Multiplatform Metabolomic Analysis Reveals Metabolic Reprogramming of Burkholderia cepacia During Polyhydroxyalkanoate Production from Oleic Acid

Multiplatform Metabolomic Analysis Reveals Metabolic Reprogramming of Burkholderia cepacia During Polyhydroxyalkanoate Production from Oleic Acid | 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 Multiplatform Metabolomic Analysis Reveals Metabolic Reprogramming of Burkholderia cepacia During Polyhydroxyalkanoate Production from Oleic Acid Alfonso E. Alarcón, Nubia C. Moreno, Daniel Pardo-Rodriguez, Mónica P. Cala, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8182922/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Polyhydroxyalkanoates (PHA) have emerged as biodegradable alternatives to conventional polymers. However, their high production cost remains the main limitation to large-scale commercialization. Metabolic engineering strategies have been implemented to optimize PHA quality and enhance process productivity, yet a comprehensive understanding of the intracellular metabolic regulation is still needed. In this study, an untargeted metabolomic analysis using GC–MS and LC–MS platforms was performed to characterize the endometabolome of Burkholderia cepacia during batch fermentation with oleic acid as the carbon source. A total of 24 significant metabolites were identified by GC–MS and 223 by LC–MS, mainly organic acids and lipids. These metabolites were associated with key pathways such as β-oxidation, the tricarboxylic acid cycle, and the pentose phosphate pathway. The results revealed a clear metabolic reprogramming driven by acetyl-CoA flux redistribution, reflecting a regulatory mechanism responsive to nutrient availability. This dynamic reorganization of the metabolic network supports the transition from growth to PHA accumulation. The integrative metabolomic approach applied here provides insights valuable for guiding future Design–Build–Test–Learn (DBTL) strategies in the rational optimization of PHA-producing bioprocesses. Burkholderia cepacia PHA production Untargeted Metabolomics Pathway enrichment analysis Metabolic reprogramming Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The pollution generated by petroleum-derived polymers and their recycling difficulties has driven the need for new and greener materials (Emadian et al., 2017 ; Mohapatra et al., 2017 ; Peptu & Kowalczuk, 2018). Polyhydroxyalkanoates (PHA) have become biodegradable candidates for the replacement of chemical polymers due to their similarity in properties with conventional plastic materials (tensile strength and flexibility) and their complete biodegradability (Kumar et al., 2020 ; Nielsen & Keasling, 2016 ; Suriyamongkol et al., 2007 ; Urtuvia et al., 2014 ). Some microorganisms make PHA inside their cells and store them as an energy source (Możejko-Ciesielska & Kiewisz, 2016 ; Sagong et al., 2018 ). Among the bacterial species capable of accumulating large quantities of PHA and its copolymers are Cupriavidus necator , Pseudomonas sp. , and Burkholderia sp . (Anjum et al., 2016 ; Chee et al., 2010 ; Urtuvia et al., 2014 ; Zhu et al., 2010 ). One of the main obstacles to the commercial application of PHA production bioprocesses is the high production cost (Bano et al., 2024 ; Mitra et al., 2020 ). Profitability is directly related to the microbial rate of growth and production, the substrate cost, and the substrate conversion efficiency (Albuquerque & Malafaia, 2018 ). To enhance production competitiveness, Akiyama et al. ( 2003 ) reported that using vegetable oils as the carbon source results in higher PHA yields (0.6–0.8 g PHA per g oil) compared to simple sugars. In addition, several strategies have been investigated to improve process profitability, including the prospecting of new strains (Cerrone et al., 2023 ; Kucera et al., 2018 ), genetic improvement to obtain high-producing strains (Hernández Jirón, 2023 ; Santolin et al., 2024 ), and the formulation of metabolically structured models to support process optimization (Torres Ospina, 2019 ). Previous studies on PHA production with Burkholderia cepacia (Ardila Arévalo & Viloria García, 2017 ; Becerra Jiménez, 2013 ; Méndez, 2016 ; Viloria et al., 2017 ) demonstrated that achieving higher biomass concentrations is essential to improve process profitability. Furthermore, in the study by Torres-Ospina & Riascos (2020), fluxomic analysis at different time points of a batch fermentation using oleic acid and a simplified metabolic network revealed that, during the growth phase, 61% of the carbon flux is directed toward biomass and energy generation, while 39% is allocated to PHA synthesis. In contrast, during the production phase, carbon distribution shifts to 43% and 57%, respectively, resulting in a PHA-to-biomass ratio of 77% (w/w). These findings indicate that the metabolic “machinery” undergoes significant changes between the two phases, and that this transition is not instantaneous, as it involves an adjustment in metabolic functioning. Given the metabolic implications of this phase shift, extending the growth phase requires the development of metabolic regulation strategies. In another effort to develop commercially competitive processes, synthetic biology and metabolic engineering techniques have been applied to enhance PHA properties and increase bioprocess yields (Bano et al., 2024 ). Metabolic engineering is based on the concept of a metabolic network as a sequence of enzyme-catalyzed reactions that convert substrates into cellular products (Sharma et al., 2019 ; Stephanopoulos & Sinskey, 1993 ). The metabolic network plays a crucial role in cellular physiology (Litsios et al., 2018 ; Min Lee et al., 2008 ), as it encompasses various regulatory mechanisms that control cellular activities through timely and coordinated physiological responses to environmental changes (Gerosa & Sauer, 2011 ; Litsios et al., 2018 ; Shen et al., 2016 ; Stephanopoulos, 1999 ). Therefore, studying intracellular metabolic processes provides opportunities to efficiently manipulate metabolic pathways and improve overall bioprocess performance. Furthermore, intracellular metabolite concentrations are critical for understanding metabolic dynamics, as they provide insight into the biochemical mechanisms operating within the system. One of the main tools for identifying and quantifying metabolites in a biological system is metabolomics (Luo et al., 2007 ; Xiao et al., 2012 ). This approach allows inference of metabolic changes in microorganisms by determining metabolite concentrations at different time points during cell culture (Buchholz et al., 2002 ; Chen et al., 2025 ; Čuperlović-Culf et al., 2010 ). Among the most widely used methods for metabolite identification and quantification in microbial metabolomics are mass spectrometry (MS) techniques coupled with chromatographic separation, such as gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS), due to their high sensitivity and separation efficiency (Ye et al., 2022 ). The metabolic changes inferred from metabolomic analysis contribute to a deeper understanding of metabolic dynamics. In a recent review, Tanaka et al. ( 2024 ) confirmed metabolomics as a fundamental tool within the Design–Build–Test–Learn (DBTL) cycle. The DBTL cycle is a workflow strategy for developing high-producing microbial strains aimed at achieving commercial-scale production. In addition to strain improvement, the optimization of cultivation conditions can also benefit from the detailed insights that metabolomics provides into metabolic behavior, as highlighted in the review by Tanaka et al. ( 2024 ). Given the challenges posed by the high costs and low competitiveness of PHA production processes, metabolomics represents a valuable approach to expanding knowledge of metabolic systems and their regulatory mechanisms, enabling the development of strategies to make bioprocesses more commercially competitive. In the present work, an untargeted metabolomic analysis was performed at different time points during batch fermentation of a Burkholderia cepacia strain using oleic acid as the carbon source. The objective was to characterize the metabolic alterations occurring throughout the cultivation process and to elucidate their association with the main metabolic pathways employed by the microorganism at different growth stages. This type of analysis provides valuable insights into microbial metabolism, helping to identify pathway bottlenecks that could guide future improvements in PHA production within the framework of the DBTL cycle. 2. Materials and Methods 2.1. Chemicals High-grade methanol, n-heptane, and acetonitrile for liquid chromatography coupled to mass spectrometry (LC–MS) (LiChrosolv®) were purchased from Merck Millipore (Massachusetts, USA), as was formic acid for LC–MS (LiChropur™). N,O-Bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane (LiChropur™), methoxyamine hydrochloride (98% purity), methyl heptadecanoate-d₃₃ in heptane (internal standard), the Fatty Acid Methyl Ester (FAME) organic reference standard (Supelco®), and analytical standards of alkanes from C8 to C20 were obtained from Sigma-Aldrich (St. Louis, MO, USA). 2.2. Fermentation 2.2.1. Strain and culture conditions A modified strain of Burkholderia cepacia (B27), obtained by spontaneous mutation at the Institute of Biotechnology, Universidad Nacional de Colombia (Moreno et al., 2007 ), was used in this study. This strain was developed as part of a long-term effort to establish a commercial PHA production process. Although B. cepacia B27 is capable of accumulating PHAs from various vegetable oils, oleic acid was selected as the sole carbon source to simplify omics analyses (fluxomics in Torres and Riascos, 2020; metabolomics in the present work). Strain activation was carried out following the methodology described by Torres-Ospina (2019). One milliliter of the culture bank was inoculated into 10 mL of LB medium and incubated at 30°C for 24 h. The resulting culture was transferred to 100 mL of fermentation medium and incubated at 30°C for an additional 12 h. Finally, this culture was used to inoculate a Biostat® reactor with a working volume of 1 L, operated at 30°C, 9 × g agitation, pH 6.5, and an aeration rate of 2 vvm for 24 h. 2.2.2. Sampling Six biological replicates were obtained, each consisting of 50 mL of fermentation broth. Samples were collected at 6, 12, 16, and 22 h post-inoculation, centrifuged at 4°C and 7000 × g for 7 min, and the resulting pellets were washed with 50 mL of phosphate-buffered saline (PBS, 4°C). After homogenization by vortexing for 30 s, samples were centrifuged again, and the supernatant discarded. The washing step was repeated, and the final pellets were homogenized by dilution and stored at − 80°C until analysis (Marques & Justino, 2023 ; Patejko et al., 2017 ; Van Gulik et al., 2013 ). 2.3. Sample Analysis 2.3.1. Sample preparation Samples were extracted using an acetonitrile:methanol:water (40:40:20, v/v/v) solution, vortexed for 10 min, and centrifuged at 4°C and 24,000 × g for 10 min. The supernatant was filtered through Agilent Technologies PTFE filters (13 mm, 0.2 µm) and aliquoted (50 µL) into glass vials, which were stored at − 80°C until analysis by LC–QTOF–MS and GC–QTOF–MS (Jaiyesimi et al., 2021 ; Marques & Justino, 2023 ). For GC–QTOF–MS, 30 µL of supernatant was evaporated to dryness for 1 h at 35°C using a SpeedVac concentrator. Ten microliters of O-methoxyamine in pyridine (15 mg/mL) were added, vortexed for 5 min, and incubated in darkness at room temperature for 16 h (Rey-Stolle et al., 2022 ). Silylation was carried out by adding 10 µL of N,O-bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane, vortexing for 5 min, and incubating at 70°C for 1 h. After cooling to room temperature (30 min), 60 µL of methyl heptadecanoate-d₃₃ in heptane (2 mg/L) was added as an internal standard and vortexed for 5 min (Rey-Stolle et al., 2022 ). 2.3.2. LC–QTOF–MS analysis Metabolomic analysis was performed using an Agilent 1260 Infinity LC system coupled to an Agilent 6545 Q-TOF MS (Agilent Technologies, Palo Alto, CA, USA). Ten microliters of the extracted sample were injected into an InfinityLab Poroshell 120 EC-C18 column (2.1 × 150 mm, 2.7 µm, Agilent), maintained at 30°C. The elution gradient consisted of 0.1% (v/v) formic acid in Milli-Q water (Phase A) and 0.1% (v/v) formic acid in acetonitrile (Phase B), at a flow rate of 0.4 mL/min. The gradient program was adapted from León-Inga et al. ( 2024 ): starting at 2% B, linearly increasing to 30% B at 10 min, then to 98% B at 20 min, held for 2 min, and re-equilibrated for 5 min. MS detection was conducted in both positive and negative ESI modes, scanning 50–1100 m/z . Reference masses were used for real-time mass correction: 121.0509 and 922.0098 for positive mode, and 112.9856 and 1033.9881 for negative mode. Nitrogen served as the nebulizing gas (50 psi), with a drying temperature of 325°C and flow of 8 L/min. The sheath gas was set at 350°C and 11 L/min. Capillary and fragmentor voltages were 3000 V and 175 V, respectively. Nitrogen (99.999%) was used as the collision gas. Data were acquired in centroid mode at a scan rate of 1.00 spectra per 24 seconds. 2.3.3. GC–QTOF–MS analysis A 7890B gas chromatograph coupled to a 7250 QTOF MS detector (Agilent Technologies, Palo Alto, CA, USA) was used, equipped with a split/splitless injector (250°C, split ratio = 30) and an Agilent 7693A autosampler. Electron ionization (EI) was performed at 70 eV. One microliter of derivatized sample was injected into a J&W HP-5MS column (30 m × 0.25 mm × 0.25 µm, Agilent Technologies), with helium as the carrier gas (0.7 mL/min). The oven temperature was programmed as follows: 60°C (1 min), ramped at 10°C/min to 325°C. Transfer line, ion source, and quadrupole temperatures were maintained at 280°C, 230°C, and 150°C, respectively. Data were collected in the 50–600 m/z range at 5 spectra/s (Rey-Stolle et al., 2022 ). 2.3.4. Quality control samples Quality control (QC) samples were prepared by pooling equal aliquots of each extracted sample. QC runs were performed prior to sample analysis to ensure system stability, followed by randomized injections of one QC sample after every four analytical runs (Dudzik et al., 2018 ; Kirwan et al., 2022 ; Mosley et al., 2024 ). 2.4. Data Processing Raw LC–QTOF–MS and GC–QTOF–MS data were processed as described by Cala et al. ( 2019 ). LC–MS data were analyzed using Agilent MassHunter Profinder B.10.0 for deconvolution, alignment, and integration. GC–MS data were processed using Agilent MassHunter Unknowns Analysis B.10.00, with metabolite identification based on the Fiehn (2015) and NIST17 libraries. Retention time alignment was performed with Agilent Mass Profiler Professional B.12.1, and integration using MassHunter Quantitative B.10.00. Manual curation was applied to remove background noise. Only metabolites present in 100% of biological replicates per group and with a coefficient of variation < 20% in QC samples were retained for analysis. 2.5. Statistical Analysis To identify statistically significant differences among metabolomic profiles, univariate (UVA) and multivariate (MVA) analyses were applied (Cambiaghi et al., 2017 ; Liland, 2011 ; Pakkir Shah et al., 2024 ; Vinaixa et al., 2012 ). Principal Component Analysis (PCA) was first used to assess data quality and ensure QC clustering consistency (Broadhurst et al., 2018 ; Worley & Powers, 2016 ). Subsequently, Partial Least Squares–Discriminant Analysis (PLS–DA) models were constructed to maximize class separation and identify discriminatory metabolites. Model performance was validated through permutation and cross-validation tests (Westad & Marini, 2015 ; Westerhuis et al., 2010 ; Worley & Powers, 2013 ). Data were auto-scaled prior to analysis (Pang et al., 2021 ). Significant metabolites were selected according to two criteria: (1) VIP > 1 and p 1 for upregulated or < 1 for downregulated metabolites (Farrés et al., 2015 ; Vinaixa et al., 2012 ). All analyses were performed using the MetaboAnalyst 5.0 platform (Pang et al., 2021 ). 2.5 Statistical analysis To determine statistically significant differences between metabolomic profiles, univariate (UVA) and multivariate (MVA) statistical analyses were performed (Cambiaghi et al., 2017 ; Liland, 2011 ; Pakkir Shah et al., 2024 ; Vinaixa et al., 2012 ). Principal Component Analysis (PCA) was applied as a preprocessing step that reduces data dimensionality, in that way the first and second component can be used to evaluate the quality of the acquired data, verifying that the quality control samples were correctly grouped in these models to ensure the stability of the analytical system (Broadhurst et al., 2018 ; Herrera-Rocha et al., 2021 ; Uarrota et al., 2014 ; Worley & Powers, 2016 ). Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) models were built to maximize and inspect the differences between the study groups and select the metabolites responsible for separating the groups; model performance was assessed through permutation and cross-validation tests (Westad & Marini, 2015 ; Westerhuis et al., 2010 ; Worley & Powers, 2013 ). Before statistical analysis, data auto-scaling was used (Pang et al., 2021 ). Significant variables were selected for all platform data only based on the following two criteria: 1) multivariate analysis (MVA) criteria — namely, significant Variables in Projection (VIP > 1) from the PLS-DA model and p values 1 for increased metabolites and FC < 1 for decreased metabolites) (Farrés et al., 2015 ; Vinaixa et al., 2012 ). All analyses were conducted using the MetaboAnalyst 5.0 server (Pang et al., 2021 ). 2.6. Metabolite Identification Monoisotopic mass searches were conducted using CEU Mass Mediator (Gil de la Fuente et al., 2018) and cross-referenced with the following databases: Human Metabolome Database (HMDB) ( http://hmdb.ca ), MassBank ( https://massbank.eu/MassBank/ ), Lipid MAPS ( http://lipidmaps.org ), GNPS, BioCyc (Karp et al., 2018 ), METLIN (Montenegro-Burke et al., 2020 ), and KEGG (Aoki-Kinoshita, 2006 ). MS/MS spectra were annotated automatically using MS-DIAL (Lai et al., 2018 ), SIRIUS (Dührkop et al., 2019 ), MZmine, and Lipid Annotator software, followed by manual verification using MassHunter Qualitative Analysis v10.0. Metabolite identification confidence levels followed the criteria described by Blaženović et al. ( 2018 ). 2.7. Metabolic Pathway Mapping Metabolic pathway analysis was conducted in MetaboAnalyst 5.0 ( http://www.metaboanalyst.ca/ ), integrating pathway enrichment and topology analyses. The KEGG IDs of the identified metabolites were uploaded using the Burkholderia mallei ATCC 23344 reference library (Pang et al., 2021 ; Xia & Wishart, 2011 ). 3. Results and discussion 3.1 Definition of sampling times The selection of sampling times was aimed at monitoring the metabolic shift previously proposed by Torres-Ospina and Riascos (2020), who reported that the metabolic machinery of B. cepacia reorganizes according to nutrient availability in the culture medium during the exponential growth phase. The growth curve of B. cepacia using oleic acid as the sole carbon source was established, and the specific growth rate was determined at different time points to define the metabolic phases of the bioprocess. As shown in Fig. 1 , the growth rate remained constant (µ = 0.32 h⁻¹) between 6 and 16 hours of cultivation, indicating that the process was in the exponential growth phase. At 22 hours, a decrease in the specific growth rate was observed (µ = 0.16 h⁻¹), suggesting a transition from exponential to stationary phase. Based on these observations, four sampling points were selected: two within the exponential phase (6 and 12 h), one in the transition phase (16 h), and one in the stationary phase (22 h). These time points enabled the evaluation of metabolite concentration dynamics and their correlation with the activity of the main metabolic pathways during cultivation. PHA production was detected throughout the entire process, showing a nearly constant accumulation between 6 and 22 hours. This behavior confirms that in B. cepacia , PHA biosynthesis begins at the onset of the exponential phase and continues during subsequent growth stages. 3.2 Quality Control Analysis. Unsupervised PCA models were applied to assess the performance of the QC samples (Broadhurst et al., 2018 ). All analytical platforms showed tight QC clustering, confirming data consistency and indicating that the observed group separations originated from biological rather than analytical variation. The differences among experimental groups throughout the fermentation were further explored and maximized using Partial Least Squares Discriminant Analysis (PLS-DA). As shown in Fig. 2 , the models derived from the three analytical platforms revealed a clear separation of metabolic profiles corresponding to the different sampling times (6, 12, 16, and 22 h). This consistent separation across all platforms indicates substantial metabolic reorganization during the batch fermentation of B. cepacia and agreement between analytical approaches. Beyond the clear discrimination of the groups, the sequential distribution of the clusters along the principal component axes reflects a temporal evolution of the metabolic fingerprint, in line with the expected physiological transitions during cultivation. This trend supports the occurrence of a dynamic metabolic reprogramming as fermentation progresses. The robustness of the metabolomic data is supported by high coefficients of determination (R² = 0.95–0.99), indicating a strong correlation between metabolite abundance and fermentation time, and by high cross-validated coefficients (Q² = 0.89–0.91), confirming the predictive reliability of the models. The observed changes in metabolite abundance are consistent with the progressive depletion of nutrients in the culture medium, which drives shifts in cellular metabolism. Similar patterns have been reported in previous studies (Behera et al., 2022 ; Koller, 2020 ; Torres-Ospina & Riascos, 2020). 3.3 Metabolite profiling at different fermentation times using multiple analytical platforms. Statistically significant features were identified by combining multivariate (MVA) and univariate (UVA) criteria: variables with Variable Importance in Projection (VIP) > 1 (with Jack-Knifing validation) and percentage change > 20% with p < 0.05 were selected. Figure 3 shows a Venn diagram illustrating the number of significantly altered metabolites detected by each analytical platform, categorized by chemical family. A total of 133 metabolites were uniquely detected in the LC-QTOF-MS(–) platform (yellow), mainly classified as lipids, nucleosides, organic acids, organic oxygen compounds, and organoheterocyclic compounds. The LC-QTOF-MS(+) platform (blue) identified 90 unique metabolites, displaying greater chemical diversity that included benzenoids, polyketides, and hydrocarbons, in addition to the classes mentioned above. Meanwhile, the GC-QTOF-MS platform (green) annotated 28 unique metabolites, primarily lipids, nucleosides, and organic acids. Shared metabolites among the analytical platforms are represented in the overlapping regions of the Venn diagram. The relatively small intersection areas indicate limited redundancy between techniques, underscoring their complementary nature and their combined ability to cover a broader spectrum of metabolite classes. To investigate the dynamics of metabolite abundance throughout the fermentation process, a heatmap was generated using MetaboAnalyst 5.0 (Fig. 4 ). Prior to visualization, the list of identified metabolites was curated against the genomic information of Burkholderia cepacia ATCC using the KEGG and BioCyc databases to ensure the biological relevance of the analyzed compounds. After curation, 111 metabolites were retained and used for pathway analysis and interpretation of metabolic changes during fermentation. Most of the excluded metabolites corresponded to long-chain fatty acids not recorded in the aforementioned databases, preventing their association with specific B. cepacia metabolic pathways. The heatmap displays the relative abundance of metabolites showing significant variations across the four fermentation time points (6, 12, 16, and 22 hours). Red indicates increased abundance, while blue denotes decreased abundance. The clustering pattern reveals two well-defined metabolite groups, each exhibiting distinct temporal trends during the culture process. The first cluster (top section of the heatmap) comprises metabolites that display low abundance during the early stages of fermentation and progressively increase over time. This group shows a heterogeneous chemical composition, with a notable subset of CoA derivatives, including propanoyl-CoA, butanoyl-CoA, and hydroxymethylbutyryl-CoA. Additionally, several amino acids—such as proline, phenylalanine, glutamine, and ornithine—and carbohydrate intermediates like phosphoenolpyruvate, oxalosuccinate, and erythrose-phosphate are also present within this cluster. The elevated levels of amino acids and specific carbohydrates toward the end of the fermentation process suggest metabolic shifts involving pathways such as gluconeogenesis, the pentose phosphate pathway, and the Entner–Doudoroff pathway, all of which are directly associated with energy generation for cellular growth and maintenance (Koller, 2020 ). These findings are consistent with those reported by Fukui et al. ( 2014 ), who observed that the use of octanoate as a carbon source in R. eutropha H16 increased acetyl-CoA concentration, leading to the accumulation of carboxylic acids and stimulating PEP and triose phosphate formation via oxaloacetate. In contrast, the second cluster (bottom section of the heatmap) exhibits the opposite pattern, with metabolites present at higher levels during the initial fermentation stages and decreasing over time. Notably, this group includes oleic acid, the supplied carbon source, which shows significant depletion, suggesting an intense substrate uptake that temporarily exceeds the metabolic capacity for its conversion during early fermentation. Similarly, linoleic acid and hexadecanoic acid follow the same trend, reinforcing the notion of early lipid utilization through β-oxidation, one of the main pathways in B. cepacia for energy and PHA production when fatty acids are used as carbon sources (Escapa et al., 2012 ; Koller, 2020 ). These results are consistent with those reported by Gao et al. ( 2011 ). This cluster also includes important metabolites such as nucleosides (adenosine, guanosine, uridine, and adenine), pyridoxal, and CDP-glycerol. The elevated nucleoside levels during the early stages may reflect increased nucleotide turnover and biosynthetic activity associated with cell replication, underscoring the dynamic changes in nucleotide metabolism and cofactor availability as fermentation progresses. Overall, these findings reveal a clear metabolic transition throughout the fermentation process. In the early stages, fatty acids such as oleic, linoleic, and hexadecanoic acids undergo intense degradation, serving as the primary energy and carbon sources. As fermentation progresses, the metabolic pattern shifts toward the catabolism of amino acids and carbohydrates, evidenced by their increased abundance at later time points. This transition likely represents an adaptive metabolic reprogramming triggered by changes in substrate availability and the energetic demands of the cells during the different phases of B. cepacia fermentation. 3.4 Analysis of metabolic pathways during fermentation In bacteria, PHA biosynthetic pathways are closely interconnected with central metabolic routes, including glycolysis, the TCA cycle, β-oxidation, fatty acid degradation, amino acid catabolism, the pentose phosphate pathway, and the serine pathway (Koller, 2020 ; Tan et al., 2014 ). During fermentation, the dynamics of these pathways in B. cepacia can be elucidated by analyzing the temporal evolution of the identified metabolites. Metabolic pathway analysis was performed by integrating pathway enrichment and topology analyses. The enrichment analysis evaluated whether the identified metabolites were significantly represented within the theoretical pathways predicted from KEGG, using the Burkholderia mallei ATCC 23344 genome as reference. The topology analysis assessed the relative importance of each metabolite within the network, treating each as a node. As illustrated in Fig. 5 , these analyses enabled the identification of 58 distinct pathways. Among these, 10 pathways exhibited statistically significant enrichment with an effect size greater than zero. Particularly relevant were the fatty acid degradation (β-oxidation), TCA cycle, arginine biosynthesis, glyoxylate metabolism, phenylalanine metabolism, purine metabolism, cysteine metabolism, tryptophan metabolism, pyrimidine metabolism, and the pentose phosphate pathway. β-oxidation is the main pathway responsible for fatty acid metabolism under aerobic conditions (Sun et al., 2003 ; Thamarai et al., 2024 ). The analysis identified several metabolites associated with this pathway, including hexadecanoate, butanoyl-CoA, glutaryl-CoA, acetyl-CoA, dodecanoyl-CoA, decanoyl-CoA, hexanoyl-CoA, and octanoyl-CoA. The key product of this pathway is acetyl-CoA, a central intermediate that connects multiple core biosynthetic routes in B. cepacia during PHA production (Koller, 2020 ). In PHA-producing microorganisms such as Cupriavidus necator , Chromatium vinosum and Pseudomonas aeruginosa , the metabolic flux from acetyl-CoA toward PHA synthesis is strongly influenced by nutrient availability (Steinbüchel & Hein, 2001). A similar pattern is observed in B. cepacia . As shown in Fig. 4 , β-oxidation intermediates increase during the exponential growth phase, along with higher acetyl-CoA levels. This trend suggests that, at this stage, acetyl-CoA is primarily directed toward energy generation and biomass formation. Concurrently, certain β-oxidation intermediates, particularly (S)-3-hydroxyacyl-CoA, are likely diverted to PHA biosynthesis. This dual channeling explains the simultaneous increase in biomass and polymer accumulation observed since the exponential phase, consistent with the findings reported by Torres (2019). These results are also in agreement with Tanadchangsaeng and Roytrakul ( 2020 ), who reported that in C. necator grown on glycerol, enhanced expression of gluconeogenic enzymes was associated with reduced PHA synthesis, supporting the close relationship between central carbon metabolism and polymer production. Glucose anabolism in B. cepacia occurs through gluconeogenesis, a pathway activated via glyoxylate metabolism, where phosphoenolpyruvate (PEP) is generated from oxaloacetate by PEP carboxykinase (Muñoz-Elías & McKinney, 2006 ; Oh et al., 2002 ). This pathway subsequently stimulates the pentose phosphate (PP) pathway, which plays a key role in cell growth. As shown in Fig. 6 , higher abundances of gluconeogenic and PP pathway intermediates —such as PEP, erythrose-4-phosphate (E4P), ribose-5-phosphate (R5P), and phenylalanine— were observed during the stationary phase, whereas sedoheptulose-7-phosphate exhibited the opposite pattern. These findings are consistent with those reported by Fukui et al. ( 2014 ), who studied Ralstonia eutropha ( Cupriavidus necator ) H16 cultivated on octanoate and observed the activation of gluconeogenesis mediated by the glyoxylate cycle during PHA accumulation. Similarly, the fluxomic analysis of B. cepacia cultured with oleic acid (Torres-Ospina & Riascos, 2020) demonstrated that gluconeogenesis is active during the exponential phase, supporting the generation of E4P and R5P, precursors for phenylalanine and nucleotides, respectively, which are essential for biomass formation. In contrast, the metabolomic data from the present study show lower abundances of E4P and R5P at early growth stages (6 and 12 h) and their accumulation when biomass production decreases (16 and 22 h). Interestingly, nucleosides such as adenosine, guanosine, and uridine displayed the opposite behavior, being more abundant during early growth (6 and 12 h) and decreasing toward the stationary phase (16 and 22 h). This inverse relationship with R5P, a nucleotide precursor, suggests a regulatory mechanism that enhances R5P accumulation by downregulating nucleotide synthesis. Future studies using multi-omics approaches could provide deeper insights into the interaction between these metabolites and their underlying regulatory mechanisms. Finally, the progressive increase in oxalosuccinate observed between the exponential and stationary phases suggests an activation of the tricarboxylic acid (TCA) cycle, with a possible redirection of its intermediates toward PHA biosynthesis. This adaptation likely enables B. cepacia to maximize nutrient utilization in response to the changing culture conditions. Together, these results reveal a clear metabolic transition throughout the fermentation process. During the early stages, there is an intense degradation of fatty acids, such as oleic, linoleic, and hexadecanoic acids, which appear to serve as primary energy sources. As fermentation progresses, this metabolic pattern shifts toward the catabolism of amino acids and carbohydrates, as reflected by their increased abundance in later time points. This transition likely represents a form of metabolic reprogramming, where B. cepacia reorganizes its metabolic fluxes in response to nutrient depletion and changing energetic demands. Such metabolic reprogramming optimizes carbon redistribution between energy production, biosynthesis, and polymer accumulation, ensuring cellular adaptation and maintenance across the different phases of fermentation. Conclusions A comprehensive metabolomic analysis of Burkholderia cepacia is essential to advance the understanding of the metabolic dynamics governing its fermentation process. The multiplatform analytical strategy, combined with rigorous quality control procedures, enabled the identification of a broad spectrum of metabolites that exhibited significant temporal variations in abundance during batch fermentation for PHA production using oleic acid as the carbon source. Metabolic pathway analysis revealed the key routes involved in B. cepacia metabolism, highlighting acetyl-CoA as the central intermediate connecting core biosynthetic pathways with PHA synthesis. The results confirmed the existence of a metabolic switch occurring during the exponential phase, mediated by a complex regulatory system responsive to nutrient availability in the culture medium. This metabolic reorganization involves a shift from biomass formation toward energy generation and polymer accumulation, suggesting an adaptive response that reallocates the carbon flux to sustain cellular maintenance and survival under changing environmental conditions. Moreover, the integration of data from LC-QTOF-MS(±) and GC-QTOF-MS platforms demonstrated the complementarity of analytical techniques for achieving comprehensive metabolome coverage, underscoring the importance of multiplatform metabolomics in deciphering microbial metabolism. These findings confirmed the existence of a metabolic switch during the exponential phase, mediated by a complex regulatory system responsive to nutrient availability in the culture medium. This switch represents a form of metabolic reprogramming, through which B. cepacia dynamically adjusts its central metabolism to balance energy generation, biosynthesis, and polymer accumulation. Understanding this adaptive reorganization provides a foundation for the rational design of improved PHA-producing strains. In the context of the Design–Build–Test–Learn (DBTL) framework, the metabolomic insights obtained in this study contribute to the Learn phase, enabling data-driven strategies for the next Design and Build iterations aimed at optimizing metabolic fluxes and enhancing PHA yield and productivity. Declarations Data Availability All datasets generated during this study will be publicly available in the Metabolomics Workbench repository https://www.metabolomicsworkbench.org. The corresponding accession numbers will be included in the final published version of the article. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Credit Author Statement: A.E.A.O. and N.C.M.S. collection and processing of samples. 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Supplementary Files S3.Pathwayanalysis.xlsx S1.Statisticalanalysis.docx S2.TableMetabolomicsfermentation.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviews received at journal 18 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 22 Nov, 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. 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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-8182922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573666176,"identity":"8a765223-2add-40b2-aaf7-2e702d6551d2","order_by":0,"name":"Alfonso E. Alarcón","email":"","orcid":"","institution":"Universidad Nacional de Colombia","correspondingAuthor":false,"prefix":"","firstName":"Alfonso","middleName":"E.","lastName":"Alarcón","suffix":""},{"id":573666178,"identity":"0624f218-cd39-4e9a-9f9d-72bc98e14ea6","order_by":1,"name":"Nubia C. 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07:10:21","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212349,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/df6293df1498cdde2cb7ccb4.html"},{"id":100369363,"identity":"8a1402fd-ac30-49c8-9242-fbe7bacd832d","added_by":"auto","created_at":"2026-01-16 07:58:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70596,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth phases and sampling times for metabolomic analysis of \u003cem\u003eB. cepacia\u003c/em\u003e with oleic acid as the carbon source. Data represent the mean of six biological replicates; error bars indicate standard deviation; dotted lines delimit the exponential growth phase.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/95ba7c50c81913e66e26add7.png"},{"id":100208697,"identity":"4579faea-6ee6-446a-8b6b-841759f0f484","added_by":"auto","created_at":"2026-01-14 07:10:23","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191316,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA score plots showing the metabolic profile separation at different fermentation times of \u003cem\u003eB. cepacia\u003c/em\u003e. (A) LC-QTOF-MS(+): R² = 0.96, Q² = 0.90; (B) LC-QTOF-MS(–): R² = 0.99, Q² = 0.91; (C) GC-QTOF-MS: R² = 0.95, Q² = 0.89. Ovals of different colors group samples from each fermentation time (red: 6 h; green: 12 h; purple: 16 h; cyan: 22 h). Each oval contains six biological replicates.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/eaa3d5285ba53c2c283d7658.jpeg"},{"id":100208669,"identity":"48e32089-0ca9-4579-b38f-91604de5b634","added_by":"auto","created_at":"2026-01-14 07:10:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50469,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram showing the number of metabolites identified by each analytical platform: LC-QTOF-MS(+) (blue), LC-QTOF-MS(–) (yellow), and GC-QTOF-MS (green).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/0b9a3db50128af3c76800edc.jpeg"},{"id":100208677,"identity":"af09649a-4c0a-4a86-96e9-c8f422c5b41d","added_by":"auto","created_at":"2026-01-14 07:10:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":293696,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of metabolite abundance changes in the \u003cem\u003eBurkholderia cepacia\u003c/em\u003e endometabolome during fermentation using oleic acid as the carbon source. Metabolite intensities are normalized across samples. Blue indicates low relative abundance, while red indicates high relative abundance.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/395d93a88bf51660e6b1fd3a.png"},{"id":100369976,"identity":"8ac0e57c-f6ac-486a-bcc5-dbb28436b8a7","added_by":"auto","created_at":"2026-01-16 07:59:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePathway analysis of \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB. cepacia\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e metabolism using oleic acid as the carbon source.\u003c/em\u003e Generated with MetaboAnalyst. The y-axis represents pathway enrichment, and the x-axis represents pathway impact. Bubble size indicates the relative impact of each pathway, and color intensity reflects the enrichment level.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/f920f8a6819f3c74030d668c.png"},{"id":100208671,"identity":"7f294c26-c9d8-475a-9dff-9769c5507d7b","added_by":"auto","created_at":"2026-01-14 07:10:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":133157,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic pathways involved in PHA synthesis from oleic acid in \u003cem\u003eBurkholderia cepacia\u003c/em\u003e. 3-HV-CoA: 3-hydroxyvaleryl-CoA; 3-HB-CoA: 3-hydroxybutyryl-CoA; TCA: tricarboxylic acid cycle; PEP: phosphoenolpyruvate; G3P: glyceraldehyde-3-phosphate; G6P: glucose-6-phosphate.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/66cf04f683d2dc657cbdae46.png"},{"id":100406216,"identity":"3da67810-913d-4169-9a12-ee93021e5236","added_by":"auto","created_at":"2026-01-16 12:57:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1642525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/9a7086ba-8595-48b2-bf42-35abb7c642ea.pdf"},{"id":100370070,"identity":"bc006da3-51b8-4536-a16a-441cb6b31906","added_by":"auto","created_at":"2026-01-16 07:59:54","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20531,"visible":true,"origin":"","legend":"","description":"","filename":"S3.Pathwayanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/07109c19ec739fa249bcc201.xlsx"},{"id":100208673,"identity":"389f7c40-2840-41c8-bf52-ecf0d5a7a88d","added_by":"auto","created_at":"2026-01-14 07:10:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90236,"visible":true,"origin":"","legend":"","description":"","filename":"S1.Statisticalanalysis.docx","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/7885fadf5f458d4e1460eb43.docx"},{"id":100208667,"identity":"75a11b21-c8fe-4ab8-b7ae-e975818aeb04","added_by":"auto","created_at":"2026-01-14 07:10:21","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":97732,"visible":true,"origin":"","legend":"","description":"","filename":"S2.TableMetabolomicsfermentation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8182922/v1/f7e13f2c81f1bd0adcbc027d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multiplatform Metabolomic Analysis Reveals Metabolic Reprogramming of Burkholderia cepacia During Polyhydroxyalkanoate Production from Oleic Acid","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe pollution generated by petroleum-derived polymers and their recycling difficulties has driven the need for new and greener materials (Emadian et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mohapatra et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Peptu \u0026amp; Kowalczuk, 2018). Polyhydroxyalkanoates (PHA) have become biodegradable candidates for the replacement of chemical polymers due to their similarity in properties with conventional plastic materials (tensile strength and flexibility) and their complete biodegradability (Kumar et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nielsen \u0026amp; Keasling, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Suriyamongkol et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Urtuvia et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Some microorganisms make PHA inside their cells and store them as an energy source (Możejko-Ciesielska \u0026amp; Kiewisz, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sagong et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Among the bacterial species capable of accumulating large quantities of PHA and its copolymers are \u003cem\u003eCupriavidus necator\u003c/em\u003e, \u003cem\u003ePseudomonas sp.\u003c/em\u003e, and \u003cem\u003eBurkholderia sp\u003c/em\u003e. (Anjum et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chee et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Urtuvia et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the main obstacles to the commercial application of PHA production bioprocesses is the high production cost (Bano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mitra et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Profitability is directly related to the microbial rate of growth and production, the substrate cost, and the substrate conversion efficiency (Albuquerque \u0026amp; Malafaia, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To enhance production competitiveness, Akiyama et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) reported that using vegetable oils as the carbon source results in higher PHA yields (0.6\u0026ndash;0.8 g PHA per g oil) compared to simple sugars. In addition, several strategies have been investigated to improve process profitability, including the prospecting of new strains (Cerrone et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kucera et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), genetic improvement to obtain high-producing strains (Hern\u0026aacute;ndez Jir\u0026oacute;n, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Santolin et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the formulation of metabolically structured models to support process optimization (Torres Ospina, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies on PHA production with \u003cem\u003eBurkholderia cepacia\u003c/em\u003e (Ardila Ar\u0026eacute;valo \u0026amp; Viloria Garc\u0026iacute;a, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Becerra Jim\u0026eacute;nez, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; M\u0026eacute;ndez, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Viloria et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that achieving higher biomass concentrations is essential to improve process profitability. Furthermore, in the study by Torres-Ospina \u0026amp; Riascos (2020), fluxomic analysis at different time points of a batch fermentation using oleic acid and a simplified metabolic network revealed that, during the growth phase, 61% of the carbon flux is directed toward biomass and energy generation, while 39% is allocated to PHA synthesis. In contrast, during the production phase, carbon distribution shifts to 43% and 57%, respectively, resulting in a PHA-to-biomass ratio of 77% (w/w). These findings indicate that the metabolic \u0026ldquo;machinery\u0026rdquo; undergoes significant changes between the two phases, and that this transition is not instantaneous, as it involves an adjustment in metabolic functioning. Given the metabolic implications of this phase shift, extending the growth phase requires the development of metabolic regulation strategies.\u003c/p\u003e \u003cp\u003eIn another effort to develop commercially competitive processes, synthetic biology and metabolic engineering techniques have been applied to enhance PHA properties and increase bioprocess yields (Bano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Metabolic engineering is based on the concept of a metabolic network as a sequence of enzyme-catalyzed reactions that convert substrates into cellular products (Sharma et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stephanopoulos \u0026amp; Sinskey, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The metabolic network plays a crucial role in cellular physiology (Litsios et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Min Lee et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), as it encompasses various regulatory mechanisms that control cellular activities through timely and coordinated physiological responses to environmental changes (Gerosa \u0026amp; Sauer, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Litsios et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stephanopoulos, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Therefore, studying intracellular metabolic processes provides opportunities to efficiently manipulate metabolic pathways and improve overall bioprocess performance.\u003c/p\u003e \u003cp\u003eFurthermore, intracellular metabolite concentrations are critical for understanding metabolic dynamics, as they provide insight into the biochemical mechanisms operating within the system. One of the main tools for identifying and quantifying metabolites in a biological system is metabolomics (Luo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This approach allows inference of metabolic changes in microorganisms by determining metabolite concentrations at different time points during cell culture (Buchholz et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Čuperlović-Culf et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Among the most widely used methods for metabolite identification and quantification in microbial metabolomics are mass spectrometry (MS) techniques coupled with chromatographic separation, such as gas chromatography\u0026ndash;mass spectrometry (GC\u0026ndash;MS) and liquid chromatography\u0026ndash;mass spectrometry (LC\u0026ndash;MS), due to their high sensitivity and separation efficiency (Ye et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe metabolic changes inferred from metabolomic analysis contribute to a deeper understanding of metabolic dynamics. In a recent review, Tanaka et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirmed metabolomics as a fundamental tool within the Design\u0026ndash;Build\u0026ndash;Test\u0026ndash;Learn (DBTL) cycle. The DBTL cycle is a workflow strategy for developing high-producing microbial strains aimed at achieving commercial-scale production. In addition to strain improvement, the optimization of cultivation conditions can also benefit from the detailed insights that metabolomics provides into metabolic behavior, as highlighted in the review by Tanaka et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the challenges posed by the high costs and low competitiveness of PHA production processes, metabolomics represents a valuable approach to expanding knowledge of metabolic systems and their regulatory mechanisms, enabling the development of strategies to make bioprocesses more commercially competitive. In the present work, an untargeted metabolomic analysis was performed at different time points during batch fermentation of a \u003cem\u003eBurkholderia cepacia\u003c/em\u003e strain using oleic acid as the carbon source. The objective was to characterize the metabolic alterations occurring throughout the cultivation process and to elucidate their association with the main metabolic pathways employed by the microorganism at different growth stages. This type of analysis provides valuable insights into microbial metabolism, helping to identify pathway bottlenecks that could guide future improvements in PHA production within the framework of the DBTL cycle.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Chemicals\u003c/h2\u003e \u003cp\u003eHigh-grade methanol, n-heptane, and acetonitrile for liquid chromatography coupled to mass spectrometry (LC\u0026ndash;MS) (LiChrosolv\u0026reg;) were purchased from Merck Millipore (Massachusetts, USA), as was formic acid for LC\u0026ndash;MS (LiChropur\u0026trade;). N,O-Bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane (LiChropur\u0026trade;), methoxyamine hydrochloride (98% purity), methyl heptadecanoate-d₃₃ in heptane (internal standard), the Fatty Acid Methyl Ester (FAME) organic reference standard (Supelco\u0026reg;), and analytical standards of alkanes from C8 to C20 were obtained from Sigma-Aldrich (St. Louis, MO, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Fermentation\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Strain and culture conditions\u003c/h2\u003e \u003cp\u003eA modified strain of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e (B27), obtained by spontaneous mutation at the Institute of Biotechnology, Universidad Nacional de Colombia (Moreno et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), was used in this study. This strain was developed as part of a long-term effort to establish a commercial PHA production process. Although \u003cem\u003eB. cepacia\u003c/em\u003e B27 is capable of accumulating PHAs from various vegetable oils, oleic acid was selected as the sole carbon source to simplify omics analyses (fluxomics in Torres and Riascos, 2020; metabolomics in the present work).\u003c/p\u003e \u003cp\u003eStrain activation was carried out following the methodology described by Torres-Ospina (2019). One milliliter of the culture bank was inoculated into 10 mL of LB medium and incubated at 30\u0026deg;C for 24 h. The resulting culture was transferred to 100 mL of fermentation medium and incubated at 30\u0026deg;C for an additional 12 h. Finally, this culture was used to inoculate a Biostat\u0026reg; reactor with a working volume of 1 L, operated at 30\u0026deg;C, 9 \u0026times; g agitation, pH 6.5, and an aeration rate of 2 vvm for 24 h.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Sampling\u003c/h2\u003e \u003cp\u003eSix biological replicates were obtained, each consisting of 50 mL of fermentation broth. Samples were collected at 6, 12, 16, and 22 h post-inoculation, centrifuged at 4\u0026deg;C and 7000 \u0026times; g for 7 min, and the resulting pellets were washed with 50 mL of phosphate-buffered saline (PBS, 4\u0026deg;C). After homogenization by vortexing for 30 s, samples were centrifuged again, and the supernatant discarded. The washing step was repeated, and the final pellets were homogenized by dilution and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis (Marques \u0026amp; Justino, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patejko et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van Gulik et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Sample preparation\u003c/h2\u003e \u003cp\u003eSamples were extracted using an acetonitrile:methanol:water (40:40:20, v/v/v) solution, vortexed for 10 min, and centrifuged at 4\u0026deg;C and 24,000 \u0026times; g for 10 min. The supernatant was filtered through Agilent Technologies PTFE filters (13 mm, 0.2 \u0026micro;m) and aliquoted (50 \u0026micro;L) into glass vials, which were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis by LC\u0026ndash;QTOF\u0026ndash;MS and GC\u0026ndash;QTOF\u0026ndash;MS (Jaiyesimi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marques \u0026amp; Justino, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor GC\u0026ndash;QTOF\u0026ndash;MS, 30 \u0026micro;L of supernatant was evaporated to dryness for 1 h at 35\u0026deg;C using a SpeedVac concentrator. Ten microliters of O-methoxyamine in pyridine (15 mg/mL) were added, vortexed for 5 min, and incubated in darkness at room temperature for 16 h (Rey-Stolle et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Silylation was carried out by adding 10 \u0026micro;L of N,O-bis(trimethylsilyl)trifluoroacetamide with 1% trimethylchlorosilane, vortexing for 5 min, and incubating at 70\u0026deg;C for 1 h. After cooling to room temperature (30 min), 60 \u0026micro;L of methyl heptadecanoate-d₃₃ in heptane (2 mg/L) was added as an internal standard and vortexed for 5 min (Rey-Stolle et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. LC\u0026ndash;QTOF\u0026ndash;MS analysis\u003c/h2\u003e \u003cp\u003eMetabolomic analysis was performed using an Agilent 1260 Infinity LC system coupled to an Agilent 6545 Q-TOF MS (Agilent Technologies, Palo Alto, CA, USA). Ten microliters of the extracted sample were injected into an InfinityLab Poroshell 120 EC-C18 column (2.1 \u0026times; 150 mm, 2.7 \u0026micro;m, Agilent), maintained at 30\u0026deg;C. The elution gradient consisted of 0.1% (v/v) formic acid in Milli-Q water (Phase A) and 0.1% (v/v) formic acid in acetonitrile (Phase B), at a flow rate of 0.4 mL/min. The gradient program was adapted from Le\u0026oacute;n-Inga et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e): starting at 2% B, linearly increasing to 30% B at 10 min, then to 98% B at 20 min, held for 2 min, and re-equilibrated for 5 min.\u003c/p\u003e \u003cp\u003eMS detection was conducted in both positive and negative ESI modes, scanning 50\u0026ndash;1100 \u003cem\u003em/z\u003c/em\u003e. Reference masses were used for real-time mass correction: 121.0509 and 922.0098 for positive mode, and 112.9856 and 1033.9881 for negative mode. Nitrogen served as the nebulizing gas (50 psi), with a drying temperature of 325\u0026deg;C and flow of 8 L/min. The sheath gas was set at 350\u0026deg;C and 11 L/min. Capillary and fragmentor voltages were 3000 V and 175 V, respectively. Nitrogen (99.999%) was used as the collision gas. Data were acquired in centroid mode at a scan rate of 1.00 spectra per 24 seconds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. GC\u0026ndash;QTOF\u0026ndash;MS analysis\u003c/h2\u003e \u003cp\u003eA 7890B gas chromatograph coupled to a 7250 QTOF MS detector (Agilent Technologies, Palo Alto, CA, USA) was used, equipped with a split/splitless injector (250\u0026deg;C, split ratio\u0026thinsp;=\u0026thinsp;30) and an Agilent 7693A autosampler. Electron ionization (EI) was performed at 70 eV. One microliter of derivatized sample was injected into a J\u0026amp;W HP-5MS column (30 m \u0026times; 0.25 mm \u0026times; 0.25 \u0026micro;m, Agilent Technologies), with helium as the carrier gas (0.7 mL/min).\u003c/p\u003e \u003cp\u003eThe oven temperature was programmed as follows: 60\u0026deg;C (1 min), ramped at 10\u0026deg;C/min to 325\u0026deg;C. Transfer line, ion source, and quadrupole temperatures were maintained at 280\u0026deg;C, 230\u0026deg;C, and 150\u0026deg;C, respectively. Data were collected in the 50\u0026ndash;600 \u003cem\u003em/z\u003c/em\u003e range at 5 spectra/s (Rey-Stolle et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Quality control samples\u003c/h2\u003e \u003cp\u003eQuality control (QC) samples were prepared by pooling equal aliquots of each extracted sample. QC runs were performed prior to sample analysis to ensure system stability, followed by randomized injections of one QC sample after every four analytical runs (Dudzik et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kirwan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mosley et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data Processing\u003c/h2\u003e \u003cp\u003eRaw LC\u0026ndash;QTOF\u0026ndash;MS and GC\u0026ndash;QTOF\u0026ndash;MS data were processed as described by Cala et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). LC\u0026ndash;MS data were analyzed using Agilent MassHunter Profinder B.10.0 for deconvolution, alignment, and integration. GC\u0026ndash;MS data were processed using Agilent MassHunter Unknowns Analysis B.10.00, with metabolite identification based on the Fiehn (2015) and NIST17 libraries. Retention time alignment was performed with Agilent Mass Profiler Professional B.12.1, and integration using MassHunter Quantitative B.10.00. Manual curation was applied to remove background noise.\u003c/p\u003e \u003cp\u003eOnly metabolites present in 100% of biological replicates per group and with a coefficient of variation\u0026thinsp;\u0026lt;\u0026thinsp;20% in QC samples were retained for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e \u003cp\u003eTo identify statistically significant differences among metabolomic profiles, univariate (UVA) and multivariate (MVA) analyses were applied (Cambiaghi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liland, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pakkir Shah et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vinaixa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Principal Component Analysis (PCA) was first used to assess data quality and ensure QC clustering consistency (Broadhurst et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Worley \u0026amp; Powers, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Subsequently, Partial Least Squares\u0026ndash;Discriminant Analysis (PLS\u0026ndash;DA) models were constructed to maximize class separation and identify discriminatory metabolites. Model performance was validated through permutation and cross-validation tests (Westad \u0026amp; Marini, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Westerhuis et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Worley \u0026amp; Powers, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData were auto-scaled prior to analysis (Pang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Significant metabolites were selected according to two criteria: (1) VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; and (2) fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1 for upregulated or \u0026lt;\u0026thinsp;1 for downregulated metabolites (Farr\u0026eacute;s et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vinaixa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All analyses were performed using the MetaboAnalyst 5.0 platform (Pang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eTo determine statistically significant differences between metabolomic profiles, univariate (UVA) and multivariate (MVA) statistical analyses were performed (Cambiaghi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liland, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pakkir Shah et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vinaixa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Principal Component Analysis (PCA) was applied as a preprocessing step that reduces data dimensionality, in that way the first and second component can be used to evaluate the quality of the acquired data, verifying that the quality control samples were correctly grouped in these models to ensure the stability of the analytical system (Broadhurst et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Herrera-Rocha et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uarrota et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Worley \u0026amp; Powers, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Subsequently, Partial Least Squares Discriminant Analysis (PLS-DA) models were built to maximize and inspect the differences between the study groups and select the metabolites responsible for separating the groups; model performance was assessed through permutation and cross-validation tests (Westad \u0026amp; Marini, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Westerhuis et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Worley \u0026amp; Powers, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore statistical analysis, data auto-scaling was used (Pang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Significant variables were selected for all platform data only based on the following two criteria: 1) multivariate analysis (MVA) criteria \u0026mdash; namely, significant Variables in Projection (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1) from the PLS-DA model and p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the univariate analysis (UVA) test, and 2) Fold Change (FC\u0026thinsp;\u0026gt;\u0026thinsp;1 for increased metabolites and FC\u0026thinsp;\u0026lt;\u0026thinsp;1 for decreased metabolites) (Farr\u0026eacute;s et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vinaixa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All analyses were conducted using the MetaboAnalyst 5.0 server (Pang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Metabolite Identification\u003c/h2\u003e \u003cp\u003eMonoisotopic mass searches were conducted using CEU Mass Mediator (Gil de la Fuente et al., 2018) and cross-referenced with the following databases: Human Metabolome Database (HMDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hmdb.ca\u003c/span\u003e\u003cspan address=\"http://hmdb.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), MassBank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://massbank.eu/MassBank/\u003c/span\u003e\u003cspan address=\"https://massbank.eu/MassBank/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Lipid MAPS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lipidmaps.org\u003c/span\u003e\u003cspan address=\"http://lipidmaps.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GNPS, BioCyc (Karp et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), METLIN (Montenegro-Burke et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and KEGG (Aoki-Kinoshita, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). MS/MS spectra were annotated automatically using MS-DIAL (Lai et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), SIRIUS (D\u0026uuml;hrkop et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), MZmine, and Lipid Annotator software, followed by manual verification using MassHunter Qualitative Analysis v10.0. Metabolite identification confidence levels followed the criteria described by Blaženović et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Metabolic Pathway Mapping\u003c/h2\u003e \u003cp\u003eMetabolic pathway analysis was conducted in MetaboAnalyst 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"http://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), integrating pathway enrichment and topology analyses. The KEGG IDs of the identified metabolites were uploaded using the \u003cem\u003eBurkholderia mallei\u003c/em\u003e ATCC 23344 reference library (Pang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xia \u0026amp; Wishart, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Definition of sampling times\u003c/h2\u003e \u003cp\u003eThe selection of sampling times was aimed at monitoring the metabolic shift previously proposed by Torres-Ospina and Riascos (2020), who reported that the metabolic machinery of B. cepacia reorganizes according to nutrient availability in the culture medium during the exponential growth phase. The growth curve of \u003cem\u003eB. cepacia\u003c/em\u003e using oleic acid as the sole carbon source was established, and the specific growth rate was determined at different time points to define the metabolic phases of the bioprocess. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the growth rate remained constant (\u0026micro;\u0026thinsp;=\u0026thinsp;0.32 h⁻\u0026sup1;) between 6 and 16 hours of cultivation, indicating that the process was in the exponential growth phase. At 22 hours, a decrease in the specific growth rate was observed (\u0026micro;\u0026thinsp;=\u0026thinsp;0.16 h⁻\u0026sup1;), suggesting a transition from exponential to stationary phase.\u003c/p\u003e \u003cp\u003eBased on these observations, four sampling points were selected: two within the exponential phase (6 and 12 h), one in the transition phase (16 h), and one in the stationary phase (22 h). These time points enabled the evaluation of metabolite concentration dynamics and their correlation with the activity of the main metabolic pathways during cultivation. PHA production was detected throughout the entire process, showing a nearly constant accumulation between 6 and 22 hours. This behavior confirms that in \u003cem\u003eB. cepacia\u003c/em\u003e, PHA biosynthesis begins at the onset of the exponential phase and continues during subsequent growth stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Quality Control Analysis.\u003c/h2\u003e \u003cp\u003eUnsupervised PCA models were applied to assess the performance of the QC samples (Broadhurst et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). All analytical platforms showed tight QC clustering, confirming data consistency and indicating that the observed group separations originated from biological rather than analytical variation.\u003c/p\u003e \u003cp\u003eThe differences among experimental groups throughout the fermentation were further explored and maximized using Partial Least Squares Discriminant Analysis (PLS-DA). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the models derived from the three analytical platforms revealed a clear separation of metabolic profiles corresponding to the different sampling times (6, 12, 16, and 22 h). This consistent separation across all platforms indicates substantial metabolic reorganization during the batch fermentation of \u003cem\u003eB. cepacia\u003c/em\u003e and agreement between analytical approaches. Beyond the clear discrimination of the groups, the sequential distribution of the clusters along the principal component axes reflects a temporal evolution of the metabolic fingerprint, in line with the expected physiological transitions during cultivation. This trend supports the occurrence of a dynamic metabolic reprogramming as fermentation progresses.\u003c/p\u003e \u003cp\u003eThe robustness of the metabolomic data is supported by high coefficients of determination (R\u0026sup2; = 0.95\u0026ndash;0.99), indicating a strong correlation between metabolite abundance and fermentation time, and by high cross-validated coefficients (Q\u0026sup2; = 0.89\u0026ndash;0.91), confirming the predictive reliability of the models. The observed changes in metabolite abundance are consistent with the progressive depletion of nutrients in the culture medium, which drives shifts in cellular metabolism. Similar patterns have been reported in previous studies (Behera et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Koller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Torres-Ospina \u0026amp; Riascos, 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Metabolite profiling at different fermentation times using multiple analytical platforms.\u003c/h2\u003e \u003cp\u003eStatistically significant features were identified by combining multivariate (MVA) and univariate (UVA) criteria: variables with Variable Importance in Projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 (with Jack-Knifing validation) and percentage change\u0026thinsp;\u0026gt;\u0026thinsp;20% with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a Venn diagram illustrating the number of significantly altered metabolites detected by each analytical platform, categorized by chemical family. A total of 133 metabolites were uniquely detected in the LC-QTOF-MS(\u0026ndash;) platform (yellow), mainly classified as lipids, nucleosides, organic acids, organic oxygen compounds, and organoheterocyclic compounds. The LC-QTOF-MS(+) platform (blue) identified 90 unique metabolites, displaying greater chemical diversity that included benzenoids, polyketides, and hydrocarbons, in addition to the classes mentioned above. Meanwhile, the GC-QTOF-MS platform (green) annotated 28 unique metabolites, primarily lipids, nucleosides, and organic acids.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eShared metabolites among the analytical platforms are represented in the overlapping regions of the Venn diagram. The relatively small intersection areas indicate limited redundancy between techniques, underscoring their complementary nature and their combined ability to cover a broader spectrum of metabolite classes.\u003c/p\u003e \u003cp\u003eTo investigate the dynamics of metabolite abundance throughout the fermentation process, a heatmap was generated using MetaboAnalyst 5.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Prior to visualization, the list of identified metabolites was curated against the genomic information of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e ATCC using the KEGG and BioCyc databases to ensure the biological relevance of the analyzed compounds. After curation, 111 metabolites were retained and used for pathway analysis and interpretation of metabolic changes during fermentation. Most of the excluded metabolites corresponded to long-chain fatty acids not recorded in the aforementioned databases, preventing their association with specific \u003cem\u003eB. cepacia\u003c/em\u003e metabolic pathways.\u003c/p\u003e \u003cp\u003eThe heatmap displays the relative abundance of metabolites showing significant variations across the four fermentation time points (6, 12, 16, and 22 hours). Red indicates increased abundance, while blue denotes decreased abundance. The clustering pattern reveals two well-defined metabolite groups, each exhibiting distinct temporal trends during the culture process. The first cluster (top section of the heatmap) comprises metabolites that display low abundance during the early stages of fermentation and progressively increase over time. This group shows a heterogeneous chemical composition, with a notable subset of CoA derivatives, including propanoyl-CoA, butanoyl-CoA, and hydroxymethylbutyryl-CoA. Additionally, several amino acids\u0026mdash;such as proline, phenylalanine, glutamine, and ornithine\u0026mdash;and carbohydrate intermediates like phosphoenolpyruvate, oxalosuccinate, and erythrose-phosphate are also present within this cluster. The elevated levels of amino acids and specific carbohydrates toward the end of the fermentation process suggest metabolic shifts involving pathways such as gluconeogenesis, the pentose phosphate pathway, and the Entner\u0026ndash;Doudoroff pathway, all of which are directly associated with energy generation for cellular growth and maintenance (Koller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings are consistent with those reported by Fukui et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who observed that the use of octanoate as a carbon source in \u003cem\u003eR. eutropha\u003c/em\u003e H16 increased acetyl-CoA concentration, leading to the accumulation of carboxylic acids and stimulating PEP and triose phosphate formation via oxaloacetate.\u003c/p\u003e \u003cp\u003eIn contrast, the second cluster (bottom section of the heatmap) exhibits the opposite pattern, with metabolites present at higher levels during the initial fermentation stages and decreasing over time. Notably, this group includes oleic acid, the supplied carbon source, which shows significant depletion, suggesting an intense substrate uptake that temporarily exceeds the metabolic capacity for its conversion during early fermentation. Similarly, linoleic acid and hexadecanoic acid follow the same trend, reinforcing the notion of early lipid utilization through β-oxidation, one of the main pathways in \u003cem\u003eB. cepacia\u003c/em\u003e for energy and PHA production when fatty acids are used as carbon sources (Escapa et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Koller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These results are consistent with those reported by Gao et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This cluster also includes important metabolites such as nucleosides (adenosine, guanosine, uridine, and adenine), pyridoxal, and CDP-glycerol. The elevated nucleoside levels during the early stages may reflect increased nucleotide turnover and biosynthetic activity associated with cell replication, underscoring the dynamic changes in nucleotide metabolism and cofactor availability as fermentation progresses.\u003c/p\u003e \u003cp\u003eOverall, these findings reveal a clear metabolic transition throughout the fermentation process. In the early stages, fatty acids such as oleic, linoleic, and hexadecanoic acids undergo intense degradation, serving as the primary energy and carbon sources. As fermentation progresses, the metabolic pattern shifts toward the catabolism of amino acids and carbohydrates, evidenced by their increased abundance at later time points. This transition likely represents an adaptive metabolic reprogramming triggered by changes in substrate availability and the energetic demands of the cells during the different phases of \u003cem\u003eB. cepacia\u003c/em\u003e fermentation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of metabolic pathways during fermentation\u003c/h2\u003e \u003cp\u003eIn bacteria, PHA biosynthetic pathways are closely interconnected with central metabolic routes, including glycolysis, the TCA cycle, β-oxidation, fatty acid degradation, amino acid catabolism, the pentose phosphate pathway, and the serine pathway (Koller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). During fermentation, the dynamics of these pathways in \u003cem\u003eB. cepacia\u003c/em\u003e can be elucidated by analyzing the temporal evolution of the identified metabolites. Metabolic pathway analysis was performed by integrating pathway enrichment and topology analyses. The enrichment analysis evaluated whether the identified metabolites were significantly represented within the theoretical pathways predicted from KEGG, using the \u003cem\u003eBurkholderia mallei\u003c/em\u003e ATCC 23344 genome as reference. The topology analysis assessed the relative importance of each metabolite within the network, treating each as a node.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, these analyses enabled the identification of 58 distinct pathways. Among these, 10 pathways exhibited statistically significant enrichment with an effect size greater than zero. Particularly relevant were the fatty acid degradation (β-oxidation), TCA cycle, arginine biosynthesis, glyoxylate metabolism, phenylalanine metabolism, purine metabolism, cysteine metabolism, tryptophan metabolism, pyrimidine metabolism, and the pentose phosphate pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eβ-oxidation is the main pathway responsible for fatty acid metabolism under aerobic conditions (Sun et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Thamarai et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The analysis identified several metabolites associated with this pathway, including hexadecanoate, butanoyl-CoA, glutaryl-CoA, acetyl-CoA, dodecanoyl-CoA, decanoyl-CoA, hexanoyl-CoA, and octanoyl-CoA. The key product of this pathway is acetyl-CoA, a central intermediate that connects multiple core biosynthetic routes in \u003cem\u003eB. cepacia\u003c/em\u003e during PHA production (Koller, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In PHA-producing microorganisms such as \u003cem\u003eCupriavidus necator\u003c/em\u003e, \u003cem\u003eChromatium vinosum\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, the metabolic flux from acetyl-CoA toward PHA synthesis is strongly influenced by nutrient availability (Steinb\u0026uuml;chel \u0026amp; Hein, 2001). A similar pattern is observed in \u003cem\u003eB. cepacia\u003c/em\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, β-oxidation intermediates increase during the exponential growth phase, along with higher acetyl-CoA levels. This trend suggests that, at this stage, acetyl-CoA is primarily directed toward energy generation and biomass formation. Concurrently, certain β-oxidation intermediates, particularly (S)-3-hydroxyacyl-CoA, are likely diverted to PHA biosynthesis. This dual channeling explains the simultaneous increase in biomass and polymer accumulation observed since the exponential phase, consistent with the findings reported by Torres (2019). These results are also in agreement with Tanadchangsaeng and Roytrakul (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported that in \u003cem\u003eC. necator\u003c/em\u003e grown on glycerol, enhanced expression of gluconeogenic enzymes was associated with reduced PHA synthesis, supporting the close relationship between central carbon metabolism and polymer production.\u003c/p\u003e \u003cp\u003eGlucose anabolism in \u003cem\u003eB. cepacia\u003c/em\u003e occurs through gluconeogenesis, a pathway activated via glyoxylate metabolism, where phosphoenolpyruvate (PEP) is generated from oxaloacetate by PEP carboxykinase (Mu\u0026ntilde;oz-El\u0026iacute;as \u0026amp; McKinney, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Oh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This pathway subsequently stimulates the pentose phosphate (PP) pathway, which plays a key role in cell growth. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, higher abundances of gluconeogenic and PP pathway intermediates \u0026mdash;such as PEP, erythrose-4-phosphate (E4P), ribose-5-phosphate (R5P), and phenylalanine\u0026mdash; were observed during the stationary phase, whereas sedoheptulose-7-phosphate exhibited the opposite pattern. These findings are consistent with those reported by Fukui et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who studied \u003cem\u003eRalstonia eutropha\u003c/em\u003e (\u003cem\u003eCupriavidus necator\u003c/em\u003e) H16 cultivated on octanoate and observed the activation of gluconeogenesis mediated by the glyoxylate cycle during PHA accumulation.\u003c/p\u003e \u003cp\u003eSimilarly, the fluxomic analysis of \u003cem\u003eB. cepacia\u003c/em\u003e cultured with oleic acid (Torres-Ospina \u0026amp; Riascos, 2020) demonstrated that gluconeogenesis is active during the exponential phase, supporting the generation of E4P and R5P, precursors for phenylalanine and nucleotides, respectively, which are essential for biomass formation. In contrast, the metabolomic data from the present study show lower abundances of E4P and R5P at early growth stages (6 and 12 h) and their accumulation when biomass production decreases (16 and 22 h). Interestingly, nucleosides such as adenosine, guanosine, and uridine displayed the opposite behavior, being more abundant during early growth (6 and 12 h) and decreasing toward the stationary phase (16 and 22 h). This inverse relationship with R5P, a nucleotide precursor, suggests a regulatory mechanism that enhances R5P accumulation by downregulating nucleotide synthesis. Future studies using multi-omics approaches could provide deeper insights into the interaction between these metabolites and their underlying regulatory mechanisms.\u003c/p\u003e \u003cp\u003eFinally, the progressive increase in oxalosuccinate observed between the exponential and stationary phases suggests an activation of the tricarboxylic acid (TCA) cycle, with a possible redirection of its intermediates toward PHA biosynthesis. This adaptation likely enables \u003cem\u003eB. cepacia\u003c/em\u003e to maximize nutrient utilization in response to the changing culture conditions. Together, these results reveal a clear metabolic transition throughout the fermentation process. During the early stages, there is an intense degradation of fatty acids, such as oleic, linoleic, and hexadecanoic acids, which appear to serve as primary energy sources. As fermentation progresses, this metabolic pattern shifts toward the catabolism of amino acids and carbohydrates, as reflected by their increased abundance in later time points. This transition likely represents a form of metabolic reprogramming, where \u003cem\u003eB. cepacia\u003c/em\u003e reorganizes its metabolic fluxes in response to nutrient depletion and changing energetic demands. Such metabolic reprogramming optimizes carbon redistribution between energy production, biosynthesis, and polymer accumulation, ensuring cellular adaptation and maintenance across the different phases of fermentation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA comprehensive metabolomic analysis of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e is essential to advance the understanding of the metabolic dynamics governing its fermentation process. The multiplatform analytical strategy, combined with rigorous quality control procedures, enabled the identification of a broad spectrum of metabolites that exhibited significant temporal variations in abundance during batch fermentation for PHA production using oleic acid as the carbon source.\u003c/p\u003e \u003cp\u003eMetabolic pathway analysis revealed the key routes involved in \u003cem\u003eB. cepacia\u003c/em\u003e metabolism, highlighting acetyl-CoA as the central intermediate connecting core biosynthetic pathways with PHA synthesis. The results confirmed the existence of a metabolic switch occurring during the exponential phase, mediated by a complex regulatory system responsive to nutrient availability in the culture medium. This metabolic reorganization involves a shift from biomass formation toward energy generation and polymer accumulation, suggesting an adaptive response that reallocates the carbon flux to sustain cellular maintenance and survival under changing environmental conditions.\u003c/p\u003e \u003cp\u003eMoreover, the integration of data from LC-QTOF-MS(\u0026plusmn;) and GC-QTOF-MS platforms demonstrated the complementarity of analytical techniques for achieving comprehensive metabolome coverage, underscoring the importance of multiplatform metabolomics in deciphering microbial metabolism.\u003c/p\u003e \u003cp\u003eThese findings confirmed the existence of a metabolic switch during the exponential phase, mediated by a complex regulatory system responsive to nutrient availability in the culture medium. This switch represents a form of metabolic reprogramming, through which \u003cem\u003eB. cepacia\u003c/em\u003e dynamically adjusts its central metabolism to balance energy generation, biosynthesis, and polymer accumulation. Understanding this adaptive reorganization provides a foundation for the rational design of improved PHA-producing strains. In the context of the Design\u0026ndash;Build\u0026ndash;Test\u0026ndash;Learn (DBTL) framework, the metabolomic insights obtained in this study contribute to the Learn phase, enabling data-driven strategies for the next Design and Build iterations aimed at optimizing metabolic fluxes and enhancing PHA yield and productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated during this study will be publicly available in the Metabolomics Workbench repository https://www.metabolomicsworkbench.org. The corresponding accession numbers will be included in the final published version of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit Author Statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.E.A.O. and N.C.M.S. collection and processing of samples. A.E.A.O., D.P.R., and M.P.C. \u0026nbsp;acquisition and processing of metabolomic data. A.E.A.O., D.P.R., M.P.C., and C.A.M.R. analysis and interpretation of metabolomics results, discussion of findings, and manuscript drafting. All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAkiyama, M., Tsuge, T., \u0026amp; Doi, Y. (2003). Environmental life cycle comparison of polyhydroxyalkanoates produced from renewable carbon resources by bacterial fermentation. \u003cem\u003ePolymer Degradation and Stability\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(1), 183\u0026ndash;194. https://doi.org/10.1016/S0141-3910(02)00400-7\u003c/li\u003e\n \u003cli\u003eAlbuquerque, P. B. S., \u0026amp; Malafaia, C. B. (2018). Perspectives on the production, structural characteristics and potential applications of bioplastics derived from polyhydroxyalkanoates. \u003cem\u003eInternational Journal of Biological Macromolecules\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(PartA), 615\u0026ndash;625. https://doi.org/10.1016/j.ijbiomac.2017.09.026\u003c/li\u003e\n \u003cli\u003eAnjum, A., Zuber, M., Zia, K. 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Microbial metabolomics: From novel technologies to diversified applications. In \u003cem\u003eTrAC - Trends in Analytical Chemistry\u003c/em\u003e (Vol. 148). Elsevier B.V. https://doi.org/10.1016/j.trac.2022.116540\u003c/li\u003e\n \u003cli\u003eZhu, C., Nomura, C. T., Perrotta, J. A., Stipanovic, A. J., \u0026amp; Nakas, J. P. (2010). Production and characterization of poly-3-hydroxybutyrate from biodiesel-glycerol by \u003cem\u003eBurkholderia cepacia\u003c/em\u003e ATCC 17759. \u003cem\u003eBiotechnology Progress\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 424\u0026ndash;430. https://doi.org/10.1002/btpr.355\u0026nbsp;\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Burkholderia cepacia, PHA production, Untargeted Metabolomics, Pathway enrichment analysis, Metabolic reprogramming","lastPublishedDoi":"10.21203/rs.3.rs-8182922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8182922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolyhydroxyalkanoates (PHA) have emerged as biodegradable alternatives to conventional polymers. However, their high production cost remains the main limitation to large-scale commercialization. Metabolic engineering strategies have been implemented to optimize PHA quality and enhance process productivity, yet a comprehensive understanding of the intracellular metabolic regulation is still needed. In this study, an untargeted metabolomic analysis using GC\u0026ndash;MS and LC\u0026ndash;MS platforms was performed to characterize the endometabolome of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e during batch fermentation with oleic acid as the carbon source. A total of 24 significant metabolites were identified by GC\u0026ndash;MS and 223 by LC\u0026ndash;MS, mainly organic acids and lipids. These metabolites were associated with key pathways such as β-oxidation, the tricarboxylic acid cycle, and the pentose phosphate pathway. The results revealed a clear metabolic reprogramming driven by acetyl-CoA flux redistribution, reflecting a regulatory mechanism responsive to nutrient availability. This dynamic reorganization of the metabolic network supports the transition from growth to PHA accumulation. The integrative metabolomic approach applied here provides insights valuable for guiding future Design\u0026ndash;Build\u0026ndash;Test\u0026ndash;Learn (DBTL) strategies in the rational optimization of PHA-producing bioprocesses.\u003c/p\u003e","manuscriptTitle":"Multiplatform Metabolomic Analysis Reveals Metabolic Reprogramming of Burkholderia cepacia During Polyhydroxyalkanoate Production from Oleic Acid","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 07:10:06","doi":"10.21203/rs.3.rs-8182922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-14T00:45:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"181600556324267147761417797526654650528","date":"2026-03-04T04:45:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T01:06:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-31T08:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160066981124916301317182844894327401710","date":"2026-01-15T06:31:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194666832122594540407925410646133727278","date":"2026-01-13T05:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181600556324267147761417797526654650528","date":"2026-01-12T20:32:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264458738794596015196294340602464154805","date":"2026-01-12T11:25:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T11:07:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T11:36:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T11:34:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2025-11-23T01:25:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3041b192-02b5-49e7-9071-f523eb0d3a48","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T14:11:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 07:10:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8182922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8182922","identity":"rs-8182922","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}