FDH knockout and TsFDH transformation led to enhance growth rate of Escherichia coli

FDH knockout and TsFDH transformation led to enhance growth rate of Escherichia coli | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FDH knockout and TsFDH transformation led to enhance growth rate of Escherichia coli Roya Razavipour, Saman Hosseini Ashtiani, Abbas Akhavan Sepahy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3921353/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, we sought to reduce the released CO 2 into the atmosphere from bacterial growth by reducing formic acid conversion into CO 2 . Since E. coli is the biotechnological workhorse and its higher growth rate is desirable, another goal was to monitor the bacterial biomass after the metabolic engineering. The conversion of formic acid to CO 2 is a crucial reaction. Therefore, we compared the growth of control strains, alongside two strains in which two different genes coding two formate dehydrogenase (FDH) subunits were deleted. The knockout bacteria grew better than the controls. Thiobacillus FDH ( Ts FDH) transformation increased the growth of both knockouts of E.coli compared with the controls and the knockouts strain without Ts FDH. Through a transcriptomics-level analysis of the strain knockout genes, the genes negatively correlated with the target genes were shown to belong to tRNA-related pathways. Observing higher cell biomass for the knockout and transformed strains indicates possible underlying mechanisms leading to reduced carbon leakage and increased carbon assimilation, which need more detailed investigations. Gene expression correlations and pathway analysis outcomes suggested possible over-expression of the genes involved in tRNA processing and charging pathways. Formate Dehydrogenase Metabolic CO2 Leak Glycerol minimal medium Spearman's rank correlation coefficient RNA-seq Principal Component Analysis (PCA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Background CO 2 was easily formed by the oxidation of organic molecules during respiration in living organisms or combustion in regular mechanical engines. This molecule is thermodynamically stable with a low chemical activity.Nowadays,the atmospheric CO 2 concentration is approaching alarming levels (from 300 ppm to 417 ppm in about 50 years).Greenhouse gases has led to elevate frequencies of extreme climate conditions like drought, flooding, wild fire and tropical storms in different regions of the world (Solomon et al., 2009 ). The development of innovative methods for reducing the released CO 2 into the atmosphere and assimilating CO 2 into organic matter is in demand more than ever (Gong et al., 2018 ). The respiration process, which is more economical for extracting chemical energy from organic material compared to anaerobic fermentation. Engineering bacterial strains has been involved in biotechnological processes with the aim of reducing carbon dioxide release into the atmosphere or even fixing the atmospheric CO 2 into biomass has environmental and economic advantages. There have been many efforts to reduce the carbon dioxide to release during biomass production by metabolic engineering methods. (Cotton et al., 2018 ). The central pathways and cycles of metabolism are the first targets for manipulating enzymes responsible for critical biochemical reactions or regulatory proteins controlling the expression of certain enzymes to reduce CO 2 release (Hädicke et al., 2018 ). The formate dehydrogenase is One of exciting candidates for reducing the amount of released CO 2 . Theoretically, FDHs are enzymes capable of reversible conversion of CO 2 to formate, which is the simplest organic acid (Moon et al., 2020 ). However, the major drawback of the biotechnological application of FDHs is the fact that majority of these enzymes prefer the oxidation of formate to produce CO 2 under physiological conditions (Hoelsch et al. , 2012). In this study, FDH from Thiobacillus sp . KNK65MA ( Ts FDH) as an enzyme with high catalytic efficiency toward reduction of CO 2 has been chosen. (Choe et al., 2014 ). The crystal structure of the active enzyme (PDB: 3WR5) consists of a homo-tetramer of 406 amino acid-long polypeptide. There are five extra residues at the N-terminal of the recombinant protein, compared with the sequence in UniProt (Consortium et al. , 2021) (accession code: Q76EB7). In this study, we sought to monitor the growth of E. coli fdhD and fdhF knockout strains (JW3866 and JW4040, respectively) with and without Ts FDH, as well as control strains, i.e., K12 and BL21. For consistency's sake, we will refer to knockout strains JW3866 and JW4040 as ∆ fdhD and ∆ fdhF , respectively. Both knockout strains demonstrated growth advantage in LB and M9 + glycerol media compared with the control E. coli strains with wild-type FDH. Here, we demonstrate high growth rate advantage in knockouts expressing the recombinant enzyme ( Ts FDH) compared with knockouts and controls without Ts FDH, particularly in M9 + Glycerol medium. In order to explain the observed results, we perform an in silico study by examining the transcriptomic-level correlations between the target (knockout) genes and the rest of the genes in E. coli . The correlation analysis, based on an independent E.coli RNA-seq gene expression profile data set, followed by pathway analysis disclosed that all the genes significantly anti-correlated with both target genes belong to tRNA charging or tRNA processing pathways. Results Experimental results Growth measures of bacterial cells with or without plasmid on LB and M9 + Glycerol are presented in Figs. 1 and 2 , respectively. Samples were examined within 24 hours at different time intervals. On LB media the preferential growth dynamics of knockout strains with or without Ts FDH over control strains were recognizable more clearly after eight hours post inoculation up to 24 hours. In samples, cell growth in M9 + Glycerol media was observed to pronounce shift in the growth divergence of FDH knockout strains with or without Ts FDH from control strains during earlier time intervals up to 24 hours than LB medium. On M9 + Glycerol, BL21 with Ts FDH showed higher growth up to eight hours compared with the standard BL21. Moreover, on M9 + Glycerol, both knockouts with Ts FDH showed relatively higher growth rates from 12 hours onward compared with the respective growth rates on LB. SDS-PAGE analysis of the strains confirms the expression of Ts FDH under the experimental conditions (Fig. S3). In silico analysis results correlation analysis All correlations with each of the knockout genes were calculated (Table S3 and S4), and the ones with FDR-adjusted p-values < 0.01 were chosen (Table S5 and S6). PCA analysis According to the PCA results (Fig. S4a), it is postulated that the top anti-correlated genes for both knockouts are closely associated with one another. As a comparison, the PCA plot was generated using all the genes, indicating that the other genes show more expression divergence. Moreover, the second PCA plot (Fig. S4b) could be indicate that the dataset, being based on different growth media, reflects a wide range of expression levels for different genes in each data point, which is critical for reflecting the correlations between the fluctuating gene expression levels. Metabolic pathways Since the knockout of FDH main subunits is synonymous to absolute down regulation of the FDH activity in knockout strains, we initially focused on the significantly negatively correlated genes, which may reveal the genes that undergo up regulation accordingly. Using BioCyc database of microbial genomes and metabolic pathways, all the significantly negatively correlated genes (with FDR adjusted p-values < 0.01) were shown to be involved in tRNA charging pathway and tRNA processing pathway (PWY0-1479). To evaluate whether the mentioned pathways, are more frequent among the negatively or positively correlated gene, all the positively correlated genes assigned to the same pathways, i.e., tRNA charging and tRNA processing pathways, were selected for comparison. All the pathways other than the two mentioned pathways for both negatively and positively correlated genes were categorized as background pathways and were named "All other pathways". For better visualization of the distribution of all positive and negative correlation coefficients for both mentioned pathways the corresponding density plots were used (Fig. 3 . and 4.). The comparisons of the percentages of the number of correlated genes in each pathway category was given for both fdhD and fdhF genes (Table 2 ). Table 1 Correlations with fdhF and fdhD : Comparison of the number of positively and negatively correlated genes assigned to the unique set of pathways. Knockout gene Pathway Negatively correlated genes Positively correlated genes fdhF t-RNA charging or processing 96% 1% All other pathways 4% 99% fdhD t-RNA charging or processing 100% 0.8% All other pathways 0% 99.2% Discussions Increasing the growth efficacy of industrially essential microorganisms is a novel goal in biotechnological applications. One of the strategies to boost the bacterial growth rate is to reduce the organic carbon leak, i.e., the release of CO 2 as one of primary end products in the respiration process. Different E.coli strains are the workhorse for the production of some well-known biopharmaceuticals like G-CSF, Romiplostim and Asparaginase. Therefore, higher growth rates are on demand particularly in Biotechnology. Since the Kelvin cycle is the mainstream CO 2 fixation pathway in plants, algae and Cyanobacteria.most engineering efforts were directed towards the Kelvin cycle for converting CO 2 into valuable materials. Heterotrophic microorganisms generally do not assimilate CO 2 through the central metabolism (Gong et al., 2015 ). Over the past decade, there has been great success in the production of CO 2 derivatives, which have the potential to be used as fuel and valuable chemicals by bacteria (Hartmann and Leimkühler., 2013). Previously, other researchers have approached this challenge by defining fermentation conditions and controlling aeration rates (Riesenberg et al., 1991 ) or genetically overexpressing ArcA transcription factor (Basan et al., 2017 ). Here, we introduce a new approach by targeting one of the main enzymes responsible for converting organic formate into inorganic wasteful CO 2 , i.e., FDH. There were three known FDHs in E. coli namely respiratory FDH, anaerobically expressed FDH and newly identified pressure induced FDH (FHL). FDHF is the cytosolic form, while FDHN and FDHO are membrane bound,FDHN is responsible for Nitrogen cycle and FDHO active in Sulfur metabolism (Iwadate et al., 2017 ). All these enzymes prefer the oxidation of formate into CO 2 under physiological conditions. Scanning BRENDA for FDHs a tendency towards producing of formate from CO 2 revealed that there are few candidate FDHs with relatively higher formate production (CO 2 reduction) catalytic efficiency. A comprehensive search the metabolism of E.coli using the Regulon DB(Santos-Zavaleta,Salgado, Gama-Castro,Sánchez-Pérez,Gómez-Romero, Ledezma-Tejeida,García-Sotelo,Alquicira-Hernández,Muñiz-Rascado,Peña-Loredo,Ishida-Gutiérrez, David A.Velázquez-Ramírez, et al. , 2019) were showed that the formate dehydrogenase enzymes successfully expressed in recombinant form in E. coli (Santos-Zavaleta,Salgado,Gama-Castro,Sánchez-Pérez, Gómez-Romero, Ledezma-Tejeida, García-Sotelo, Alquicira-Hernández, Muñiz-Rascado, Peña-Loredo,Ishida-Gutiérrez,David A Velázquez-Ramírez, et al. , 2019). According to a study, E. coli ’s FDHs have a strong tendency to regenerate CO 2 from formate. (Gong et al., 2015 ).Among the studied formate dehydrogenases, Ts FDH has potential advantages as a biocatalyst in CO 2 reduction (Choe et al., 2014 ). We hypothesized that the E. coli strains harboring Ts FDH could lead to developing bacterial strains for biotechnological applications with higher biomass to carbon source ratio owing to the possible lowering of carbon dioxide leakage. This possible solution has so far been remained out of sight and to the best of our knowledge has not tried yet. We expressed the recombinant fdhD gene from Thiobacillus in Escherichia coli strains K12 and BL21. In order to better compare the role of Ts FDH in growth efficacy on similar growth conditions (media), we also transformed two FDH knockout strains from K12 and BL21 with pET + TsFDH. Significant growth rate differences were observed between the knockout strains and the recombinant knockouts containing Ts FDH on either growth medium. On the other hand, both knockouts with Ts FDH has showed vividly higher growth rate starting from 8 hours to 24 hours on both media after induction. Additionally, the growth advantage of the control strains (K12 and BL21) with Ts FDH lasted up to 12 hours on both media. A possible reason for the less increase in the growth rate of BL21 cells transformed with Ts FDH plasmid compared with the transformed knockouts is the presence of the original FDH in these strains. BL21 also contains IPTG as a gene expression inducer thanks to having a chromosomally encoded bacteriophage T7 RNA polymerase (T7 RNAP), which may be responsible for some metabolic leakage leading to less growth compared with K12-derived knockouts and transformed knockouts (Studier and Moffatt., 1986). The original K12 cells and mutants derived from this cell lack the complementary T7 RNA-polymerase. In this study, we showed that removing either subunit of the wild-type formate dehydrogenase gene from E. coli and its replacement with the formate dehydrogenase gene from Thiobacillus Sp. KNK65MA can increase the growth rate of E. coli cells. A mechanistic interpretation, from a metabolic point of view, is that by reducing the breakdown of formate to CO 2 , the intracellular concentration of formate will increase. This mechanism may lead to elevated expression of the pyruvate lyase gene. This enzyme will produce pyruvate to enhanced anabolic pathways including citrate cycle. By enhancing intermediates of Citrate cycle, the starting materials for amino acids and nucleotides biosynthesis could be more abundant (Fig. S5). In a previous study by Palsson et al. , the whole cell in silico model of the E. coli metabolic network predicted that glycerol should be a preferred carbon source over glucose. However, the experimental findings were not consistent with the mentioned predictions. They indicated the adaptive evolution phenomenon for the bacteria to go from sub-optimal to the predicted optimal growth rate on glycerol (Ibarra et al., 2002 ; Cheng et al., 2014 ; K et al. , 2012). Concerning our results, the replacing of the native FDH with TsFDH might lead to potential metabolic rewirings leading, to an increased glycerol efficiency as a carbon source. Our outcomes may suggest an initial clue to start more mechanistic metabolic investigating flux balance analysis and CO 2 leakage measurements to address these discrepancies between the in silico predictions and the experimental outcomes. Considering our in silico outcomes, we hypothesized that the omission of the original E. coli FDH may ultimately lead to the increased expression of some genes playing roles in tRNA charging and processing pathways. These in silico findings could be considered as a gene expression-level study related to our experimental observations; nonetheless, more detailed investigations are necessary to verify the hypotheses arising from our results. Conclusions The main problems in biotechnological applications of most known FDHs so far are protein instability, sensitivity to oxygen and the low conversion rate. The FDH of this study ( Ts FDH) shows some biochemical advantages over the previously studied FDHs such as Candida bolidini's FDH including higher turnover number and insensitivity to the environmental oxygen as well as profound tendency towards reducing CO 2 to formate (Yamamoto et al., 2005 ; Slusarczyk et al., 2000 ; Hartmann and Leimkühler., 2013). We showed that both knockouts and transformation with Ts FDH lead to increased growth in all strains. In the initial incubation periods, the growth rates were approximately equal between transformed and non-transformed knockouts on LB medium between 8-24h, the growth rates of the transformed and knockout bacteria were much higher than those of controls in both media, which implies the negative effect of wild type FDH gene on the E. coli growth rate. In other words, there would be a higher growth rate by eliminating the wild- type FDH chains. The increased growth rate of the transformed knockouts on both media might be the result from decreased CO 2 leakage due to less formate oxidation by Ts FDH compared with the wild-type FDH. One plausible hypothesis regarding the relatively more accentuated growth of the transformed knockouts on glycerol medium could be the preferred utilization of glycerol after knockout and transformation besides the probable decrease of CO 2 leakage due to conjecture as mentioned earlier. These hypotheses to evaluate, further studies are necessary such as CO 2 absorption/emission measurements and flux balance analysis (FBA). These observations could also be a starting point for more sophisticated molecular-level studies on Ts FDHꞌs contribution to the growth efficiency of E. coli on glycerol as the carbon source. Methods Escherichia coli Strains, Plasmids and Media All E. coli strains and media used in this study were presented in Table 1. Escherichia coli BL21(DE3) was used to express of the recombinant FDH from Thiobacillus sp. KNK65MA ( Ts FDH). ∆ fdhD and ∆ fdhF , Two FDH knockout strains were purchased from Keio Collection. Table 1?....... Table 2 Bacterial strains and media. In this study ,all bacterial strains and plasmids to be used and their characteristics and the sources they were obtained. Strains and plasmids Related characteristics Source Strains E. coli K12 E. coli BL21(DE3) E. coli JW3866 E. coli JW4040 Plasmids pET-21α pET-21α-TsFDH Wild type [ lon ] omp T gal (λ DE3) [ dcm ] ∆ hsd S K12 ∆ fdhD K12 ∆ fdhF Ap R , T7 promoter, lac operator pET-21α, containing TsFDH gene from Thiobacilusis sp KNK65MA NIGEB stocks Invitrogen Dharmacon Dharmacon Novagen This research M9 +glycerol and LB media all containing 30 µg kanamycin were used for measuring bacterial growth. For BL21 with pET+TsFDH, the same media with ampicillin were used. In all samples containing pET+TsFDH, IPTG (0.5 mM final concentration) were added to the media. The metabolic reactions consuming or producing formate (map01200 and C00058) were obtained from KEGG (Kanehisa et al. , 2017) (https://www.genome.jp/pathway/map01200+C00058). Using the results from the KEGG pathway search for all the carbon fixation reactions, the contributing FDHs were identified. The kinetic parameters including K cat and K m of FDHs (EC: 1.17.1.9) for formate formation were obtained from the Brenda enzyme data bank (Chang et al. , 2021) and the published articles were reviewed and compared in different bacteria (Table S1). This approach revealed some interesting FDHs with relatively better kinetic parameters. The results were obtained by Ts FDH might be interesting, to assume,there are still some FDHs that deserve attention for replacing the indigenous FDHs of E. coli to improve the growth efficiency. Our assumption was based on the ambiguity of assay conditions for some of the reported FDHs and the lack of a gold standard for the kinetics comparisons. Scanning the kinetic parameters for a desired FDH suggested Thiobacillus sp . KNK65MA FDH (Choe et al. , 2014) . Amino acid and nucleotide sequences of Thiobacilusis sp KNK65MA formate dehydrogenase were obtained from UniProt (accession # Q76EB7). cDNA of Ts FDH was synthesized in pET21a by ZistEghtesadMad based on reference sequence (Q76EB7). Two knockout strains of K12 Escherichia coli , ∆ fdhD, and ∆ fdhF , with the deletion of fdhD and fdhF genes, respectively, were purchased from Dharmacon. The stocks of the Knockout E. coli strains were cultured on LB broth and M9+Glycerol media, followed by incubation at 37° C for 24 hours (Sambrook, 2012) . Strains K12 and BL21 were used controls to compare the growth rates. All strains were cultured at the same time under the same conditions on LB broth media at 37 ° C and 200 rpm. Competent cells of the BL21, E. coli ∆ fdhF , and E. coli ∆ fdhD were prepared as previously mentioned (Sambrook, 2012) . pET21, a plasmid containing a fusion gene to express format dehydrogenase of Thiobacillu s sp. KNK65MA (pET+TsFDH), was transformed into competent BL21cells, E. coli ∆ fdhD and E. coli ∆ fdhF on LB Agar with Amp (100 mg/ml) followed by incubation overnight at 37° C . The colonies containing plasmid were selected and cultured on a 10 ml LB broth with Amp as a primary culture and were incubated at 37 °C , 200 rpm for 24 hours. Then the primary culture was carried out in 200 ml of the LB broth containing Amp (100 mg /ml), and they were incubated at 37° C and 200 rpm for 24 hours. Also, the bacteria K12, BL21, E. coli ∆ fdhD ,and E. coli ∆ fdhF lacking the plasmid were simultaneously cultivated and incubated on LB broth and M9-Glycerol + 50 µg/ml Kanamycin under identical conditions with plasmid-containing strains. Media and cultures M9 medium + glycerol containing 30 µg kanamycin was used for measuring bacterial growth with and without pET+TsFDH. The same media with ampicillin were also used. In all BL21 samples containing pET+TsFDH, IPTG (0.5 mM final concentration) was added to the medium. LB media was purchased from Merck. Growth measurements Bacteria were grown in batch cultures at 37 o C in shaker incubator in 50 mL flasks. 1000 µL samples were taken in triplicate at indicated time intervals, and the absorbance was measured at 600nm. E. coli cell without plasmid as well as plasmid containing cells were sampled during incubation at different times, namely zero, two, four, six, eight, ten, twelve and 24hrs (Table S2). The growth rates of bacteria were determined at selected time intervals by optical absorption at 600 nm wavelength. In silico analysis Data preparation To perform a transcriptomic-level study related to our observations, we searched for an independent E. coli expression dataset that could reflect the maximum possible transcriptional variations so that we would be able to achieve significant correlations between as many genes as possible. Moreover, the number of genes involved in the gene expression profile was essential to calculate as many correlations as possible. With this aspiration, we fetched an E. coli RNA-seq dataset comprising 152 RNA-seq count samples under 34 different growth conditions (GEO accession GSE94117). These samples were taken from both exponential and stationary phases. One unique aspect of this highly pertinent dataset is the fact that it is sampled under 34 different growth condition, leading to a broader range of differentially expressed genes thanks to different metabolic needs (Caglar et al. , 2017) . Subsequently, correlation analysis, PCA, and pathway analysis were applied to this data set. Data preprocessing Using Python version 3.6.1, 152 samples of RNA-seq count files were merged. The counts were converted into count per million (CPM) and were log2 transformed. The resulting data were z-score transformed per gene across all samples. Quality control was performed as sample-level box plots before and after data preprocessing (Fig. S1 and S2). Correlation analysis The Spearman rank-order correlation coefficient (Spearman's ρ), being a nonparametric measure, examines the monotonic relationship between the ordinal values of the variables. Contrary to the Pearson correlation, the Spearman's rank correlation was not based on the assumption that the variables were normally distributed. Spearman's correlation coefficient spans between -1 and +1 indicating no correlation. Correlation coefficients of -1 or +1 imply a perfect monotonic relationship. The spearman function was used from the sub-package scipy.stats (Kokoska and Zwillinger., 2000) . the correlations between each of the two target genes (knockout genes), and the rest of the genes were calculated. The correlated genes were chosen for further analysis, all with FDR-adjusted p-values 0.4. Principal Component Analysis (PCA) PCA was used as a dimensionality reduction technique to compare the gene expression profile dispersion of the bacteria based on the variations of their genes expression levels. " PCA " function from mixOmics R package was used for this purpose (Rohart et al. , 2017) . Pathway analysis BioCyc (Karp et al. , 2018) database of microbial genomes and metabolic pathways were used to find the pathways each of the correlated genes were assigned. Concerning our results, replacing of the native FDH with TsFDH might lead to potential metabolic rewirings, leading to an increased glycerol efficiency as a carbon source. Our outcomes may suggest an initial clue to start more mechanistic metabolic investigations such as flux balance analysis and CO 2 leakage measurements to address these discrepancies between the in silico predictions and the experimental outcomes. Considering our in silico outcomes, we hypothesized that the omission of the original E. coli FDH may ultimately lead to the increased expression of some genes playing roles in tRNA charging and processing pathways. These in silico findings could be considered as a gene expression-level study related to our experimental observations; nonetheless, more detailed investigations are necessary to verify the hypotheses arising from our results. Number and insensitivity to environmental oxygen (Yamamoto et al. , 2005; Slusarczyk et al. , 2000; Hartmann and Leimkühler., 2013) . We showed that both knockouts and transformation with Ts FDH lead to increased growth in all strains. In the initial incubation periods, the growth rates were approximately equal between transformed and non-transformed knockouts on LB medium, between 8-24 h the growth rates of the transformed and knockout bacteria were much higher than those of controls in both media, which implies the negative effect of wild type FDH gene on the E. coli growth rate. In other words, it showed higher growth rate by eliminating the wild-type FDH chains. The increased growth rate of the transformed knockouts on both media might be result from decreased CO 2 leakage due to less formate oxidation by Ts FDH compared with the wild-type FDH. One plausible hypothesis regarding the relatively more accentuated growth of the transformed knockouts on glycerol medium could be the preferred utilization of glycerol after knockout and transformation besides the probable decrease of CO 2 leakage due to the conjecture as mentioned earlier. To evaluate these hypotheses, further studies are necessary, such as CO 2 absorption/emission measurements and flux balance analysis (FBA). These observations could also be a starting point for more sophisticated molecular-level studies on Ts FDHꞌs contribution to the growth efficiency of E. coli on glycerol as the carbon source. Abbreviations PCA Principal Component Analysis FDH Formate Dehydrogenase TsFDH Thiobacillus sp. KNK65MA DE3 Escherichia coli BL21 CPM Count Per Million Declarations Availability of data and materials All the data supporting the conclusions of this paper were included in the context of the paper and the additional files. All the codes for the preprocessing and the analysis of the RNA-seq dataset are available at https://github.com/SamanAshtiani/ecoli_fdh.git. Competing Interest The authors declare that there are no competing interests that could have affected the study presented in this paper. Funding International Cooperation for Applied Research Development (ICARD) grant to BB. Authors' contributions RR and BB conducted experiments. AAS and MHM helped this research. SHA conceived and executed the bioinformatics analysis sections. AE helped with the bioinformatics analyses and visualizations. BB, RR, and SHA wrote the manuscript. All authors read and approved the manuscript. Acknowledgements We acknowledge the fund by EU-ITN project ProteinFactory (MSCA-ITN-2014-ETN-642836) and the Swedish Research Council (Grant 2016-03798). We thank Payam Emami, Rui Benfeitas, and Paulo Czarnewski at the National Bioinformatics Infrastructure Sweden (NBIS) at SciLifeLab for their fruitful discussions and help with the RNA-seq data analyses. We also thank Roghaieh Ghaderi Ternik for her help. References Basan M, Hui S, Williamson JR (2017) ArcA overexpression induces fermentation and results in enhanced growth rates of E. coli. Sci Rep 7(1):11866. 10.1038/s41598-017-12144-6 Caglar MU, Houser JR, Barnhart CS, Boutz DR, Carroll SM, Dasgupta A, Wilke CO (2017) The E. coli molecular phenotype under different growth conditions. Scientific reports , 7 (1), 45303. 10.1038/srep45303 (2017) Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Schomburg D (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49(D1):D498–D508 Cheng KK, Lee BS, Masuda T, Ito T, Ikeda K, Hirayama A, Robert M (2014) Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol. Nature communications , 5 (1), 3233. 10.1038/ncomms4233 (2014) Choe H, Joo JC, Cho DH, Kim MH, Lee SH, Jung KD, Kim YH (2014) Efficient CO2-reducing activity of NAD-dependent formate dehydrogenase from Thiobacillus sp. KNK65MA for formate production from CO2 gas. PLoS ONE 9(7):e103111. 10.1371/journal.pone.0103111 ?? Consortium TU et al (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480–D489 Cotton CA, Edlich-Muth C, Bar-Even A (2018) Reinforcing carbon fixation: CO 2 reduction replacing and supporting carboxylation. Curr Opin Biotechnol 49:49–56. 10.1016/j.copbio.2017.07.014 Gong F, Zhu H, Zhang Y, Li Y (2018) Biological carbon fixation: from natural to synthetic. J CO2 Utilization 28:221–227. 10.1016/j.jcou.2018.09.014 Gong F, Liu G, Zhai X, Zhou J, Cai Z, Li Y (2015) Quantitative analysis of an engineered CO 2-fixing Escherichia coli reveals great potential of heterotrophic CO 2 fixation. Biotechnol Biofuels 8:1–10. 10.1186/s13068-015-0268-1 Hädicke O, von Kamp A, Aydogan T, Klamt S (2018) OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli. PLoS Comput Biol 14(9):e1006492. 10.1371/journal.pcbi.1006492 Hartmann T, Leimkühler S (2013) The oxygen-tolerant and NAD+‐dependent formate dehydrogenase from Rhodobacter capsulatus is able to catalyze the reduction of CO 2 to formate. FEBS J 280(23):6083–6096. 10.1111/febs.12528 Hoelsch K, Sührer I, Heusel M, Weuster-Botz D (2013) Engineering of formate dehydrogenase: synergistic effect of mutations affecting cofactor specificity and chemical stability. Appl Microbiol Biotechnol 97:2473–2481. 10.1007/s00253-012-4142-9 Ibarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186–189. 10.1038/nature01149 Iwadate Y, Funabasama N, Kato JI (2017) Involvement of formate dehydrogenases in stationary phase oxidative stress tolerance in Escherichia coli. FEMS Microbiol Lett 364(20):fnx193. 10.1093/femsle/fnx19 Martínez-Gómez K, Flores N, Castañeda HM, Martínez-Batallar G, Hernández-Chávez G, Ramírez OT, Bolivar F (2012) New insights into Escherichia coli metabolism: carbon scavenging, acetate metabolism and carbon recycling responses during growth on glycerol. Microb Cell Fact 11:1–21. 10.1186/1475-2859-11-46 Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–D361. 10.1093/nar/gkw1092 Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Subhraveti P (2019) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform 20(4):1085–1093. 10.1093/bib/bbx085 Kokoska S, Zwillinger D (2000) CRC standard probability and statistics tables and formulae. Crc Moon M, Park GW, Lee JP, Lee JS, Min K (2020) Recent progress in formate dehydrogenase (FDH) as a non-photosynthetic CO2 utilizing enzyme: A short review. J CO2 Utilization 42:101353. 10.1016/j.jcou.2020.101353 Riesenberg D, Schulz V, Knorre WA, Pohl HD, Korz D, Sanders EA, Deckwer WD (1991) High cell density cultivation of Escherichia coli at controlled specific growth rate. J Biotechnol 20(1):17–27. 10.1016/0168-1656(91)90032-Q Rohart F, Gautier B, Singh A, Lê Cao KA (2017) mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol 13(11):e1005752. 10.1371/journal.pcbi.1005752 Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual, vol 2. Cold Spring Harbor Laboratory Press Santos-Zavaleta A, Salgado H, Gama-Castro S, Sánchez-Pérez M, Gómez-Romero L, Ledezma-Tejeida D, Collado-Vides J (2019) RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12. Nucleic Acids Res 47(D1):D212–D220. 10.1093/nar/gky1077 Slusarczyk H, Felber S, Kula MR, Pohl M (2000) Stabilization of NAD-dependent formate dehydrogenase from Candida boidinii by site‐directed mutagenesis of cysteine residues. Eur J Biochem 267(5):1280–1289. 10.1046/j.1432-1327.2000.01123.x Solomon S, Plattner GK, Knutti R, Friedlingstein P (2009) Irreversible climate change due to carbon dioxide emissions. Proceedings of the national academy of sciences , 106 (6), 1704–1709. 10.1073/pnas.0812721106 Studier FW, Moffatt BA (1986) Use of bacteriophage T7 RNA polymerase to direct selective high-level expression of cloned genes. J Mol Biol 189(1):113–113. 10.1016/0022-2836(86)90385-2 Yamamoto H, Mitsuhashi K, Kimoto N, Kobayashi Y, Esaki N (2005) Robust NADH-regenerator: Improved α-haloketone-resistant formate dehydrogenase. Appl Microbiol Biotechnol 67:33–39. doi./10.1007/s00253-004-1728-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3921353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270795343,"identity":"68ba9081-1524-43db-b842-08a54257f3b1","order_by":0,"name":"Roya Razavipour","email":"","orcid":"","institution":"Science and Research Branch IAU","correspondingAuthor":false,"prefix":"","firstName":"Roya","middleName":"","lastName":"Razavipour","suffix":""},{"id":270795344,"identity":"fc530054-8e51-4a68-8b4d-22ad2485bbb0","order_by":1,"name":"Saman Hosseini Ashtiani","email":"","orcid":"","institution":"Stockholm University","correspondingAuthor":false,"prefix":"","firstName":"Saman","middleName":"Hosseini","lastName":"Ashtiani","suffix":""},{"id":270795345,"identity":"f506f5cf-6a42-4d38-8695-82bf5ebe01d8","order_by":2,"name":"Abbas Akhavan Sepahy","email":"","orcid":"","institution":"North Branch IAU","correspondingAuthor":false,"prefix":"","firstName":"Abbas","middleName":"Akhavan","lastName":"Sepahy","suffix":""},{"id":270795346,"identity":"dd020945-a97d-4076-be23-5f286831e7d3","order_by":3,"name":"Mohammad Hossein Modarressi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hossein","lastName":"Modarressi","suffix":""},{"id":270795347,"identity":"bc875c04-144c-430e-96cd-6207a15e6aee","order_by":4,"name":"Bijan Bambai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACAwYGNiBpAyLZwCJsRGpJI1kLw2GiFEOAOfvptAc/Cs7n8fEvPvaAocaOgU/6AH4tlj252w17DG4Xs0k8SzdgOJbMwMaXQMBhB3K3SfAY3E5skzhjJsHAdoCBjYeQX86/3Sb5x+AcUMv5bxIM/4jRciN3mzSPwYHENv4eNgnGNiK0WM54u91YxiAZaAubuUFiXzIPQS3m/LnbHr75Y5c4v//wswcfvtnJyfcQ0IIAEgkMDEBEyA5kwH+ABMWjYBSMglEwogAArvQ8FYta6TgAAAAASUVORK5CYII=","orcid":"","institution":"National Institute for Genetic Engineering and Biotechnology (NIGEB)","correspondingAuthor":true,"prefix":"","firstName":"Bijan","middleName":"","lastName":"Bambai","suffix":""}],"badges":[],"createdAt":"2024-02-02 15:14:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3921353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3921353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50719956,"identity":"295b4435-29a0-477f-8c7c-b75532d6641f","added_by":"auto","created_at":"2024-02-06 09:36:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth rate comparison among bacteria on LB medium at OD 600 nm at different time points:\u003c/strong\u003eAt earlier time points there is no considerable difference between the bacterial species with and without TsFDH. From eight hours onward, there is a significant difference between all bacterial species with and without TsFDH.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3921353/v1/8ff9cd8b9cffe42c9be9f92b.png"},{"id":50719960,"identity":"1ca736ed-b429-4868-9b58-2b6b265199b0","added_by":"auto","created_at":"2024-02-06 09:36:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGrowth rate comparison among bacteria on M9+Glycerol medium at OD 600 nm at different time points: \u003c/strong\u003eAt earlier hours, unlike on LB medium, on M9+Glycerol, the growth rates of the bacterial species with TsFDH are vividly higher than those without TsFDH except for K12. Particularly from 12 hours onward, the difference between the knockouts with and without TsFDH is relatively more accentuated compared with the growth rates on LB at the same time points.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3921353/v1/d8716e09436937fe055ff59a.png"},{"id":50719957,"identity":"959d3e4c-fff4-4766-9643-338e75197270","added_by":"auto","created_at":"2024-02-06 09:36:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6428,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations distribution for fdhF.\u003c/strong\u003e The distribution of all genes' correlations with fdhF for each pathway. The gold density plot represents the probability density of achieving either tRNA charging or tRNA processing for the corresponding correlation coefficients on the x-axis. The gray density plot represents the probability density of getting any pathway other than the two mentioned ones for the corresponding correlation coefficients on the x-axis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3921353/v1/b451cb6ed2e39b3cd19060bd.png"},{"id":50719955,"identity":"7358dbf0-3719-4700-a776-0b4791676404","added_by":"auto","created_at":"2024-02-06 09:36:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations distribution for fdhD.\u003c/strong\u003eThe distribution of all genes' correlations with fdhD for each pathway. The gold density plot represents the probability density of achieving either tRNA charging or tRNA processing for the corresponding correlation coefficients on the x-axis. The gray density plot indicates the probability density of getting any pathway other than the two mentioned ones for the corresponding correlation coefficients on the x-axis.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3921353/v1/aa33a4f8ede2f878d5e67274.png"},{"id":51771482,"identity":"fabe0977-0a72-4770-a6c1-a013a6198727","added_by":"auto","created_at":"2024-02-28 19:33:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":426062,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3921353/v1/6e01e7bf-6b69-444b-8df4-886199d053de.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FDH knockout and TsFDH transformation led to enhance growth rate of Escherichia coli","fulltext":[{"header":"Background","content":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e was easily formed by the oxidation of organic molecules during respiration in living organisms or combustion in regular mechanical engines. This molecule is thermodynamically stable with a low chemical activity.Nowadays,the atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration is approaching alarming levels (from 300 ppm to 417 \u003cem\u003eppm\u003c/em\u003e in about 50 years).Greenhouse gases has led to elevate frequencies of extreme climate conditions like drought, flooding, wild fire and tropical storms in different regions of the world (Solomon et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The development of innovative methods for reducing the released CO\u003csub\u003e2\u003c/sub\u003e into the atmosphere and assimilating CO\u003csub\u003e2\u003c/sub\u003e into organic matter is in demand more than ever (Gong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe respiration process, which is more economical for extracting chemical energy from organic material compared to anaerobic fermentation. Engineering bacterial strains has been involved in biotechnological processes with the aim of reducing carbon dioxide release into the atmosphere or even fixing the atmospheric CO\u003csub\u003e2\u003c/sub\u003e into biomass has environmental and economic advantages. There have been many efforts to reduce the carbon dioxide to release during biomass production by metabolic engineering methods. (Cotton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The central pathways and cycles of metabolism are the first targets for manipulating enzymes responsible for critical biochemical reactions or regulatory proteins controlling the expression of certain enzymes to reduce CO\u003csub\u003e2\u003c/sub\u003e release (H\u0026auml;dicke et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe formate dehydrogenase is One of exciting candidates for reducing the amount of released CO\u003csub\u003e2\u003c/sub\u003e. Theoretically, FDHs are enzymes capable of reversible conversion of CO\u003csub\u003e2\u003c/sub\u003e to formate, which is the simplest organic acid (Moon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the major drawback of the biotechnological application of FDHs is the fact that majority of these enzymes prefer the oxidation of formate to produce CO\u003csub\u003e2\u003c/sub\u003e under physiological conditions (Hoelsch \u003cem\u003eet al.\u003c/em\u003e, 2012). In this study, FDH from \u003cem\u003eThiobacillus sp\u003c/em\u003e. KNK65MA (\u003cem\u003eTs\u003c/em\u003eFDH) as an enzyme with high catalytic efficiency toward reduction of CO\u003csub\u003e2\u003c/sub\u003e has been chosen. (Choe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The crystal structure of the active enzyme (PDB: 3WR5) consists of a homo-tetramer of 406 amino acid-long polypeptide. There are five extra residues at the N-terminal of the recombinant protein, compared with the sequence in UniProt (Consortium \u003cem\u003eet al.\u003c/em\u003e, 2021) (accession code: Q76EB7).\u003c/p\u003e \u003cp\u003eIn this study, we sought to monitor the growth of \u003cem\u003eE. coli\u003c/em\u003e fdhD and fdhF knockout strains (JW3866 and JW4040, respectively) with and without \u003cem\u003eTs\u003c/em\u003eFDH, as well as control strains, i.e., K12 and BL21. For consistency's sake, we will refer to knockout strains JW3866 and JW4040 as ∆\u003cem\u003efdhD\u003c/em\u003e and ∆\u003cem\u003efdhF\u003c/em\u003e, respectively. Both knockout strains demonstrated growth advantage in LB and M9\u0026thinsp;+\u0026thinsp;glycerol media compared with the control \u003cem\u003eE. coli\u003c/em\u003e strains with wild-type FDH. Here, we demonstrate high growth rate advantage in knockouts expressing the recombinant enzyme (\u003cem\u003eTs\u003c/em\u003eFDH) compared with knockouts and controls without \u003cem\u003eTs\u003c/em\u003eFDH, particularly in M9\u0026thinsp;+\u0026thinsp;Glycerol medium. In order to explain the observed results, we perform an \u003cem\u003ein silico\u003c/em\u003e study by examining the transcriptomic-level correlations between the target (knockout) genes and the rest of the genes in \u003cem\u003eE. coli\u003c/em\u003e. The correlation analysis, based on an independent \u003cem\u003eE.coli\u003c/em\u003e RNA-seq gene expression profile data set, followed by pathway analysis disclosed that all the genes significantly anti-correlated with both target genes belong to tRNA charging or tRNA processing pathways.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental results\u003c/h2\u003e \u003cp\u003eGrowth measures of bacterial cells with or without plasmid on LB and M9\u0026thinsp;+\u0026thinsp;Glycerol are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively. Samples were examined within 24 hours at different time intervals. On LB media the preferential growth dynamics of knockout strains with or without \u003cem\u003eTs\u003c/em\u003eFDH over control strains were recognizable more clearly after eight hours post inoculation up to 24 hours. In samples, cell growth in M9\u0026thinsp;+\u0026thinsp;Glycerol media was observed to pronounce shift in the growth divergence of FDH knockout strains with or without \u003cem\u003eTs\u003c/em\u003eFDH from control strains during earlier time intervals up to 24 hours than LB medium. On M9\u0026thinsp;+\u0026thinsp;Glycerol, BL21 with \u003cem\u003eTs\u003c/em\u003eFDH showed higher growth up to eight hours compared with the standard BL21. Moreover, on M9\u0026thinsp;+\u0026thinsp;Glycerol, both knockouts with \u003cem\u003eTs\u003c/em\u003eFDH showed relatively higher growth rates from 12 hours onward compared with the respective growth rates on LB. SDS-PAGE analysis of the strains confirms the expression of \u003cem\u003eTs\u003c/em\u003eFDH under the experimental conditions (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eanalysis results\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ecorrelation analysis\u003c/h2\u003e \u003cp\u003eAll correlations with each of the knockout genes were calculated (Table S3 and S4), and the ones with FDR-adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were chosen (Table S5 and S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePCA analysis\u003c/h2\u003e \u003cp\u003eAccording to the PCA results (Fig. S4a), it is postulated that the top anti-correlated genes for both knockouts are closely associated with one another. As a comparison, the PCA plot was generated using all the genes, indicating that the other genes show more expression divergence. Moreover, the second PCA plot (Fig. S4b) could be indicate that the dataset, being based on different growth media, reflects a wide range of expression levels for different genes in each data point, which is critical for reflecting the correlations between the fluctuating gene expression levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic pathways\u003c/h2\u003e \u003cp\u003eSince the knockout of FDH main subunits is synonymous to absolute down regulation of the FDH activity in knockout strains, we initially focused on the significantly negatively correlated genes, which may reveal the genes that undergo up regulation accordingly. Using BioCyc database of microbial genomes and metabolic pathways, all the significantly negatively correlated genes (with FDR adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were shown to be involved in tRNA charging pathway and tRNA processing pathway (PWY0-1479). To evaluate whether the mentioned pathways, are more frequent among the negatively or positively correlated gene, all the positively correlated genes assigned to the same pathways, i.e., tRNA charging and tRNA processing pathways, were selected for comparison. All the pathways other than the two mentioned pathways for both negatively and positively correlated genes were categorized as background pathways and were named \"All other pathways\". For better visualization of the distribution of all positive and negative correlation coefficients for both mentioned pathways the corresponding density plots were used (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. and 4.). The comparisons of the percentages of the number of correlated genes in each pathway category was given for both fdhD and fdhF genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelations with fdhF and fdhD\u003c/b\u003e: Comparison of the number of positively and negatively correlated genes assigned to the unique set of pathways.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnockout gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegatively correlated genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePositively correlated genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003efdhF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-RNA charging or processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll other pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003efdhD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-RNA charging or processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll other pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eIncreasing the growth efficacy of industrially essential microorganisms is a novel goal in biotechnological applications. One of the strategies to boost the bacterial growth rate is to reduce the organic carbon leak, i.e., the release of CO\u003csub\u003e2\u003c/sub\u003e as one of primary end products in the respiration process. Different \u003cem\u003eE.coli\u003c/em\u003e strains are the workhorse for the production of some well-known biopharmaceuticals like G-CSF, Romiplostim and Asparaginase. Therefore, higher growth rates are on demand particularly in Biotechnology.\u003c/p\u003e \u003cp\u003eSince the Kelvin cycle is the mainstream CO\u003csub\u003e2\u003c/sub\u003e fixation pathway in plants, algae and Cyanobacteria.most engineering efforts were directed towards the Kelvin cycle for converting CO\u003csub\u003e2\u003c/sub\u003e into valuable materials. Heterotrophic microorganisms generally do not assimilate CO\u003csub\u003e2\u003c/sub\u003e through the central metabolism (Gong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Over the past decade, there has been great success in the production of CO\u003csub\u003e2\u003c/sub\u003e derivatives, which have the potential to be used as fuel and valuable chemicals by bacteria (Hartmann and Leimk\u0026uuml;hler., 2013). Previously, other researchers have approached this challenge by defining fermentation conditions and controlling aeration rates (Riesenberg et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) or genetically overexpressing ArcA transcription factor (Basan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we introduce a new approach by targeting one of the main enzymes responsible for converting organic formate into inorganic wasteful CO\u003csub\u003e2\u003c/sub\u003e, i.e., FDH. There were three known FDHs in \u003cem\u003eE. coli\u003c/em\u003e namely respiratory FDH, anaerobically expressed FDH and newly identified pressure induced FDH (FHL). FDHF is the cytosolic form, while FDHN and FDHO are membrane bound,FDHN is responsible for Nitrogen cycle and FDHO active in Sulfur metabolism (Iwadate et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All these enzymes prefer the oxidation of formate into CO\u003csub\u003e2\u003c/sub\u003e under physiological conditions. Scanning BRENDA for FDHs a tendency towards producing of formate from CO\u003csub\u003e2\u003c/sub\u003e revealed that there are few candidate FDHs with relatively higher formate production (CO\u003csub\u003e2\u003c/sub\u003e reduction) catalytic efficiency. A comprehensive search the metabolism of \u003cem\u003eE.coli\u003c/em\u003e using the Regulon DB(Santos-Zavaleta,Salgado, Gama-Castro,S\u0026aacute;nchez-P\u0026eacute;rez,G\u0026oacute;mez-Romero, Ledezma-Tejeida,Garc\u0026iacute;a-Sotelo,Alquicira-Hern\u0026aacute;ndez,Mu\u0026ntilde;iz-Rascado,Pe\u0026ntilde;a-Loredo,Ishida-Guti\u0026eacute;rrez, David A.Vel\u0026aacute;zquez-Ram\u0026iacute;rez,\u003cem\u003eet al.\u003c/em\u003e, 2019) were showed that the formate dehydrogenase enzymes successfully expressed in recombinant form in \u003cem\u003eE. coli\u003c/em\u003e (Santos-Zavaleta,Salgado,Gama-Castro,S\u0026aacute;nchez-P\u0026eacute;rez, G\u0026oacute;mez-Romero, Ledezma-Tejeida, Garc\u0026iacute;a-Sotelo, Alquicira-Hern\u0026aacute;ndez, Mu\u0026ntilde;iz-Rascado, Pe\u0026ntilde;a-Loredo,Ishida-Guti\u0026eacute;rrez,David A Vel\u0026aacute;zquez-Ram\u0026iacute;rez, \u003cem\u003eet al.\u003c/em\u003e, 2019). According to a study, \u003cem\u003eE. coli\u003c/em\u003e\u0026rsquo;s FDHs have a strong tendency to regenerate CO\u003csub\u003e2\u003c/sub\u003e from formate. (Gong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).Among the studied formate dehydrogenases, \u003cem\u003eTs\u003c/em\u003eFDH has potential advantages as a biocatalyst in CO\u003csub\u003e2\u003c/sub\u003e reduction (Choe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We hypothesized that the \u003cem\u003eE. coli\u003c/em\u003e strains harboring \u003cem\u003eTs\u003c/em\u003eFDH could lead to developing bacterial strains for biotechnological applications with higher biomass to carbon source ratio owing to the possible lowering of carbon dioxide leakage. This possible solution has so far been remained out of sight and to the best of our knowledge has not tried yet.\u003c/p\u003e \u003cp\u003eWe expressed the recombinant fdhD gene from \u003cem\u003eThiobacillus\u003c/em\u003e in \u003cem\u003eEscherichia coli\u003c/em\u003e strains K12 and BL21. In order to better compare the role of \u003cem\u003eTs\u003c/em\u003eFDH in growth efficacy on similar growth conditions (media), we also transformed two FDH knockout strains from K12 and BL21 with pET\u0026thinsp;+\u0026thinsp;TsFDH. Significant growth rate differences were observed between the knockout strains and the recombinant knockouts containing \u003cem\u003eTs\u003c/em\u003eFDH on either growth medium. On the other hand, both knockouts with \u003cem\u003eTs\u003c/em\u003eFDH has showed vividly higher growth rate starting from 8 hours to 24 hours on both media after induction. Additionally, the growth advantage of the control strains (K12 and BL21) with \u003cem\u003eTs\u003c/em\u003eFDH lasted up to 12 hours on both media. A possible reason for the less increase in the growth rate of BL21 cells transformed with \u003cem\u003eTs\u003c/em\u003eFDH plasmid compared with the transformed knockouts is the presence of the original FDH in these strains. BL21 also contains IPTG as a gene expression inducer thanks to having a chromosomally encoded bacteriophage T7 RNA polymerase (T7 RNAP), which may be responsible for some metabolic leakage leading to less growth compared with K12-derived knockouts and transformed knockouts (Studier and Moffatt., 1986). The original K12 cells and mutants derived from this cell lack the complementary T7 RNA-polymerase.\u003c/p\u003e \u003cp\u003eIn this study, we showed that removing either subunit of the wild-type formate dehydrogenase gene from \u003cem\u003eE. coli\u003c/em\u003e and its replacement with the formate dehydrogenase gene from \u003cem\u003eThiobacillus\u003c/em\u003e Sp. KNK65MA can increase the growth rate of \u003cem\u003eE. coli\u003c/em\u003e cells. A mechanistic interpretation, from a metabolic point of view, is that by reducing the breakdown of formate to CO\u003csub\u003e2\u003c/sub\u003e, the intracellular concentration of formate will increase. This mechanism may lead to elevated expression of the pyruvate lyase gene. This enzyme will produce pyruvate to enhanced anabolic pathways including citrate cycle. By enhancing intermediates of Citrate cycle, the starting materials for amino acids and nucleotides biosynthesis could be more abundant (Fig. S5). In a previous study by Palsson \u003cem\u003eet al.\u003c/em\u003e, the whole cell \u003cem\u003ein silico\u003c/em\u003e model of the \u003cem\u003eE. coli\u003c/em\u003e metabolic network predicted that glycerol should be a preferred carbon source over glucose. However, the experimental findings were not consistent with the mentioned predictions. They indicated the adaptive evolution phenomenon for the bacteria to go from sub-optimal to the predicted optimal growth rate on glycerol (Ibarra et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; K \u003cem\u003eet al.\u003c/em\u003e, 2012).\u003c/p\u003e \u003cp\u003eConcerning our results, the replacing of the native FDH with TsFDH might lead to potential metabolic rewirings leading, to an increased glycerol efficiency as a carbon source. Our outcomes may suggest an initial clue to start more mechanistic metabolic investigating flux balance analysis and CO\u003csub\u003e2\u003c/sub\u003e leakage measurements to address these discrepancies between the \u003cem\u003ein silico\u003c/em\u003e predictions and the experimental outcomes. Considering our \u003cem\u003ein silico\u003c/em\u003e outcomes, we hypothesized that the omission of the original \u003cem\u003eE. coli\u003c/em\u003e FDH may ultimately lead to the increased expression of some genes playing roles in tRNA charging and processing pathways. These \u003cem\u003ein silico\u003c/em\u003e findings could be considered as a gene expression-level study related to our experimental observations; nonetheless, more detailed investigations are necessary to verify the hypotheses arising from our results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe main problems in biotechnological applications of most known FDHs so far are protein instability, sensitivity to oxygen and the low conversion rate. The FDH of this study (\u003cem\u003eTs\u003c/em\u003eFDH) shows some biochemical advantages over the previously studied FDHs such as Candida bolidini's FDH including higher turnover number and insensitivity to the environmental oxygen as well as profound tendency towards reducing CO\u003csub\u003e2\u003c/sub\u003e to formate (Yamamoto et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Slusarczyk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hartmann and Leimk\u0026uuml;hler., 2013).\u003c/p\u003e \u003cp\u003eWe showed that both knockouts and transformation with \u003cem\u003eTs\u003c/em\u003eFDH lead to increased growth in all strains. In the initial incubation periods, the growth rates were approximately equal between transformed and non-transformed knockouts on LB medium between 8-24h, the growth rates of the transformed and knockout bacteria were much higher than those of controls in both media, which implies the negative effect of wild type FDH gene on the \u003cem\u003eE. coli\u003c/em\u003e growth rate. In other words, there would be a higher growth rate by eliminating the wild- type FDH chains.\u003c/p\u003e \u003cp\u003eThe increased growth rate of the transformed knockouts on both media might be the result from decreased CO\u003csub\u003e2\u003c/sub\u003e leakage due to less formate oxidation by \u003cem\u003eTs\u003c/em\u003eFDH compared with the wild-type FDH. One plausible hypothesis regarding the relatively more accentuated growth of the transformed knockouts on glycerol medium could be the preferred utilization of glycerol after knockout and transformation besides the probable decrease of CO\u003csub\u003e2\u003c/sub\u003e leakage due to conjecture as mentioned earlier. These hypotheses to evaluate, further studies are necessary such as CO\u003csub\u003e2\u003c/sub\u003e absorption/emission measurements and flux balance analysis (FBA). These observations could also be a starting point for more sophisticated molecular-level studies on \u003cem\u003eTs\u003c/em\u003eFDHꞌs contribution to the growth efficiency of \u003cem\u003eE. coli\u003c/em\u003e on glycerol as the carbon source.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e Strains, Plasmids and Media\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll \u003cem\u003eE.\u003c/em\u003e \u003cem\u003ecoli\u003c/em\u003e strains and media used in this study were presented in Table 1. \u003cem\u003eEscherichia coli\u003c/em\u003e BL21(DE3) was used to express of the recombinant FDH from \u003cem\u003eThiobacillus sp.\u003c/em\u003e KNK65MA (\u003cem\u003eTs\u003c/em\u003eFDH).\u0026nbsp;∆\u003cem\u003efdhD\u0026nbsp;\u003c/em\u003eand ∆\u003cem\u003efdhF\u003c/em\u003e, Two FDH knockout strains\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere purchased from Keio Collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1?.......\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Bacterial strains and media.\u003c/strong\u003e In this study ,all bacterial strains and plasmids to be used and their characteristics and \u0026nbsp;the sources they were obtained.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.470588235294116%\" valign=\"top\"\u003e\n \u003cp\u003eStrains and plasmids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.76470588235294%\" valign=\"top\"\u003e\n \u003cp\u003eRelated characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.764705882352942%\" valign=\"top\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.470588235294116%\" valign=\"top\"\u003e\n \u003cp\u003eStrains\u003c/p\u003e\n \u003cp\u003eE. coli K12\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e BL21(DE3)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e JW3866 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e JW4040\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePlasmids\u003c/p\u003e\n \u003cp\u003epET-21\u0026alpha;\u003c/p\u003e\n \u003cp\u003epET-21\u0026alpha;-TsFDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"56.76470588235294%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eWild type\u003c/p\u003e\n \u003cp\u003e[\u003cem\u003elon\u003c/em\u003e] \u003cem\u003eomp\u003c/em\u003eT \u003cem\u003egal\u003c/em\u003e (\u0026lambda; DE3) [\u003cem\u003edcm\u003c/em\u003e] ∆\u003cem\u003ehsd\u003c/em\u003eS\u003c/p\u003e\n \u003cp\u003eK12 ∆\u003cem\u003efdhD\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eK12 ∆\u003cem\u003efdhF\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAp\u003csup\u003eR\u003c/sup\u003e, \u003cem\u003eT7\u003c/em\u003e promoter, lac operator\u003c/p\u003e\n \u003cp\u003epET-21\u0026alpha;, containing TsFDH gene from \u003cem\u003eThiobacilusis\u003c/em\u003e \u003cem\u003esp\u0026nbsp;\u003c/em\u003eKNK65MA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.764705882352942%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNIGEB stocks\u003c/p\u003e\n \u003cp\u003eInvitrogen\u003c/p\u003e\n \u003cp\u003eDharmacon\u003c/p\u003e\n \u003cp\u003eDharmacon\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNovagen\u003c/p\u003e\n \u003cp\u003eThis research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eM9 +glycerol and LB media all containing 30 \u0026micro;g kanamycin were used for measuring bacterial growth. For BL21 with pET+TsFDH, the same media with ampicillin were used. In all samples containing pET+TsFDH, IPTG (0.5 mM final concentration) were added to the media. The metabolic reactions consuming or producing formate (map01200 and C00058) were obtained from KEGG \u003cspan lang=\"EN-US\"\u003e(Kanehisa \u003cem\u003eet al.\u003c/em\u003e, 2017)\u003c/span\u003e (https://www.genome.jp/pathway/map01200+C00058). Using the results from the KEGG \u0026nbsp;pathway search for all the carbon fixation reactions, the contributing FDHs were identified. The kinetic parameters including K\u003csub\u003ecat\u0026nbsp;\u003c/sub\u003eand K\u003csub\u003em\u003c/sub\u003e of FDHs (EC: 1.17.1.9) for formate formation were obtained from the Brenda enzyme data bank \u003cspan lang=\"EN-US\"\u003e(Chang \u003cem\u003eet al.\u003c/em\u003e, 2021)\u003c/span\u003e and the published articles were reviewed and compared in different bacteria (Table S1). This approach revealed some interesting FDHs with relatively better kinetic parameters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results were obtained by \u003cem\u003eTs\u003c/em\u003eFDH might be interesting, to assume,there are still some FDHs that deserve attention for replacing the indigenous FDHs of \u003cem\u003eE. coli\u003c/em\u003e to improve the growth efficiency. Our assumption was based on the ambiguity of assay conditions for some of the reported FDHs and the lack of a gold standard for the kinetics comparisons. Scanning the kinetic parameters for a desired FDH suggested \u003cem\u003eThiobacillus sp\u003c/em\u003e. KNK65MA FDH \u003cspan lang=\"EN-US\"\u003e(Choe \u003cem\u003eet al.\u003c/em\u003e, 2014)\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAmino acid and nucleotide sequences of \u003cem\u003eThiobacilusis\u003c/em\u003e \u003cem\u003esp\u0026nbsp;\u003c/em\u003eKNK65MA\u003cem\u003e\u0026nbsp;\u003c/em\u003eformate\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003edehydrogenase were obtained from UniProt (accession # Q76EB7). cDNA of \u003cem\u003eTs\u003c/em\u003eFDH was synthesized in pET21a by ZistEghtesadMad based on reference sequence (Q76EB7). Two knockout strains of K12 \u003cem\u003eEscherichia coli\u003c/em\u003e,\u0026nbsp;∆\u003cem\u003efdhD,\u0026nbsp;\u003c/em\u003eand\u0026nbsp;∆\u003cem\u003efdhF\u003c/em\u003e, with the deletion of fdhD and fdhF genes, respectively, were purchased from Dharmacon. The stocks of the Knockout \u003cem\u003eE. coli\u003c/em\u003e strains were cultured on LB broth and M9+Glycerol media, followed by incubation at 37\u0026deg;\u003csup\u003eC\u003c/sup\u003e for 24 hours \u003cspan lang=\"EN-US\"\u003e(Sambrook, 2012)\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStrains K12 and BL21 were used controls to compare the growth rates. All strains were cultured at the same time under the same conditions on LB broth media at 37 \u0026deg;\u003csup\u003eC\u003c/sup\u003e and 200 rpm. Competent cells of the BL21, \u003cem\u003eE. coli\u003c/em\u003e ∆\u003cem\u003efdhF\u003c/em\u003e\u003cem\u003e, and E. coli\u003c/em\u003e ∆\u003cem\u003efdhD\u003c/em\u003e were prepared as previously mentioned \u003cspan lang=\"EN-US\"\u003e(Sambrook, 2012)\u003c/span\u003e. pET21, a plasmid containing a fusion gene to express format dehydrogenase of \u003cem\u003eThiobacillu\u003c/em\u003es \u003cem\u003esp.\u0026nbsp;\u003c/em\u003eKNK65MA (pET+TsFDH), was transformed into competent BL21cells, \u003cem\u003eE. coli\u003c/em\u003e ∆\u003cem\u003efdhD\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e ∆\u003cem\u003efdhF\u003c/em\u003e on LB Agar with Amp (100 mg/ml) followed by incubation overnight at 37\u0026deg;\u003csup\u003eC\u003c/sup\u003e. The colonies containing plasmid were selected and cultured on a 10 ml LB broth\u0026nbsp;with\u0026nbsp;Amp as a primary culture and were incubated at 37\u003csup\u003e\u0026deg;C\u003c/sup\u003e, 200 rpm for 24 hours.\u0026nbsp;Then the primary culture was carried out\u0026nbsp;in 200 ml of the LB broth containing Amp (100 mg /ml), and they were incubated at 37\u0026deg;\u003csup\u003eC\u003c/sup\u003e and 200 rpm for 24 hours. Also, the bacteria K12, BL21, \u003cem\u003eE. coli\u003c/em\u003e ∆\u003cem\u003efdhD\u003c/em\u003e ,and \u003cem\u003eE. coli\u003c/em\u003e ∆\u003cem\u003efdhF\u0026nbsp;\u003c/em\u003elacking the plasmid were simultaneously cultivated and incubated on LB broth and M9-Glycerol + 50 \u0026micro;g/ml Kanamycin under identical conditions with plasmid-containing strains.\u003c/p\u003e\n\u003ch2\u003eMedia and cultures\u003c/h2\u003e\n\u003cp\u003eM9 medium + glycerol containing 30 \u0026micro;g kanamycin was used for measuring bacterial growth with and without pET+TsFDH. The same media with ampicillin were also used. In all BL21 samples containing pET+TsFDH, IPTG (0.5 mM final concentration) was added to the medium. LB media was purchased from Merck.\u003c/p\u003e\n\u003ch2\u003eGrowth measurements\u003c/h2\u003e\n\u003cp\u003eBacteria were grown in batch cultures at 37\u003csup\u003eo\u003c/sup\u003eC in shaker incubator in 50 mL flasks. 1000 \u0026micro;L samples were taken in triplicate at indicated time intervals, and the absorbance was measured at 600nm. E. coli cell without plasmid as well as plasmid containing cells were sampled during incubation at different times, namely zero, two, four, six, eight, ten, twelve and 24hrs (Table S2). The growth rates of bacteria were determined at selected time intervals by optical absorption at 600 nm wavelength.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eIn silico\u003c/em\u003e analysis\u003c/h2\u003e\n\u003ch3\u003eData preparation\u003c/h3\u003e\n\u003cp\u003eTo perform a transcriptomic-level study related to our observations, we searched for an independent \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003eexpression dataset that could reflect the maximum possible transcriptional variations so that we would be able to achieve significant correlations between as many genes as possible. Moreover, the number of genes involved in the gene expression profile was essential to calculate as many correlations as possible. With this aspiration, we fetched an \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003eRNA-seq dataset comprising 152 RNA-seq count samples under 34 different growth conditions (GEO accession GSE94117). These samples were taken from both exponential and stationary phases. One unique aspect of this highly pertinent dataset is the fact that it is sampled under 34 different growth condition, leading to a broader range of differentially expressed genes thanks to different metabolic needs \u003cspan lang=\"EN-US\"\u003e(Caglar \u003cem\u003eet al.\u003c/em\u003e, 2017)\u003c/span\u003e. Subsequently, correlation analysis, PCA, and pathway analysis were applied to this data set.\u003c/p\u003e\n\u003ch3\u003eData preprocessing\u003c/h3\u003e\n\u003cp\u003eUsing Python version 3.6.1, 152 samples of RNA-seq count files were merged. The counts were converted into count per million (CPM) and were log2 transformed. The resulting data were z-score transformed per gene across all samples. Quality control was performed as sample-level box plots before and after data preprocessing (Fig. S1 and S2).\u003c/p\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003eThe Spearman rank-order correlation coefficient (Spearman\u0026apos;s \u0026rho;), being a nonparametric measure, examines the monotonic relationship between the ordinal values of the variables. Contrary to the Pearson correlation, the Spearman\u0026apos;s rank correlation was not based on the assumption that the variables were normally distributed. Spearman\u0026apos;s correlation coefficient spans between -1 and +1 indicating no correlation. Correlation coefficients of -1 or +1 imply a perfect monotonic relationship. The spearman function was used from the sub-package scipy.stats \u003cspan lang=\"EN-US\"\u003e(Kokoska and Zwillinger., 2000)\u003c/span\u003e. the correlations between each of the two target genes (knockout genes), and the rest of the genes were calculated. The correlated genes were chosen for further analysis, all with FDR-adjusted p-values \u0026lt; 0.01 and |Spearman\u0026apos;s \u0026rho;| \u0026gt; 0.4.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePrincipal Component Analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePCA was used as a dimensionality reduction technique to compare the gene expression profile dispersion of the bacteria based on the variations of their genes expression levels. \u0026quot; PCA \u0026quot; function from mixOmics R package was used for this purpose \u003cspan lang=\"EN-US\"\u003e(Rohart \u003cem\u003eet al.\u003c/em\u003e, 2017)\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003ePathway analysis\u003c/h3\u003e\n\u003cp\u003eBioCyc \u003cspan lang=\"EN-US\"\u003e(Karp \u003cem\u003eet al.\u003c/em\u003e, 2018)\u003c/span\u003e database of microbial genomes and metabolic pathways were used to find the pathways each of the correlated genes were assigned.\u003c/p\u003e\n\u003cp\u003eConcerning our results, replacing of the native FDH with TsFDH might lead to potential metabolic rewirings, leading to an increased glycerol efficiency as a carbon source. Our outcomes may suggest an initial clue to start more mechanistic metabolic investigations such as flux balance analysis and CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eleakage measurements to address these discrepancies between the \u003cem\u003ein silico\u003c/em\u003e predictions and the experimental outcomes. Considering our \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eoutcomes, we hypothesized that the omission of the original \u003cem\u003eE. coli\u003c/em\u003e FDH may ultimately lead to the increased expression of some genes playing roles in tRNA charging and processing pathways. These \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003efindings could be considered as a gene expression-level study related to our experimental observations; nonetheless, more detailed investigations are necessary to verify the hypotheses arising from our results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumber and insensitivity to environmental oxygen \u003cspan lang=\"EN-US\"\u003e(Yamamoto \u003cem\u003eet al.\u003c/em\u003e, 2005; Slusarczyk \u003cem\u003eet al.\u003c/em\u003e, 2000; Hartmann and Leimk\u0026uuml;hler., 2013)\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe showed that both knockouts and transformation with \u003cem\u003eTs\u003c/em\u003eFDH lead to increased growth in all strains. In the initial incubation periods, the growth rates were approximately equal between transformed and non-transformed knockouts on LB medium, between 8-24 h the growth rates of the transformed and knockout bacteria were much higher than those of controls in both media, which implies the negative effect of wild type FDH gene on the \u003cem\u003eE. coli\u003c/em\u003e growth rate. In other words, it showed higher growth rate by eliminating the wild-type FDH chains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe increased growth rate of the transformed knockouts on both media might be result from decreased CO\u003csub\u003e2\u003c/sub\u003e leakage due to less formate oxidation by \u003cem\u003eTs\u003c/em\u003eFDH compared with the wild-type FDH. One plausible hypothesis regarding the relatively more accentuated growth of the transformed knockouts on glycerol medium could be the preferred utilization of glycerol after knockout and transformation besides the probable decrease of CO\u003csub\u003e2\u003c/sub\u003e leakage due to the conjecture as mentioned earlier. To evaluate these hypotheses, further studies are necessary, such as CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eabsorption/emission measurements and flux balance analysis (FBA). These observations could also be a starting point for more sophisticated molecular-level studies on \u003cem\u003eTs\u003c/em\u003eFDHꞌs contribution to the growth efficiency of \u003cem\u003eE. coli\u003c/em\u003e on glycerol as the carbon source.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFormate Dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTsFDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThiobacillus sp. KNK65MA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDE3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEscherichia coli BL21\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCount Per Million\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data supporting the conclusions of this paper were included in the context of the paper and the additional files. All the codes for the preprocessing and the analysis of the RNA-seq dataset are available at https://github.com/SamanAshtiani/ecoli_fdh.git.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests that could have affected the study presented in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInternational Cooperation for Applied Research Development (ICARD) grant to BB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRR and BB conducted experiments. AAS and MHM helped this research. SHA conceived and executed the bioinformatics analysis sections. AE helped with the bioinformatics analyses and visualizations. BB, RR, and SHA wrote the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the fund by EU-ITN project ProteinFactory (MSCA-ITN-2014-ETN-642836) and the Swedish Research Council (Grant 2016-03798). We thank Payam Emami, Rui Benfeitas, and Paulo Czarnewski at the National Bioinformatics Infrastructure Sweden (NBIS) at SciLifeLab for their fruitful discussions and help with the RNA-seq data analyses. We also thank Roghaieh Ghaderi Ternik for her help.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBasan M, Hui S, Williamson JR (2017) ArcA overexpression induces fermentation and results in enhanced growth rates of E. coli. Sci Rep 7(1):11866. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-017-12144-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-12144-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaglar MU, Houser JR, Barnhart CS, Boutz DR, Carroll SM, Dasgupta A, Wilke CO (2017) The E. coli molecular phenotype under different growth conditions. \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 45303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep45303\u003c/span\u003e\u003cspan address=\"10.1038/srep45303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Schomburg D (2021) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49(D1):D498\u0026ndash;D508\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng KK, Lee BS, Masuda T, Ito T, Ikeda K, Hirayama A, Robert M (2014) Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1), 3233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms4233\u003c/span\u003e\u003cspan address=\"10.1038/ncomms4233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoe H, Joo JC, Cho DH, Kim MH, Lee SH, Jung KD, Kim YH (2014) Efficient CO2-reducing activity of NAD-dependent formate dehydrogenase from Thiobacillus sp. KNK65MA for formate production from CO2 gas. PLoS ONE 9(7):e103111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0103111\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0103111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e?? Consortium TU et al (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480\u0026ndash;D489\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotton CA, Edlich-Muth C, Bar-Even A (2018) Reinforcing carbon fixation: CO\u003csub\u003e2\u003c/sub\u003e reduction replacing and supporting carboxylation. Curr Opin Biotechnol 49:49\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.copbio.2017.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.copbio.2017.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong F, Zhu H, Zhang Y, Li Y (2018) Biological carbon fixation: from natural to synthetic. J CO2 Utilization 28:221\u0026ndash;227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcou.2018.09.014\u003c/span\u003e\u003cspan address=\"10.1016/j.jcou.2018.09.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong F, Liu G, Zhai X, Zhou J, Cai Z, Li Y (2015) Quantitative analysis of an engineered CO 2-fixing Escherichia coli reveals great potential of heterotrophic CO 2 fixation. Biotechnol Biofuels 8:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13068-015-0268-1\u003c/span\u003e\u003cspan address=\"10.1186/s13068-015-0268-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;dicke O, von Kamp A, Aydogan T, Klamt S (2018) OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli. PLoS Comput Biol 14(9):e1006492. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pcbi.1006492\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1006492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartmann T, Leimk\u0026uuml;hler S (2013) The oxygen-tolerant and NAD+‐dependent formate dehydrogenase from Rhodobacter capsulatus is able to catalyze the reduction of CO\u003csub\u003e2\u003c/sub\u003e to formate. FEBS J 280(23):6083\u0026ndash;6096. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/febs.12528\u003c/span\u003e\u003cspan address=\"10.1111/febs.12528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoelsch K, S\u0026uuml;hrer I, Heusel M, Weuster-Botz D (2013) Engineering of formate dehydrogenase: synergistic effect of mutations affecting cofactor specificity and chemical stability. Appl Microbiol Biotechnol 97:2473\u0026ndash;2481. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00253-012-4142-9\u003c/span\u003e\u003cspan address=\"10.1007/s00253-012-4142-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbarra RU, Edwards JS, Palsson BO (2002) Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature 420(6912):186\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature01149\u003c/span\u003e\u003cspan address=\"10.1038/nature01149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwadate Y, Funabasama N, Kato JI (2017) Involvement of formate dehydrogenases in stationary phase oxidative stress tolerance in Escherichia coli. FEMS Microbiol Lett 364(20):fnx193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/femsle/fnx19\u003c/span\u003e\u003cspan address=\"10.1093/femsle/fnx19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-G\u0026oacute;mez K, Flores N, Casta\u0026ntilde;eda HM, Mart\u0026iacute;nez-Batallar G, Hern\u0026aacute;ndez-Ch\u0026aacute;vez G, Ram\u0026iacute;rez OT, Bolivar F (2012) New insights into Escherichia coli metabolism: carbon scavenging, acetate metabolism and carbon recycling responses during growth on glycerol. Microb Cell Fact 11:1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1475-2859-11-46\u003c/span\u003e\u003cspan address=\"10.1186/1475-2859-11-46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353\u0026ndash;D361. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkw1092\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkw1092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Subhraveti P (2019) The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform 20(4):1085\u0026ndash;1093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbx085\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbx085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKokoska S, Zwillinger D (2000) CRC standard probability and statistics tables and formulae. Crc\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoon M, Park GW, Lee JP, Lee JS, Min K (2020) Recent progress in formate dehydrogenase (FDH) as a non-photosynthetic CO2 utilizing enzyme: A short review. J CO2 Utilization 42:101353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jcou.2020.101353\u003c/span\u003e\u003cspan address=\"10.1016/j.jcou.2020.101353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiesenberg D, Schulz V, Knorre WA, Pohl HD, Korz D, Sanders EA, Deckwer WD (1991) High cell density cultivation of Escherichia coli at controlled specific growth rate. J Biotechnol 20(1):17\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0168-1656(91)90032-Q\u003c/span\u003e\u003cspan address=\"10.1016/0168-1656(91)90032-Q\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohart F, Gautier B, Singh A, L\u0026ecirc; Cao KA (2017) mixOmics: An R package for \u0026lsquo;omics feature selection and multiple data integration. PLoS Comput Biol 13(11):e1005752. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pcbi.1005752\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1005752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual, vol 2. Cold Spring Harbor Laboratory Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos-Zavaleta A, Salgado H, Gama-Castro S, S\u0026aacute;nchez-P\u0026eacute;rez M, G\u0026oacute;mez-Romero L, Ledezma-Tejeida D, Collado-Vides J (2019) RegulonDB v 10.5: tackling challenges to unify classic and high throughput knowledge of gene regulation in E. coli K-12. Nucleic Acids Res 47(D1):D212\u0026ndash;D220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gky1077\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky1077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlusarczyk H, Felber S, Kula MR, Pohl M (2000) Stabilization of NAD-dependent formate dehydrogenase from Candida boidinii by site‐directed mutagenesis of cysteine residues. Eur J Biochem 267(5):1280\u0026ndash;1289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1046/j.1432-1327.2000.01123.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1432-1327.2000.01123.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon S, Plattner GK, Knutti R, Friedlingstein P (2009) Irreversible climate change due to carbon dioxide emissions. \u003cem\u003eProceedings of the national academy of sciences\u003c/em\u003e, \u003cem\u003e106\u003c/em\u003e(6), 1704\u0026ndash;1709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0812721106\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0812721106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStudier FW, Moffatt BA (1986) Use of bacteriophage T7 RNA polymerase to direct selective high-level expression of cloned genes. J Mol Biol 189(1):113\u0026ndash;113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0022-2836(86)90385-2\u003c/span\u003e\u003cspan address=\"10.1016/0022-2836(86)90385-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto H, Mitsuhashi K, Kimoto N, Kobayashi Y, Esaki N (2005) Robust NADH-regenerator: Improved α-haloketone-resistant formate dehydrogenase. Appl Microbiol Biotechnol 67:33\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi./10.1007/s00253-004-1728-x\u003c/span\u003e\u003cspan address=\"doi./10.1007/s00253-004-1728-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Formate Dehydrogenase, Metabolic CO2 Leak, Glycerol minimal medium, Spearman's rank correlation coefficient, RNA-seq, Principal Component Analysis (PCA)","lastPublishedDoi":"10.21203/rs.3.rs-3921353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3921353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, we sought to reduce the released CO\u003csub\u003e2\u003c/sub\u003e into the atmosphere from bacterial growth by reducing formic acid conversion into CO\u003csub\u003e2\u003c/sub\u003e. Since \u003cem\u003eE. coli\u003c/em\u003e is the biotechnological workhorse and its higher growth rate is desirable, another goal was to monitor the bacterial biomass after the metabolic engineering. The conversion of formic acid to CO\u003csub\u003e2\u003c/sub\u003e is a crucial reaction. Therefore, we compared the growth of control strains, alongside two strains in which two different genes coding two formate dehydrogenase (FDH) subunits were deleted. The knockout bacteria grew better than the controls. \u003cem\u003eThiobacillus\u003c/em\u003e FDH (\u003cem\u003eTs\u003c/em\u003eFDH) transformation increased the growth of both knockouts of \u003cem\u003eE.coli\u003c/em\u003e compared with the controls and the knockouts strain without \u003cem\u003eTs\u003c/em\u003eFDH. Through a transcriptomics-level analysis of the strain knockout genes, the genes negatively correlated with the target genes were shown to belong to tRNA-related pathways. Observing higher cell biomass for the knockout and transformed strains indicates possible underlying mechanisms leading to reduced carbon leakage and increased carbon assimilation, which need more detailed investigations. Gene expression correlations and pathway analysis outcomes suggested possible over-expression of the genes involved in tRNA processing and charging pathways.\u003c/p\u003e","manuscriptTitle":"FDH knockout and TsFDH transformation led to enhance growth rate of Escherichia coli","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 09:36:23","doi":"10.21203/rs.3.rs-3921353/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b873cab2-1e7b-48eb-9bd0-3dac1050a238","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-28T19:32:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 09:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3921353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3921353","identity":"rs-3921353","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}