Effect of thioredoxin tag, oxidizing environment, and temperature on the global metabolome of E. coli strains

Effect of thioredoxin tag, oxidizing environment, and temperature on the global metabolome of E. coli strains | 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 Effect of thioredoxin tag, oxidizing environment, and temperature on the global metabolome of E. coli strains Snehal D Ganjave, Meghna Srivastava, Prajval Nakrani, Vivek Mishra, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6382221/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Applied Biochemistry and Biotechnology → Version 1 posted 4 You are reading this latest preprint version Abstract Achieving soluble expression of disulfide bond-containing recombinant proteins in Escherichia coli is challenging. While strategies such as low post-induction temperature, fusion tags, and engineered strains have been employed to achieve soluble protein expression, their specific effects on E. coli metabolism and its relation to soluble protein expression remain unclear. Here, we performed untargeted metabolomics to study the key metabolic changes associated with co-expression of fusion tags in E. coli strains at low and high cultivation temperatures. Using a mass spectrometry-based approach, we identified 121 differentially abundant metabolites. The metabolomes of BL21 (DE3) and SHuffle strains exhibited distinct intracellular pools of amino acids and redox regulators. We further studied the expression of platelet-derived growth factor (PDGF) as a model disulfide-rich protein that generally tends to aggregate when expressed in E. coli . A lower induction temperature and the addition of a thioredoxin tag were observed to be crucial for obtaining soluble PDGF in both strains. However, SHuffle showed heightened metabolic stress during PDGF production compared to BL21. Soluble PDGF expression was associated with higher levels of peptides, nucleotides, and glycolysis and TCA cycle intermediates, while PDGF expression as inclusion bodies was associated with higher levels of amino acids, nucleobases, and pentose phosphate pathway intermediates. These results have implications for enhancing strain performance and bioprocess optimization for producing “difficult-to-express” proteins in E. coli . Untargeted metabolomics LCMS E. coli BL21 (DE3) SHuffle thioredoxin tag PDGF Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The heterologous expression of recombinant proteins containing multiple disulfide bonds in Escherichia coli is a challenge[ 1 ] due to the presence of highly active reducing systems such as thioredoxin reductase (trxB) and glutathione reductase (gor) in the cytoplasm [ 2 ]. In nature, these proteins are translocated to the periplasmic compartment in gram-negative bacterial hosts such as E. coli , which provides the necessary oxidative environment for disulfide bond formation [ 3 ]. On an industrial scale, these proteins are commonly targeted to the periplasm or produced as inclusion bodies (IBs) and then refolded [ 4 ]. Various strategies have been developed to facilitate soluble expression of disulfide-rich proteins in E. coli , including the modification of cultivation conditions, use of fusion tags such as thioredoxin (Trx) and disulfide bond oxidoreductase (DsbA) and the development of engineered strains such as Origami and SHuffle that facilitate disulfide bond formation in the cytoplasm by lowering the activity of the cytoplasmic reductive pathway [ 2 , 5 – 9 ]. However, despite these advancements, achieving soluble expression of recombinant disulfide-rich proteins still involves significant trial and error. Metabolomics, a relatively new omics tool, focuses on the comprehensive analysis of intracellular and extracellular metabolites and provides valuable insights into the physiological state of E. coli cultures. Exposure of E. coli to different non-optimal stress conditions can cause unique system-wide changes in pathways that reflect specific responses [ 10 ]. Several metabolomics studies have investigated the overall changes in the E. coli metabolome caused by altered environmental conditions, nutrient availability, genetic modifications, and antibiotic treatments [ 11 , 12 ]. The induction of recombinant protein expression disrupts the cellular physiology of E. coli , redirecting several metabolic fluxes from their typical pathways to meet the high demand for protein production [ 2 , 13 ]. Therefore, analysis of metabolite trends can facilitate a deeper understanding of the physiological state and inform engineering efforts to create a favorable environment for protein production [ 14 , 15 ]. Recently, researchers have studied E. coli metabolic responses to the general stress induced by heterologous gene expression [ 16 ], and the metabolome changes associated with different protein production outcomes [ 17 ]. However, further metabolomics studies are needed to elucidate the global metabolic changes resulting from host engineering and the processing of recombinant disulfide-rich proteins, particularly in terms of their diversion into soluble and insoluble fractions. In the present study, we expressed platelet-derived growth factor-BB subunit (PDGF) as a model disulfide-rich protein in the cytoplasm of E. coli hosts BL21(DE3) and SHuffle to investigate the effects of fusion tag, induction temperature, and cellular environment on their metabolite composition. PDGF contains three intramolecular disulfide bonds in its mature functional structure and tend to aggregate as IBs when expressed in E. coli [ 18 – 20 ]. Specifically, we utilized liquid/gas chromatography coupled to mass spectrometry (LCMS and GCMS) to analyze the intracellular and extracellular metabolic profiles of recombinant E. coli strains during recombinant PDGF expression at different temperatures, the partitioning of PDGF between soluble and insoluble fraction, and the variations in nutrient utilization patterns. Our findings highlight the potential of LCMS-based metabolite characterization in understanding the capacities of different host strains for protein processing and in guiding metabolic engineering for improved recombinant protein folding and expression in E. coli . 2. Materials and Methods 2.1 Plasmid constructs Enzymes and kits for molecular biology were purchased from New England Biolabs (Ipswich, MA, USA) and GeneAll (Seoul, Korea). The codon-optimized PDGF gene was synthesized in our previous study [ 18 ]. The PDGF gene was cloned with different tags in pET vectors, including pET28a (His-tagged PDGF), pET39b (DsbA-tagged PDGF), and pET32b (Trx-tagged PDGF). PDGF without any tag was cloned in the pET39b vector at Nde I- Bam HI. Details of the cloning strategy are described in Supplementary Fig. S1 . 2.2 Cultivation of recombinant strains for PDGF production All the recombinant plasmids carrying tagged or untagged PDGF were transformed into E. coli BL21 (DE3) (Novagen Inc, Madison, WI, USA) and SHuffle T7 (New England Biolabs) strains. A single colony was picked from the freshly transformed agar plate and inoculated into Luria-Bertani (LB) broth (HiMedia, Mumbai, India) supplemented with the appropriate antibiotic. This seed culture grown overnight at 37°C was used to inoculate 100 mL of LB (initial OD 600 = 0.05). After culturing at 32°C with 180 rpm shaking until OD 600 reached 0.6–0.8, protein expression was induced with 0.5 mM IPTG and further incubated at 37°C for 4 h, 30°C for 8 h, or at 16°C for 20 h. Cells were harvested by centrifugation at 10,000 rpm for 15 min at 4°C, and the pellets were processed as described previously [ 18 ] to yield soluble and insoluble fractions. For performing LCMS analysis to study the temperature-induced metabolic alterations associated with PDGF expression, the cultures were grown at 37°C (higher) and 16°C (lower) post-induction. 2.3 Extraction of metabolites Intracellular metabolites were extracted in the aqueous phase using an improved biphasic method developed by our group [ 21 ]. Briefly, mid-exponential phase cultures (OD 600 0.6–0.8) were induced with 0.5 mM IPTG and harvested post 1 h and 6 h for 37°C and 16°C cultures, respectively. Samples for LCMS analysis were prepared by filtering the culture through a 0.8 µm nylon membrane followed by washing the filter paper with 50mM ammonium bicarbonate, then quenching the cells with an 80:20 methanol: water mixture at room temperature, and lysing the cells in the solvent mixture at -80°C. The resulting cell-solvent mixture was vortexed, and 0.2 M ammonium hydroxide was added to facilitate phase separation. The top aqueous-rich layer was collected, dried under vacuum, and stored at -80°C. Prior to LCMS analysis, the sample was reconstituted in 100 µL of 50/50 acetonitrile-water mixture and filtered (0.2 µm) before injection. To analyze the extracellular metabolites, culture samples were obtained at 0 h (immediately after inoculation) and 1 h and 6 h post-induction, as described above. An uninoculated LB medium was used as a control. The culture supernatant (200 µL) was mixed with 600 µL of 100% methanol and vigorously vortexed at room temperature for 30 min. The resulting mixture was centrifuged, and 200 µL of the supernatant was collected, dried, and stored at -80°C for subsequent analysis. 2.4 LCMS data acquisition and analysis LCMS analysis was performed on a Triple TOF 5600 + mass spectrophotometer (SCIEX, Framingham, MA, USA) coupled with a UHPLC system (Shimadzu, Nexera LC-30 AD, Singapore). Reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC) were used for the analysis based on their ability to separate and identify different classes of compounds. Detailed LCMS methods are given in Supplementary Information. This study utilized three biological replicates unless stated otherwise. Data pre-processing was performed using the in-house tool MetAnalyzer (freely available at https://msone.claritybiosystems.com/ ) and MS-DIAL [ 22 ]. Metabolic features were defined as ions with unique m/z and retention time, and putative matches were identified using freely available databases. After pre-processing of the data, manual peak curation was performed for all the annotated metabolic features using MetAnalyzer. Data normalization was performed with a QC regression line for each batch separately (with 3 QC injections) before statistical analysis with MetaStat ( https://msone.claritybiosystems.com/ ). Normalized area ratios were used for all statistical analyses. Features with p-value < 0.05 and a fold change threshold of 1.5 were deemed to be significantly different between conditions. 2.5 GCMS data acquisition and analysis For GCMS analysis, the extracellular samples were first derivatized with MOX-Pyridine-MSTFA as described previously[ 23 ]. The dried samples were mixed with norvaline as an internal standard during derivatization. The derivatized samples were injected on a GCMS TQ8040 Triple Quadrupole (Shimadzu, Kyoto, Japan) fitted with a DB-5 column (15 m length, 0.25 µm inner diameter, 0.25 µm film thickness) (Agilent Technologies, Santa Clara, CA, USA). The methods for both GC and MS were the same as previously described [ 23 ]. Two biological replicates were used for all conditions to examine the extracellular profile. LabSolutions CS software (GCMS Solution version 4.4.2, Shimadzu) was used for data pre-processing, followed by targeted analysis of 80 compounds previously established using pure standards. Norleucine was used as an internal standard for relative quantification of amino acids among different conditions. 3. Results We first performed comprehensive intracellular metabolomic analysis of E. coli host strains BL21 (DE3) and SHuffle to compare their cellular environments and the metabolic response to conditions that favor the expression of heterologous disulfide-rich proteins. We investigated the following strains and conditions: (i) The BL21(DE3) and SHuffle host strains at 16°C and 37°C, (ii) these two strains expressing PDGF with a Trx tag at 16°C and 37°C and without a Trx tag at 16°C, (iii) the two strains expressing just a Trx tag at 16°C. A total of 8324 m/z (mass to charge ratio) features were detected using HILIC, with 1337 putatively annotated in E. coli , while RP chromatography detected 9393 m/z features, with 447 putatively annotated. Applying one-way ANOVA, we identified 284 significant features, with 156 showing significantly different abundances (1.5-fold threshold) between BL21 and SHuffle at 16°C, and 126 features at 37°C. For further comparison, 121 metabolites with good peak quality and MS/MS matching were selected (Fig. 1 A). 3.1 Metabolomic differences between E. coli host strains BL21 (DE3) and SHuffle Principal component analysis (PCA) highlighted more distinct metabolic differences between the two strains at 16°C than at 37°C (Fig. 1 B), indicating an interplay between temperature stress and strain-specific responses. The SHuffle strain exhibited higher levels of amino acids, dipeptides, and Tri-Carboxylic Acid (TCA) cycle-derived amino acids at both temperatures, while BL21 accumulated TCA cycle intermediates (Fig. 1 C-F). Additionally, SHuffle displayed an increased abundance of Pentose Phosphate Pathway (PPP) derived amino acids and intermediates, suggesting higher flux toward PPP. Glycolytic intermediates varied, with 3-phosphoglycerate upregulated in BL21, while pyruvate and acetyl Co-A were significantly upregulated in SHuffle (Fig. 1 F). Furthermore, SHuffle showed a significant upregulation of guanine nucleotides, indicating differential nucleotide metabolism regulation (Fig. 1 D). Meanwhile, BL21 exhibited upregulation of coenzymes NAD + and FAD, suggesting a more balanced redox environment and optimal growth conditions. SHuffle, with mutations in trxB and gor, showed increased oxidative stress, impacting amino acid levels. Elevated levels of asparagine, arginine, and leucine, known to accumulate under oxidative stress, were observed. SHuffle also exhibited higher levels of oxidized glutathione, proline, and other oxidative stress indicators compared to BL21 at both temperatures (Fig. 1 E). 3.2 Effect of thioredoxin tag on host strains We examined the effect of expressing the TrxA fusion tag on the metabolome profiles of BL21 and SHuffle strains. TrxA is a 11.6 kDa protein from E. coli , known for high solubility and thermal stability, which may be conferred to TrxA fusion proteins to facilitate their soluble expression. The TrxA tag has also been shown to facilitate disulfide bond formation in a reducing background [ 24 – 26 ]. In BL21 without the Trx tag, we observed that the intracellular metabolome remained consistent regardless of the growth temperature (Fig. S2). However, when the Trx tag was expressed in BL21 (DE3) (Trx-BL21) at 16°C, its metabolite pattern aligned with that of the host and Trx-expressing SHuffle strain (Fig. S2), indicating similarities in intracellular environments. Specifically, redox metabolites, including GSH, GSSG, and ophthalmic acid, as well as amino acids originating from glycolysis (histidine and isoleucine) and TCA cycle (asparagine and glutamic acid), were upregulated in Trx-BL21 achieving levels similar to those in the host and Trx tag-expressing SHuffle strains (Fig. S2). 3.3 Effect of trxA tag and lower temperature on soluble PDGF production PDGF tends to aggregate as misfolded inclusion bodies when expressed in the cytoplasm of E. coli BL21 at 37°C and requires refolding [ 19 ]. In this study, PDGF cloned without any tag, or with His-tag or DsbA tags[ 27 ] formed IBs in BL21, regardless of the post-induction temperature (Table 1 ). However, fusion with the Trx tag rendered partial soluble expression of PDGF in BL21 at 30°C and 37°C, with a larger soluble fraction achieved at 16°C (Fig. 2 A). Similar PDGF expression patterns were obtained in SHuffle as well. These results suggest that the oxidized cellular environment of SHuffle host strain alone might not be sufficient to facilitate proper folding of PDGF, and that the Trx fusion tag[ 7 ] and a low post-induction temperature may be critical to achieving soluble PDGF expression (Table 1 , Fig. 2 A). These findings are consistent with previous reports that soluble expression of disulfide bond-containing proteins is a function of the fusion tag, target protein, and the cellular redox milieu. Human growth hormone (hGH) fused with Trx expressed in a soluble form in BL21, while untagged hGH mostly formed IBs in SHuffle at 16°C, similar to PDGF [ 28 ]. On the other hand, FGF19 required both the Trx tag and Rosetta-gami or SHuffle host for soluble expression, while FGF15 only formed IBs under all conditions [ 29 ]. Table 1 Production of PDGF expressed with different fusion tags in BL21 (DE3) and Shuffle strains at 16°C and 37°C. E. coli strain Fusion tag Plasmid Soluble expression at 37°C Soluble expression at 16°C BL21 (DE3) No tag pET39 - - His pET21 - - pET28 - - DsbA pET39 - - Thioredoxin (TrxA) pET32 ≤ 10% 30–40% SHuffle T7 No tag pET39 - - DsbA pET39 - - Thioredoxin (TrxA) pET32 10–10% 30–40% The identity of PDGF was further confirmed using SDS-PAGE analysis followed by in-gel trypsin digestion and MS analysis (Fig. S3). Soluble Trx-PDGF with an N-terminal His tag was purified using Ni-affinity chromatography (Fig. 2 B) and was analyzed using dot blot with anti-PDGF antibody (Fig. 2 C). CD spectroscopy verified the presence of characteristic antiparallel β-sheets in the soluble PDGF structure which was not maintained in the denatured PDGF IBs (Fig. 2 D). 3.4 Metabolic profiling of E. coli strains producing soluble and insoluble PDGF The presence of Trx tag and incubation at 16°C were crucial for obtaining soluble PDGF in both strains. To understand their effects on the host metabolome, we examined the metabolic signatures of the recombinant BL21 and SHuffle strains producing soluble or insoluble PDGF. PCA revealed a clear separation in metabolite profiles between the strains expressing PDGF with and without the Trx tag, responsible for the production of soluble and insoluble PDGF, respectively, showed distinct metabolome profiles, independent of growth temperature (Fig. 3 ). These results paved the way for further detailed metabolic investigations to characterize the metabolic burden on host metabolism resulting from soluble PDGF protein production in both E. coli strains. 3.4.1 Metabolic stress indicators The diversion of metabolites and energy for recombinant protein production is known to elicit a cellular stress response (CSR) [ 30 ], that serves as a protective mechanism against excessive resource allocation, vital for cell survival [ 31 ]. To gather information on the metabolic load elicited by soluble PDGF production we performed intra- and extracellular metabolite profiling to characterize the differential regulation of oxidative stress markers and osmoprotectants between the recombinant E. coli BL21 (DE3) and SHuffle strains expressing soluble Trx-tagged PDGF. The SHuffle strain has been reported to be under oxidative stress [ 32 ], which is further exacerbated by the addition of the Trx tag, resulting in increased levels of oxidized glutathione (Fig. S2). Moreover, SHuffle showed significantly elevated levels of glutamate and stress-related polyamines such as spermidine and n-acetyl spermidine derived from glutamate. While BL21(DE3) exhibited high intracellular lysine levels, SHuffle accumulated stress markers originating from the lysine degradation pathway, such as spermidine, acetyl spermidine, and L-Saccharopine (Fig. 4 A), as well as osmoprotectants such as isomaltulose and L-pipecolic acid. Elevated intracellular cAMP and GMP levels in SHuffle suggested a general stress response related to energy storage, with cAMP-related proteins associated with oxidative and general stress responses [ 33 ]. Nevertheless, the two strains showed a similar extracellular abundance of these stress markers. (Fig. 4 B). In summary, despite Trx-PDGF being expressed in soluble form in both strains, SHuffle exhibited elevated stress markers and osmoregulators, indicating increased oxidative stress and potential damage to cellular components associated with PDGF expression. Combining the learnings from our metabolomics and protein expression analyses on the host and PDGF-expressing strains, we concluded that SHuffle may not be optimal for producing soluble PDGF. Based on these findings, we selected BL21 (DE3) as the expression host to investigate factors affecting PDGF solubility when expressed in E. coli. 3.4.2 General metabolomic response Out of 154 identified metabolites, 79 showed significant differences between the two conditions, including peptides, nucleotides, and central pathway metabolites (Fig. 5 ). To exemplify, soluble PDGF expression was associated with lower levels of PP pathway metabolites and upregulation of glycolytic and TCA cycle intermediates. compared to IB expression. Notably, soluble PDGF expression led to elevated levels of di- and tri-peptides, particularly those containing glutamate (Fig. 5 ). While the role of peptides in E. coli metabolism is less explored, they have been reported to act as transient reservoirs of amino acids in cyanobacteria [ 34 , 35 ]. γ-Glutamyl peptides have also been shown to accumulate in cells under osmotic stress [ 36 ]. Besides glutamate, a few proline-containing peptides, such as leu-Pro, Ile-pro-Ile, and Pro-Arg, were also elevated under soluble PDGF expression. Furthermore, soluble PDGF expression was linked with increased intracellular pyruvate levels and reduced secretion of amino acids derived from pyruvate. This suggests that these amino acids may be converted into peptides within the cell, serving as a reservoir of carbon and nitrogen, rather than being utilized as free amino acids. Soluble PDGF expression was also associated with elevated levels of several nucleotides, which are critical for cellular adaptation to environmental and growth challenges. Recent studies have highlighted the impact of intracellular nucleotide pools on RNA polymerase activity, tRNA synthesis, mRNA translation, and ribosome biogenesis [ 37 ]. Among the 19 nucleotide metabolism intermediates identified in the present study, most of the mono, di-, and triphosphates were upregulated under soluble PDGF production, indicating a high turnover in nucleotide metabolism, whereas nucleosides were accumulated when PDGF was produced as IBs. Quantifying GTP (a ppGpp precursor), ATP, and ADP may reveal changes in energy metabolism linked to soluble protein expression. 3.4.3 Extracellular profiling Extracellular profiling investigates metabolites that are consumed or secreted by the cells, providing insights into substrate uptake patterns and the metabolic state of the growing culture, respectively [ 23 , 38 ]. Figure 6 shows the amino acid consumption and secretion profiles for the PDGF-expressing and host strains and the initial LB medium. Serine and aspartate were prominently consumed, which have been shown to be preferred for exponential growth, with aspartate functioning as an important nitrogen source [ 39 ]. Further, several free amino acids were secreted into the medium upon induction with IPTG, like leucine, valine, isoleucine, alanine and tyrosine, implying a high flux towards their synthesis. Discussion Recombinant protein production exerts a significant strain on the E. coli metabolism, necessitating the redirection of several metabolic pathways. Previous studies investigating the metabolic adaptation of E. coli to recombinant protein production primarily focused on characterizing the overall metabolic burden exerted on the host strain[ 40 , 41 ]. Omics-guided efforts, including the application of external NaCl stress [ 42 ] or specific gene knockouts [ 43 ] have yielded some success in improving the soluble expression of different proteins. However, a comprehensive understanding of metabolic differences between soluble and insoluble protein expression remains lacking. In addition, a systematic understanding of how various protein expression strategies, such as the use of a fusion tag or an engineered host strain (SHuffle) modulate the cytoplasmic expression of a disulfide-rich protein is required. In the present study, using PDGF as a model protein, we investigated the global effects of soluble protein expression strategies on the metabolome of E. coli BL21 (DE3) and SHuffle strains. Our goal was to uncover the global metabolic consequences of strain mutations associated with recombinant protein expression, which can then be addressed via metabolic engineering or nutrient supplementation. Metabolic profiles are generally more stress-specific than changes in gene expression, as the metabolome reacts faster and in a more targeted manner to stress conditions [ 44 ]. Studies have shown that E. coli responds to oxidative stress by altering key metabolic pathways, including lipid, nucleotide, amino acid, and carbohydrate metabolism[ 45 , 46 ]. In this study, we observed higher oxidative stress in the SHuffle strain, which exhibited significant metabolomic changes compared to BL21 (DE3). Specifically, SHuffle displayed elevated levels of amino acids, dipeptides, and TCA cycle-derived amino acids at both temperatures, while BL21 accumulated more TCA intermediates (Fig. 1 C-F). Additionally, SHuffle had increased levels of Pentose Phosphate Pathway (PPP) intermediates and guanine nucleotides, indicating altered nucleotide metabolism and greater flux toward the PPP (Fig. 1 D). In contrast, BL21 upregulated NAD + and FAD, suggesting a more balanced redox environment. SHuffle, with mutations in trxB and gor, showed higher oxidative stress, which impacted amino acid levels, including proline, asparagine, arginine, glutamate, methionine, and leucine, all of which are known to accumulate under oxidative stress (Fig. 1 E). SHuffle also exhibited higher levels of oxidative stress markers like oxidized glutathione and proline, while glycolytic intermediates varied between the strains, with pyruvate and acetyl Co-A significantly upregulated in SHuffle (Fig. 1 F). Several of these amino acids and PPP intermediates are general responders to oxidative stress [ 47 , 48 ]. Glutamate plays a crucial role in stress-related biosynthesis, while methionine is vital for mitigating oxidative stress by modulating the oxidative branch of the PPP [ 49 ]. Enhancing SHuffle’s performance by mitigating oxidative stress, possibly through PPP enzyme modulation or antioxidant supplementation, could further improve its capabilities. Elevated amino acid and mononucleotide levels in the host SHuffle strain compared to BL21 suggested that the genetic manipulations targeting redox pathways effect a more global metabolic response. Increased accumulation of proline and arginine and PPP intermediates implicated an interplay between oxidative stress, amino acid metabolism, and cellular redox homeostasis. Mitigation of oxidative stress, potentially involving modulation of PPP enzymes or antioxidant supplementation, can be used to enhance performance of the SHuffle strain. Our investigation demonstrated that successful soluble expression of PDGF in E. coli requires the combination of Trx fusion tag and low post-induction temperature, even in the presence of an oxidizing environment (as in SHuffle). The effects of thioredoxin tag at the metabolome levels showed similarity between BL21 expressing the tag and SHuffle strain, with or without the tag, mainly metabolites responsible for redox balance and amino acids. This indicates the effect of thioredoxin tag, on metabolome level is similar to the mutations in SHuffle. On cultivating the strains to high cell density, the final biomass concentration of the SHuffle strain expressing PDGF was 1.3-fold lower than that of recombinant BL21 (DE3), despite similar initial DO profiles. Metabolomics analysis revealed differential regulation of stress markers and osmoprotectants in SHuffle expressing soluble Trx-tagged PDGF, indicating increased oxidative stress associated with PDGF expression in the SHuffle strain. Despite the inherently more oxidizing environment of the SHuffle strain, our findings demonstrate that incorporation of the Trx tag is critical for soluble PDGF expression. Combining the insights from growth and metabolomics data, we recommend BL21 as the host system for potential large-scale PDGF production. It is important to note though that the downstream removal of the tag remains a limitation with this strategy. Next, our investigation into the metabolomes associated with soluble versus insoluble expression of PDGF in E. coli BL21 (DE3) highlighted significant differences in intracellular and extracellular metabolite profiles. Soluble PDGF expression is associated with altered levels of glycolytic and TCA cycle intermediates, elevated di- and tri-peptide levels, and increased nucleotide turnover. Notably, this mode of expression fosters a potential strategy for carbon and nitrogen storage through peptide formation. Furthermore, extracellular profiling reveals amino acid consumption and secretion patterns, shedding light on the preferred substrates for protein biosynthesis and growth. Serine and aspartate were observed to be consumed in the exponential phase, consistent with previous reports on amino acid uptake during E. coli growth [ 50 ]. Since aspartate serves as an important nitrogen source, supplementing aspartate and providing other nitrogen source such as glutamine and asparagine could help improve protein yield. Glutamine will not only serve as a nitrogen source but also a sensor for cell to not trigger the nitrogen limiting stress conditions [ 51 , 52 ]. The secretion of branched-chain amino acids (BCAAs) such as leucine, valine, and isoleucine suggest a flux towards their synthesis. Overexpressing regulatory proteins that prevent excessive synthesis of BCAAs could increase carbon flux towards central metabolic pathways, potentially enhancing overall protein synthesis [ 53 ]. Additionally, deleting or downregulating enzymes responsible for their export or synthesis might help maintain intracellular levels of these amino acids, promoting protein production. The secretion of amino acids like alanine and tyrosine could be a sign of metabolic burden caused by overproduction of certain metabolites. Reducing this burden, either by modulating the expression of key metabolic genes could lead to enhanced metabolic efficiency, translating to increased protein yields. Since serine is consumed at high levels, it might also be limiting for protein production, therefore, increasing the availability of serine in the medium could improve intracellular serine levels, boosting protein biosynthesis. Further investigations into temporal amino acid consumption profiles hold promise for refining dynamic external supplementation strategies to further enhance PDGF production and cell growth [ 23 ]. Overall, this study provides insights into the metabolic consequences of growth temperature, thioredoxin tag addition, and strain mutations associated with recombinant protein production. The comprehensive intracellular untargeted metabolomic analysis of BL21 (DE3) and SHuffle strains at different temperatures revealed distinct cellular environments. The findings highlight the role of TrxA fusion tag and growth temperature in modulating the cellular metabolism of E. coli and facilitating soluble expression of recombinant PDGF. Understanding these metabolic changes and their underlying mechanisms will pave the way for more efficient protein production and resource utilization in E. coli -based biotechnological applications. Further studies integrating metabolomics with other omics approaches will be required to enhance our understanding and provide more tailored strategies for optimized recombinant protein production in E. coli . Declarations Acknowledgments and Funding This work was partially supported by the Department of Biotechnology (DBT) under DBT‐Pan IIT Centre for Bioenergy Phase 2 (BT/PR41982/PBD/26/822/2021). The authors would like to thank Avinash Sunder for useful suggestions. SDG acknowledges the award of INSPIRE Fellowship by the Department of Science and Technology (DST), Government of India. MS acknowledges the Department of Biotechnology, Government of India for Ph.D. fellowship. Competing Interests PPW holds equity inClarity Bio Systems India Pvt. Ltd. All other authors have no relevant competing interests to declare. Author contributions SDG, MS, and PPW conceptualized the research and designed the experiments. SDG and MS performed the research. PN and VM helped in data acquisition and analysis. SDG, MS, and PPW analyzed the data and wrote the manuscript. All authors read and approved the final manuscript. PPW supervised the research and acquired the funding. Ethics Approval Not applicable Consent to Participate Not applicable Consent to Publish Not applicable Availability of data and materials All data supporting the findings of this study are available within the paper and its Supplementary Information. References Ma Y, Lee CJ, Park JS (2020) Strategies for Optimizing the Production of Proteins and Peptides with Multiple Disulfide Bonds. 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Sci Rep 4:1–6. https://doi.org/10.1038/srep04500 Chung WJ, Huang CL, Gong HY, et al (2015) Recombinant production of biologically active giant grouper (Epinephelus lanceolatus) growth hormone from inclusion bodies of Escherichia coli by fed-batch culture. Protein Expr Purif 110:79–88. https://doi.org/10.1016/J.PEP.2015.02.012 Kong B, Guo GL (2014) Soluble Expression of Disulfide Bond Containing Proteins FGF15 and FGF19 in the Cytoplasm of Escherichia coli. PLoS One 9:e85890. https://doi.org/10.1371/JOURNAL.PONE.0085890 Steinchen W, Zegarra V, Bange G (2020) (p)ppGpp: Magic Modulators of Bacterial Physiology and Metabolism. Front Microbiol 11:575222. https://doi.org/10.3389/FMICB.2020.02072/BIBTEX Spira B, Ospino K (2020) Diversity in E. coli (p)ppGpp Levels and Its Consequences. Front Microbiol 11:564096. https://doi.org/10.3389/FMICB.2020.01759/BIBTEX Lobstein J, Emrich CA, Jeans C, et al (2012) SHuffle, a novel Escherichia coli protein expression strain capable of correctly folding disulfide bonded proteins in its cytoplasm. Microb Cell Fact 11:1–16. https://doi.org/10.1186/1475-2859-11-56/TABLES/3 Barth E, Gora K V., Gebendorfer KM, et al (2009) Interplay of cellular cAMP levels, σS activity and oxidative stress resistance in Escherichia coli. Microbiology (N Y) 155:1680. https://doi.org/10.1099/MIC.0.026021-0 Jaiswal D, Wangikar PP (2020) Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle. https://doi.org/10.1016/j.isci.2020.101704 Jaiswal D, Nenwani M, Mishra V, Wangikar PP (2022) Probing the metabolism of γ-glutamyl peptides in cyanobacteria via metabolite profiling and 13C labeling. The Plant Journal 109:708–726. https://doi.org/10.1111/TPJ.15564 McLaggan D, Logan TM, Lynn DG, Epstein W (1990) Involvement of γ-glutamyl peptides in osmoadaptation of Escherichia coli. J Bacteriol 172:3631–3636. https://doi.org/10.1128/JB.172.7.3631-3636.1990 Leiva LE, Zegarra V, Bange G, Ibba M (2023) At the Crossroad of Nucleotide Dynamics and Protein Synthesis in Bacteria. Microbiology and Molecular Biology Reviews. https://doi.org/10.1128/MMBR.00044-22 Pinu FR, Granucci N, Daniell J, et al (2018) Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test. Metabolomics 14:. https://doi.org/10.1007/S11306-018-1339-7 Schubert C, Zedler S, Strecker A, Unden G (2021) L-Aspartate as a high-quality nitrogen source in Escherichia coli: Regulation of L-aspartase by the nitrogen regulatory system and interaction of L-aspartase with GlnB. Mol Microbiol 115:526–538. https://doi.org/10.1111/MMI.14620 Chae YK, Kim SH, Markley JL (2017) Relationship between recombinant protein expression and host metabolome as determined by two-dimensional NMR spectroscopy. PLoS One 12:e0177233. https://doi.org/10.1371/JOURNAL.PONE.0177233 Nadeem MS, Razeeth M, Choudhry HMZ, et al (2020) LC-MS/MS-based metabolic profiling of Escherichia coli under heterologous gene expression stress. J Cell Biochem 121:125–134. https://doi.org/10.1002/JCB.28962 Chae YK, Kim SH, Um Y (2019) Relationship between Protein Expression Pattern and Host Metabolome Perturbation as Monitored by Two-Dimensional NMR Spectroscopy. Bull Korean Chem Soc 40:634–641. https://doi.org/10.1002/BKCS.11743 Zhou L, Ma Y, Wang K, et al (2023) Omics-guided bacterial engineering of Escherichia coli ER2566 for recombinant protein expression. Appl Microbiol Biotechnol 107:853–865. https://doi.org/10.1007/S00253-022-12339-6/FIGURES/7 Jozefczuk S, Klie S, Catchpole G, et al (2010) Metabolomic and transcriptomic stress response of Escherichia coli. Mol Syst Biol 6:. https://doi.org/10.1038/MSB.2010.18/SUPPL_FILE/MSB201018-SUP-0001-SOURCEDATA-S1.TXT Bhatia SS, Pillai SD (2019) A comparative analysis of the metabolomic response of electron beam inactivated E. Coli O26:H11 and Salmonella Typhimurium ATCC 13311. Front Microbiol 10:414162. https://doi.org/10.3389/FMICB.2019.00694/BIBTEX Tian X, Yu Q, Yao D, et al (2018) New insights into the response of metabolome of Escherichia coli O157:H7 to ohmic heating. Front Microbiol 9:419797. https://doi.org/10.3389/FMICB.2018.02936/BIBTEX Christodoulou D, Link H, Fuhrer T, et al (2018) Reserve Flux Capacity in the Pentose Phosphate Pathway Enables Escherichia coli’s Rapid Response to Oxidative Stress. Cell Syst 6:569-578.e7. https://doi.org/10.1016/J.CELS.2018.04.009/ATTACHMENT/E2CAAF68-2482-4E9C-80E4-CBE5F3C5854C/MMC6.PDF Pan Y, Cheng JH, Sun DW (2021) Metabolomic analyses on microbial primary and secondary oxidative stress responses. Compr Rev Food Sci Food Saf 20:5675–5697. https://doi.org/10.1111/1541-4337.12835 Campbell K, Vowinckel J, Keller MA, Ralser M (2016) Methionine Metabolism Alters Oxidative Stress Resistance via the Pentose Phosphate Pathway. Antioxid Redox Signal 24:543–547. https://doi.org/10.1089/ARS.2015.6516/ASSET/IMAGES/LARGE/FIGURE1.JPEG Maser A, Peebo K, Vilu R, Nahku R (2020) Amino acids are key substrates to Escherichia coli BW25113 for achieving high specific growth rate. Res Microbiol 171:185–193. https://doi.org/10.1016/J.RESMIC.2020.02.001 Reitzer L (2003) Nitrogen Assimilation and Global Regulation in Escherichia coli. Annu Rev Microbiol 57:155–176. https://doi.org/10.1146/ANNUREV.MICRO.57.030502.090820/CITE/REFWORKS Wang J, Yan D, Dixon R, Wang YP (2016) Deciphering the Principles of Bacterial Nitrogen Dietary Preferences: a Strategy for Nutrient Containment. mBio 7:e00792-16. https://doi.org/10.1128/MBIO.00792-16 Marreddy RKR, Geertsma ER, Permentier HP, et al (2010) Amino Acid Accumulation Limits the Overexpression of Proteins in Lactococcus lactis. PLoS One 5:e10317. https://doi.org/10.1371/JOURNAL.PONE.0010317 Supplementary Files PDGFmanuscriptSupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2025 Read the published version in Applied Biochemistry and Biotechnology → Version 1 posted Reviewers agreed at journal 10 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor invited by journal 17 Apr, 2025 First submitted to journal 16 Apr, 2025 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. 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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-6382221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450459483,"identity":"7612761f-f58f-4abd-ac29-9cd1e6c0a567","order_by":0,"name":"Snehal D Ganjave","email":"","orcid":"","institution":"IITB: Indian Institute of Technology Bombay","correspondingAuthor":false,"prefix":"","firstName":"Snehal","middleName":"D","lastName":"Ganjave","suffix":""},{"id":450459484,"identity":"686aa538-b596-4300-961b-e8aa4eaf0969","order_by":1,"name":"Meghna Srivastava","email":"","orcid":"","institution":"IITB: Indian Institute of Technology Bombay","correspondingAuthor":false,"prefix":"","firstName":"Meghna","middleName":"","lastName":"Srivastava","suffix":""},{"id":450459485,"identity":"33f8ddd3-980f-4a48-80ab-ec09c93e305b","order_by":2,"name":"Prajval Nakrani","email":"","orcid":"","institution":"Clarity Bio Systems India Pvt. Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Prajval","middleName":"","lastName":"Nakrani","suffix":""},{"id":450459486,"identity":"3622ce4d-7b63-418b-86bc-df4c5323088d","order_by":3,"name":"Vivek Mishra","email":"","orcid":"","institution":"Clarity Bio Systems India Pvt. Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Vivek","middleName":"","lastName":"Mishra","suffix":""},{"id":450459487,"identity":"c35524c8-81f6-472b-97d7-1c669133dd6e","order_by":4,"name":"Pramod Wangikar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3OsQuCQBTH8ScParFcc/JfeCJILfWvHARNUo6Bg4VQo6vQ/9EsCN1itDoKLQ0ORktjp0ub3hh03+kOfh94ACrVr8ZSAAO1HbY/lCVmpO0jeQKCUApaJDUmfs0eZT7fOByPdx/mFgxHaTfJ16spK5azcyYOS2Bp73DMOombei6xGsltiA7IAHXqJreqISE5UUtCCVJ4TsmKjAhbkvWTRVG5wHJOE3HYKSFuH/qIGXvO830JyIh5+fK3gWUYeTcRDSbftxgP+vYirCVGKpVK9c99AD3+Qh471Ov6AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0108-7585","institution":"Indian Institute of Technology Bombay","correspondingAuthor":true,"prefix":"","firstName":"Pramod","middleName":"","lastName":"Wangikar","suffix":""}],"badges":[],"createdAt":"2025-04-05 13:13:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6382221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6382221/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12010-025-05438-3","type":"published","date":"2025-11-06T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82061355,"identity":"f4edb9d7-622f-4ce3-b6c2-f474288b7c13","added_by":"auto","created_at":"2025-05-06 11:42:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntracellular metabolome analysis with LC–MS/MS. \u003c/strong\u003e(A) Classification of the 121 metabolites studied (out of 284 identified) in \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) and SHuffle strains using RP and HILIC chromatography paired with ESI-MS/MS. “Other metabolites” included secondary metabolic pathway intermediates and amino and nucleotide sugars. (B) Principal component analysis (PCA) two-dimensional scores plot for all annotated and significant metabolites for both strains reveals better separation in metabolic profile based on growth temperature. (C-F):\u003cstrong\u003e \u003c/strong\u003eHeat maps of differentially expressed metabolites in BL21 (DE3) and SHuffle strains\u003cstrong\u003e. \u003c/strong\u003eSamples for LC-MS analysis were drawn 1 h post-induction from 37 °C cultures and 6 h post-induction from 16 °C cultures. The map represents the area value of each metabolite normalized to that of the Quality Control (QC) samples, log2 transformed, and mean-centered for each metabolite. Warmer (red) colors indicate elevated metabolite abundance and colder (blue) colors indicate reduced abundance. The metabolites belonged to the following major categories: (C) Amino acids, (D) Peptides, (E) Nucleotides and derivatives, (F) Central pathway metabolites. Note: The abscissa and the ordinate represent the scores of PC1 and PC2, respectively. All PCA plots and heatmaps were generated with the MetaStat tool, and the data were log-transformed and normalized with QC (significance was set at p \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/b107840a0479da2e7d36d507.jpg"},{"id":82060598,"identity":"e191a4f7-632a-4b81-8d0e-0d96318f69c6","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePDGF expression in different \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eE. coli\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e strains and characterization of soluble PDGF. \u003c/strong\u003eA) Effect of temperature on soluble expression of PDGF expressed with Trx-tag in different \u003cem\u003eE. coli\u003c/em\u003e strains\u003cstrong\u003e.\u003c/strong\u003e The numbers represent temperatures (in °C). Purified soluble PDGF was used as a control. S: cell lysate, P: Pellet obtained after sonication. \u003cstrong\u003e(\u003c/strong\u003eB) Representative purification of Trx-tagged PDGF expressed in BL21 (DE3); M: Protein marker, S: supernatant containing soluble PDGF, P: Pellet containing PDGF IBs, E1-E5: Elution fractions containing purified PDGF. (C) Dot blot showing confirmation of soluble expression of PDGF using a PDGF-specific antibody\u003cstrong\u003e. (\u003c/strong\u003eD)\u003cstrong\u003e \u003c/strong\u003eSecondary structure analysis of denatured PDGF and purified soluble PDGF obtained from CD spectroscopy at wavelengths of 190–260 nm\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/295931fcf2fe031f75262a66.jpg"},{"id":82060593,"identity":"1ac4dcac-2ee8-4a7a-8b0a-4951a5a65623","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component analysis (PCA) two-dimensional scores plot reveals the effect of thioredoxin tag and growth temperature on the metabolism of recombinant strains expressing PDGF. \u003c/strong\u003eThe PCA plot depicts variation in the intracellular metabolic profile, obtained by LC-MS/MS, of thioredoxin-tagged and untagged PDGF expressed in BL21 (DE3) and SHuffle strains grown at 16 °C and 37 °C. Note: The abscissa and the ordinate represent the scores of PC1 and PC2, respectively. PCA plot was generated with Metaexplorer software (available freely at msone.claritybiosystems.com), and the data was log-transformed and normalized with a QC sample. Statistical significance was determined at p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/873173e8caa3f85f4e6ed0b5.jpg"},{"id":82060597,"identity":"d5c5145f-c6a2-4ff0-9073-626edb3d9b78","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed metabolites under RPP stress at 16 °C. \u003c/strong\u003e(A) Intracellular metabolome profile, (B) Extracellular metabolome profile. Red bars represent differentially expressed metabolites that are upregulated in BL21 (DE3); Blue bars represent metabolites that are upregulated in SHuffle. The figure represents the ratio of the normalized area values of each metabolite identified in recombinant Shuffle strain to that of BL21 (DE3) producing soluble PDGF. The error bars indicate the standard error of the mean calculated for two biological replicates\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/6193a831e492bcae0018e4a1.jpg"},{"id":82060600,"identity":"a5b89af6-cf85-4772-bee3-3c0167b2d530","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential regulation of intracellular metabolites of the BL21 (DE3) strain producing soluble (with TrxA) and insoluble (without TrxA) PDGF at 16 °C. \u003c/strong\u003e(A) Peptides, (B) Central pathway metabolites, (C) Nucleotides and derivatives. Metabolites with a ratio \u0026gt;1 were considered to be upregulated in BL21 (DE3) expressing Trx-tagged PDGF, while metabolites with a ratio \u0026lt;1 were upregulated in BL21 (DE3) expressing untagged PDGF. The error bars indicate the standard error of the mean calculated for two biological replicates.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/8a1612765ae62cd916c702a7.jpg"},{"id":82060595,"identity":"31b31c7a-9f33-4b7b-a783-13c01cbb7a18","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential regulation of amino acids in extracellular metabolite pool of the host strain and recombinant BL21 (DE3). \u003c/strong\u003eThe extracellular metabolite profile was studied using a triple quadrupole GC-MS/MS. The initial inoculation media was used as a reference to study the secretion and consumption of various amino acids. The area values are normalized with norvaline as an internal standard, added prior to derivatization. The error bars indicate the standard error of mean calculated for two biological replicates\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/6005504465237aef7d5258e5.jpg"},{"id":95564103,"identity":"b22c8dc2-e224-4e61-9872-f5785eb5d55e","added_by":"auto","created_at":"2025-11-10 16:07:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1416770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/9412985f-de44-4ac0-b871-6bfd1f5a5fa1.pdf"},{"id":82060615,"identity":"f7534054-4621-4139-ac37-ed96e7f3fa31","added_by":"auto","created_at":"2025-05-06 11:34:09","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":638197,"visible":true,"origin":"","legend":"","description":"","filename":"PDGFmanuscriptSupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6382221/v1/006f447a9b760a63fc3bef3f.docx"}],"financialInterests":"","formattedTitle":"Effect of thioredoxin tag, oxidizing environment, and temperature on the global metabolome of E. coli strains","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe heterologous expression of recombinant proteins containing multiple disulfide bonds in \u003cem\u003eEscherichia coli\u003c/em\u003e is a challenge[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] due to the presence of highly active reducing systems such as thioredoxin reductase (trxB) and glutathione reductase (gor) in the cytoplasm [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In nature, these proteins are translocated to the periplasmic compartment in gram-negative bacterial hosts such as \u003cem\u003eE. coli\u003c/em\u003e, which provides the necessary oxidative environment for disulfide bond formation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. On an industrial scale, these proteins are commonly targeted to the periplasm or produced as inclusion bodies (IBs) and then refolded [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Various strategies have been developed to facilitate soluble expression of disulfide-rich proteins in \u003cem\u003eE. coli\u003c/em\u003e, including the modification of cultivation conditions, use of fusion tags such as thioredoxin (Trx) and disulfide bond oxidoreductase (DsbA) and the development of engineered strains such as Origami and SHuffle that facilitate disulfide bond formation in the cytoplasm by lowering the activity of the cytoplasmic reductive pathway [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, despite these advancements, achieving soluble expression of recombinant disulfide-rich proteins still involves significant trial and error.\u003c/p\u003e \u003cp\u003eMetabolomics, a relatively new omics tool, focuses on the comprehensive analysis of intracellular and extracellular metabolites and provides valuable insights into the physiological state of \u003cem\u003eE. coli\u003c/em\u003e cultures. Exposure of \u003cem\u003eE. coli\u003c/em\u003e to different non-optimal stress conditions can cause unique system-wide changes in pathways that reflect specific responses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Several metabolomics studies have investigated the overall changes in the \u003cem\u003eE. coli\u003c/em\u003e metabolome caused by altered environmental conditions, nutrient availability, genetic modifications, and antibiotic treatments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The induction of recombinant protein expression disrupts the cellular physiology of \u003cem\u003eE. coli\u003c/em\u003e, redirecting several metabolic fluxes from their typical pathways to meet the high demand for protein production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, analysis of metabolite trends can facilitate a deeper understanding of the physiological state and inform engineering efforts to create a favorable environment for protein production [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recently, researchers have studied \u003cem\u003eE. coli\u003c/em\u003e metabolic responses to the general stress induced by heterologous gene expression [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and the metabolome changes associated with different protein production outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, further metabolomics studies are needed to elucidate the global metabolic changes resulting from host engineering and the processing of recombinant disulfide-rich proteins, particularly in terms of their diversion into soluble and insoluble fractions.\u003c/p\u003e \u003cp\u003eIn the present study, we expressed platelet-derived growth factor-BB subunit (PDGF) as a model disulfide-rich protein in the cytoplasm of \u003cem\u003eE. coli\u003c/em\u003e hosts BL21(DE3) and SHuffle to investigate the effects of fusion tag, induction temperature, and cellular environment on their metabolite composition. PDGF contains three intramolecular disulfide bonds in its mature functional structure and tend to aggregate as IBs when expressed in \u003cem\u003eE. coli\u003c/em\u003e [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, we utilized liquid/gas chromatography coupled to mass spectrometry (LCMS and GCMS) to analyze the intracellular and extracellular metabolic profiles of recombinant \u003cem\u003eE. coli\u003c/em\u003e strains during recombinant PDGF expression at different temperatures, the partitioning of PDGF between soluble and insoluble fraction, and the variations in nutrient utilization patterns. Our findings highlight the potential of LCMS-based metabolite characterization in understanding the capacities of different host strains for protein processing and in guiding metabolic engineering for improved recombinant protein folding and expression in \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plasmid constructs\u003c/h2\u003e \u003cp\u003eEnzymes and kits for molecular biology were purchased from New England Biolabs (Ipswich, MA, USA) and GeneAll (Seoul, Korea). The codon-optimized PDGF gene was synthesized in our previous study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The PDGF gene was cloned with different tags in pET vectors, including pET28a (His-tagged PDGF), pET39b (DsbA-tagged PDGF), and pET32b (Trx-tagged PDGF). PDGF without any tag was cloned in the pET39b vector at \u003cem\u003eNde\u003c/em\u003eI-\u003cem\u003eBam\u003c/em\u003eHI. Details of the cloning strategy are described in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cultivation of recombinant strains for PDGF production\u003c/h2\u003e \u003cp\u003eAll the recombinant plasmids carrying tagged or untagged PDGF were transformed into \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) (Novagen Inc, Madison, WI, USA) and SHuffle T7 (New England Biolabs) strains. A single colony was picked from the freshly transformed agar plate and inoculated into Luria-Bertani (LB) broth (HiMedia, Mumbai, India) supplemented with the appropriate antibiotic. This seed culture grown overnight at 37\u0026deg;C was used to inoculate 100 mL of LB (initial OD\u003csub\u003e600\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.05). After culturing at 32\u0026deg;C with 180 rpm shaking until OD\u003csub\u003e600\u003c/sub\u003e reached 0.6\u0026ndash;0.8, protein expression was induced with 0.5 mM IPTG and further incubated at 37\u0026deg;C for 4 h, 30\u0026deg;C for 8 h, or at 16\u0026deg;C for 20 h. Cells were harvested by centrifugation at 10,000 rpm for 15 min at 4\u0026deg;C, and the pellets were processed as described previously [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to yield soluble and insoluble fractions. For performing LCMS analysis to study the temperature-induced metabolic alterations associated with PDGF expression, the cultures were grown at 37\u0026deg;C (higher) and 16\u0026deg;C (lower) post-induction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Extraction of metabolites\u003c/h2\u003e \u003cp\u003eIntracellular metabolites were extracted in the aqueous phase using an improved biphasic method developed by our group [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Briefly, mid-exponential phase cultures (OD\u003csub\u003e600\u003c/sub\u003e 0.6\u0026ndash;0.8) were induced with 0.5 mM IPTG and harvested post 1 h and 6 h for 37\u0026deg;C and 16\u0026deg;C cultures, respectively. Samples for LCMS analysis were prepared by filtering the culture through a 0.8 \u0026micro;m nylon membrane followed by washing the filter paper with 50mM ammonium bicarbonate, then quenching the cells with an 80:20 methanol: water mixture at room temperature, and lysing the cells in the solvent mixture at -80\u0026deg;C. The resulting cell-solvent mixture was vortexed, and 0.2 M ammonium hydroxide was added to facilitate phase separation. The top aqueous-rich layer was collected, dried under vacuum, and stored at -80\u0026deg;C. Prior to LCMS analysis, the sample was reconstituted in 100 \u0026micro;L of 50/50 acetonitrile-water mixture and filtered (0.2 \u0026micro;m) before injection.\u003c/p\u003e \u003cp\u003eTo analyze the extracellular metabolites, culture samples were obtained at 0 h (immediately after inoculation) and 1 h and 6 h post-induction, as described above. An uninoculated LB medium was used as a control. The culture supernatant (200 \u0026micro;L) was mixed with 600 \u0026micro;L of 100% methanol and vigorously vortexed at room temperature for 30 min. The resulting mixture was centrifuged, and 200 \u0026micro;L of the supernatant was collected, dried, and stored at -80\u0026deg;C for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 LCMS data acquisition and analysis\u003c/h2\u003e \u003cp\u003eLCMS analysis was performed on a Triple TOF 5600\u0026thinsp;+\u0026thinsp;mass spectrophotometer (SCIEX, Framingham, MA, USA) coupled with a UHPLC system (Shimadzu, Nexera LC-30 AD, Singapore). Reverse phase (RP) and hydrophilic interaction liquid chromatography (HILIC) were used for the analysis based on their ability to separate and identify different classes of compounds. Detailed LCMS methods are given in Supplementary Information. This study utilized three biological replicates unless stated otherwise. Data pre-processing was performed using the in-house tool MetAnalyzer (freely available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://msone.claritybiosystems.com/\u003c/span\u003e\u003cspan address=\"https://msone.claritybiosystems.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and MS-DIAL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Metabolic features were defined as ions with unique m/z and retention time, and putative matches were identified using freely available databases. After pre-processing of the data, manual peak curation was performed for all the annotated metabolic features using MetAnalyzer. Data normalization was performed with a QC regression line for each batch separately (with 3 QC injections) before statistical analysis with MetaStat (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://msone.claritybiosystems.com/\u003c/span\u003e\u003cspan address=\"https://msone.claritybiosystems.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Normalized area ratios were used for all statistical analyses. Features with p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change threshold of 1.5 were deemed to be significantly different between conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 GCMS data acquisition and analysis\u003c/h2\u003e \u003cp\u003eFor GCMS analysis, the extracellular samples were first derivatized with MOX-Pyridine-MSTFA as described previously[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The dried samples were mixed with norvaline as an internal standard during derivatization. The derivatized samples were injected on a GCMS TQ8040 Triple Quadrupole (Shimadzu, Kyoto, Japan) fitted with a DB-5 column (15 m length, 0.25 \u0026micro;m inner diameter, 0.25 \u0026micro;m film thickness) (Agilent Technologies, Santa Clara, CA, USA). The methods for both GC and MS were the same as previously described [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Two biological replicates were used for all conditions to examine the extracellular profile. LabSolutions CS software (GCMS Solution version 4.4.2, Shimadzu) was used for data pre-processing, followed by targeted analysis of 80 compounds previously established using pure standards. Norleucine was used as an internal standard for relative quantification of amino acids among different conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eWe first performed comprehensive intracellular metabolomic analysis of \u003cem\u003eE. coli\u003c/em\u003e host strains BL21 (DE3) and SHuffle to compare their cellular environments and the metabolic response to conditions that favor the expression of heterologous disulfide-rich proteins. We investigated the following strains and conditions: (i) The BL21(DE3) and SHuffle host strains at 16°C and 37°C, (ii) these two strains expressing PDGF with a Trx tag at 16°C and 37°C and without a Trx tag at 16°C, (iii) the two strains expressing just a Trx tag at 16°C. A total of 8324 m/z (mass to charge ratio) features were detected using HILIC, with 1337 putatively annotated in \u003cem\u003eE. coli\u003c/em\u003e, while RP chromatography detected 9393 m/z features, with 447 putatively annotated. Applying one-way ANOVA, we identified 284 significant features, with 156 showing significantly different abundances (1.5-fold threshold) between BL21 and SHuffle at 16°C, and 126 features at 37°C. For further comparison, 121 metabolites with good peak quality and MS/MS matching were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Metabolomic differences between \u003cem\u003eE. coli\u003c/em\u003e host strains BL21 (DE3) and SHuffle\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) highlighted more distinct metabolic differences between the two strains at 16°C than at 37°C (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating an interplay between temperature stress and strain-specific responses. The SHuffle strain exhibited higher levels of amino acids, dipeptides, and Tri-Carboxylic Acid (TCA) cycle-derived amino acids at both temperatures, while BL21 accumulated TCA cycle intermediates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F). Additionally, SHuffle displayed an increased abundance of Pentose Phosphate Pathway (PPP) derived amino acids and intermediates, suggesting higher flux toward PPP. Glycolytic intermediates varied, with 3-phosphoglycerate upregulated in BL21, while pyruvate and acetyl Co-A were significantly upregulated in SHuffle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Furthermore, SHuffle showed a significant upregulation of guanine nucleotides, indicating differential nucleotide metabolism regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Meanwhile, BL21 exhibited upregulation of coenzymes NAD + and FAD, suggesting a more balanced redox environment and optimal growth conditions. SHuffle, with mutations in trxB and gor, showed increased oxidative stress, impacting amino acid levels. Elevated levels of asparagine, arginine, and leucine, known to accumulate under oxidative stress, were observed. SHuffle also exhibited higher levels of oxidized glutathione, proline, and other oxidative stress indicators compared to BL21 at both temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effect of thioredoxin tag on host strains\u003c/h2\u003e \u003cp\u003eWe examined the effect of expressing the TrxA fusion tag on the metabolome profiles of BL21 and SHuffle strains. TrxA is a 11.6 kDa protein from \u003cem\u003eE. coli\u003c/em\u003e, known for high solubility and thermal stability, which may be conferred to TrxA fusion proteins to facilitate their soluble expression. The TrxA tag has also been shown to facilitate disulfide bond formation in a reducing background [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In BL21 without the Trx tag, we observed that the intracellular metabolome remained consistent regardless of the growth temperature (Fig. S2). However, when the Trx tag was expressed in BL21 (DE3) (Trx-BL21) at 16°C, its metabolite pattern aligned with that of the host and Trx-expressing SHuffle strain (Fig. S2), indicating similarities in intracellular environments. Specifically, redox metabolites, including GSH, GSSG, and ophthalmic acid, as well as amino acids originating from glycolysis (histidine and isoleucine) and TCA cycle (asparagine and glutamic acid), were upregulated in Trx-BL21 achieving levels similar to those in the host and Trx tag-expressing SHuffle strains (Fig. S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effect of trxA tag and lower temperature on soluble PDGF production\u003c/h2\u003e \u003cp\u003ePDGF tends to aggregate as misfolded inclusion bodies when expressed in the cytoplasm of \u003cem\u003eE. coli\u003c/em\u003e BL21 at 37°C and requires refolding [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, PDGF cloned without any tag, or with His-tag or DsbA tags[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] formed IBs in BL21, regardless of the post-induction temperature (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, fusion with the Trx tag rendered partial soluble expression of PDGF in BL21 at 30°C and 37°C, with a larger soluble fraction achieved at 16°C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similar PDGF expression patterns were obtained in SHuffle as well. These results suggest that the oxidized cellular environment of SHuffle host strain alone might not be sufficient to facilitate proper folding of PDGF, and that the Trx fusion tag[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and a low post-induction temperature may be critical to achieving soluble PDGF expression (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These findings are consistent with previous reports that soluble expression of disulfide bond-containing proteins is a function of the fusion tag, target protein, and the cellular redox milieu. Human growth hormone (hGH) fused with Trx expressed in a soluble form in BL21, while untagged hGH mostly formed IBs in SHuffle at 16°C, similar to PDGF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, FGF19 required both the Trx tag and Rosetta-gami or SHuffle host for soluble expression, while FGF15 only formed IBs under all conditions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eProduction of PDGF expressed with different fusion tags in BL21 (DE3) and Shuffle strains at 16°C and 37°C.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli strain\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFusion tag\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlasmid\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoluble expression at 37°C\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoluble expression at 16°C\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eBL21 (DE3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo tag\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDsbA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThioredoxin (TrxA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e≤ 10%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30–40%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSHuffle T7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo tag\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDsbA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThioredoxin (TrxA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epET32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10–10%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30–40%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe identity of PDGF was further confirmed using SDS-PAGE analysis followed by in-gel trypsin digestion and MS analysis (Fig. S3). Soluble Trx-PDGF with an N-terminal His tag was purified using Ni-affinity chromatography (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and was analyzed using dot blot with anti-PDGF antibody (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). CD spectroscopy verified the presence of characteristic antiparallel β-sheets in the soluble PDGF structure which was not maintained in the denatured PDGF IBs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Metabolic profiling of \u003cem\u003eE. coli\u003c/em\u003e strains producing soluble and insoluble PDGF\u003c/h2\u003e \u003cp\u003eThe presence of Trx tag and incubation at 16°C were crucial for obtaining soluble PDGF in both strains. To understand their effects on the host metabolome, we examined the metabolic signatures of the recombinant BL21 and SHuffle strains producing soluble or insoluble PDGF. PCA revealed a clear separation in metabolite profiles between the strains expressing PDGF with and without the Trx tag, responsible for the production of soluble and insoluble PDGF, respectively, showed distinct metabolome profiles, independent of growth temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results paved the way for further detailed metabolic investigations to characterize the metabolic burden on host metabolism resulting from soluble PDGF protein production in both \u003cem\u003eE. coli\u003c/em\u003e strains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Metabolic stress indicators\u003c/h2\u003e \u003cp\u003eThe diversion of metabolites and energy for recombinant protein production is known to elicit a cellular stress response (CSR) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], that serves as a protective mechanism against excessive resource allocation, vital for cell survival [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To gather information on the metabolic load elicited by soluble PDGF production we performed intra- and extracellular metabolite profiling to characterize the differential regulation of oxidative stress markers and osmoprotectants between the recombinant \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) and SHuffle strains expressing soluble Trx-tagged PDGF. The SHuffle strain has been reported to be under oxidative stress [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which is further exacerbated by the addition of the Trx tag, resulting in increased levels of oxidized glutathione (Fig. S2). Moreover, SHuffle showed significantly elevated levels of glutamate and stress-related polyamines such as spermidine and n-acetyl spermidine derived from glutamate. While BL21(DE3) exhibited high intracellular lysine levels, SHuffle accumulated stress markers originating from the lysine degradation pathway, such as spermidine, acetyl spermidine, and L-Saccharopine (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), as well as osmoprotectants such as isomaltulose and L-pipecolic acid. Elevated intracellular cAMP and GMP levels in SHuffle suggested a general stress response related to energy storage, with cAMP-related proteins associated with oxidative and general stress responses [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNevertheless, the two strains showed a similar extracellular abundance of these stress markers. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In summary, despite Trx-PDGF being expressed in soluble form in both strains, SHuffle exhibited elevated stress markers and osmoregulators, indicating increased oxidative stress and potential damage to cellular components associated with PDGF expression. Combining the learnings from our metabolomics and protein expression analyses on the host and PDGF-expressing strains, we concluded that SHuffle may not be optimal for producing soluble PDGF. Based on these findings, we selected BL21 (DE3) as the expression host to investigate factors affecting PDGF solubility when expressed in \u003cem\u003eE. coli.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 General metabolomic response\u003c/h2\u003e \u003cp\u003eOut of 154 identified metabolites, 79 showed significant differences between the two conditions, including peptides, nucleotides, and central pathway metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). To exemplify, soluble PDGF expression was associated with lower levels of PP pathway metabolites and upregulation of glycolytic and TCA cycle intermediates. compared to IB expression. Notably, soluble PDGF expression led to elevated levels of di- and tri-peptides, particularly those containing glutamate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). While the role of peptides in \u003cem\u003eE. coli\u003c/em\u003e metabolism is less explored, they have been reported to act as transient reservoirs of amino acids in cyanobacteria [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. γ-Glutamyl peptides have also been shown to accumulate in cells under osmotic stress [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Besides glutamate, a few proline-containing peptides, such as leu-Pro, Ile-pro-Ile, and Pro-Arg, were also elevated under soluble PDGF expression. Furthermore, soluble PDGF expression was linked with increased intracellular pyruvate levels and reduced secretion of amino acids derived from pyruvate. This suggests that these amino acids may be converted into peptides within the cell, serving as a reservoir of carbon and nitrogen, rather than being utilized as free amino acids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSoluble PDGF expression was also associated with elevated levels of several nucleotides, which are critical for cellular adaptation to environmental and growth challenges. Recent studies have highlighted the impact of intracellular nucleotide pools on RNA polymerase activity, tRNA synthesis, mRNA translation, and ribosome biogenesis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Among the 19 nucleotide metabolism intermediates identified in the present study, most of the mono, di-, and triphosphates were upregulated under soluble PDGF production, indicating a high turnover in nucleotide metabolism, whereas nucleosides were accumulated when PDGF was produced as IBs. Quantifying GTP (a ppGpp precursor), ATP, and ADP may reveal changes in energy metabolism linked to soluble protein expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Extracellular profiling\u003c/h2\u003e \u003cp\u003eExtracellular profiling investigates metabolites that are consumed or secreted by the cells, providing insights into substrate uptake patterns and the metabolic state of the growing culture, respectively [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the amino acid consumption and secretion profiles for the PDGF-expressing and host strains and the initial LB medium. Serine and aspartate were prominently consumed, which have been shown to be preferred for exponential growth, with aspartate functioning as an important nitrogen source [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Further, several free amino acids were secreted into the medium upon induction with IPTG, like leucine, valine, isoleucine, alanine and tyrosine, implying a high flux towards their synthesis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecombinant protein production exerts a significant strain on the \u003cem\u003eE. coli\u003c/em\u003e metabolism, necessitating the redirection of several metabolic pathways. Previous studies investigating the metabolic adaptation of \u003cem\u003eE. coli\u003c/em\u003e to recombinant protein production primarily focused on characterizing the overall metabolic burden exerted on the host strain[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Omics-guided efforts, including the application of external NaCl stress [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] or specific gene knockouts [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] have yielded some success in improving the soluble expression of different proteins. However, a comprehensive understanding of metabolic differences between soluble and insoluble protein expression remains lacking. In addition, a systematic understanding of how various protein expression strategies, such as the use of a fusion tag or an engineered host strain (SHuffle) modulate the cytoplasmic expression of a disulfide-rich protein is required. In the present study, using PDGF as a model protein, we investigated the global effects of soluble protein expression strategies on the metabolome of \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) and SHuffle strains. Our goal was to uncover the global metabolic consequences of strain mutations associated with recombinant protein expression, which can then be addressed via metabolic engineering or nutrient supplementation.\u003c/p\u003e\u003cp\u003eMetabolic profiles are generally more stress-specific than changes in gene expression, as the metabolome reacts faster and in a more targeted manner to stress conditions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Studies have shown that \u003cem\u003eE. coli\u003c/em\u003e responds to oxidative stress by altering key metabolic pathways, including lipid, nucleotide, amino acid, and carbohydrate metabolism[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this study, we observed higher oxidative stress in the SHuffle strain, which exhibited significant metabolomic changes compared to BL21 (DE3). Specifically, SHuffle displayed elevated levels of amino acids, dipeptides, and TCA cycle-derived amino acids at both temperatures, while BL21 accumulated more TCA intermediates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F). Additionally, SHuffle had increased levels of Pentose Phosphate Pathway (PPP) intermediates and guanine nucleotides, indicating altered nucleotide metabolism and greater flux toward the PPP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In contrast, BL21 upregulated NAD + and FAD, suggesting a more balanced redox environment. SHuffle, with mutations in trxB and gor, showed higher oxidative stress, which impacted amino acid levels, including proline, asparagine, arginine, glutamate, methionine, and leucine, all of which are known to accumulate under oxidative stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). SHuffle also exhibited higher levels of oxidative stress markers like oxidized glutathione and proline, while glycolytic intermediates varied between the strains, with pyruvate and acetyl Co-A significantly upregulated in SHuffle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Several of these amino acids and PPP intermediates are general responders to oxidative stress [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Glutamate plays a crucial role in stress-related biosynthesis, while methionine is vital for mitigating oxidative stress by modulating the oxidative branch of the PPP [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Enhancing SHuffle’s performance by mitigating oxidative stress, possibly through PPP enzyme modulation or antioxidant supplementation, could further improve its capabilities.\u003c/p\u003e\u003cp\u003eElevated amino acid and mononucleotide levels in the host SHuffle strain compared to BL21 suggested that the genetic manipulations targeting redox pathways effect a more global metabolic response. Increased accumulation of proline and arginine and PPP intermediates implicated an interplay between oxidative stress, amino acid metabolism, and cellular redox homeostasis. Mitigation of oxidative stress, potentially involving modulation of PPP enzymes or antioxidant supplementation, can be used to enhance performance of the SHuffle strain.\u003c/p\u003e\u003cp\u003eOur investigation demonstrated that successful soluble expression of PDGF in \u003cem\u003eE. coli\u003c/em\u003e requires the combination of Trx fusion tag and low post-induction temperature, even in the presence of an oxidizing environment (as in SHuffle). The effects of thioredoxin tag at the metabolome levels showed similarity between BL21 expressing the tag and SHuffle strain, with or without the tag, mainly metabolites responsible for redox balance and amino acids. This indicates the effect of thioredoxin tag, on metabolome level is similar to the mutations in SHuffle. On cultivating the strains to high cell density, the final biomass concentration of the SHuffle strain expressing PDGF was 1.3-fold lower than that of recombinant BL21 (DE3), despite similar initial DO profiles. Metabolomics analysis revealed differential regulation of stress markers and osmoprotectants in SHuffle expressing soluble Trx-tagged PDGF, indicating increased oxidative stress associated with PDGF expression in the SHuffle strain. Despite the inherently more oxidizing environment of the SHuffle strain, our findings demonstrate that incorporation of the Trx tag is critical for soluble PDGF expression. Combining the insights from growth and metabolomics data, we recommend BL21 as the host system for potential large-scale PDGF production. It is important to note though that the downstream removal of the tag remains a limitation with this strategy.\u003c/p\u003e\u003cp\u003eNext, our investigation into the metabolomes associated with soluble versus insoluble expression of PDGF in \u003cem\u003eE. coli\u003c/em\u003e BL21 (DE3) highlighted significant differences in intracellular and extracellular metabolite profiles. Soluble PDGF expression is associated with altered levels of glycolytic and TCA cycle intermediates, elevated di- and tri-peptide levels, and increased nucleotide turnover. Notably, this mode of expression fosters a potential strategy for carbon and nitrogen storage through peptide formation. Furthermore, extracellular profiling reveals amino acid consumption and secretion patterns, shedding light on the preferred substrates for protein biosynthesis and growth. Serine and aspartate were observed to be consumed in the exponential phase, consistent with previous reports on amino acid uptake during \u003cem\u003eE. coli\u003c/em\u003e growth [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Since aspartate serves as an important nitrogen source, supplementing aspartate and providing other nitrogen source such as glutamine and asparagine could help improve protein yield. Glutamine will not only serve as a nitrogen source but also a sensor for cell to not trigger the nitrogen limiting stress conditions [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The secretion of branched-chain amino acids (BCAAs) such as leucine, valine, and isoleucine suggest a flux towards their synthesis. Overexpressing regulatory proteins that prevent excessive synthesis of BCAAs could increase carbon flux towards central metabolic pathways, potentially enhancing overall protein synthesis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Additionally, deleting or downregulating enzymes responsible for their export or synthesis might help maintain intracellular levels of these amino acids, promoting protein production. The secretion of amino acids like alanine and tyrosine could be a sign of metabolic burden caused by overproduction of certain metabolites. Reducing this burden, either by modulating the expression of key metabolic genes could lead to enhanced metabolic efficiency, translating to increased protein yields. Since serine is consumed at high levels, it might also be limiting for protein production, therefore, increasing the availability of serine in the medium could improve intracellular serine levels, boosting protein biosynthesis. Further investigations into temporal amino acid consumption profiles hold promise for refining dynamic external supplementation strategies to further enhance PDGF production and cell growth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, this study provides insights into the metabolic consequences of growth temperature, thioredoxin tag addition, and strain mutations associated with recombinant protein production. The comprehensive intracellular untargeted metabolomic analysis of BL21 (DE3) and SHuffle strains at different temperatures revealed distinct cellular environments. The findings highlight the role of TrxA fusion tag and growth temperature in modulating the cellular metabolism of \u003cem\u003eE. coli\u003c/em\u003e and facilitating soluble expression of recombinant PDGF. Understanding these metabolic changes and their underlying mechanisms will pave the way for more efficient protein production and resource utilization in \u003cem\u003eE. coli\u003c/em\u003e-based biotechnological applications. Further studies integrating metabolomics with other omics approaches will be required to enhance our understanding and provide more tailored strategies for optimized recombinant protein production in \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments and Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the Department of Biotechnology (DBT) under DBT‐Pan IIT Centre for Bioenergy Phase 2 (BT/PR41982/PBD/26/822/2021). The authors would like to thank Avinash Sunder for useful suggestions. SDG acknowledges the award of INSPIRE Fellowship by the Department of Science and Technology (DST), Government of India. MS acknowledges the Department of Biotechnology, Government of India for Ph.D. fellowship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePPW holds equity inClarity Bio Systems India Pvt. Ltd. All other authors have no relevant competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSDG, MS, and PPW conceptualized the research and designed the experiments. SDG and MS performed the research. PN and VM helped in data acquisition and analysis. SDG, MS, and PPW analyzed the data and wrote the manuscript. All authors read and approved the final manuscript. PPW supervised the research and acquired the funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMa Y, Lee CJ, Park JS (2020) Strategies for Optimizing the Production of Proteins and Peptides with Multiple Disulfide Bonds. 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Annu Rev Microbiol 57:155\u0026ndash;176. https://doi.org/10.1146/ANNUREV.MICRO.57.030502.090820/CITE/REFWORKS\u003c/li\u003e\n\u003cli\u003eWang J, Yan D, Dixon R, Wang YP (2016) Deciphering the Principles of Bacterial Nitrogen Dietary Preferences: a Strategy for Nutrient Containment. mBio 7:e00792-16. https://doi.org/10.1128/MBIO.00792-16\u003c/li\u003e\n\u003cli\u003eMarreddy RKR, Geertsma ER, Permentier HP, et al (2010) Amino Acid Accumulation Limits the Overexpression of Proteins in Lactococcus lactis. PLoS One 5:e10317. https://doi.org/10.1371/JOURNAL.PONE.0010317\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Untargeted metabolomics, LCMS, E. coli BL21 (DE3), SHuffle, thioredoxin tag, PDGF","lastPublishedDoi":"10.21203/rs.3.rs-6382221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6382221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAchieving soluble expression of disulfide bond-containing recombinant proteins in \u003cem\u003eEscherichia coli\u003c/em\u003e is challenging. While strategies such as low post-induction temperature, fusion tags, and engineered strains have been employed to achieve soluble protein expression, their specific effects on \u003cem\u003eE. coli\u003c/em\u003e metabolism and its relation to soluble protein expression remain unclear. Here, we performed untargeted metabolomics to study the key metabolic changes associated with co-expression of fusion tags in \u003cem\u003eE. coli\u003c/em\u003e strains at low and high cultivation temperatures. Using a mass spectrometry-based approach, we identified 121 differentially abundant metabolites. The metabolomes of BL21 (DE3) and SHuffle strains exhibited distinct intracellular pools of amino acids and redox regulators. We further studied the expression of platelet-derived growth factor (PDGF) as a model disulfide-rich protein that generally tends to aggregate when expressed in \u003cem\u003eE. coli\u003c/em\u003e. A lower induction temperature and the addition of a thioredoxin tag were observed to be crucial for obtaining soluble PDGF in both strains. However, SHuffle showed heightened metabolic stress during PDGF production compared to BL21. Soluble PDGF expression was associated with higher levels of peptides, nucleotides, and glycolysis and TCA cycle intermediates, while PDGF expression as inclusion bodies was associated with higher levels of amino acids, nucleobases, and pentose phosphate pathway intermediates. These results have implications for enhancing strain performance and bioprocess optimization for producing \u0026ldquo;difficult-to-express\u0026rdquo; proteins in \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Effect of thioredoxin tag, oxidizing environment, and temperature on the global metabolome of E. coli strains","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 11:34:04","doi":"10.21203/rs.3.rs-6382221/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-10T05:41:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-01T01:58:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Applied Biochemistry and Biotechnology","date":"2025-04-17T07:55:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Biochemistry and Biotechnology","date":"2025-04-17T00:59:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"64551676-98ad-4a01-80ca-9d40270adb3b","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:03:39+00:00","versionOfRecord":{"articleIdentity":"rs-6382221","link":"https://doi.org/10.1007/s12010-025-05438-3","journal":{"identity":"applied-biochemistry-and-biotechnology","isVorOnly":false,"title":"Applied Biochemistry and Biotechnology"},"publishedOn":"2025-11-06 15:58:14","publishedOnDateReadable":"November 6th, 2025"},"versionCreatedAt":"2025-05-06 11:34:04","video":"","vorDoi":"10.1007/s12010-025-05438-3","vorDoiUrl":"https://doi.org/10.1007/s12010-025-05438-3","workflowStages":[]},"version":"v1","identity":"rs-6382221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6382221","identity":"rs-6382221","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}