applied sciences Article The Effect of the Expression of the Antiapoptotic BHRF1 Gene on the Metabolic Behavior of a Hybridoma Cell Line Iván Martínez-Monge 1,2, * , Pere Comas 2 , David Catalán-Tatjer 1 , Jordi Prat 2 , Antoni Casablancas 2 , Carlos Paredes 2 , Martí Lecina 2,3 and Jordi Joan Cairó 2 1 The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark; dacata@biosustain.dtu.dk 2 Chemical, Biological and Environmental Engineering Department, Campus Bellatera, Universitat Autònoma de Barcelona, Edifici Q, Carretera de Cerdanyola, s/n., 08193 Bellaterra, Spain; pere.comas@uab.cat (P.C.); jordipra60@yahoo.es (J.P.); antoni.casablancas@uab.cat (A.C.); carlesparedes@hotmail.com (C.P.); marti.lecina@iqs.url.edu (M.L.); JordiJoan.Cairo@uab.cat (J.J.C.) 3 Bioengineering Department, IQS-Universitat Ramon Llull, 08017 Barcelona, Spain * Correspondence: ivanmar@biosustain.dtu.dk Abstract: One of the most important limitations of mammalian cells-based bioprocesses, and particu- larly hybridoma cell lines, is the accelerated metabolism related to glucose and glutamine consump- tion. The high uptake rates of glucose and glutamine (i.e., the main sources of carbon, nitrogen and energy) lead to the production and accumulation of large amounts of lactate and ammonia in culture  broth. Lactate and/or ammonia accumulation, together with the depletion of the main nutrients, are  the major causes of apoptosis in hybridoma cell cultures. The KB26.5 hybridoma cell line, producing Citation: Martínez-Monge, I.; an IgG3 , was engineered with BHRF1 (KB26.5-BHRF1), an Epstein–Barr virus-encoded early protein Comas, P.; Catalán-Tatjer, D.; Prat, J.; homologous to the antiapoptotic protein Bcl-2, with the aim of protecting the hybridoma cell line Casablancas, A.; Paredes, C.; Lecina, from apoptosis. Surprisingly, besides achieving effective protection from apoptosis, the expression of M.; Cairó, J.J. The Effect of the BHRF1 modified the metabolism of the hybridoma cell line. Cell physiology and metabolism analyses Expression of the Antiapoptotic of the original KB26.5 and KB26.5-BHRF1 revealed an increase of cell growth rate, a reduction of BHRF1 Gene on the Metabolic Behavior of a Hybridoma Cell Line. glucose and glutamine consumption, as well as a decrease in lactate secretion in KB26.5-BHRF1 cells. Appl. Sci. 2021, 11, 6258. https:// A flux balance analysis allowed us to quantify the intracellular fluxes of both cell lines. The main doi.org/10.3390/app11146258 metabolic differences were identified in glucose consumption and, consequently, the production of lactate. The lactate production flux was reduced by 60%, since the need for NADH regeneration Academic Editors: Alessandra in the cytoplasm decreased due to a more than 50% reduction in glucose uptake. In general terms, Procentese and Francesca Raganati the BHRF1 engineered cell line showed a more efficient metabolism, with an increase in biomass volumetric productivity under identical culture conditions. Received: 14 June 2021 Accepted: 3 July 2021 Keywords: hybridoma; genome-scale metabolic model; antiapoptotic gene; mitochondrial trans- Published: 6 July 2021 port; BHRF1 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- 1. Introduction iations. The demand for monoclonal antibodies has significantly increased in recent years, mainly due to new applications in therapy, but also for clinical diagnoses and highly spe- cific purification processes [1,2]. In this regard, the capacity of mammalian cells to perform complex post-translational modifications to yield biologically active proteins has led to Copyright: © 2021 by the authors. their preferential use for biopharmaceuticals production. About 70–80% of all biophar- Licensee MDPI, Basel, Switzerland. maceuticals, including monoclonal antibodies, viral vaccines and gene therapy vectors, This article is an open access article distributed under the terms and are produced in mammalian cells. Among the top ten selling protein biopharmaceuticals conditions of the Creative Commons in 2014, six were antibodies or antibody-derived proteins. It is therefore not surprising Attribution (CC BY) license (https:// that in 2016, monoclonal antibody-based drug production using mammalian cell-based creativecommons.org/licenses/by/ systems almost doubled that of 2010 [1,3]. In recent years, monoclonal antibodies indus- 4.0/). Appl. Sci. 2021, 11, 6258. https://doi.org/10.3390/app11146258 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 6258 2 of 16 trial manufacturing has been based on mammalian cell lines such as hybridoma, among others [3–6]. Mammalian cell-based processes present important limitations regarding apoptosis: the accumulation of metabolic byproducts (i.e., lactate and ammonia) up to cytotoxic con- centration, and the depletion of essential nutrients, which triggers apoptosis (programmed cell death) [7]. The prevention of apoptosis during cell growth has a critical effect on the productivity of the final process, as an increase of cell life-span results in an increase in the synthesis and accumulation of the product of interest, since cells remain productive for a longer time, even after the exponential cell growth phase [8]. Moreover, more robust cell lines which are less sensitive to apoptosis make it possible to design high cell density culture strategies based on consistently low nutrient concentrations in narrower ranges [9]. In recent years, strategies to generate stress-resistant cell lines preventing apoptosis have been focused on blocking the apoptotic transduction pathways [10,11]. Although different pathways control the activation of signaling cascades of cell apoptosis activation, many of the apoptosis signals converge on the mitochondria, which stores numerous molecules that activate apoptosis [7]. The most widely used strategy to prevent apoptosis has been the overexpression of bcl-2 or bcl-xL genes, which inhibits the release of pro- apoptotic molecules from the mitochondria [10,12]. This strategy has been successfully applied in different mammalian cell lines, such as CHO or hybridoma, showing higher viabilities and improved robustness in cell culture [13–16]. The expression of mcl-1, another antiapoptotic gene with mechanism similar to that of bcl-2/bcl-xL, has also shown good results [17]. Another approach has been to directly target the caspase cascade, expressing X-linked inhibitor of apoptosis (XIAP) or cytokine response modifier CrmA, which both act to directly inhibit the caspases [18]. The expression of different viral proteins has also been reported to have antiapoptotic effects in hybridoma cell cultures, such as ksblc-2 from Karposi’s sarcoma-associated herpesvirus [19] and bhrf-1 from Epstein-Barr virus [20]. Furthermore, the downregulation of the pro-apoptotic genes Bak, Bax and Casp3 has been shown to reduce apoptosis in CHO cells [21]. Additionally, antiapoptotic genes have been shown to have an effect on metabolism, although this is not yet fully understood [22,23]. This is remarkable, as cell-based processes present an important limitation regarding metabolism: deregulation of the uptake of substrates (i.e., high consumption rates of mainly glucose and glutamine), linked to the secretion and accumulation of lactate and ammonia as byproducts of metabolism [24]. The reduction of the secretion and accumulation of lactate remains a hot topic in the biomanufacturing industry. Many approaches have been explored to reduce or delay lactate generation in cell culture, including media design by the substitution of glucose for alterna- tive sources of carbon, like fructose or galactose [25], different fed-batch strategies limiting glucose concentration [9,26] and several cell engineering approaches such as the expression of pyruvate carboxylase [27] and downregulation of lactate dehydrogenase [28]. In all of the described scenarios, a reduction, but never a total depletion, in lactate accumulation was observed. In the present work, a murine hybridoma cell line (KB26.5) was engineered to over- express the BHRF-1 gene (KB26.5-BHRF1). Besides the observed protective effect against apoptosis, the overexpression of BHRF-1 had an unexpected direct effect on cell physiology and metabolism. Mainly higher cell growth rate and more efficient nutrient usage were observed in batch cultures, significantly reducing the production of lactate. Therefore, metabolic flux balancing techniques were applied to better understand the interactions and effects of BHRF-1 expression with cell metabolism. Analysis of the obtained intracellular fluxes provided us with a better understanding of the effects of BHRF-1 expression on the improved cell metabolism, and gave rise to some hypotheses about possible BHRF-1 inter- actions with the metabolic pathways. Nowadays, the prevention of apoptosis continues to be a relevant research topic, e.g., in a review by Henry et al. (2020) [11], the effects of the various antiapoptotic strategies which have been applied in recent years were presented and discussed. Appl. Sci. 2021, 11, 6258 3 of 16 2. Materials and Methods 2.1. Cell Lines and Cell Maintenance The murine hybridoma cell line used in this work was KB26.5, which produced an IgG3 antibody against antigen A1 of human red blood cells (used to distinguish among ABO groups in blood tests). The KB26.5 cell line was transfected with BHRF1 (BamHI rightward reading frame 1)-enconding vector, as described by Juanola et al. (2009) [20]. Both cells lines were maintained in a 125 mL polycarbonate shake flask (Corning Inc., Corning, NY, USA) with a working volume of 20 mL, incubated in a humidified atmosphere at 37 ◦ C with 5% CO2 (Steri-cult 2000 Incubator). The agitation was set at 110 rpm with an orbital shaking platform (Stuart SSL110). Cell passaging was performed every 2 or 3 days at an initial seeding density of 1.5·105 cells/mL. 2.2. Culture Medium Dulbecco’s modified Eagle’s media (DMEN) (D5796, Sigma, St. Louis, MO, USA), containing 25 mM glucose and 4 mM glutamine, was used. Basal media was supplemented with 2 mM glutamine (Sigma), 5% FBS (v/v) (Sigma), 0.5% Antifoam C Emulsion (Sigma) and 2% Kolliphor® P 188 (Sigma). 2.3. Shake-Flasks Culturing Platform A set of 250 mL polycarbonate shake-flask (Corning Inc.) with 50 mL working volume was used for preliminary experiments. Identical conditions to those described under “cell maintenance” were maintained. The seeding density was about 3.0 × 105 cells/mL, and the sampling volume was 1 mL. 2.4. Bioreactor and Operational Conditions Bioreactor cell cultures were run in a 2 L Biostat Bplus (Sartorius Stedim Biotech, Gottingen, Germany). The bioreactor was equipped with the necessary probes to maintain the desired culture conditions regarding pH, pO2 and temperature, stirred with two marine impellers at 60 rpm. The dissolved oxygen (DO) concentration was measured with an optical probe (VisiFerm DO, Hamilton). DO was set at 40% saturation with a constant aeration inflow of 0.20 L/min, with variable composition controlled by a gas mix air/oxygen station. pH was measured with a standard electrode (EASYFERM PLUS, Hamilton) and maintained at 7.1 through the addition of NaOH 0.5 M solution when buffer capacity was not enough to compensate for acidification. CO2 inlet gas enrichment was fixed at 5% during the entire process. The temperature was set at 37 ◦ C. 2.5. Analytical Methods 2.5.1. Cell Number Cell number was determined by manual counting using a Neubauer hemocytometer and a phase contrast microscope (Nikon eclypse TS100). Cell viability was determined by the trypan blue dye exclusion method (1:1 mixture of a 0.2% trypan blue (Gibco Thermo Fisher Scientific, Waltham, MA, USA) in phosphate buffer saline and cell sample). 2.5.2. Metabolite Concentrations For the metabolite analysis, samples were centrifuged at 3000× g for 3 min (Spec- trafuge) and the pellet was discarded. Daily measurements of centrifuged samples were taken for glucose, lactate, glutamine, glutamate, ammonia, K+ , Na+ and osmolality, using the BioProfile 400 (Nova Biomedical, Waltham, MA, USA). Prior to the amino acid analysis, supernatants were filtrated using 96-well filter plates (AcroPrep Advance, 0.2 µm Supor Short Tip Natural PP, Pall Corporation, New York, NY, USA). Amino acids were quantified using a method based on that of Valgepea et al. (2017) with the following modifications: amino acids were derivatized in the HPLC autosampler (Dionex Ultimate 3000), and the sample was injected into an AdvanceBio AAA (2.7 um, 4.6 × 100 mm, Agilent PN: 655950-802) with a guard column (AdvanceBio AAA 2.7 um, Appl. Sci. 2021, 11, 6258 4 of 16 4.6 × 5 mM PN: 820750-931). The HPLC gradient was 5–22% B from 0–7 min, 22–23% B from 7–8 min, 23–30% B from 8–9 min, 30–40% B from 9–14 min, 40–50% B from 14–16 min, 50–55% B from 16–18 min, 55–100% B from 18–18.5 min and kept at 100% to 20.1 min, but decreased to 5% B until 24 min, when chromatography was completed. Buffer A was 40 mM Na2 HPO4 , 0.02% NaN3 (w/v) at pH 7.8. Buffer B was 45% (v/v) acetonitrile, 45% (v/v) methanol and 10% (v/v) water. Flow rate was 1 mL/min from 0–18.5 min, 1.5 mL/min from 18.5–22.1 min, and thereafter, 1 mL/min to 24 min. Derivatized amino acids were monitored using a fluorescence detector. OPA-derivatized amino acids were detected at 340ex and 450em nm, and FMOC-derivatized amino acids at 266ex and 305em nm. Quantifications were based on standard curves derived from serial dilutions of an in-house prepared mixed amino acid standard. The upper and lower limits of quantification were 75 and 0.5 µg/mL, respectively. Chromatograms were integrated using Chromeleon version 7.1.3. 2.5.3. Product Concentration Product concentration was measured using Valita® TITER Assay at the moment the cells reached the maximum density in bioreactor. The Valita® TITER Assay is a fluorescence polarization assay for the quantitative detection of IgG antibodies in cell culture. Standard curve was performed using a known concentration IgG standard (Human IgG Isotype Control, Thermo Fisher, Waltham, MA, USA) diluted in the same cell culture media as that used in the experiments. An eight-point standard curve performed in triplicated was used. The Valitacell Evaluation tool software was used to analyze the titer assays. 2.6. Oxygen Uptake Rate (OUR) Determination of the oxygen uptake rate (OUR) was performed by applying the dynamic method [29,30]. This method is based on interrupting the air inflow to the bioreactor for a few minutes; then, the dissolved oxygen concentration decreases due to the respiratory activity of the cells and to the oxygen desorption from the liquid phase to the bioreactor headspace. The specific methodology used in this work is detailed in the literature [31,32]. 2.7. Estimation of Specific Consumption and Production Rates The methodology followed to calculate the specific consumption and production rates for the metabolites analyzes is detailed in a report by Martínez-Monge et al. (2019) [33]. The specific productivity (qp ) of the cells in the process was calculated using the integral of viable cells (IVC) and the product concentration between the first and last points, as presented in Equation (1). IVC was calculated using the trapezoidal rule with the function trapz included in MATLAB 2019a (Mathworks). [Product]final qp = (1) IVC For volumetric productivity (VP ), the final product concentration and the total process time was used, as shown in Equation (2). [Product]final VP = (2) ttotal 2.8. Reduced Genome-Scale Metabolic Model In this study, the reduced genome scale metabolic model published by Martínez et al. (2013) [34] was further modified to adapt it to the experimental conditions. The original model is freely available in Systems Biology Markup Language (SBML) [34]. The model was derived from the complete Hybridoma genome-scale metabolic model constructed from a generic genome-scale metabolic model of Mus musculus [35,36]. The adaptation process to obtain a reduced model which is able to perform flux analysis for the experiments performed in hybridoma was detailed in Martínez-Monge et al. (2019) [33]. The base Appl. Sci. 2021, 11, 6258 5 of 16 metabolic model did not contain the biomass formation equation, since the defined objective function depended on maximizing or minimizing of the ATP yield. So, the base model was modified by adding 14 reactions involved in the synthesis of all of the metabolite precursors needed for biomass formation, as detailed in Martínez-Monge et al. (2019) [33]. The final model is composed of 362 internal and 36 external reactions that collectively involve 395 metabolites. A complete list of all included reactions and metabolites is available in the Supplementary Data 1 in xlsx format. 2.9. Flux Balance Analysis of the Reduced Model A Parsimonious Flux Balance Analysis (p-FBA) was performed using Optflux 3.4 Software [37], a user-friendly computational tool for metabolic engineering applications. p-FBA is a variant of flux balance analysis that minimizes the total material flow to achieve an objective while maintaining optimal growth [38]. A graphical representation of the p-FBA was generated using the Omix Visualization Software (GmbH&Co.KG), as de- scribed by Droste et al. (2011) [39], in which only the most significant fluxes for this study were represented. As usual in FBA with genomic models, the system was underdetermined with some degrees of freedom. To find the optimal state, p-FBA optimizes a certain objective function, in this case, the maximum value of ATP generation (EF0002). The cytoplasmic malic enzyme was considered to be inactive, and NADPH required for biosynthesis could only be regen- erated by the pentose phosphate pathway, as this degree of freedom can only be resolved by using more advanced fluxomic techniques (R00216 [0 0]) [40]. Glutaminolysis was constrained to go in the direction of oxoglutarate generation (R00243mitoc [0 10000]) [41]. The specific consumption/production rates of the measured experimental metabolites were added to the model using the mean of the duplicates ± standard deviation. 3. Results 3.1. Effect of BHRF1 on Cell Physiology 3.1.1. Noncontrolled Culture Conditions: Shake Flask Cultures After engineering KB26.5 hybridoma cell lines with the BHRF1 gene, the transformed cell population was kept under selective pressure for several passages. Then, differences in cell physiology between the cell line KB26.5 and the engineered KB26.5-BHRF1 were observed after comparing both cell lines in shake flask cultures. The time profiles for cell growth, cell viability, glucose and lactate concentrations for both cell lines (A1 for KB26.5 and B1 for KB26.5-BHRF1) are displayed in Figure 1. A Napierian logarithm of viable cells versus time is plotted to better understand the variations in cell growth, as will be discussed later (A2 for KB26.5 and B2 for KB26.5-BHRF1). Considering that the experiments were carried out in duplicate (same time points), the mean values are plotted together with the standard deviation. The parental KB26.5 cell line reached a cell density of 1.79 ± 0.3 × 106 cells/mL after 72 h of culture, at which point cell viability decreased sharply. In contrast, KB26.5-BHRF1 reached 3.92 ± 0.03 × 106 cell/mL in about the same time. Regarding glucose and lactate concentration, high consumption and production rates respectively could be deduced by the sharp decay/increase of the concentration profiles, obtaining more lactate generation in the parental KB26.5 cell line. The slopes of the Napierian logarithm plots of viable cells showed a higher cell growth rate for the KB26.5-BHRF1 compared with the parental line. Parallel to the plateau of the cell growth profile, glucose consumption and lactate production also indicated a metabolic arrest at 72 h of culture, when their concentration values became almost constant. No morphology changes were observed between KB26.5 and KB26.5-BHRF1 on different days from manual counting using a microscope (data not shown). Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 20 Appl. Sci.Sci. Appl. 2021, 11,11, 2021, x FOR 6258 PEER REVIEW 6 of 616of 19 Appl. Sci. 2021, 11, x FOR PEER REVIEW 6 of 20 results show the same behavior encountered in the previous shake-flask experiments, i.e., A1 KB26.5-BHRF1 showed higher final cellB1 density, higher growth rate and less lactate pro- 100 5 A1 duction. The final titer 35obtained100was also 5 B1 higher for KB26.5-BHRF1. 35 90 100 5 35 100 90 5 35 30 30 Viable Cell Density6 (10 cells/mL) Viable Cell Density (10 cells/mL) 8090 4 A.1 90 80 4 A.2 30 30 (10 cells/mL) Viable Cell Density (10 cells/mL) 100 5 35 25 140 100 5 35 25 140 Glucose/Lactate (mM) Glucose/Lactate (mM) 7080 4 80 70 4 6 6 90 90 Glucose/Lactate (mM) Glucose/Lactate (mM) 25 25 Viability (%) Viability (%) 6070 3 30 120 70 60 3 30 120 20 20 (10 cells/mL) Viable Cell Density (10 cells/mL) 80 4 6 80 4 Viability (%) Viability (%) 5060 3 20 60 50 3 20 Glucose/Lactate (mM) Glucose/Lactate (mM) 70 25 100 70 25 100 Cell Density 15 15 405060 50 40 6 6 2 3 2 3 Viability (%) Viability (%) IgG3 (mg/L) IgG3 (mg/L) 60 20 15 80 20 15 80 Cell Density 40 2 40 2 30 50 10 30 50 10 3040 15 60 30 15 60 20 1 2 10 20 40 1 2 10 Viable 2030 1 5 20 1 5 10 40 30 10 10 10 40 Viable 5 5 1020 1 10 20 1 0 0 5 0 20 0 0 5 0 20 010 00 24 48 72 96 0 0 10 0 0 24 48 72 0 96 0 24 48 72 96 0 24 48 72 96 0 0 Time (h) 0 0 0 0 Time (h) 0 0 0 24 48Time(h) 72 96 0 24 Time48(h) 72 96 A2 Time (h) B2 Time (h) 1.5 A2 1.5 B2 1.5 B.1 1.5 B.2 100 5 35 140 100 5 35 140 90 1.0 90 1.0 1.0 1.0 30 120 30 120 Viable Cell Density (10 cells/mL) Viable Cell Density (10 cells/mL) 80 4 80 4 0.5 0.5 Glucose/Lactate (mM) Glucose/Lactate (mM) 70 0.5 25 100 70 0.5 25 100 6 6 ln (VCD) ln (VCD) Viability (%) Viability (%) IgG3 (mg/L) IgG3 (mg/L) 60 3 ln (VCD) ln (VCD) 60 3 20 80 20 80 0.0 0.0 0.0 50 50 0.0 15 60 15 60 40 2 40 2 -0.5 -0.5 -0.5-0.5 30 10 40 30 10 40 20 1 20 1 -1.0 -1.0 5 20 -1.0-1.0 5 20 10 10 0 0 0 0 0 0 0 0 -1.5 -1.5 0 24 48 72 96 -1.5-1.5 0 24 48 72 96 00 24 24 48 48 72 72 9696 0 0 24 24 48 48 72 72 96 96 Time (h) Time (h) Time (h) Time (h) (h) (h) TimeTime Figure 2. Two L bioreactor batch cultures of the parental KB26.5 cell line (A.1 and A.2) and the engineered KB26.5-BHRF1 (B11.and Figure Figure Figure 1.1. B2).profile Time Time Time Time profile profile profile of of cell viable ofviable of viable cell cell density density ((▲), density (▲), cell cell N), cell (▲), viability viability cellviability viability ♦(◊); ((◊), glucose glucose ), glucose (◊), glucose (●) ((●) and ) and (●)and lactate lactate andlactate ((( ))(concentration lactate )and product (bars) concentration of concentration ofofbatch ) concentration batch cultures cultures batch cultures run run run of in ininbatch shake shake shake cultures. flasks Duplicate (A1: cultures parental ((A1):parental flasks ((A1): KB26.5 parentalKB26.5 are and KB26.5 and shown B1: for each engineered and (B1): engineered cell line. KB26.5-BHRF1). engineered KB26.5-BHRF1). Napierian KB26.5-BHRF1).Napierian Napierian logarithm logarithm of viable of viable logarithm cell of viable density cellcell density density versus versus time versustime (●) time(●) (A2: ((A2):parental (•) ((A2): parental parentalKB26.5 KB26.5 KB26.5 and and B2: and engineered (B2): engineered (B2): KB26.5-BHRF1). engineeredKB26.5-BHRF1). Data KB26.5-BHRF1). obtained Data obtained from Data obtainedfromduplicate duplicatecultures culturesinin from duplicate shake shake in cultures flasks; shake the theerror flasks; flasks; bar barcorresponds the error error to to the bar corresponds corresponds the standard to deviation. the standard standard deviation. deviation. 3.1.2.The The parental Bioreactor Controlled KB26.5 cellCultures parental KB26.5 cell line reached a cell density of 1.79 ± 0.3 × 106 cells/mL line reached a cell density of 1.79 ± 0.3 × 106 cells/mL after 72 after 72 h of culture, at which point cell viability decreased sharply. In contrast, KB26.5-BHRF1 h of culture, In orderattowhich confirmpoint the cell viability beneficial decreased effect of BHRF1 sharply. on cellIn contrast, KB26.5-BHRF1 physiology (i.e., on cell reached 3.92 ± 0.03 × 106 6cell/mL in about the same time. Regarding glucose and lactate growth and reached 3.92nutrients ± 0.03 × usage), 10 cell/mLcultures of boththe in about cellsame lines time. were performed Regardinginglucose a 2 L bioreactor. and lactate concentration, high consumption and production rates respectively could be deduced by Bioreactor cultures should not only allow us to disregard the possibility concentration, high consumption and production rates respectively could be deduced by that the differences the sharp decay/increase of the concentration profiles, obtaining more lactate generation encountered the sharp previously were decay/increase related to the changing conditions when culturing cells in in the parental KB26.5 cell of the line. concentration The slopes of the profiles, Napierian obtaining logarithmmore plotslactate of viablegeneration cells shake in flasks (non the parental pH andcelldissolved oxygen control), but also logarithm to generateplotsenough data tocells showed a higherKB26.5 cell growth line. rateThe slopes for the of the Napierian KB26.5-BHRF1 compared with the of viable parental line. performathe showed flux balance higher cell analysis. growth rate In foraddition, the product concentration KB26.5-BHRF1 compared (IgG with3 ) the wasparental measuredline. Parallel from the tomoment the plateau the of thereached cells cell growth profile, glucose the maximum consumption density. and lactate Figure 2 shows the time produc- profile Parallel tion also to the plateau indicated a of thearrest metabolic cell growth at 72 h of profile, culture, glucose when consumption their concentration and lactate values of cell density and viability, as well as the evolution of glucose, lactate and product production became alsoconstant. almost indicatedNo a metabolic morphology arrest at 72were changes h of culture, when theirKB26.5 concentration concentrations. As the experiments were carried out in the observed between same bioreactor in duplicateand but values became almost KB26.5-BHRF1 constant. No morphology changesusing wereaobserved between KB26.5 not in parallel (different time points), both replicates are plotted for each cell line,(data on different days from manual counting microscope not showing and KB26.5-BHRF1 shown). comparable behavior. onAdifferent Napierian days from manual logarithm of viablecounting using cells versus timea microscope is plotted for(dataeach not shown). replicate in Figure 3, to better understand the changes in cell growth. The results show the 3.1.2. sameControlled Bioreactor Cultures behavior encountered in the previous shake-flask experiments, i.e., KB26.5-BHRF1 3.1.2. In showedControlled order higher Bioreactor to final confirm the Cultures beneficial cell density, higher effect growth ofrate BHRF1 on cell and less physiology lactate (i.e.,The production. on final cell growth titerIn and obtained ordernutrients was to usage), also confirm thecultures higher of both for KB26.5-BHRF1. beneficial cellof effect lines were performed BHRF1 in a 2 L bioreactor. on cell physiology (i.e., on cell Bioreactor cultures Finally, before should not preforming only flux allow balance us to disregard analysis (FBA), the the possibility specific that the consumption growth and nutrients usage), cultures of both cell lines were performed in a 2 L bioreactor. differ- rates ences for allencountered the metabolitespreviously measured were in related bioreactor to the changing experiments wereconditions calculated Bioreactor cultures should not only allow us to disregard the possibility that the when in the culturing exponen- tial growth cells in shakephase differences flasks(Table (non 1). encountered pH Values werewere and dissolved previously calculated oxygen relatedas the to mean control), the of thetodata butchanging also obtained generate from enough conditions when duplicate data to cultures. perform the In general, flux balanceKB26.5-BHRF1 analysis. In showed addition, more productefficient substrate concentration culturing cells in shake flasks (non pH and dissolved oxygen control), but also to generate consump- (IgG 3) was tion (especially measured for glucose fromtothe moment and glutamine), theflux cellsbalance reachedand thereduced maximumbyproduct density.generation (especially Figure 2 shows the enough data perform the analysis. In addition, product concentration lactate, time ammonia profile of celland alanine). density and viability, as well as the evolution of glucose, lactate and (IgG3) was measured from the moment the cells reached the maximum density. Figure 2 product concentrations. As the experiments were carried out in the same bioreactor in shows the time profile of cell density and viability, as well as the evolution of glucose, duplicate but not in parallel (different time points), both replicates are plotted for each cell lactate and product concentrations. As the experiments were carried out in the same line, showing comparable behavior. A Napierian logarithm of viable cells versus time is bioreactor in duplicate but not in parallel (different time points), both replicates are plotted for each replicate in Figure 3, to better understand the changes in cell growth. The plotted for each cell line, showing comparable behavior. A Napierian logarithm of viable cells versus time is plotted for each replicate in Figure 3, to better understand the changes Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 19 Appl. Sci. 2021, 11, x FOR PEER REVIEW 7 of 20 Appl. Sci.Sci. 2021, 11, 11, x FOR in cell growth. The results show the same behavior encountered in the previous shake- Appl. 2021, 6258PEER REVIEW 6 of720 of 16 flask experiments, results i.e., KB26.5-BHRF1 show the same showed behavior encountered in higher final cell the previous density, experiments, shake-flask higher growth rate i.e., and less lactate production. The final titer obtained was also higher for KB26.5-BHRF1. KB26.5-BHRF1 showed higher final cell density, higher growth rate and less lactate pro- duction. The final titer obtained was also B1 higher for KB26.5-BHRF1. A.1 A1 A.2 100 5 35 100 5 35 100 5 35 140 100 5 35 140 90 90 A.1 90 90 A.2 30 30 3035 120 30 120 (10 cells/mL) Viable Cell Density (10 cells/mL) 100 5 140 100 5 35 140 Viable Cell Density (10 cells/mL) (10 cells/mL) 80 4 80 4 80 4 80 4 90 90 Glucose/Lactate (mM) Glucose/Lactate (mM) 70 25 70 25 2530 100 25 100 (mM) (mM) Glucose/Lactate (mM) 70 120 70 30 120 (10 cells/mL) (10 cells/mL) 80 4 6 6 80 4 6 6 Viability (%) Viability (%) 60 60 3 60 3 Viability (%) (%) (%) IgG3 (mg/L) 3 IgG3 (mg/L) 60 3 Glucose/Lactate 20 20 20 Glucose/Lactate (mM) 70 2025 80 100 70 25 100 80 Cell Density Cell Density Viability 50 50 50 6 6 50 Viability (%) IgG3 (mg/L) IgG3 (mg/L) 60 3 60 3 Glucose/Lactate 1520 15 80 60 20 15 15 80 60 Cell Density Cell Density 40 40 2 2 40 40 2 2 Viability 50 50 30 3040 2 10 15 10 40 60 30 30 15 10 10 60 40 40 2 Viable ViableViable 20 2030 1 1 20 20 30 1 1 10 40 10 40 Viable 5 5 20 5 5 20 10 1020 1 10 10 20 1 5 20 5 20 0 010 0 0 0 0 0 0 100 0 0 0 0 0 0 0 24 24 48 48 72 72 96 96 0 0 24 24 48 48 72 72 96 96 0 0 0 0 0 0 0 0 0 24 Time48 (h) Time (h) 72 96 0 24 Time (h) (h) Time48 72 96 B.1A2 B25 B.2 Time (h) Time (h) 100 5 35 140 100 35 140 1.5 B.1 1.5 B.2 90 100 5 35 140 100 90 5 35 140 30 120 30 120 Viable Cell Density (10 cells/mL) cells/mL) 90 90 1.0 80 4 1.0 80 4 30 120 30 120 Viable Cell Density (10 cells/mL) cells/mL) 80 4 25 100 80 4 25 100 (mM) Glucose/Lactate (mM) 70 70 (mM) Glucose/Lactate (mM) 25 100 25 100 6 6 70 0.5 70 0.5 (10(10 Viability (%) (%)(%) 60 3 3 (mg/L) IgG3 (mg/L) 60 3 Glucose/Lactate 20 80 20 80 6 6 Viability (%) 3 (mg/L) IgG3 (mg/L) Density 60 3 ln (VCD) ln (VCD) 60 3 Glucose/Lactate Viability 50 20 80 50 20 80 Density Viability 0.0 50 0.0 IgGIgG 50 15 60 15 60 40 2 40 2 15 60 15 60 Cell 40 2 40 2 Cell 30 10 40 30 10 40 -0.5 -0.5 Viable 30 10 40 30 10 40 Viable 20 1 20 1 20 1 5 20 20 1 5 20 10 -1.0 5 20 10-1.0 5 20 10 10 0 0 0 0 0 0 0 0 0 0 0 24 48 72 96 0 0 0 0 0 0 -1.5 -1.5 0 24 48 72 96 0 24 48 72 96 0 24 48 72 96 0 24 Time (h)48 72 96 0 24 48 Time (h) 72 96 Time (h) Time (h) Time (h) Time (h) Figure Figure2. Two Figure 2. Two 2. TwoLL bioreactor L bioreactor bioreactor batch batchcultures batch culturesof cultures ofthe of theparental the parental KB26.5 parental KB26.5 KB26.5 cellcellline cell line(A.1 line (A.1and (A.1 andA.2) and A.2)and A.2) andthe and the the engineered engineered engineered KB26.5-BHRF1 KB26.5-BHRF1 KB26.5-BHRF1 (B.1 Figureand (B1 (B.1 1. B.2). and B2). Time B.2).Time Timeprofile Time profileprofile of ofcell of of viable profile celldensity cell (▲), ((▲), density (▲), cell cell N), cell viability((◊), cellviability viability viability (◊); ♦(◊); glucose glucose glucose ); glucose (●) (●) ((●) and and ) and and lactate lactate lactate lactate ((( )()concentration )and andproduct )and product product of(bars) (bars) concentration concentration batch cultures concentration of batch run of in cultures. batch shake flasksDuplicate cultures. Duplicate (A1: cultures parentalcultures KB26.5are areshown andshown B1: for for each each engineered of batch cultures. Duplicate cultures are shown for each cell line. cell cell line. KB26.5-BHRF1). Napierian logarithm of viable cell density versus time (●) (A2: parental KB26.5 and B2: engineered KB26.5-BHRF1). Data obtained from duplicate cultures in shake flasks; the error bar corresponds to Effect 3.2. the standard of BHRF1 deviation. on Cell Metabolism: Flux Balance Analysis The metabolism of animal cells growing in high glucose media is characterized by The parental KB26.5 cell line reached a cell density of 1.79 ± 0.3 × 106 cells/mL after 72 high rates of glycolysis fluxes. The metabolism of glucose by aerobic microorganisms h of culture, at which point cell viability decreased sharply. In contrast, KB26.5-BHRF1 yields pyruvate, which is partially converted into acetyl-CoA, and then completely oxi- reached 3.92 ± 0.03 × 106 cell/mL in about the same time. Regarding glucose and lactate dized to CO2 and H2 O in the TCA cycle. However, in mammalian cell lines, pyruvate is concentration, high consumption and production rates respectively could be deduced by primarily converted into lactate [24]. The generation and accumulation of large amounts of the sharp decay/increase of the concentration profiles, obtaining more lactate generation lactate, since glucose cannot be completely oxidized, is the major consequence of such high in the parental glycolytic KB26.5 fluxes, cell line. leading The slopes ofmetabolism. to unbalanced the Napierian logarithm This metabolicplots of viable behavior hascells been showed a higher cell growth rate for the KB26.5-BHRF1 compared with the observed in hybridoma cell lines, regardless of the level of dissolved oxygen in the culture. parental line. Parallel Lactateto generation the plateaufrom of thepyruvate cell growthseemsprofile, to beglucose necessaryconsumption to fulfill theand NADHlactate produc- regeneration tion requirements in the cytoplasm [42] due to the limiting transport rates of NADHvalues also indicated a metabolic arrest at 72 h of culture, when their concentration into the became almost constant. mitochondria, where it No can morphology changeseven also be regenerated, werethough observed between lactate KB26.5 formation is aand much KB26.5-BHRF1 less energeticallyon different days from efficient process thanmanual counting its oxidation using in the Krebsa microscope cycle [34]. (data not shown).Flux Balance Analysis (FBA) is a useful tool to obtain information about the redistri- bution of internal metabolic fluxes. It also helped us to generate some hypotheses about 3.1.2. theControlled Bioreactor effects of BHRF1 Cultures Figures 4 and 5 show the distribution of the metabolic on metabolism. In order fluxes usingtotheconfirm the beneficial genome-derived modeleffect forofMusBHRF1 on cell musculus. physiology Metabolic flux (i.e., on cell balances were growth and nutrients conducted using the usage), cultures of data obtained both from thecell lines were bioreactor performed in a 2 L bioreactor. cultures. FBAcultures Bioreactor led to deregulated should not metabolism only allow in us both cell lines,the to disregard characterized possibility by high that theglycolytic differ- fluxes ences and consequent encountered lactatewere previously generation related and secretion. to the changing However, conditionsglycolytic fluxes were when culturing reduced cells in shake byflasks 54% (bottom (non pHpart andofdissolved glycolysisoxygen as a reference) control),duebut to thetoreduction also generate in glucose enough consumption (51%) in KB26.5-BHRF1. Lactate generation flux data to perform the flux balance analysis. In addition, product concentration (IgG3) wasdropped more than 60%. measured from the moment the cells reached the maximum density. Figure 2 shows the to Despite the lower fluxes in glycolysis presented by the KB26.5-BHRF1 strain compared KB26.5, time profilethe of rate cell of carbonand density influx from cytoplasm viability, as well as to themitochondria evolution of through glucose, pyruvate lactate and was 14% higher product (320 versusAs concentrations. 274 thenmol/(mg experiments·h)). were In other words, carried outwhen in thecalculating the fluxes same bioreactor in in mass units duplicate (mginmetabolite/(mg but not parallel (different DCWtime·h)), onlyboth points), 13% replicates of the totalareglucose plottedconsumed for each cell had entered the mitochondria of KB26.5 cells, whereas this increased line, showing comparable behavior. A Napierian logarithm of viable cells versus time is to 32% in the engineered plotted for each replicate in Figure 3, to better understand the changes in cell growth. The Appl. Sci. 2021, 11, 6258 8 of 16 KB26.5-BHRF1. When performing similar calculations to estimate the lactate generation Appl. Sci. 2021, 11, x FOR PEER REVIEW ratio regardless of glucose consumption, lactate formation represented about 87% and8 69% of 19 of the consumed glucose in KB26.5 and KB26.5-BHRF1 respectively. Figure 3. Figure 3. Two Two LL bioreactor bioreactor batch batch cultures cultures of of the the parental parental KB26.5 KB26.5 cell cell line line (A.1 (A.1 and and A.2) A.2) and and the the engineered engineered KB26.5-BHRF1 KB26.5-BHRF1 (B.1 and B.2). Napierian logarithm of viable cell density versus time (●). Duplicate cultures are presented for each (B.1 and B.2). Napierian logarithm of viable cell density versus time (•). Duplicate cultures are presented for each cell cell line. line. Finally, before preforming flux balance analysis (FBA), the specific consumption Table 1. Specific consumption rates of glucose, amino acids and oxygen, and production rates of rates for all the metabolites measured in bioreactor experiments were calculated in the lactate and ammonia for KB26.5 and KB26.5-BHRF1 cell lines. Cultures were run in 2 L bioreactors. exponential growth phase (Table 1). Values were calculated as the mean of the data The specific rates are part of the 38 external measured fluxes used as modeling constraints, and are obtained from duplicate cultures. In general, KB26.5-BHRF1 showed more efficient expressed in nmol/(mg·h) units. substrate consumption (especially for glucose and glutamine), and reduced byproduct generation Parameter (especially (nmol/mg·lactate, h−1 ) ammonia and alanine). KB26.5 KB26.5-BHRF1 Alanine 132.493 ± 12.954 48.305 ± 4.158 Table 1. Specific consumption rates of glucose, Arginine −14.726amino ± 2.812acids and oxygen, − and production 15.517 ± 8.825 rates of lactate andAsparagine ammonia for KB26.5 and KB26.5-BHRF1 cell 5.420 ± 1.302 lines. Cultures were3.058 ± 20.681 run in L bioreactors. The specific rates are Aspartic part of the 38 external4.304 acid measured ± 6.017fluxes used as modeling constraints, 0.487 ± 0.801 and are expressed inBiomass nmol/(mg·h) units. 0.036 ± 0.006 0.048 ± 0.007 Cysteine − 5.024 ± 2.111 − 4.825 ± 4.079 Parameter (nmol/mg·h−1) Glucose KB26.5 −933.447 ± 134.567 −KB26.5−BHRF1 542.206 ± 177.457 Alanine Glutamic acid 132.493 7.858 ±± 0.265 12.954 48.305 0.841 ±± 0.956 4.158 Glutamine Arginine − 258.541 ± 76.627 −14.726 ± 2.812 − ± −15.517 ± 122.421 179.660 8.825 Glycine Asparagine − 11.597 ± 6.102 5.420 ± 1.302 −3.058 12.094±± 6.784 0.681 Aspartic acid 4.304 ± 6.017 0.487 ± 0.801 Biomass 0.036 ± 0.006 0.048 ± 0.007 Cysteine −5.024 ± 2.111 −4.825 ± 4.079 Glucose −933.447 ± 134.567 −542.206 ± 177.457 Appl. Sci. 2021, 11, 6258 9 of 16 Table 1. Cont. Parameter (nmol/mg·h−1 ) KB26.5 KB26.5-BHRF1 Histidine −6.744 ± 3.693 −5.368 ± 3.796 Isoleucine −24.989 ± 5.918 −18.098 ± 16.359 Lactate 1435.500 ± 348.745 654.556 ± 34.663 Leucine −28.854 ± 6.149 −24.593 ± 17.043 Lysine −22.759 ± 9.314 −21.579 ± 17.480 Methionine −7.315 ± 0.948 −6.511 ± 4.841 NH4+ 156.137 ± 11.064 85.090 ± 15.910 Phenylalanine −9.685 ± 2.050 −10.009 ± 8.848 Proline 13.080 ± 4.084 1.993 ± 8.586 Oxygen −701.500 ± 160.513 −674.500 ± 12.021 Serine −15.150 ± 6.181 −13.913 ± 11.826 Threonine −16.883 ± 3.216 −18.522 ± 18.500 Tryptophan −2.585 ± 3.297 −1.078 ± 0.722 Appl. Sci. 2021, 11, x FOR PEER REVIEW Tyrosine −8.340 ± 1.832 −8.392 ± 7.97410 of 19 Valine −24.539 ± 4.938 −20.236 ± 18.013 Figure Figure 4. Metabolic 4. Metabolic flux flux distribution distribution ofofthe theparental parentalKB26.5 KB26.5 hybridoma hybridoma cell cellline. line.The Thearrows arrowsindicate thethe indicate direction of the direction of the reaction; the arrow thickness is proportional to the flux rate. Different coloring was used for each pathway, and the model reaction; the arrow thickness is proportional to the flux rate. Different coloring was used for each pathway, and the model was structured in cytoplasmic and mitochondrial reactions (mitochondrion is limited by a discontinued line). The values was in structured the boxesin cytoplasmic correspond andflux to the mitochondrial rate in nmol reactions (mitochondrion metabolite/(mg DCW·h). is limited by a discontinued line). The values in the boxes correspond to the flux rate in nmol metabolite/(mg DCW·h). Appl. Sci. Sci. Appl. 2021, 11, 11, 2021, 6258x FOR PEER REVIEW 1110 of of 1916 Figure Figure 5. Metabolic 5. Metabolic fluxflux distribution distribution of theofengineered the engineered hybridoma hybridoma cellKB26.5-BHRF1. cell line line KB26.5-BHRF1. The arrows The arrows indicateindicate the the direction direction of the reaction; the arrow thickness is proportional to the flux rate. Different coloring was used for each of the reaction; the arrow thickness is proportional to the flux rate. Different coloring was used for each pathway, and the pathway, and the model was structured in cytoplasmic and mitochondrial reactions (mitochondrion is limited by a discontinued model was structured in cytoplasmic and mitochondrial reactions (mitochondrion is limited by a discontinued line). The line). The values in the boxes correspond to the flux rate in nmol metabolite/mg DCW·h. values in the boxes correspond to the flux rate in nmol metabolite/mg DCW·h. FBA led to deregulated metabolism in both cell lines, characterized by high glycolytic In this case, FBA shows that the rate of transport of reduction power from cyto- fluxes and consequent lactate generation and secretion. However, glycolytic fluxes were plasm to mitochondria through the malate-aspartate shuttle seems to be a bit higher in reduced by 54% (bottom part of glycolysis as a reference) due to the reduction in glucose KB26.5-BHRF1 than in KB26.5, with a 19% increase, i.e., a rate of 228 versus 192 nmol/(mg·h) consumption (51%) in KB26.5-BHRF1. Lactate generation flux dropped more than 60%. in the aspartate-glutamate mitochondrial symport transport. However, in both cases, the Despite the lower fluxes in glycolysis presented by the KB26.5-BHRF1 strain compared to high glycolytic fluxes caused the cells to generate lactate, as the malate-aspartate shuttle KB26.5, the rate of carbon influx from cytoplasm to mitochondria through pyruvate was seemed to be insufficient to couple with the cytoplasmic NADH regeneration. In addition, 14% higher (320 versus 274 nmol/(mg·h)). In other words, when calculating the fluxes in a mass slightunits increase (mginmetabolite/(mg the pathways related DCW·h)), with onlythe13% formation of a biomass of the total glucose was observed consumed hadin KB26.5-BHRF1 due to the higher growth rate; as Pentose Phosphate Pathway entered the mitochondria of KB26.5 cells, whereas this increased to 32% in the engineered to generate nucleotides KB26.5-BHRF1. or citrate When export from the performing mitochondria similar to generate calculations lipids. to estimate the lactate generation Due to the high growth rate of KB26.5-BHRF1, an increase ratio regardless of glucose consumption, lactate formation represented in the about TCA fluxes 87% andwas to69% be expected, producing more energy and, therefore, of the consumed glucose in KB26.5 and KB26.5-BHRF1 respectively. increased biomass. However, in comparing theFBA In this case, TCAshows cyclesthat forthe both ratecell lines, noofsignificant of transport increase reduction power was from observed, cytoplasm which was in accordance with the similar observed oxygen consumption to mitochondria through the malate-aspartate shuttle seems to be a bit higher in KB26.5- rate (Table 1). What is clear from the FBA is that there was a collapse between glycolysis BHRF1 than in KB26.5, with a 19% increase, i.e., a rate of 228 versus 192 nmol/(mg·h) and the TCA in pathways in both cases, as the high glucose uptake rate collapses the transport of NADH to Appl. Sci. 2021, 11, 6258 11 of 16 mitochondria, and NADH have to be regenerated in cytoplasm using pyruvate by means of lactate dehydrogenase. The reduction of the glycolytic pathway in KB26.5-BHRF1 involved a reduction of ATP production in the cytoplasm, as well as a reduction of NADH formation. Consequently, the need to oxidize the NADH formed in the cytoplasm also decreased, and, together with the slightly higher rates related to Malate-Aspartate Shuttle, yielded to a reduction in the formation of lactate. To further examine the results, an analysis of the synthesis Appl. Sci. 2021, 11, x FOR PEER REVIEW 13 of 19 and consumption of ATP was performed. Figure 6 shows the distribution of ATP formation (with regard to the reaction in which ATP was generated) and its consumption. Distribution Figure6.6.Distribution Figure of ATP of ATP generation generation (concerning (concerning the reaction the reaction in ATP in which which wasATP was generated) generated) and consumption in the in and consumption KB26.5 and KB26.5-BHRF1 the KB26.5 cell lines. and KB26.5-BHRF1 cellATP generation lines. (A) and(A) ATP generation consumption (B) and consumption (nmol ATP/ATP/ (B) (nmol (mg DCW·h)). (mg DCW·h)). The results show a significant reduction in the total ATP generation and, as a consequence, ATP consumption in KB26.5-BHRF1, compared with the parental cell line. As pointed out above, higher amounts of ATP were synthetized during glycolysis in parental KB26.5 (in proportion with the increase in glycolytic flux). However, an increase in the oxidative phosphorylation pathway was also observed; this is very important for Appl. Sci. 2021, 11, 6258 12 of 16 The results show a significant reduction in the total ATP generation and, as a con- sequence, ATP consumption in KB26.5-BHRF1, compared with the parental cell line. As pointed out above, higher amounts of ATP were synthetized during glycolysis in parental KB26.5 (in proportion with the increase in glycolytic flux). However, an increase in the oxidative phosphorylation pathway was also observed; this is very important for the present discussion, given its high capacity to generate ATP. Regarding ATP consumption, KB26.5-BHRF1 needs more ATP to generate more biomass, which also includes other pathways such as lipids and nucleotides generation. 4. Discussion The growth, viability, glucose and lactate profiles obtained from the KB26.5-BHRF1 cultures were vastly different than those from the parental cell line. Cell culture expansion was much faster, and the maximum cell density reached 3.92 ± 0.03 × 106 cell/mL, repre- senting a two-fold increase. Additionally, the cell viability profile after the cell density peak remained at over 90% for 24 h after the maximum cell density had been achieved, indicating a delay in apoptosis due to the effects of BHRF1, as anticipated by Juanola et al. (2009) [20]. The glucose and lactate profiles showed an initial phase of glucose consumption and lactate generation. The glucose concentration profile decrease was similar to that obtained in the KB26.5 culture, but lactate production rates were reduced by almost half. However, since the cell density almost doubled, the specific glucose consumption rate was affected by BHRF1 expression and, consequently, by lactate production. In order to better quantify the differences, Table 2 presents the main physiological parameters for both cell lines, i.e., growth rate (µ), doubling time (td ), specific glucose consumption rate (qGlu ), specific lactate production rate (qLac ), glucose yield on biomass (YGlu/X ), lactate yield on biomass (YLac/X ) and maximum VCD (VCDmax ). Table 2. Main physiological parameters determined for KB26.5 and KB26.5-BHRF1 in shake flask cultures. Values were calculated as the means of data obtained from duplicate cultures. Parameter KB26.5 KB26.5-BHRF1 µ (h−1 ) 0.027 ± 0.001 0.048 ± 0.002 td (h) 25.500 ± 0.928 14.560 ± 0.627 qGlu (nmol/(mg·h)) −802.325 ± 71.750 −682.100 ± 55.536 qLac (nmol/(mg·h)) 1337.750 ± 175.857 663.780 ± 13.831 YGlu/X (mmol/L)/(106 cell/mL) 14.515 ± 0.120 4.645 ± 0.007 YLac/X (mmol/L)/(106 cell/mL) 24.265 ± 1.039 5.035 ± 1.704 VCDmax (106 cell/mL) 1.79 ± 0.30 3.92 ± 0.03 In general terms, the expression of BHRF1 was beneficial for cell physiology and growth. The cell growth rate (µ) increased by 78%, the specific glucose consumption rate (qGlu ) decreased by 15% and the specific lactate production rate (qLac ) reduced by more than 50%. This resulted in a decrease in both glucose yield on biomass (YGlu/X ) and lactate yield on biomass (YLac/X ) in KB26.5-BHRF1, meaning that more efficient glucose consumption had been achieved, thereby generating less lactate. Overall, this resulted in a more efficient use of the main carbon source. This fact translated into a more than two-fold increase in total cell density under the same media and culture conditions. The results obtained in 2 L bioreactors showed similarities to those observed in shake flask cultures. The maximum cell density was slightly improved for KB26.5, reaching 2.34 ± 0.39 × 106 cell/mL, probably due to the pH control. Even though lactate was pro- duced at similar rates, the lack of pH control in the shake flaks might have negatively affected cell growth, a phenomenon which was reversed in the bioreactor cultures. In the case of KB26.5-BHRF1, the maximum cell density observed was significantly increased, reaching 3.93 ± 0.01 × 106 cell/mL. In addition, the viability strongly decayed after 48 h for KB26.5, while it was maintained at above 90% in BHRF1-KB26.5. Appl. Sci. 2021, 11, 6258 13 of 16 Despite the concordance with the results observed in the shake flask cultures, another difference was that the glucose was completely depleted in the KB26.5 culture, indicat- ing the extension of the culture time due to the pH control, which may have had an effect on cell density increase. To further compare the performance of both strains in the bioreactor, the main physiological parameters were calculated and compiled in Table 3, including maximum titer obtained (IgG3 ,max), specific productivity (qP ) and volumetric productivity (VP ). Table 3. Main physiological parameters determined for KB26.5 and KB26.5-BHRF1 cultures in 2 L bioreactors. Values were calculated as the means of data obtained from duplicate cultures. Parameter KB26.5 KB26.5-BHRF1 (h−1 ) µ 0.036 ± 0.006 0.048 ± 0.007 td (h) 19.823 ± 3.588 14.593 ± 2.001 qGlu (nmol/(mg·h)) −933.447 ± 134.567 −542.206 ± 177.457 qLac (nmol/(mg·h)) 1435.500 ± 348.745 654.556 ± 34.663 YGlu/X (mmol/L)/(106 cell/mL) 8.805 ± 0.361 3.735 ± 0.346 YLac/X (mmol/L)/(106 cell/mL) 14.010 ± 0.665 4.730 ± 1.160 VCDmax (106 cell/mL) 2.34 ± 0.39 3.93 ± 0.01 IgG3 ,max (mg/L) 12.90 ± 3.82 47.70 ± 2.69 qP (µg/(106 cell·h)) 0.24 ± 0.08 0.48 ± 0.02 VP ((µg/L)/h) 252.12 ± 74.63 750.22 ± 0.85 Similar to shake flask culture results, cell growth improved by more than 30% in KB26.5-BHRF1, with a reduction of 42% and 54% in specific glucose consumption rate and specific lactate production rate, respectively. Therefore, the differences between strains may have been related to the effects of BHRF1 on cell physiology, rather than a consequence of the culture conditions. The same was observed regarding glucose and lactate yield on biomass, as both were reduced in KB26.5-BHRF1. Due to the extension of the growth phase, KB26.5-BHRF1 increased the final product concentration by 3.7 times. The specific and volumetric productivity were also increased two- and three-fold, respectively. The results showed lower specific consumption rates for glucose and almost all amino acids, as well as lower production rates of byproducts, which might indicate efficient cell metabolism mediated by the expression of BHRF1. Additionally, the significant reduction in glutamine consumption, and therefore, in the production of ammonia, well known as a toxic byproduct in mammalian cell cultures [43], yielded the dual benefits of lactate and ammonia reduction in culture. Alanine generation was also greatly reduced in KB26.5-BHRF1, an amino acid that often accumulates in mammalian cell cultures [40]. In any case, a metabolic flux balance is required to evaluate and discuss the possible effects of BHRF1 on the general behavior of the engineered line with respect to the parental cell line. Interestingly, the expression of BHRF1 has an effect on metabolism, improving the efficiency of nutrient usage, which is necessary for cell growth (see Tables 1–3) and the reduction of byproducts. The engineered KB26.5-BHRF1 hybridoma cell line suffered a modification in its metabolism. BHRF1 is an antiapoptotic protein located in the inner mitochondrial mem- brane [44]. For this reason, BHRF1 may have somehow affected carbon and NADH/NAD+ transport between cytoplasm and mitochondria. Most of the antiapoptotic genes reported in the literature are known to be bind at the mitochondrial membrane, regulating apoptosis through modulation of mitochondrial permeability, but also playing an important role in the metabolic processes of mitochondria [45]. Dorai et al. (2009) [22] reported the effect of two antiapoptotic genes on the metabolism of CHO, showing an important reduction in the final lactate concentration due to lactate consumption in culture. In addition, engineered cells showed a more efficient nutrient consumption profile and produced fewer byproducts, such as ammonia or alanine, as observed in this study. Lactate generation in mammalian cell cultures is a well-known challenge that has been extensively studied in recent years [46,47]. At present, the most accepted hypothesis Appl. Sci. 2021, 11, 6258 14 of 16 for the production of lactate is based on the regeneration of the reducing power (NADH) in the cytoplasm due to the high glycolytic fluxes [46,48]. In this regard, there are two ways to regenerate NADH into the cytoplasm: (1) lactate generation and (2) malate-aspartate shuttle [48]. It should be noted that the malate-aspartate shuttle facilitates the regeneration of NADH and increases the TCA cycle flux (importing malate), thereby generating energy in the form of ATP. However, the generation of lactate provokes a total loss of both carbon sources and ATP generation. Therefore, the mechanism of lactate generation may lie in the flux limit of the Malate-Aspartate Shuttle, leading cells to generate lactate and display this wasteful metabolism. In the breakdown of glucose into two pyruvate molecules, two molecules of ATP and two NADH are generated. Since the inner mitochondrial membrane is impermeable to NADH, the malate-aspartate shuttle works as an indirect transport system. It has been reported that the flux through this transport occurs at lower rates than glycolysis [49], and that the increased LDH activity is due to the inability to transport NADH through the shuttle at the same rates at which it is generated. Under conditions of increased cellular energy demand, higher glycolytic fluxes are observed, and consequently, NADH produc- tion rates increase proportionally. Such an increase in the need for NAD+ regeneration is compensated for by higher LDH activity in the cytosol, as not much increase is observed in the malate-aspartate shuttle fluxes [50]. 5. Conclusions The results presented in this work provide evidence for a beneficial effect of antiapop- totic gene BHRF1 in hybridoma in terms of cell growth, productivity and metabolism, characterized by a more efficient use of nutrients, doubling the cell density in the culture under identical conditions and using the same culture media. The specific productivity of the cells increased two fold, yielding a three-fold increase in the final concentration of IgG3. The study and comparison of the intracellular fluxes by means of a flux balance analy- sis of the KB26.5 and KB26.5-BHRF1 engineered cell lines highlighted the interactions and effects of the BHRF1 protein on the metabolic pathways. In short, BHRF1 primarily affected glucose uptake rate, reducing glycolysis by 50% and, consequently, reducing the generation of lactate by more than 60%. Interestingly, the total ATP generation in the engineered KB26.5-BHRF1 cell line decreased significantly due to the lower energy requirements for maintenance, probably due to the lower energy requirements for maintaining ion gradients (reduction in the generation of lactate), although this hypothesis should be tested in future metabolic flux analysis experiments. The use of antiapoptotic genes has been extensively explored in recent years, as manifest in a recent review about the attenuation of apoptosis in the CHO cell line [12]. Many efforts have been made to determine how these genes affect cell apoptosis; however, the impact they have on protein production, and especially on metabolism, has not yet been clarified. To address this, a strategy to obtain isogenic cell lines should be implemented, as this would make it possible to compare different antiapoptotic genes and their effects. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/app11146258/s1: A complete list of all included reactions and metabolites used is freely available in the Supplementary Data 1 in xlsx format. Author Contributions: Conceptualization, J.J.C., I.M.-M., A.C., C.P. and M.L.; methodology, J.J.C., I.M.-M., P.C., J.P., A.C., C.P. and M.L.; software, P.C. and I.M.-M.; validation, P.C. and I.M.-M.; for- mal analysis, J.J.C. and I.M.-M.; investigation, J.J.C., I.M.-M., P.C., D.C.-T., J.P., A.C., C.P. and M.L.; resources, J.J.C. and I.M.-M.; data curation, I.M.-M. and D.C.-T.; writing—original draft preparation, I.M.-M. and D.C.-T.; writing—review and editing, J.J.C., I.M.-M., D.C.-T., M.L., A.C. and C.P.; visual- ization, P.C. and I.M.-M.; supervision, I.M.-M. and J.J.C.; project administration, J.J.C.; funding A.C.; quisition, J.J.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by FI-DGR (Generalitat de Catalunya, Catalonia, Spain), grant number FI-DGR. Appl. Sci. 2021, 11, 6258 15 of 16 Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. 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