b and Ogdh) and siRNA mediated silencing of the Idh2 gene using a single siRNA Aconitase is sensitive to superoxide mediated oxidative duplex also reduced cell viability (≥ 40%) (Table 1). The dif- inactivation and is known to generate hydrogen peroxide ference in viable cell concentration between un-transfected ­(H2O2) along with redox-active iron (Cantu et al. 2009). In WT cells and negative control transfections was attributed this study, we also recorded an increase in H­ 2O2 reactive to the cellular stress caused by transfecting a foreign RNA oxygen species post-siRNA transfections (p ≤ 0.0001) for into the cell (Fiszer-Kierzkowska et al. 2011; Jacobsen et al. the Aco2 knocked-down samples in comparison to controls 2009). To counter any effects on cells because of the trans- (Fig. 3). Low levels of ­H2O2 species were measured in nega- fection process, we have compared all the data presented tive control samples and was potentially due to the oxidative in this paper with the negative control transfected samples. stress caused by siRNA transfections. A decline in H­ 2O2 Except Aco2, all other genes which demonstrated decline species from 24 to 48 h post-transfection was measured for in viability are firstly, part of complexes and catalyse the controls.

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Table 1 Summary of siRNA transfections targeting TCA cycle genes (72 h post-transfection) Gene siRNA Viability % Viable cell Adenylate Catabolic pool/single density energy charge reduction charge (cells/mL)

Pdha1 Pool 69.6 4.8x105 0.55 0.22 Dlat Pool 86.5 7.8x105 0.73 0.14 Dld Pool 86.9 9.8x105 0.80 0.14 Cs Pool 72.6 4.0x105 0.69 0.19 Aco2 Pool 40.4 1.9x105 0.31 0.30 Idh2 a Single 63.3 4.0x105 0.49 0.22 Idh3aa Single 80.7 8.4x105 0.76 0.18 Idh3b Pool 54.3 3.2x105 0.42 0.25 Idh3g Pool 73.4 6.0x105 0.51 0.21 Ogdh Pool 58.3 2.3x105 0.45 0.22 Dlsta Single 91.3 11.7 x105 0.83 0.14 Suclg1 Pool 78.5 6.3x105 0.74 0.19 Sucla2 Pool 77.5 4.0x105 0.75 0.15 Suclg2 Pool 74.3 4.4x105 0.69 0.17 Sdha Pool 84.8 6.9x105 0.71 0.18 Sdhb Pool 81.8 5.5x105 0.63 0.15 Fh Pool 84.6 6.2x105 0.82 0.13 Mdh2 Pool 76.9 4.5x105 0.37 0.26 GFP-D Negative 88.2 6.8x105 0.79 0.15 control WT 99.0 30.4 x105 0.81 0.15

Data represents mean of n = 4 independent siRNA transfections. The colour coding in the table represents the magnitude of effects on each measured criterion in comparison to the negative control siRNA transfections, red = highest, amber = intermediate and green = no effect a siRNA duplexes were screened for GC% (30–50%) and for some of the TCA cycle genes only one siRNA duplex was found viable

3.3 Gene expression and metabolic profiling of cells measured sixfold increase in pyruvate concentration in the under oxidative stress Aco2 silenced cells as compared to the controls using a col- orimetric pyruvate assay (Fig. S5). The expressions of TCA Quantitative RT-PCR arrays (qPCR-arrays) were used for cycle genes were down-regulated. The highest effect on gene expression estimation of a specific number of genes mRNA expression was recorded for the Aco2 gene (~ 27-fold functional in the glucose and amino acid metabolism with down-regulation) in the samples transfected with siRNA tar- the aim of identifying indirect effects due to cellular dis- geting the Aco2 gene. turbance owing to Aco2 interference. Greater than twofold The amino acid metabolism PCR array represented genes change in gene expression threshold indicated significant from the metabolism of tryptophan, arginine, alanine, aspar- regulation of siRNA mediated knock-down (Aco2 gene) agine, aspartate, cysteine, serine, glycine, threonine, pro- test samples compared to the negative control samples. The line, and glutamine and glutamate amino acids. Only amino glucose metabolism PCR contained genes from , acids which were linked to the TCA cycle pathway were gluconeogenesis, regulation, TCA cycle, pentose phosphate evaluated. Multiple genes on this array were functional in pathway and glycogen synthesis, degradation and regula- more than one pathway (Table S3). The Aco2 knock-down tion. In the glucose metabolism PCR array, we recorded an sample demonstrated down-regulation of biosynthesis of up-regulation of glycolysis genes—hexokinase, GAPDH, serine, aspartate biosynthesis from oxaloacetate, glutamate phosphoglycerate mutase and pyruvate kinase (Fig. 4a and related pathways like ornithine production, biosynthesis Table S2). In conjunction to the gene expression, we also of proline and creatine synthesis from Arginine. However,

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Fig. 2 Inhibition of cell growth of CHO cells and effect on energy Aco2 gene. c Adenylate metabolites, d pyridine nucleotides—72 h metabolites post-siRNA mediated knock-down of Aco2 gene a via- post-siRNA transfections, the lysates were generated using the CCLR bility, b VCD—the siRNA transfections were performed using the method. The adenylate energy charge was calculated from the ATP, Lipofectamine® RNAiMAX transfection reagent. The cell growth ADP and AMP metabolite concentration measured by the ATP bio- was measured 0–72 h post-transfection using the Vi-CELL XR Cell luminescence assay. The catabolic reduction charge was calculated Viability Analyzer. The siRNA duplex targeting the GFP gene was using the ­NAD+ and NADH concentrations measured using the used as a negative control. The data represented cell growth data NAD/NADH Assay. Data points represent group mean ± SD (n = 8) from n = 8 (mean ± SD) independent siRNA transfections targeting up-regulation of intracellular sources of glycine—betaine, intra-cellular metabolite profiling was performed. The nutri- choline etc. indicated an increase in production of glycine ent metabolites (glutamine, glutamate, glucose and lactate) (Fig. 4b and Table S3). and electrolytes (ammonium, sodium, potassium and cal- Furthermore, up-regulation of asparagine synthesis and cium) were measured on a BioProfile FLEX Analyzer (Nova tryptophan metabolism was observed in the knock-down biomedical, Cheshire, UK). Linear regression plots were samples. Genes associated with ROS induced oxidative plotted for extracellular metabolites as a function of change stress were also up-regulated post-siRNA mediated silenc- in integral viable cell concentration (IVCC) over time post- ing of the Aco2 gene (Fig. 4b and Table S3). transfection (data not shown). The regression equations While analysis of amino acid metabolism recorded up- generated best-fit value of the slopes and intercepts, along regulation of all metabolic pathways leading to production with standard deviation (Supplementary text). The two-way of pyruvate, anaplerotic pathways leading to production of ANOVA on the slope and R2 values did not demonstrate TCA cycle intermediates like acetyl-CoA, α-ketoglutarate significant statistical difference between the test and control and fumarate were down-regulated. samples. To further evaluate the effect on CHO cell metabolism Nevertheless, intracellular metabolite analysis high- post-siRNA mediated silencing of the Aco2 gene, extra- and lighted differences between the Aco2 knock-down and

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(data not shown) were generated from the GC–MS data for identification of metabolites causing variance for each time point for both the sample sets (Aco2 and negative control) and ANOVA statistical analysis was performed to identify the most discriminatory metabolites in the samples. The most-significant metabolites identified from the loadings plot and subsequent ANOVA analysis, were plotted using box-and-whisker plots (Fig. 5). Although no statistical dif- ference (p ≥ 0.05) was recorded in concentrations of intra- cellular metabolites between Aco2 and negative controls for 0–48 h post-siRNA transfection, at 72 h post-transfection, an increase in relative peak intensity (p ≤ 0.05) was recorded for metabolites presented in Fig. 5. Majority of the metabolites demonstrating higher concentration at 72 h post-silencing in Aco2 knock-down samples were either amino acids, sugars or fatty acids.

3.4 Effect on recombinant protein expression on aconitase silencing

Further, siRNA transfections targeting Aco2 gene were undertaken in a recombinant CHO cell line expressing a mouse monoclonal antibody to test if the effects were repeated in the context of a clone of cells which were under increased metabolic burden due to production of a recom- binant protein. A significant decline (p ≤ 0.0001) was meas- ured for both viability and VCD of aconitase silenced cells in comparison to the controls (Fig. 6a). The Aco2 knock- down cells recorded an AEC of 0.02 and catabolic reduction charge of 0.8 whereas the negative control cultures recorded an AEC of 0.69 and reduction charge of 0.4 (p ≤ 0.0001) on day 3 post-siRNA transfections (Fig. 6b). The cell specific productivity (Qp) calculated on day 7 of the culture yielded Fig. 3 Increase in ­H2O2 reactive oxygen species concentration post- transient knock-down of Aco2 gene expression. The ROS-Glo™ twofold decrease for Aco2 knock-down samples (p ≤ 0.05) ­H2O2 assay was used to measure the levels of hydrogen peroxide (Fig. 6d). ­(H2O2) reactive oxygen species. The viability was measured using the Vi-CELL XR Cell Viability Analyzer. The negative siRNA and WT represent control samples. The data represent group mean ± SD (n = 4). The asterisks represent p value calculated by two-way 4 Discussion ANOVA analysis—****p ≤ 0.0001 Maintaining cellular growth at high cell density and expres- control samples. The GC–MS analysis yielded 80 unique sion of recombinant proteins simultaneously creates meta- metabolites by estimating the peaks of the QC samples with bolic burden on CHO cells. The TCA cycle is the metabolic definitive identification of ~ 42 metabolites (Table S6). The hub of mammalian cells therefore, we investigated the effect total number of unique metabolites were relatively few in of the TCA cycle enzymes on cell growth and energy pro- comparison to other CHO studies (Dietmair et al. 2012a; duction with a view to identify targets for cell engineering Luo et al. 2012; Sellick et al. 2011) potentially due to either approaches to cope with this metabolic stress. low concentrations of metabolites as an effect of reduced We transiently silenced each gene of TCA cycle using growth of cells post-knock-down of Aco2 gene expression siRNA in a CHO-K1 host cell line and measured its effect and/or limitations of sensitivity of the GC–MS to detect on cell growth and energy production. Expression of recom- metabolites in such samples. binant proteins exerts an additional metabolic influence on The metabolite analysis was performed for four time CHO cells therefore, for evaluation of effect of TCA cycle points post-siRNA transfections. PCA and PC-DFA plots in regulating growth of CHO cells we used WT host cells instead of a recombinant CHO cell line for our initial studies.

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Fig. 4 Heat map of PCR array showing differential expression of in green; genes with lower expression are depicted in red and genes 84 genes from a glucose, b amino acid metabolism in Aco2 knock- with no difference in black. Differences in expression were calculated down samples. The heat map represents fold regulation expression using the ΔΔCT method. PCR array plates: JGDC genomic DNA between Aco2 knock-down and negative control samples. Genes with contamination control, RTC ​reverse transcription control, PPC posi- higher expression in Aco2 samples compared to controls are depicted tive PCR control

On comparison of effects of silencing of all TCA cycle which generate ATP (Cairns et al. 2011; Hardie 2007; Har- genes, Aco2 (or aconitase) had the most lethal effect on cell die and Hawley 2001). survivability using the pooled siRNA duplexes approach. Pyridine nucleotides NAD­ + and NADH affect growth of We consequently evaluated the effect of mitochondrial acon- mammalian cells owing to their role as electron carriers in itase silencing on WT and recombinant CHO cells in culture several biological reactions (Oka et al. 2012). ­NAD+ is an using a single siRNA duplex, to avoid any off-target effects. electron acceptor and its reduction to NADH is representa- A significant decline in cell growth (p ≤ 0.0001) 48–72 h tive of progression of metabolic reactions in cells (da Veiga post-siRNA transfections in CHO cells indicated that aco- Moreira et al. 2015). In this study, we recorded a decline nitase expression is crucial for CHO cell survival in com- in ­NAD+ concentration and increase in catabolic reduction parison to other TCA cycle genes (Table 1). The substrates charge post-aconitase silencing (Figs. 2, 6). Reduction in of the TCA cycle can be generated from other anaplerotic ­NAD+ concentration can lead to cell death and stimulate reactions allowing for partial continuation of the cycle. We pro-apoptotic pathways (Ding et al. 2007). hypothesise that on inhibition of TCA cycle enzymes, CHO Aconitase is a sensor for oxidative stress and we meas- cells may be able to generate sufficient levels of ATP to ured an increase in H­ 2O2 ROS post-aconitase knock-down. sustain cellular proliferation by this mechanism. However, Increased production of ROS in CHO cells led to an oxida- a significant decline in ATP concentration and an increase tive environment in the cell, established by an increase in in AMP production were measured post-Aco2 silencing. concentration of gluconic acid (Fig. 5), which is generated Increased AMP/ATP ratio has been shown to be associated by oxidation of glucose in the pentose phosphate pathway with inhibition of cellular proliferation and reduction in (Rohatgi et al. 2014). Further proline and hydroxyproline energy utilising anabolic pathways like gluconeogenesis and amino acids which demonstrate anti-oxidant properties increases in the rate of catabolic pathways like glycolysis (Kaul et al. 2008; Phang et al. 2008; Wu et al. 2011) were

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Fig. 5 Metabolite analysis post-siRNA mediated knock-down of Aco2 gene red circles represent outliers in the Aco2 dataset and green circles represent expression. The box-and-whisker plot (Tukey) represented relative peak inten- outliers in the negative siRNA data set. The line in the ‘box’ represented the sity from the GC–MS data of the metabolites present at all time-points post- median of the data. The data was analysed using two-way ANOVA. The ‘ns’ transfection. The ‘box’ represented the ‘interquartile range’ (IQR) of the data represented no statistical difference and p ≤ 0.05 indicated statistical difference which covers 50% of the data, from the 25th and 75th percentiles. The whisk- between the tested sample and the control. Metabolites that were identified to ers extend to the most extreme data points which are not considered as outliers, MSI level 1 are marked with * and MSI level 2 are marked with **. The data and extend the box by a further ± 1.5*IQR. The data points with > 1.5*IQR represented n = 8 siRNA transfections at different time points—0 h, 24 h, 48 h away from the ‘box’ are considered as ‘outliers’ and are plotted as a circle. The and 72 h

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Fig. 5 (continued)

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Fig. 5 (continued)

Fig. 6 Reduction in cell growth and expression of a recombinant sensors on the Octet machine respectively. b Adenylate energy and protein in a CHO cell line. The siRNA transfections targeting the catabolic reduction charges were calculated on day 3 post-siRNA Aco2 gene were performed in a recombinant CHO cell line express- transfections. d Cell specific productivity (Qp) measured picograms ing a mouse monoclonal antibody using Lipofectamine® RNAiMAX of specific protein per cell per day on day 7 post-transfections. Data transfection reagent. a Cell growth and c recombinant protein expres- represents mean ± SD of three independent siRNA transfections. The sion was measured during 7-day culture period using Vi-CELL XR asterisks represent p value calculated by two-ANOVA analysis— Cell Viability Analyzer and the anti-murine IgG quantitative bio- *p ≤ 0.05; ****p ≤ 0.0001 also up-regulated in Aco2 knock-down cells. A reduction in pyruvate to sustain the TCA cycle and subsequent energy ­H2O2 species in the controls was possibly due to the pres- production to support cellular metabolic processes. Further, ence of anti-oxidant molecules like glutathione (Fig. S4) glycolysis has a faster ability to generate ATP in compari- which can maintain intracellular levels of ROS (Burdon son to oxidative phosphorylation therefore up-regulation et al. 1995; Liou and Storz 2010). of glycolysis may be a mechanism to cope with the defect The qPCR array of glucose metabolism demonstrated in mitochondrial TCA cycle due to aconitase silencing. In up-regulation of glycolytic and other cellular pathways addition, metabolic pathways like the biosynthesis of ser- for increased production of pyruvate. The decline in ATP ine which potentially divert pyruvate away from being used production in aconitase knock-down cells generated more as a source of energy were down-regulated in our work on demand on glycolysis and other pathways generating CHO cells. The main pathways of pyruvate utilisation in

1 3 Mitochondrial aconitase is a key regulator of energy production for growth and protein expression… Page 13 of 16 136 mammalian cells (Fig. S6) are conversion of pyruvate to The GC–MS analysis of aconitase knock-down cells also lactate by lactate dehydrogenase in anaerobic conditions; demonstrated increased levels of amino acids—lysine, threo- conversion to acetyl-CoA by the pyruvate dehydrogenase nine, leucine etc. Although catabolism of these amino acids complex for progression of TCA cycle; conversion of pyru- regenerates acetyl-CoA and pyruvate for continuation of the vate to oxaloacetate by ; and reversible TCA cycle (Ozturk and Hu 2005), an accumulation of these transamination of pyruvate to alanine (DeBerardinis et al. metabolites was measured. In addition, most of the cellular 2007; Jitrapakdee et al. 2008; Vander Heiden et al. 2009; reactions are mediated by ATP or ­NAD+ metabolites. In aco- Wieland 1983; Yang et al. 2009). No significant increase in nitase silenced cells, an increase in these amino acids concen- lactate concentration or up-regulation of the lactate dehy- tration was either due to catabolism of intracellular proteins drogenase, pyruvate carboxylase and alanine transaminase for nutrients and energy (Yuan et al. 2013) or down-regulation gene expression was measured using the PCR array. In addi- of pathways for utilisation of these amino acids in paucity of tion, there was no down-regulation of the pyruvate dehy- ATP and ­NAD+ metabolites. For instance, genes involved in drogenase complex measured by the gene expression array degradation of lysine to acetyl-CoA (Aadat and Ehhadh) (Goh suggesting oxidative decarboxylation of pyruvate to acetyl- et al. 2002; Kanehisa and Goto 2000) are down-regulated. We CoA was not inhibited. Acetyl-CoA could be being directed further measured an increase in acyclic branched alkanes or for mitochondrial synthesis. The increase in both hydrocarbon lipid metabolites like heneicosane, nonadecane malonate concentration which is involved in synthesising and dodecane which are formed on lipid peroxidation in pres- fatty acids (Clay et al. 2016), and palmitic acid is suggestive ence of reactive oxygen species (Rice-Evans and Burdon of this. Nevertheless, the lack of significant up-regulation of 1994). metabolic pathways to cope with the increased generation of Silencing of aconitase gene expression in a recombinant pyruvate suggests there may be other possible roles of pyru- CHO cell line also affected protein production amongst other vate in these cells. It has been previously demonstrated that cellular processes. The reduction in protein expression was pyruvate can have an anti-oxidant effect on mammalian cells due to reduced cellular growth and oxidative stress caused by in culture (O’Donnell-Tormey et al. 1987). There is therefore ROS (McKenna 2009; Tavender and Bulleid 2010). Degraded the possibility that pyruvate is exerting an anti-oxidant effect or un-folded antibodies would lose antibody interactions to the in aconitase silenced cells. conformational epitopes of the antigens therefore, measure- Furthermore, contrary to the known amphibolic nature ment of IgG concentration using anti-mouse biosensors which of the TCA cycle to regenerate its intermediates by cata- bind specifically to the F(ab′)2 portion of the mouse IgG indi- plerotic and anaplerotic reactions (Owen et al. 2002), other cate detection of an intact antibody in our experiments. It was TCA cycle genes and pathways which would regenerate postulated owing to the transient nature of siRNA transfections α-ketoglutarate and fumarate intermediates were down- and reduced cell growth during early culture stage, the activ- regulated. The silencing of aconitase gene inhibited the ity of the Aco2 gene will be restored over the period of time functioning of the TCA cycle potentially due to generation (Dykxhoorn et al. 2003). However, we recorded no increase of large amounts of ROS leading to oxidative stress. Addi- in VCD during day 7 of the batch culture suggesting silenc- tionally, majority of the TCA cycle reactions are carried out ing of aconitase gene impairs the cells ability to restore to its in presence of reduction of NAD­ + to NADH and a decline ‘normal’ proliferating state, even in absence of siRNA activity. in ­NAD+ concentration impaired the TCA cycle activity. Glutamine acts as a shuttle between carbon and nitrogen metabolism in case of inhibition of TCA cycle. Glutamine 5 Conclusion was added to the culture medium and although the glutami- nase (gls) gene catalysing the conversion of glutamine to In conclusion, our work has established knock-down of glutamate was up-regulated, subsequent glutamate dehy- aconitase gene was the most lethal amongst TCA cycle drogenase 1 (Glud1) and Ogdh genes were down-regulated genes. Knock-down of Aco2 gene expression not only cre- suggesting that glutamine was not being used up by conver- ated oxidative stress, it also led to a significant reduction sion to α-ketoglutarate in aconitase silenced cells. Glutamate in ATP and NAD production. Gene expression analysis can be further transaminated to alanine and although ala- and metabolic profiling was performed for CHO cells nine production was not specifically measured, expression under oxidative stress caused due to silencing of the Aco2 of alanine:glyoxylate aminotransferase (Agxt) gene which gene. Impairment of TCA cycle up-regulated the rate of catalyses the conversion of alanine to pyruvate was up-regu- glycolysis and fatty acid synthesis. High levels of oxida- lated (Table S3). Studies using metabolic flux analysis using tive stress can lower rate of protein production therefore isotope-labelled 13C glucose or 15N glutamine can be used pyruvate and amino acids like proline can be added to to determine the fate of pyruvate and glutamine in aconitase reduce oxidative stress during fed-batch cultures. We also silenced mammalian cells. established reduction in Aco2 gene expression impaired

1 3 136 Page 14 of 16 N. Dhami et al. the functioning of the entire TCA cycle and CHO cells Bordag, N., Janakiraman, V., Nachtigall, J., González Maldonado, S., were unable to replenish other intermediates for normal Bethan, B., Laine, J.-P., et al. (2016). Fast filtration of bacterial or mammalian suspension cell cultures for optimal metabolomics functioning of the cycle. This was first of its kind study results. PLoS ONE, 11, e0159389. demonstrating regulation of TCA cycle in CHO cells by Breusch, F. L. (1937). Citric acid in tissue metabolism. Journal Physi- aconitase gene. Aco2 plays a crucial role in growth and ological Chemistry, 250, 262–280. proliferation of CHO cells and silencing of the gene signif- Brezinsky, S. C. G., Chiang, G. G., Szilvasi, A., Mohan, S., Shapiro, R. I., Maclean, A., et al. (2003). A simple method for enriching icantly altered protein production in a recombinant CHO populations of transfected CHO cells for cells of higher specific cell line. The gene can be used for future cell engineering productivity. Journal of Immunological Methods, 277, 141–155. strategies to cope with oxidative stress or regulate the rate Burdon, R. H., Alliangana, D., & Gill, V. (1995). Hydrogen peroxide Free Radical Research, of TCA cycle for high recombinant protein expression. and the proliferation of BHK-21 cells. 23, 471–486. Acknowledgements Cairns, R. A., Harris, I. S., & Mak, T. W. (2011). Regulation of cancer The authors thank Dr Sam Heywood (UCB) for cell metabolism. Nature Reviews Cancer, 11, 85–95. his critical review of this manuscript. We would also like to thank Profs Cantu, D., Fulton, R. E., Drechsel, D. A., & Patel, M. (2011). Mito- Alan Dickson and Pedro Mendes (University of Manchester) for their chondrial aconitase knockdown attenuates paraquat-induced dopa- insight and expertise that assisted this research. minergic cell death via decreased cellular metabolism and release of iron and ­H2O2. Journal of Neurochemistry, 118, 79–92. Author contributions Conceived and designed experiments: ND DH Cantu, D., Schaack, J., & Patel, M. (2009). Oxidative inactivation of RG DM. Performed the experiments: ND. GC-MS analysis: DT ND. mitochondrial aconitase results in iron and ­H2O2-mediated neu- Data analyses: ND DH RG DT DM. Wrote and commented in the rotoxicity in rat primary mesencephalic cultures. PLoS ONE, 4, paper: ND DH RG DT DM. e7095. Chan, C. Y., Carmack, C. S., Long, D. D., Maliyekkel, A., Shao, Y., Compliance with ethical standards Roninson, I. B., & Ding, Y. (2009). A structural interpretation of the effect of GC-content on efficiency of RNA interference. BMC Bioinformatics, 10 Conflict of interest , S33–S33. The authors declare that they have no conflict of Chevaillier, P. (1993). Pest sequences in nuclear proteins. International interest. Journal of Biochemistry, 25, 479–482. Research involving human and animal participants Clay, H. B., Parl, A. K., Mitchell, S. L., Singh, L., Bell, L. N., & Mur- This article does dock, D. G. (2016). Altering the mitochondrial fatty acid synthesis not contain any studies with human participants or animals performed (mtFASII) pathway modulates cellular metabolic states and bio- by any of the authors. active lipid profiles as revealed by metabolomic profiling. PLoS ONE, 11, e0151171. Cooper, G. M. (2000). Metabolic energy. In The cell: A molecular approach (2nd ed.). Sunderland, MA: Sinauer Associates. References Costello, L. C., & Franklin, R. B. (2013). A review of the important central role of altered citrate metabolism during the process of stem cell differentiation. Journal of Regenerative Medicine & Tis- Agrawal, N., Dasaradhi, P. V., Mohmmed, A., Malhotra, P., Bhatnagar, sue Engineering, 2, 1. R. K., & Mukherjee, S. K. (2003). RNA interference: Biology, Da Veiga Moreira, J., Peres, S., Steyaert, J.-M., Bigan, E., Paulevé, L., mechanism, and applications. Microbiology Molecular Biology Nogueira, M. L. et al. (2015). Cell cycle progression is regulated Review, 67, 657–685. by intertwined redox oscillators. Theoretical Biology and Medical Akashi, H., & Gojobori, T. (2002). Metabolic efficiency and amino Modelling, 12, 10. acid composition in the proteomes of Escherichia coli and Bacil- Deberardinis, R. J., Mancuso, A., Daikhin, E., Nissim, I., Yudkoff, lus subtilis. Proceedings of the National Academy of Sciences, M., Wehrli, S., & Thompson, C. B. (2007). Beyond aerobic 99, 3695–3700. glycolysis: Transformed cells can engage in glutamine metabo- Altamirano, C., Berrios, J., Vergara, M., & Becerra, S. (2013). lism that exceeds the requirement for protein and nucleotide Advances in improving mammalian cells metabolism for recom- synthesis. Proceedings of the National Academy of Sciences of binant protein production. Electronic Journal of Biotechnology the United States of America, 104, 19345–19350. 16(3), 10 Dietmair, S., Hodson, M. P., Quek, L. E., Timmins, N. E., Chrysan- Andersen, K. B., & Von Meyenburg, K. (1977). Charges of nicotina- thopoulos, P., Jacob, S. S., et al. (2012a). Metabolite profiling of mide adenine nucleotides and adenylate energy charge as regula- CHO cells with different growth characteristics. Biotechnology tory parameters of the metabolism in Escherichia coli. The Jour- and bioengineering, 109, 1404–1414. nal of Biological Chemistry, 252, 4151–4156. Dietmair, S., Hodson, M. P., Quek, L.-E., Timmins, N. E., Gray, P., Atkinson, D. E. (1968). Energy charge of the adenylate pool as a regu- & Nielsen, L. K. (2012b). A multi-omics analysis of recombi- latory parameter. Interaction with feedback modifiers. Biochem- nant protein production in Hek293 Cells. PLoS ONE, 7, e43394. istry, 7, 4030–4034. Ding, Y., Xu, L., Jovanovic, B. D., Helenowski, I. B., Kelly, D. L., Cat- Begley, P., Francis-Mcintyre, S., Dunn, W. B., Broadhurst, D. I., Hal- alona, W. J., Yang, X. J., Pins, M., & Bergan, R. C. (2007). The sall, A., Tseng, A., et al. (2009). Development and performance of methodology used to measure differential gene expression affects a gas chromatography–time-of-flight mass spectrometry analysis the outcome. Journal of Biomolecular Techniques, 18, 321–330. for large-scale nontargeted metabolomic studies of human serum. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-Mcintyre, Analytical Chemistry, 81, 7038–7046. S., Anderson, N., et al. (2011). Procedures for large-scale meta- Beinert, H., Kennedy, M. C., & Stout, C. D. (1996). Aconitase as bolic profiling of serum and plasma using gas chromatography iron–sulfur protein, enzyme, and iron-regulatory protein. Chemi- and liquid chromatography coupled to mass spectrometry. Nature cal Reviews, 96, 2335–2374. Protocols, 6, 1060–1083.

1 3 Mitochondrial aconitase is a key regulator of energy production for growth and protein expression… Page 15 of 16 136

Dykxhoorn, D. M., Novina, C. D., & Sharp, P. A. (2003). Killing the expression and biophysical properties of monoclonal antibody messenger: Short RNAs that silence gene expression. Nature variants. Antibodies, 3, 253. Reviews Molecular Cell Biology, 4, 457–467. Mckenna, T. (2009). Oxidative stress on mammalian cell cultures Elbashir, S. M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K., & during recombinant protein expression (Doctoral dissertation, Tuschl, T. (2001). Duplexes of 21-nucleotide RNAs mediate RNA Linköping University Electronic Press). interference in cultured mammalian cells. Nature, 411, 494–498. Mulukutla, B. C., Khan, S., Lange, A., & Hu, W.-S. (2010). Glucose Fiszer-Kierzkowska, A., Vydra, N., Wysocka-Wycisk, A., Kronekova, metabolism in mammalian cell culture: New insights for tweaking Z., Jarząb, M., Lisowska, K. M., et al. (2011). Liposome-based vintage pathways. Trends in Biotechnology, 28, 476–484. DNA carriers may induce cellular stress response and change gene Murakami, K., & Yoshino, M. (1997). Inactivation of aconitase in yeast expression pattern in transfected cells. BMC Molecular Biology, exposed to oxidative stress. IUBMB Life, 41, 481–486. 12, 27. O’donnell-Tormey, J., Nathan, C. F., Lanks, K., Deboer, C. J., & De La Gardner, P. R., Raineri, I., Epstein, L. B., & White, C. W. (1995). Harpe, J. (1987). Secretion of pyruvate. An antioxidant defense Superoxide radical and iron modulate aconitase activity in mam- of mammalian cells. The Journal of Experimental Medicine, 165, malian cells. Journal of Biological Chemistry, 270, 13399–13405. 500–514. Goh, D. L., Patel, A., Thomas, G. H., Salomons, G. S., Schor, D. S., Oka, S., Hsu, C. P., & Sadoshima, J. (2012). Regulation of cell survival Jakobs, C., et al. (2002). Characterization of the human gene and death by pyridine nucleotides. Circulation Research, 111, encoding alpha-aminoadipate aminotransferase (AADAT). Molec- 611–627. ular Genetics and Metabolism, 76, 172–180. Ott, M., Gogvadze, V., Orrenius, S., & Zhivotovsky, B. (2007). Mito- Harborth, J., Elbashir, S. M., Bechert, K., Tuschl, T., & Weber, K. chondria, oxidative stress and cell death. Apoptosis, 12, 913–922. (2001). Identification of essential genes in cultured mammalian Owen, O. E., Kalhan, S. C., & Hanson, R. W. (2002). The key role of cells using small interfering RNAs. Journal of Cell Science, 114, anaplerosis and cataplerosis for function. Journal 4557–4565. of Biological Chemistry, 277, 30409–30412. Hardie, D. G. (2007). AMP-activated/SNF1 protein kinases: Conserved Ozturk, S., & Hu, W. S. (2005). Cell culture technology for pharmaceu- guardians of cellular energy. Nature Reviews Molecular Cell Biol- tical and cell-based therapies. CRC Press, Boca Raton. ogy, 8, 774–785. Phang, J. M., Donald, S. P., Pandhare, J., & Liu, Y. (2008). The metab- Hardie, D. G., & Hawley, S. A. (2001). AMP-activated protein olism of proline, a stress substrate, modulates carcinogenic path- kinase: The energy charge hypothesis revisited. BioEssays, 23, ways. Amino Acids, 35, 681–690. 1112–1119. Quek, L. E., Dietmair, S., Kromer, J. O., & Nielsen, L. K. (2010). Jacobsen, L. B., Calvin, S. A., & Lobenhofer, E. K. (2009). Transcrip- Metabolic flux analysis in mammalian cell culture. Metabolic tional effects of transfection: The potential for misinterpretation of Engineering, 12, 161–171. gene expression data generated from transiently transfected cells. Reynolds, A., Leake, D., Boese, Q., Scaringe, S., Marshall, W. S., & BioTechniques, 47, 617. Khvorova, A. (2004). Rational siRNA design for RNA interfer- Jitrapakdee, S., Maurice, M. S., Rayment, I., Cleland, W. W., Wal- ence. Nature Biotechnology, 22, 326–330. lace, J. C., & Attwood, P. V. (2008). Structure, Mechanism and Rice-Evans, C. A., & Burdon, R. H. (1994). Free radical damage and Regulation of Pyruvate Carboxylase. The Biochemical Journal, its control. Elsevier, Amsterdam. 413, 369–387. Rohatgi, N., Nielsen, T. K., Bjørn, S. P., Axelsson, I., Paglia, G., Vold- Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes borg, B. G., et al. (2014). Biochemical characterization of human and genomes. Nucleic Acids Research, 28, 27–30. gluconokinase and the proposed metabolic impact of gluconic Kaul, S., Sharma, S. S., & Mehta, I. K. (2008). Free radical scaveng- acid as determined by constraint based metabolic network analy- ing potential of L-proline: Evidence from in vitro assays. Amino sis. PLoS ONE, 9, e98760. Acids, 34, 315–320. Sellick, C. A., Hansen, R., Maqsood, A. R., Dunn, W. B., Stephens, G. Krebs, H. A., & Holzach, O. (1952). The conversion of citrate into cis- M., Goodacre, R., et al. (2009). Effective quenching processes for aconitate and isocitrate in the presence of aconitase. Biochemical physiologically valid metabolite profiling of suspension cultured Journal, 52, 527–528. mammalian cells. Analytical Chemistry, 81, 174–183. Lai, T., Yang, Y., & Ng, S. K. (2013). Advances in mammalian cell Sellick, C. A., Hansen, R., Stephens, G. M., Goodacre, R., & Dick- line development technologies for recombinant protein produc- son, A. J. (2011). Metabolite extraction from suspension-cultured tion. Pharmaceuticals, 6, 579–603. mammalian cells for global metabolite profiling. Nature Proto- León, Z., García-Cañaveras, J. C., Donato, M. T., & Lahoz, A. (2013). cols, 6, 1241–1249. Mammalian cell metabolomics: Experimental design and sample Seth, G., Hossler, P., Yee, J. C., & Hu, W. S. (2006). Engineering preparation. Electrophoresis, 34, 2762–2775. cells for cell culture bioprocessing–physiological fundamen- Li, F., Vijayasankaran, N., Shen, A., Kiss, R., & Amanullah, A. (2010). tals. Advances in Biochemical Engineering Biotechnology, 101, Cell culture processes for monoclonal antibody production. mAbs, 119–164. 2, 466–477. Tavender, T. J., & Bulleid, N. J. (2010). Peroxiredoxin IV protects Liou, G.-Y., & Storz, P. (2010). Reactive oxygen species in cancer. Free cells from oxidative stress by removing ­H2O2 produced during Radical Research, 44, 479–496. disulphide formation. Journal of Cell Science, 123, 2672–2679. Locasale, J. W., & Cantley, L. C. (2011). Metabolic flux and the regula- Templeton, N., Dean, J., Reddy, P., & Young, J. D. (2013). Peak anti- tion of mammalian cell growth. Cell Metabolism, 14, 443–451. body production is associated with increased oxidative metabo- Luo, J., Vijayasankaran, N., Autsen, J., Santuray, R., Hudson, T., lism in an industrially relevant fed-batch CHO cell culture. Bio- Amanullah, A., et al. (2012). Comparative metabolite analysis technology and Bioengineering, 110, 2013–2024. to understand lactate metabolism shift in Chinese hamster ovary Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009). cell culture process. Biotechnology and Bioengineering, 109, Understanding the Warburg effect: The metabolic requirements 146–156. of cell proliferation. Science, 324, 1029–1033. Martius, C. (1937). The metabolism of citric acid. Journal Physiologi- Vásquez-Vivar, J., Kalyanaraman, B., & Kennedy, M. C. (2000). Mito- cal Chemistry, 247, 104–110. chondrial aconitase is a source of hydroxyl radical: An electron Mason, M., Sweeney, B., Cain, K., Stephens, P., & Sharfstein, S. spin resonance investigation. Journal of Biological Chemistry, (2014). Reduced culture temperature differentially affects 275, 14064–14069.

1 3 136 Page 16 of 16 N. Dhami et al.

Villadsen, J., Nielsen, J., & Lidén, G. (2011). Chemicals from meta- metabolism: Implications for animal and human nutrition. Amino bolic pathways. In Bioreaction engineering principles. New York: Acids, 40, 1053–1063. Springer. Yan, L.-J., Levine, R. L., & Sohal, R. S. (1997). Oxidative damage Wedge, D. C., Allwood, J. W., Dunn, W., Vaughan, A. A., Simpson, K., during aging targets mitochondrial aconitase. Proceedings of the Brown, M., et al. (2011). Is serum or plasma more appropriate for National Academy of Sciences, 94, 11168–11172. intersubject comparisons in metabolomic studies? An assessment Yang, R.-Z., Park, S., Reagan, W. J., Goldstein, R., Zhong, S., Lawton, in patients with small-cell lung cancer. Analytical Chemistry, 83, M., et al. (2009). Alanine aminotransferase isoenzymes: Molecu- 6689–6697. lar cloning and quantitative analysis of tissue expression in rats Wieland, O. H. (1983). The mammalian pyruvate dehydrogenase com- and serum elevation in liver toxicity. Hepatology, 49, 598–607. plex: Structure and regulation. In Reviews of physiology, biochem- Young, J. D. (2013). Metabolic flux rewiring in mammalian cell cul- istry and pharmacology. Berlin: Springer. tures. Current Opinion in Biotechnology, 24, 1108–1115. Winterbourn, C. C. (1995). Toxicity of iron and hydrogen peroxide: Yuan, H.-X., Xiong, Y., & Guan, K.-L. (2013). Nutrient sens- The Fenton reaction. Toxicology Letters, 82–83, 969–974. ing, metabolism, and cell growth control. Molecular Cell, 49, Wu, G., Bazer, F. W., Burghardt, R. C., Johnson, G. A., Kim, S. 379–387. W., Knabe, D. A., et al. (2011). Proline and hydroxyproline

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