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Metabolic adaptations of Escherichia coli to extended zinc exposure: insights into tricarboxylic acid cycle and trehalose synthesis

BMC Microbiology volume 24, Article number: 384 (2024) Cite this article

Abstract

Balanced bacterial metabolism is essential for cell homeostasis and growth and can be impacted by various stress factors. In particular, bacteria exposed to metals, including the nanoparticle form, can significantly alter their metabolic processes. It is known that the extensive and intensive use of food and feed supplements, including zinc, in human and animal nutrition alters the intestinal microbiota and this may negatively impact the health of the host. This study examines the effects of zinc (zinc oxide and zinc oxide nanoparticles) on key metabolic pathways of Escherichia coli. Transcriptomic and proteomic analyses along with quantification of intermediates of tricarboxylic acid (TCA) were employed to monitor and study the bacterial responses. Multi-omics analysis revealed that extended zinc exposure induced mainly oxidative stress and elevated expression/production of enzymes of carbohydrate metabolism, especially enzymes for synthesis of trehalose. After the zinc withdrawal, E. coli metabolism returned to a baseline state. These findings shed light on the alteration of TCA and on importance of trehalose synthesis in metal-induced stress and its broader implications for bacterial metabolism and defense and consequently for the balance and health of the human and animal microbiome.

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Introduction

Zinc oxide (ZnO) and zinc oxide nanoparticles (ZnONPs) are inorganic compounds known for their stable chemical and physical properties, easy synthesis, and applications in various fields [1]. They are commonly used in agriculture as pesticides and fertilizers as well as food supplements for livestock animals. Due to their antimicrobial properties, they also show promise in medical applications, such as wound dressings, bandages, and disinfectants [2,3,4]. Recently, ZnONPs have received increased attention due to their effectiveness against antibiotic resistant bacteria and their low ecological footprint [5]. Their antimicrobial activity is attributed to the release of Zn2+ ions and the production of reactive oxygen species (ROS) [6, 7] leading to damage of bacterial cell membranes, leakage of cellular contents, and cell death [8, 9]. However, a long-term exposure of bacteria to ZnO and ZnONPs has been also linked to the development of antimicrobial resistance and virulence [10,11,12,13].

In our previous study, we demonstrated elevated minimum inhibitory concentrations (MICs) to cephalosporins and aminoglycosides in E. coli subjected to the ZnO treatment for 20 subcultures (ZnO20) and this resistance remained for at least 40 subcultures (ZnO40) (15). The highest resistance increase was detected to cefazolin, gentamicin and amikacin with the MICs at the clinical breakpoint. Interestingly, an extended treatment with ZnO nanoparticles (ZnONPs20 and ZnONPs40) only resulted in the higher MIC to chloramphenicol. Furthermore, the elevated biofilm formation was observed under the ZnO40 treatment in comparison to that of control (C40). In contrast, ZnONPs40 treatment resulted in decreased biofilm formation when compared to that of C40. When the zinc treatment was ceased after the 20th subculture and sub-culturing continued until the 40th subculture without zinc (ZnO20 + 20, ZnONPs20 + 20), the antibiotic resistance and virulence phenotypes returned to their original state. Zinc treatments also caused alterations in the bacterial growth and cell morphology [14]. All of these effects are intricately tied to alterations of bacterial metabolism, that can trigger changes in the expression/production of virulence and resistance genes/proteins and vice versa [15,16,17]. Previous reports have indicated that bacteria under various stresses increase production of protection molecules, including carbohydrates (trehalose, glycogen, cellulose) and proteins/enzymes (efflux pumps, DNA-repairing enzymes, heat-shock or cold-shock proteins and more) [18, 19].

It is becoming evident that ZnO and ZnONPs can alter bacterial basal metabolism resulting in changes in bacterial resistance and virulence, yet research on this topic remains limited. In this study, we analyzed major metabolic pathways of E. coli exposed to zinc through multi-omic approaches with the aim to understand how the extended zinc exposure influences gene expression and protein abundance. We also analyzed intermediate compounds in the tricarboxylic acid (TCA) cycle to gain deeper insights into metabolic changes. This study investigates the interplay between zinc and bacterial metabolism and provides new insights for understanding of bacterial responses to environmental stress and the development of antibiotic resistance and virulence mechanisms.

Results

Transcriptomic and proteomic analysis

Differentially expressed genes (DEGs) and differentially abundant proteins (DAPs) in ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 treatments (Figs. 1 and 2) were studied in comparison to that of untreated control (C40). The study focused on DEGs and DAPs with statistical significance (p ˂ 0.05) and notable changes in expression (log2 fold change > 1.5 or ˂ -1.5). Subsequently, these DEGs and DAPs were further selected based on their gene ontology (GO) terms for carbohydrate, amino acid and fatty acid metabolism. Initially, the number of DEGs (Fig. 1A) and DAPs (Fig. 2A) across the three metabolism categories were analyzed in all treatments. Subsequently, the top 10 GO pathways with the highest rich factor of DEGs (Fig. 1B) and DAPs (Fig. 2B) were identified across all treatments in each metabolism category.

Fig. 1
figure 1

Transcriptome results. (A) Number of significant DEGs in ZnO40, ZnONPs40 ZnO20 + 20, and ZnONPs20 + 20 treatments connected to carbohydrate, amino acid, and fatty acid metabolism according to GO terms. (B) Top 10 GO pathways in ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 treatments with the highest rich factors connected to (a) carbohydrate metabolism (b) amino acid metabolism (c) fatty acid metabolism

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Fig. 2
figure 2

Proteome results. (A) Number of significant DAPs in ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 treatments connected to carbohydrate, amino acid, and fatty acid metabolism according to GO terms (B) Top 10 GO pathways in ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 treatments with the highest rich factors connected to (a) carbohydrate metabolism (b) amino acid metabolism (c) fatty acid metabolism

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Extended ZnO/ZnONPs treatments mainly altered transcripts in carbohydrate metabolism

Bacterial strains subjected to extended zinc treatments exhibited the most significant changes in the number of DEGs in carbohydrate metabolism (35 DEGs in ZnO40 and 31 DEGs in ZnONPs40). This was followed by changes in amino acid metabolism (20 DEGs in ZnO40 and 13 DEGs in ZnONPs40). In both metabolism categories, these treatments resulted primarily in the up-regulation of transcripts. In contrast, the ZnONPs20 + 20 treatment led mainly to down-regulation of DEGs, especially in metabolism of carbohydrates (21 DEGs), followed by amino acid metabolism (18 DEGs), and fatty acid metabolism (9 DEGs). In all treatments, the number of DEGs involved in fatty acid metabolism was the least affected. Notably, the number of DEGs of ZnO20 + 20 treatment was similar to that of C40 (Fig. 1A).

We then focused on identification of the top 10 enriched GO pathways associated with the altered DEGs across all treatments within each metabolism category (Fig. 1B). The complete list of GO pathway terms, along with its rich factor values for the DEGs across all treatments, is presented in Figure S1. In the carbohydrate metabolism (Fig. 1Ba), the most significantly influenced DEGs pertained to trehalose metabolism; including trehalose-phosphatase activity [GO:0004805], trehalose metabolism in response to cold stress [GO:0070415], and the trehalose biosynthetic process [GO:0005992]. This was particularly notable in strains subjected to ZnO40 and ZnONPs40 treatments. The phosphatase activity of trehalose was also affected in E. coli exposed to the ZnO20 + 20 treatment. Additionally, transcripts involved in glycogen catabolism were impacted under ZnONPs40 and ZnONPs20 + 20 treatments. Furthermore, the biosynthetic processes of mannosylglycerate were altered by ZnONPs40, while mannosyl-3-phosphoglycerate phosphatase activity was affected by ZnO40.

In the amino acid metabolism (Fig. 1Bb), the most affected DEGs were involved in catabolism of arginine [GO:0019545, GO:0019544], L-lysine [GO:0019477] in ZnO40 and ZnONPs40, and catabolism of ornithine [GO:0006593] in ZnO40. Under the ZnONPs20 + 20 treatment, the most impacted DEG were involved in the transport of S-methylmethionine [GO:001586], beta-alanine [GO:0001762], and modification of peptidyl-methionine [GO:0018206]. In contrast, the DEGs involved in fatty acid metabolism (Fig. 1Bc) were the least affected by zinc exposure. In the ZnONPs20 + 20 treatment, 40% of DEGs (rich factor 0.4) (Fig. 1Bc) were altered in the monounsaturated fatty acid biosynthetic process [GO:1903966]. Similarly, in the ZnO40 treatment, 40% of DEGs (rich factor 0.4) were affected in the intermembrane phospholipid transfer [GO:0120010] (Fig. 1Bc).

Overall, among all metabolism categories, the ZnO20 + 20 treatment had the least impact on the gene expression, indicating the bacterial ability to return to its basal metabolic state prior to zinc treatments.

Extended ZnO/ZnONPs treatments mainly altered proteins in carbohydrate and amino acid metabolism

The bacterial strains subjected to extended zinc treatments exhibited the most significant changes in the number of DAPs involved in carbohydrate metabolism, with 24 DAPs in ZnO40 and 25 DAPs in ZnONPs40. The impact on amino acid metabolism was also notable with 25 DAPs in ZnO40 and 22 DAPs in ZnONPs40. In both metabolism categories, the majority of proteins was up-regulated. Across all treatments, the number of DAPs involved in fatty acid metabolism was the least affected. Overall, a higher number of altered DAPs in fatty acid metabolism was observed in strains with the withdrawal of zinc treatments (ZnO20 + 20 and ZnONPs20 + 20) (Fig. 2A).

The study then focused on identification of the top 10 enriched GO pathways associated with affected DAPs across all treatments within each metabolism category (Fig. 2B). A comprehensive list of GO pathway terms, along with rich factor values for the DAPs across all treatments, is in Figure S2. In the carbohydrate metabolism (Fig. 2Ba), the catabolic process of sorbitol [GO:0006062] was the most affected under the ZnONPs40 and ZnONPs20 + 20 treatments. In bacterial strains with extended zinc treatment, there was influence of 50% of DAPS (rich factor 0.5) (Fig. 2Bc) involved in trehalose metabolism; this included trehalose metabolism in response to cold stress [GO:0070415], trehalose biosynthetic process [GO:0005992] and trehalose transport [GO:0015771]. In all treatments, there was influence of plasma membrane pyruvate transport [GO:0006849], fructose metabolic process [GO:0006000] and the catabolic process of 6-sulfoquinovose(1-) [GO:1902777] and 6-sulfoquinovose(1-) to glycerole phosphate and 3-sulfolactaldehyde [GO:0061720].

In amino acid metabolism (Fig. 2Bb), the ZnONPs40 treatment primarily affected DAPs, especially in pathways, such as peptidyl-cysteine S-nitrosylation [GO: 0018119] and peptidyl-cysteine S-trans-nitrosylation [GO:0035606]. Additionally, along with ZnO40 treatment, there was an influence of DAPs in the transport of tyrosine [GO:0015828], p-aminobenzoyl-glutamate [GO:0015814], p-aminobenzoyl-glutamate transmembrane [GO:1902604] and tryptophan [GO:0015827]. This also included peptidyl-S-carbamoyl-L-cysteine dehydration [GO:0046892] and the catabolic processes of tryptophan [GO:0006569] and glutamate [GO:0006538].

Proteins involved in fatty acid metabolism showed the least change. The greatest changes were observed in the fatty acid beta-oxidation using acyl-CoA dehydrogenase [GO:0033539] under ZnO40 treatment across all treatments and in the anaerobic glycerol catabolic process [GO:0019588] under the ZnONPs40 and ZnO20 + 20 treatments.

Integration of transcriptomic and proteomic data

The 3D graphics in Fig. 3A illustrate the common GO pathway terms for DEGs (Fig. 3Aa) and DAPs (Fig. 3Ab), with the corresponding rich factor values for both, transcriptomic (Fig. 3Aa) and proteomic data (Fig. 3Ab) shown on the z-axis (the y-axis numbers for each treatment are in Fig. 3Ac). Among the treatments, the most significant influence of DEGs and DAPs was observed under the ZnO40 and ZnONPs40 treatments, especially within carbohydrate metabolism, focusing on trehalose metabolism pathways: trehalose biosynthetic process [GO:0005992] and trehalose metabolism in response to cold stress [GO:0070415]. In these pathways, the genes encoding trehalose-6-phosphate synthase (otsA) and trehalose-6-phosphate phosphatase (otsB), as well as the OtsB protein, were significantly affected, as evidenced by smaller rich factor values for the proteomic data (Fig. 3B). Up-regulation was observed for the genes otsA and otsB and protein OtsB (Fig. 3B).

Fig. 3
figure 3

Integration of transcriptome and proteome results for amino acid, carbohydrate, and fatty acid metabolism Aa. Transcriptome (TR) and Ab. proteome (PR) results presenting rich factor values for Ac. integrated GO pathways of treated strains ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 B. DEGs and DAPs for common GO terms related to amino acid (green), carbohydrate (orange), and fatty acid (blue) metabolism in ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20 strains from integrated GO pathways

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In amino acid metabolism, the main influence of ZnO40 and ZnONPs40 treatments was detected in the L-lysine catabolic process [GO:0019477], affecting the expression of genes encoding putrescine aminotransferase (ygjG), L-2-hydroxyglutarate dehydrogenase (lhgO), and Glutarate L-2-Hydroxylase (csiD), along with the abundance of the CsiD protein, supporting the smaller rich factor values for the proteomic data. An up-regulation was detected for genes ygjG, lhgO, csiD and the protein CsiD (Fig. 3B). Rich factor values in the other treatments did not reveal significant changes.

The log2 fold change values of all DEGs and DAPs in common GO pathway terms of carbohydrate, amino acid and fatty acid metabolisms for all treatments are presented in the heatmap (Fig. 3B). A summary of GO pathway terms with linked expressed genes, abundant proteins, their accession numbers and log2 fold change values for each treatment are in Table S1. Upon comparing the effects of extended ZnO treatment to its nanoparticle form (ZnONPs), noticeable differences in log2 fold change values emerged for several DEGs (fabB, fadE, lpxC and lpxP) and DAPs (AgaS, BglF, MalT, RpiB, RstB, FadE, and LpxP), all of which play roles in carbohydrate, amino acid, and fatty acid metabolic pathways.

Integration of metabolic, transcriptomic, and proteomic data of TCA cycle intermediates

Figure 4 presents a visual representation of the detected changes in the production of TCA cycle metabolites, gene expression, and protein abundance under various Zn treatments. Color bars indicate the production level of metabolites, with stars denoting significance based on the p-values (* p-value ˂ 0.05, ** p-value ˂ 0.01, *** p-value ˂ 0.001, **** p-value ˂ 0.0001). Additionally, the expression and abundance of enzymes involved in this cycle are depicted in the accompanying tables.

Fig. 4
figure 4

Influence of TCA cycle of E. coli by ZnO/ZnONPs: Production of metabolites related to TCA cycle [ng] for all tested strains are visualized in bar graphs: Control C40 (gray), ZnO40 (violet), ZnONPs40 (pink), ZnO20 + 20 (green), and ZnONPs20 + 20 (blue) with error bars and asterisks indicating the significance according to p-value (* p-value ˂ 0.05, ** p-value ˂ 0.01, *** p-value ˂ 0.001, **** p-value ˂ 0.0001). To compare the treatments (ZnO40, ZnONPs40, ZnO20 + 20, and ZnONPs20 + 20) with the control (C40), an unpaired t-test was performed, and the significance is represented by asterisk symbols. Furthermore, this figure includes log2 fold change values of enzymes involved in this cycle, presented in tables of DEGs on the left and DAPs on the right. Only DEGs or DAPs whose significance was p ˂ 0.05 and log2 fold change > 1.5 and ˂ -1.5, were chosen for depiction. Up-regulated genes/proteins are marked as up arrow, down-regulated genes/proteins are marked as down arrow, hyphens present not changed expression/abundance compared to that of C40, and X marks no identification of DEG/DAP for treatment

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Our analysis of TCA cycle intermediates revealed the increased production of pyruvate, citric acid, glutamic acid, ketoglutarate, and succinate in strains subjected to ZnO40 and ZnONPs40 compared to that of control (C40). Notably, E. coli exposed to ZnONPs demonstrated the higher production of pyruvate, citric acid, glutamic acid, and α-ketoglutarate than that under ZnO40. Both extended treatments resulted in a significant decrease in isocitrate, fumarate, and malate. In strains with the zinc withdrawal after 20 subcultures (ZnO20 + 20 and ZnONPs20 + 20), TCA cycle intermediate production remained unchanged relative to C40, with the exception of decrease in isocitrate, citric acid, and lactate under ZnONPs20 + 20.

Figure 4 also illustrates the log2 fold change values of DEGs and DAPs of enzymes involved in the TCA cycle, presented as a heatmap. This map provides a comprehensive view of the gene expression and protein abundance level changes within the TCA cycle, highlighting DEGs and DAPs with significant values (p ˂ 0.05, log2FC > 1.5 and ˂ -1.5). The most notable changes were observed in the expression of genes encoding pyruvate dehydrogenase (poxB) and pyruvate kinase I (pykF) and in the abundance of PoxB in extended zinc treatments. Additionally, the lower abundance of isocitrate dehydrogenase kinase/phosphatase (AceK) was detected in bacteria under the ZnO40 treatment, as well as that in ZnO20 + 20 and ZnONPs20 + 20. A heatmap representing all DEGs and DAPs of the TCA enzymes is provided in Figure S3. When comparing the impact of ZnO to that of ZnONPs on E. coli through changes in TCA cycle intermediate production, only minor differences were detected. Specifically, ZnONPs resulted in slightly higher production of α-ketoglutarate, pyruvate, citric acid and glutamic acid compared to ZnO (Figure S3).

The expression of genes encoding enzymes participating in the TCA cycle is regulated via different transcriptional factors. The major transcriptional factors regulating the TCA genes are listed in Table S2 showing log2 fold changes of expressed genes and abundant proteins with their adjusted p-values.

Discussion

Bacterial metabolism comprises a complex interconnected metabolic network that enables bacteria to process a variety of organic and inorganic compounds essential for the growth and the maintenance of cellular homeostasis [20]. A variety of environmental factors, such as temperature, pH, radiation, nutrient limitation, and oxidative or osmotic stress can lead to the disruption of various metabolic processes [21, 22]. In response to such stressors, especially from heavy metals, bacteria developed a range of adaptive mechanisms. Although these mechanisms are critical for bacterial survival under stress and result to changes in resistance and virulence, they are still not well understood [23, 24].

Understanding the metabolic networks and their regulation is crucial for gaining insights into bacterial physiology and adaptability to changing environmental conditions. Here, we focused on three major metabolic pathways of E. coli exposed to zinc that play a critical role in energy production, biosynthesis, and cellular homeostasis [25,26,27]. Extended zinc treatments (ZnO40, ZnONPs40) of E. coli resulted in alterations in gene expression and protein abundance in metabolism of carbohydrates, proteins, and lipids. Transcriptomic data revealed an increased number of DEGs, involved primarily in carbohydrate metabolism. A similar trend was observed in the proteomic data along with an increased number of DAPs in amino acid metabolism.

It is known that ZnO and ZnONPs induce the production of reactive oxygen species (ROS) in E. coli, such as hydrogen peroxide (H2O2), superoxide anions (O2−.), and hydroxyl radicals (.OH) leading to oxidative stress and potential damage to cellular components, including nucleic acids, amino acids, proteins, and lipids in cell membranes [28, 29]. Genes encoding the stress-protective enzymes (superoxide dismutase and catalase) are regulated by transcription factors through two major oxidative stress regulons, OxyR and SoxRS, as well as the general stress regulon RpoS [30, 31]. RpoS, known for its involvement in responses to multiple stresses [32], directly or indirectly regulates the expression of a large set of genes that aid bacterial adaptation and survival under adverse conditions [33]. This may explain the increased number of DEGs and DAPs linked to carbohydrate and amino acid metabolism in bacteria exposed to ZnO and ZnONPs resulting in stress alleviation. Furthermore, RpoS has been implicated in the regulation of otsA and otsB, that are involved in trehalose biosynthesis [34, 35]. Increased levels of OtsA and OtsB were observed at transcriptome and proteome of extended zinc treatments, corroborated by our previous study, where the up-regulation of otsB was confirmed in both treatments by using real-time qPCR [14]. These combined results indicate the elevated trehalose synthesis.

Trehalose is known to protect membranes and biomacromolecules against abiotic stresses, including osmotic stress, heat stress, desiccation, freezing, and other environmental conditions [36,37,38]. In our study, trehalose was likely synthesized as osmoprotectant to stabilize effects of zinc on proteins and lipids in the cell membranes and to eliminate ROS. Several studies have shown that plants and yeasts can produce trehalose in response to mild heat shock and utilize it to scavenge oxygen radicals and protect cellular proteins from damage [39, 40]. Furthermore, some plants have been found to deal with stress from heavy metals such as cadmium, zinc, and nickel through trehalose production [41, 42]. Exogenously added trehalose has also been shown to enhance stress tolerance and yield in various plant species, indicating its potential as a protective agent against metal-induced damage [43]. However, the role of trehalose in bacteria under stress from heavy metals has not been explored. One study demonstrated that Rhizobium sp. have the capacity to accumulate trehalose and this may play an important role in mediating stress responses in interactions with plants [44].

An extended treatment of E. coli with ZnO nanoparticles (ZnONPs40) led to increased catabolism of glycogen, D-allose, and sorbitol, while ZnO40 treatment enhanced the catabolism of cellobiose. Subsequently, glycolysis likely became a great source of pyruvate for the TCA cycle, generating a substantial amount of energy. The heightened production of pyruvate could be also attributed to the catabolism of 6-sulfoquinovose and the activity of deoxyribose phosphate that were notably elevated under extended ZnO/ZnONPs treatments. Furthermore, enzymes involved in the pentose phosphate shunt pathway were elevated and likely contributed to the production of pyruvate. Consequently, more pyruvate can enter the TCA cycle and this is corroborated by elevated intermediates in the TCA cycle in cells under ZnO40 and ZnONPs40 treatments. The TCA pathway plays a pivotal role in the catabolism of organic molecules in the presence of oxygen to harvest the energy for cell growth and division [45]. Increased production of the enzyme PoxB facilitates the conversion of pyruvate into acetyl-CoA. Extended zinc treatments likely augmented the catabolism of L-lysine, leading to a higher production of acetyl-CoA. This potentially contributed to an elevated abundance of citric acid in E. coli after the long-term exposure to ZnO and ZnONPs.

The most notable change in our analysis was in the production of α-ketoglutarate. Under ZnO40, the gene aceK coding for the protein responsible for activating isocitrate dehydrogenase [46] was up-regulated, potentially leading to an increased production of α-ketoglutarate. It has been demonstrated that high concentrations of zinc can alter the activity of isocitrate dehydrogenase by phosphorylation process resulting in its inactivity [47], which could lead to the synthesis of α-ketoglutarate from the L-glutamine amino acid pathway. Higher levels of NADH and GTP in strains with extended zinc treatments were likely generated by the conversion of α-ketoglutarate to succinyl-CoA and its subsequent conversion to succinate. Succinate levels were also elevated possibly due to its conversion from isocitrate, facilitated by the up-regulation of aceA and aceB in the glyoxylate pathway, as observed in ZnO40 treatment. Additionally, an increased production of succinate in the ZnONPs40 treatment could have been caused by the down-regulation of the sdhB and sdhD genes, coding for succinate: quinone oxidoreductase, responsible for the conversion of succinate to fumarate. Since the production of fumarate and malate decreased in these treatments, succinate was likely redirected to different pathways, such as the propionate synthesis pathway [48]. The oxidation of this short-chain fatty acid is then used as a source of carbons or energy [49]. High levels of intermediate compounds in the TCA cycle of E. coli can provide a greater amount of energy-rich molecules such as GTP and NADH, which can be converted to ATP to support anabolic processes, leading to cell protection, growth, and proliferation [50]. The elevated levels of TCA intermediates indicate a higher production of energy-rich molecules in cells under an extended zinc exposure.

Overall, the transcriptomic and the proteomic data suggest that cells under ZnO40 and ZnONPs treatments divert energy from catabolic pathways to gluconeogenesis. This process likely leads to the synthesis of trehalose and other carbohydrates. The role of the TCA cycle in providing ATP for trehalose synthesis has been shown previously [50]. Consequently, the elevated trehalose might inactivate the oxygen radicals and acts as a stress protectant for lipids and proteins in the cell periplasm and the cytoplasmic membrane, which could be otherwise damaged [51]. A recent study revealed that in E. coli, trehalose functions as a chemical chaperone, stabilizing denatured proteins and facilitates their refolding. Trehalose may also protect proteins against aggregation by acting as a metabolite that indirectly counteracts harmful protein acetylation [52]. When the stress is relieved, E. coli can rapidly degrade trehalose and revert to basic metabolism. Degradation of trehalose involves its transfer from the periplasm to the cytoplasm, a process facilitated by the TreB protein [53], which was indeed elevated in cells under ZnO40 and ZnONPs40. After transport, trehalose is phosphorylated and degraded by trehalases TreC and TreF, into two glucose molecules, whose expression/abundance was not changed in comparison to that of control [53, 54].

It is very likely that under extended ZnO and ZnONPs treatments, E. coli experienced oxidative stress, which triggered the up-regulation of the otsAB system and the subsequent trehalose synthesis. This response provided protection against the damaging effects of zinc ions and allowed bacteria to adapt and survive. The higher energy content in cells exposed to ZnONPs40 was likely utilized for the increased trehalose production in comparison to that of ZnO40. Upon reversal ZnO and ZnONPs treatments, E. coli returned to a metabolic state similar to that of control C40. Future studies need to focus on detection and measurement of trehalose levels as well as to generate mutant strains with inactivated trehalose synthesis to confirm our findings from multi-omics approach.

In conclusion, extended zinc treatments, in the form of ZnO and ZnONPs, greatly impacted the carbohydrate metabolic network of E. coli. Transcriptomic and proteomic analyses revealed elevated expression/abundance of enzymes involved in glycolysis and the TCA cycle which was further confirmed by analysis of TCA intermediate compounds. These metabolic shifts are indicative of the bacterial response to stress as it generates the necessary energy for protection and adaptation. Importantly, the restoration of the metabolic state after withdrawal of zinc points out the bacterial capacity to adapt and recover. Our findings provide new and comprehensive insights into the metabolic adaptations of E. coli to extended zinc exposure and indicate the role of trehalose in protection from oxidative stress induced by a heavy metal and its nanoparticles. This is the first study indicating the role of TCA cycle and trehalose in protection from stress induced by a heavy metal. Considering the extent of zinc use in human and animal nutrition, our understanding the molecular mechanisms of bacterial responses is essential for elucidating the impact of metal exposure on bacterial metabolism and for developing strategies for maintenance of balanced intestinal microbiomes and combating bacterial resistance and virulence.

Materials and methods

Sub-culturing of E. Coli strain with zinc oxide and zinc oxide nanoparticles and its processing

In our previous study, we performed the sub-culturing procedure of E. coli ATCC 25,922 strain with sub-inhibitory doses of ZnO and ZnONPs in heptaplicate (n = 7) for each strain [14]. Briefly, bacteria E. coli ATCC 25,922 were cultivated on 5% Columbia blood agar (pH 7.3 ± 0.2; Lab Media Servis, Czech Republic) at 37 °C. Overnight cultures were diluted in double-concentrated Mueller-Hinton (MH) broth (pH 7.3 ± 0.1; Sigma Aldrich, USA) to an optical density at 600 nm (OD600) of 0.08–0.13 AU and then further diluted 100×. It was then incubated with ZnO/ZnONPs in 96 well microplates at 37 °C for 24 h with shaking at 120 rpm. Next day, the absorbance (620 nm) was measured using the MultiScan EX Microplate Photometer (Thermo Fisher, Germany) and the minimum inhibitory concentrations and sub-inhibitory concentrations were determined. A loopful (10 µl) of cells from the well containing the first concentration below the MIC (sub-inhibitory concentration) was transferred onto the surface of 5% Columbia blood agar. This entire process was repeated 19 times, resulting in 20 sub-cultures across seven independent replicates, with MIC measurements taken. After reaching the 20th sub-culture, the treated bacteria were subdivided into two lines and the experiment continued for additional 20 cycles with ZnO/ZnONPs (ZnO40, ZnONPs40) or mock (ZnO20 + 20, ZnONPs20 + 20). In parallel, control bacteria were incubated under the same conditions using PBS. Final sub-cultures reaching 40 passages (C40, ZnO40, ZnONPs40, ZnO20 + 20, ZnONPs20 + 20) were preserved through freezing after 24 h incubation on 5% Columbia blood agar. Specifically, bacteria were centrifuged, and the resulting pellet was washed three times with PBS. Each bacterial pellet was stored in cryo-medium at – 80 °C until further use for transcriptomic, proteomic approach and quantitative analysis of TCA cycle intermediates. This process followed the protocols detailed in our previous study [14]. Subsequently, bacterial pellets were utilized for isolation of RNA and proteins, as outlined in our earlier work. Furthermore, in the current study, metabolites were also extracted from the bacterial pellets.

Extraction of metabolites and analysis of TCA cycle intermediates

Frozen bacterial pellets were resuspended in a cold solution containing 0.1% formic acid, 40% methanol, and 40% acetonitrile (v/v) in pentaplicate (n = 5) for each strain. Metabolites were isolated via the extraction protocol for TCA cycle products, as outlined by Carvalho et al. (2019) [55] using glass beads (Benchmark Scientific, Sayreville, NJ, USA). Prior to extraction, bacterial pellets were pre-homogenized (4500 rpm, 60s) using the MagnaLyzer and subsequently placed on ice for 30 s. The supernatant was then stored at − 80 °C until further analysis. This process was repeated four times, adding the cold solution to the remaining pellets, resulting in four frozen supernatants for each treatment. The samples were then centrifuged for 15 min at 14,000 rpm and 4 °C. Subsequently, 500 µl of the supernatant was vacuum dried and reconstituted in 200 µl of the mobile phase (1% methanol acidified with 0.2% formic acid). Then, 5.0 µl of the samples were analyzed by the LC-MS using reversed-phase chromatography. The analysis was conducted on the high-performance chromatographic system Agilent Technologies 1260 (Waldbronn, Germany) coupled to the LTQ VelosPro Orbitrap Elite hybrid mass spectrometer (Thermo Scientific, CA, USA). Chromatographic separation was achieved suing a C18 Kinetex column (150 × 2.1 mm with 5 μm particles) with a flow rate of 0.2 ml/min. The gradient of mobile phases A (0.2% formic acid in water) and B (methanol) was applied as follows: 1% of B at 1 min, 5% at 4 min, 20% at 6 min, 100% at 8 min. The spectra were evaluated using Xcalibur software v2.2 (Thermo Scientific, CA, USA). A constant amount of 13C-labeled L-lactic acid (Sigma Aldrich) was used as an internal standard, with 1 µl of the internal standard solution mixed with 99 µl of the sample/standard solution.

Transcriptome and proteome analysis

The extraction and processing of biological materials (RNA and proteins) from bacterial pellets to obtain transcriptomic and proteomic data, were described in detail in our previous study [14]. Differential expression analysis was always performed from zinc treatment (ZnO40, ZnONPs40, ZnO20 + 20 and ZnONPs20 + 20) compared to control (C40). In this study we worked only with those DEGs and DAPs whose p-value ˂ 0.05. For transcriptome, adjusted p-value threshold (˂ 0.05) was calculated through the false discovery rate (FDR). This was performed in Geneious Prime 2021.0.1. by DESeq2 package [56].

Gene ontology and rich factor calculation

The significantly regulated genes/proteins were subjected to gene enrichment analysis using the aGOtool within the UniProt keyword classification system for each treatment [57]. Generated GO terms for biological processes were then organized into appropriate metabolic categories, including carbohydrate, amino acid, and fatty acid metabolism. For each DEGs and DAPs, a rich factor was calculated. The rich factor represents the ratio of the number of genes/proteins in a specific pathway to all genes/proteins of that specific pathway. A higher rich factor indicates a greater degree of enrichment within the pathway. The total number of genes/proteins participating in each specific pathway was obtained from the EcoCyc database: https://biocyc.org/ECOLI/class-tree?object=Gene-Ontology-Terms.

Statistical analysis and data visualization

Metabolic data of TCA intermediates were processed, standard deviations were calculated, and significance in comparison to that of control strain was determined using an unpaired t-test in GraphPad Prism 8.0.1. (GraphPad Software, CA, USA). In case of transcriptome and proteome, the p-value was calculated by background-based t-test as mentioned in our previous study [14]. All graphics for the transcriptomic, proteomic and metabolomic data were generated using the ggplot2 in RStudio [58], while 3D graphics were visualized using the matplotlib in Python [59].

Data availability

Data that support the findings of this study are presented in the main article and Supplementary Information files. Raw data generated from the RNA-Seq experiment of E. coli strains were deposited in the SRA archive of NCBI database under the BioProject: PRJNA915319 (https://www.ncbi.nlm.nih.gov/sra/PRJNA915319). The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE (10.1093/nar/gky1106) partner repository with the data set identifier PXD027925.

Abbreviations

DAP:

differential abundance of protein

DEG:

differential expression of gene

ROS:

reactive oxygen species

TCA:

tricarboxylic acid

ZnO:

Zinc oxide

ZnONPs:

Zinc oxide nanoparticles

ZnO40:

Escherichia coli sub–cultured with sub–inhibitory doses of ZnO till 40th subculture

ZnONPs40:

Escherichia coli sub–cultured with sub–inhibitory doses of ZnONPs till 40th subculture

ZnO20 + 20:

Escherichia coli sub–cultured with sub–inhibitory doses of ZnO till 20th subculture and then subsequently sub–cultured without ZnO until 40th subculture

ZnONPs20 + 20:

Escherichia coli sub–cultured with sub–inhibitory doses of ZnONPs till 20th subculture and then subsequently sub–cultured without ZnONPs until 40th subculture

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Acknowledgements

The study was financially supported by ERDF “Multidisciplinary research to increase application potential of nanomaterials in agricultural practice” (No. CZ.03.1.01/0.0/0.0/16_025/0007314).

Funding

ERDF “Multidisciplinary research to increase application potential of nanomaterials in agricultural practice” (No. CZ.03.1.01/0.0/0.0/16_025/0007314).

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  1. Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, Brno, CZ-613 00, Czech Republic

    Martin Rihacek, Ludmila Kosaristanova, Tatiana Fialova, Tomas Rypar, Dagmar Skopalova Sterbova, Vojtech Adam, Ludek Zurek & Kristyna Cihalova

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Contributions

All authors made a significant contribution to the reported work. M.R. processed transcriptomic and metabolomic data, created graphics, and wrote the manuscript. L.K., T.R. and D.S.S. processed the samples and analyzed the metabolic data. T.F. analyzed proteomic data. V.A. assisted with the manuscript revisions. K.C. and L.Z. designed the project, supervised the experiments, and co-wrote the manuscript. The final text has been reviewed and approved by all authors.

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Correspondence to Kristyna Cihalova.

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Rihacek, M., Kosaristanova, L., Fialova, T. et al. Metabolic adaptations of Escherichia coli to extended zinc exposure: insights into tricarboxylic acid cycle and trehalose synthesis. BMC Microbiol 24, 384 (2024). https://doi.org/10.1186/s12866-024-03463-6

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