Metabolomic insights into petroleum hydrocarbon biodegradation by a microbial consortium: evidence of coordinated metabolic transformation pathways

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In this study, the biodegradation of petroleum hydrocarbons by a mixed microbial consortium was investigated using a metabolomics-based approach. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) enabled high-resolution profiling of metabolites formed time. The results revealed a progressive transformation of aromatic hydrocarbons into oxygenated intermediates and carboxylic acids, indicating active biodegradation processes. Although no statistically significant differences were observed (p > 0.05), clear temporal trends were identified, suggesting coordinated metabolic activity. Multivariate analysis (PCA) and correlation-based approaches demonstrated structured metabolic shifts associated with hydrocarbon degradation. Key metabolites, particularly benzoic acid, were identified as central intermediates linking initial oxidation steps to downstream degradation pathways. Integration with KEGG pathways indicated the involvement of enzymatic systems such as cytochrome P450 in the transformation of hazardous hydrocarbons. These findings provide important insights into the mechanisms governing petroleum hydrocarbon degradation and highlight the relevance of metabolomics for understanding contaminant transformation in environmental systems. The study contributes to advancing bioremediation strategies by elucidating metabolic pathways associated with the breakdown of hazardous organic pollutants. Petroleum hydrocarbon biodegradation microbial consortium metabolomics GC×GC-TOFMS aromatic hydrocarbons metabolic pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Metabolomics reveals metabolic pathways in hydrocarbon biodegradation • GC×GC-TOFMS enables high-resolution metabolite detection • Aromatic compounds are transformed into oxygenated intermediates • Benzoic acid acts as a central metabolic intermediate • Microbial consortium shows coordinated metabolic activity 1. Introduction Environmental contamination by petroleum-derived hydrocarbons represents one of the major global challenges, impacting terrestrial and aquatic ecosystems, compromising environmental quality and human health (Majeed et al., 2021; Truskewycz et al., 2019; Ossai et al., 2020; D’Ugo et al., 2021; Mekonnen et al., 2021; Al-Hawash et al., 2018; Varjani, 2017; Zhang et al., 2023). These compounds are characterized by high persistence due to their chemical complexity, including recalcitrant fractions such as polycyclic aromatic hydrocarbons (PAHs), which exhibit low biodegradability and high toxicity (Haritash & Kaushik, 2009; Vijayanand et al., 2023; Feng et al., 2020; Berríos-Rolón et al., 2023; Tian et al., 2020; Wang et al., 2008; Sun et al., 2022). Understanding the environmental fate and transformation of hazardous petroleum hydrocarbons is essential for developing efficient bioremediation strategies. In this context, bioremediation has emerged as a promising and sustainable strategy, based on the ability of microorganisms to transform and mineralize complex organic compounds into less toxic or harmless products (Das & Chandran, 2011; Azubuike et al., 2016; Duarte et al., 2023; Tyagi et al., 2011; Varjani, 2017). Despite advances in the application of microbial consortia for petroleum degradation, the metabolic mechanisms involved in these processes are still not fully understood, especially with regard to the temporal dynamics of intermediate metabolites and the associated biochemical pathways (Head et al., 2006; Atlas & Hazen, 2011). The identification of these metabolites is essential to elucidate degradation routes, understand the efficiency of biological systems, and identify possible metabolic complexities that limit the complete mineralization of contaminants. In this scenario, metabolomic approaches have emerged as powerful tools to investigate the complexity of biodegradation processes, enabling the detection and characterization of intermediate and final compounds generated over time (Li et al., 2023; Gomez et al., 2020; Xu et al., 2021). In particular, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) offers high separation capacity and resolution, being especially suitable for the analysis of complex mixtures such as those derived from petroleum (Mohler et al., 2006). This technique enables the identification of metabolites at low concentration levels, as well as the detection of previously undescribed compounds, contributing to a deeper understanding of the chemical transformations involved (Mondello et al., 2008; Adahchour & Brinkman, 2008; Korytár et al., 2005). Furthermore, the integration of metabolomic data with robust statistical analyses and functional annotation tools, such as metabolic pathway databases, allows the association of specific metabolites with biological and enzymatic processes, such as those mediated by cytochrome P450 enzymes, widely recognized for their role in the oxidation of aromatic compounds. This integrated approach enables the construction of conceptual models that describe the metabolic flux from initial compounds to final mineralization (Patti et al., 2012; Johnson et al., 2016; Kanehisa et al., 2017; Guengerich, 2008; Orth et al., 2010; Baensch et al., 2025). Despite advances in biodegradation studies, the integration between metabolomic profiling and pathway-level interpretation remains limited, especially in complex systems involving petroleum hydrocarbons. In this context, this study aims to fill this gap by combining high-resolution chemical analysis using GC×GC-TOFMS, multivariate statistics, and metabolic pathway reconstruction, providing an integrated approach to understand the dynamics of biodegradation. In addition to characterizing temporal patterns of transformation and proposing a conceptual metabolic pathway that contributes to the understanding of the mechanisms involved in the bioremediation of petroleum-contaminated environments. This study aims to investigate the metabolic organization underlying petroleum hydrocarbon biodegradation using a metabolomic approach. 2. Materials and Methods 2.1 Mixed microbial consortium Mixed Microbial Consortium The microbial consortium used in this study consists of bacterial and filamentous fungal strains. All strains were previously demonstrated to degrade hydrocarbons and have been characterized genetically. [Details on accession numbers or patent references removed for review]. 2.2 Inoculum preparation Each strain of the consortium was transferred to 100 mL of BH medium (Difco™) using an agarose disk (1 cm Ø). The mixture was incubated in an orbital shaker Tecnal™ TE-420 at 30°C ± 0.2, 153 rpm, for 6 days. From the concentrated solution, aliquots were prepared and diluted in 0.9% saline solution (w/v) to reach the desired cellular concentrations: 1 × 10⁶ CFU/mL. Cell density was determined by optical density using an LMR-96™ microplate reader (ELISA), using a wavelength of 500 nm for fungi and 600 nm for bacteria. The final stock solution was stored at 4°C until use. 2.1 Experimental design and sample collection The experiment was conducted over 0, 15, 30, and 60 days using amber glass vials with a capacity of 40 mL. For the treatment with the microbial consortium, the final volume was 10 mL, composed of 8.9 mL of BH medium, 10% consortium (1 mL), and 1% petroleum (0.1 mL), with petroleum previously diluted at a ratio of 0.8295 g in 10 mL of dichloromethane (DCM). Treated samples (medium + consortium + petroleum) were prepared in triplicate, totaling 12 samples. Initially, the vials were incubated at 153 rpm at 30°C, and subsequently maintained in a germination chamber at 30°C with a 12-hour photoperiod. 2.2 Extraction and sample preparation Metabolites were extracted from the experimental samples using standard protocols for metabolomic analysis. The samples were subjected to preparation steps including organic extraction and chemical derivatization with MSTFA (N-Methyl-N-(trimethylsilyl)-trifluoroacetamide), when necessary, to increase the detectability of compounds by mass spectrometry. Derived compounds, such as trimethylsilylated esters, were considered during data interpretation. 2.3 Mass spectrometry analysis and metabolite identification Metabolomic analysis was performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS), a high-resolution technique widely used for the characterization of complex mixtures such as petroleum-derived hydrocarbons. Prepared samples were injected into the GC×GC system, allowing chromatographic separation in two dimensions based on different physicochemical properties of the compounds. Detection was performed using time-of-flight mass spectrometry (TOFMS), enabling the acquisition of spectra with high scan rate and resolution, essential for metabolite identification in complex matrices. Compound identification was conducted by comparison of mass spectra with commercial libraries and databases, as well as by analysis of fragmentation patterns (m/z). 2.4 Statistical analysis Data were normalized and log-transformed prior to statistical analysis. Principal Component Analysis (PCA) was used to explore the variance structure of the data. Statistical significance was assessed using appropriate tests, and correlation matrices were constructed using Pearson coefficients. Statistical analyses were performed in a computational environment using the Google Colab platform, through the Python programming language and appropriate scientific libraries. 2.4.1 Normality test The normality of metabolite concentration data was assessed using the Shapiro–Wilk test, with a significance level of 5% (p < 0.05). Metabolites with zero variance were previously identified and excluded from the analysis to avoid distortions in the results. 2.4.2 Descriptive analyses Metabolite concentrations were expressed as mean ± standard error of the mean (SEM). Data variability was evaluated across different time points and among experimental replicates. 2.4.3 Exploratory analysis and visualization Data exploration included the construction of different graphical approaches, including bar plots to evaluate temporal trends, heatmaps to identify global concentration patterns, and box plots for dispersion analysis and outlier detection. 2.4.4 Multivariate analysis Principal Component Analysis (PCA) was performed to investigate global patterns and relationships between samples and metabolites. Data were previously normalized and scaled prior to applying the technique, allowing the identification of groupings associated with experimental factors. 2.4.4 Computational libraries and tools Analyses were conducted using widely recognized libraries in the Python environment, including: NumPy (numerical operations); Pandas (data manipulation); SciPy (statistical tests, including Shapiro–Wilk); Matplotlib and Seaborn (data visualization); Scikit-learn (multivariate analysis, including PCA). 2.4.6 Spectrometric data processing Raw data obtained from GC×GC-TOFMS were processed for peak detection, chromatographic alignment, and extraction of spectral features. Compound identification was based on spectral similarity, fragmentation patterns, and, when possible, matching with reference libraries. Diagnostic ions (m/z) were used as structural markers for inferring chemical classes and possible transformation pathways of detected compounds. 2.5 Metabolic annotation and pathway analysis Identified metabolites were annotated based on metabolic databases, including KEGG, allowing their association with pathways related to hydrocarbon degradation and xenobiotic metabolism. Pathways such as benzoate, toluene, styrene, naphthalene, and phenanthrene degradation were considered. 2.6 Metabolite–enzyme association The association between metabolites and enzymes was inferred based on information available in metabolic databases and specialized literature. A predominance of enzymes from the cytochrome P450 family was observed, associated with oxidation reactions of aromatic compounds. 2.7 Construction of the conceptual metabolic pathway A conceptual metabolic pathway was proposed based on the integration of identified metabolites, their enzymatic associations, and annotated metabolic pathways. This approach allowed the representation of the transformation flow from initial hydrocarbons to final mineralization products, including CO₂, H₂O, and biomass. 3. Results 3.1 Overview of metabolites and data distribution A set of key metabolites associated with the biodegradation of petroleum hydrocarbons was identified using GC×GC-TOFMS, including aromatic intermediate compounds and carboxylic acids. The assessment of normality using the Shapiro–Wilk test (Table 1 ) indicated that some metabolites did not follow a normal distribution (p 0.05), consistent temporal patterns suggest biologically meaningful metabolic transformations. Table 1 – Analysis of metabolite distribution based on the Shapiro–Wilk normality test Metabolite W_Statistic P_Value_Shapiro Shapiro_Performed Ácido benzoico 0.698263 4.443237e-04 Yes Hidroxifluoreno 0.878318 3.315026e-01 Yes Ácido 2-Hidroxicromeno-2-oico 0.750000 7.771561e-16 Yes Fenantrol 0.895607 4.095662e-01 Yes Ácido benzodioico 0.753685 5.982086e-03 Yes 3.2 Univariate analysis Univariate statistical analyses did not reveal significant differences between the experimental time points (p > 0.05). Although no statistically significant differences were detected, consistent temporal trends suggest biologically relevant transformations. However, clear temporal trends were observed in the metabolite profiles, as illustrated in the bar plots with standard error of the mean (Fig. 1 ), suggesting that the biodegradation process occurs in a gradual and coordinated manner. For phenanthrene, only one time point (60 days) was observed, with low mean concentration and high variability (large error bar), suggesting heterogeneity among replicates or possible instability in the degradation process. In the case of hydroxyphenanthrene, also at 15 days, the mean concentration is higher and relatively consistent, with lower variability compared to phenanthrene, indicating stable formation of this intermediate metabolite. Benzoic acid showed expressive variations, with an increase from T0 to T15 and 60 and a slight reduction at T30, in addition to high variability among replicates, suggesting heterogeneity in the biological response. In contrast, benzodioic acid showed more discrete variations and lower dispersion, indicating greater consistency among replicates. The metabolite 2-hydroxychromene-2-oic acid did not present sufficient data for graphical analysis, highlighting limitations in data completeness. The data indicate a typical dynamic of aromatic hydrocarbon biodegradation, with the formation of intermediate (hydroxylated) metabolites followed by the accumulation of aromatic acids over time. The variability observed at some points suggests the influence of biological or experimental factors, making it important to consider replication and statistical tests to confirm the observed trends. The increase of benzoic acid at specific time points suggests its role as a key intermediate in the degradation of aromatic hydrocarbons. This accumulation may indicate a metabolic bottleneck, possibly associated with enzymatic limitations in subsequent steps of the pathway. In contrast, the more stable behavior of compounds such as benzodioic acid suggests their participation in more advanced or less dynamic stages of the degradation process. 3.3 Correlation between chemical classes The analysis of the correlation matrix (Fig. 2 ) revealed distinct patterns of association between chemical classes throughout the biodegradation process. A strong positive correlation was observed between carboxylic acids and heterocyclic acids (r = 0.95), suggesting that these classes are metabolically related and possibly involved in subsequent stages of degradation. In contrast, polycyclic aromatic hydrocarbons showed negative correlation with carboxylic acids (r = -0.27) and heterocyclic acids (r = -0.21), indicating an inverse behavior consistent with their conversion into more oxygenated compounds over time. Additionally, the strong positive correlation between polycyclic aromatic hydrocarbons and carboxylic acid esters (r = 0.87) suggests that these compounds may share intermediate metabolic pathways or reflect parallel transformation processes. On the other hand, the negative correlation between carboxylic acid esters and carboxylic acids (r = -0.38) reinforces the hypothesis of chemical conversion between these classes, possibly associated with hydrolysis or oxidation reactions. Overall, the observed patterns support a sequential degradation model, in which aromatic hydrocarbons are progressively transformed into more polar compounds, including alcohols and organic acids, reflecting the metabolic dynamics of the microbial consortium. 3.4 Global metabolite patterns: heatmap analysis Complementarily, the heatmap of mean concentrations (Fig. 3 ) confirmed these temporal trends, highlighting the increase of benzoic acid throughout the experiment and the late formation of benzodioic acid. The metabolite 2-hydroxychromene-2-oic acid showed low concentrations and missing values, which limits its interpretation. This approach allowed the identification of global patterns of accumulation and metabolic transformation over time. 3.5 Multivariate analysis (PCA) Multivariate analysis by PCA (Fig. 4 ) reinforced the existence of structural patterns in the dataset, with the first two principal components explaining 65.54% of the total variance (PC1 = 41.84%; PC2 = 23.70%). A clear separation of samples was observed according to replicates and time points, indicating that both factors significantly influence metabolomic profiles. Metabolites such as benzoic acid and 2-hydroxychromene-2-oic acid contributed strongly to variation along PC1, while benzodioic acid and phenanthrol were more associated with PC2. These results highlight the existence of a temporal metabolic dynamics associated with the biodegradation process. 3.6 Integration of metabolomic data and pathway reconstruction The integration of metabolomic data allowed the proposal of a conceptual metabolic pathway (Fig. 5 ), in which initial petroleum compounds are progressively transformed through hydroxylation reactions, generating intermediates such as hydroxyfluorene and phenanthrol, which are subsequently converted into organic acids, such as benzoic acid. This metabolite acts as a central intermediate, connecting initial and final stages of degradation, culminating in mineralization into CO₂, H₂O, and biomass. The analysis of metabolite–enzyme associations (Table 2 ) revealed the predominance of enzymes from the cytochrome P450 family, suggesting that hydroxylation reactions play a central role in the biotransformation of the detected compounds. These enzymes are essential for increasing the reactivity of hydrocarbons, facilitating subsequent degradation steps. Table 2 Identified metabolites and their associated enzymes. Metabolite Associated Enzymes 0 Benzoic Acid [Cytochrome P450] 1 Hydroxyfluorene [Cytochrome P450] 2 Phenanthrol [Cytochrome P450] 3 Hydroxyphenanthrenic Acid [Cytochrome P450] 4 Hydroxyphenanthrenic Acid [Cytochrome P450] 5 Hydroxyfluorene [Cytochrome P450] The functional characterization of the metabolites (Table 3 ) indicated that they are predominantly aromatic compounds and organic acids associated with xenobiotic metabolism, reinforcing their origin in the degradation of petroleum-derived compounds. The presence of trimethylsilylated derivatives suggests the influence of the analytical process on the detection of some compounds. Table 3 Functional and metabolic profile of the identified metabolites. Metabolite Compound Type Main Metabolic Pathway Biological Function Benzoic acid Organic acid Xenobiotic metabolism Antimicrobial, intermediate Hydroxyfluorene Aromatic compound Biotransformation, xenobiotic metabolism Intermediate, exposure marker 2-Hydroxychromene-2-oic acid Organic acid Degradation pathway Derived metabolite Phenanthrol Aromatic compound Biotransformation, xenobiotic metabolism Intermediate, exposure marker Benzodioic acid Organic acid Xenobiotic metabolism Intermediate, carboxylic acid metabolism Hydroxy-phenanthrenic acid Aromatic compound Biotransformation, xenobiotic metabolism Intermediate, exposure marker Benzenedioic acid Organic acid Xenobiotic metabolism Intermediate, carboxylic acid metabolism Hydroxy-phenanthrenic acid Aromatic compound Biotransformation, xenobiotic metabolism Intermediate, exposure marker Hydroxyfluorene Aromatic compound Biotransformation, xenobiotic metabolism Intermediate, exposure marker 1,4-Benzenedicarboxylic acid, bis(trimethylsilyl) ester Ester (derivative) Xenobiotic metabolism Analytical derivative, intermediate Finally, annotation in metabolic databases (KEGG) (Table 4 ) revealed the association of metabolites with relevant hydrocarbon degradation pathways, including benzoate, toluene, styrene, naphthalene, and phenanthrene degradation, in addition to xenobiotic metabolism mediated by cytochrome P450. These results confirm the activation of specific metabolic routes involved in the biodegradation of aromatic compounds. Table 4 Metabolites associated with KEGG hydrocarbon degradation pathways. Metabolite KEGG_ID Pathway_Name 1,4-Benzenedicarboxylic acid, bis(trimethylsil... ko00362 Benzoate degradation via CoA ligation Fenantrol ko00624 Naphthalene degradation Fenantrol ko00640 Phenanthrene degradation Hidroxifluoreno ko00620 Toluene degradation Hidroxifluoreno ko00642 Styrene degradation 4. Discussion The results obtained in this study highlight the complexity and multiphasic nature of petroleum hydrocarbon biodegradation, emphasizing the importance of integrating metabolomic, statistical, and functional annotation analyses to understand the processes involved. The predominance of aromatic compounds and organic acids among the identified metabolites reinforces the central role of oxidation and transformation reactions of polycyclic aromatic hydrocarbons (PAHs) and alkanes during bioremediation. The observed transformation of aromatic hydrocarbons into more polar compounds suggests a reduction in environmental persistence and potential toxicity, reinforcing the applicability of microbial consortia in bioremediation strategies (Haritash & Kaushik, 2009; Ghosal et al., 2016; Rojo, 2009; Patti et al., 2012; Truskewycz et al., 2019; Ossai et al., 2020; Sharma et al., 2024; Zhang et al., 2023). The identification of metabolites such as hydroxyfluorene, phenanthrol, and phenanthrene derivatives indicates that the initial stage of degradation is characterized by hydroxylation reactions, which increase the polarity and reactivity of these molecules. This process is essential to enable subsequent degradation steps, such as aromatic ring cleavage and the formation of simpler compounds. In this context, the strong association observed with enzymes of the cytochrome P450 family suggests that these monooxygenases play a key role in the initial activation of compounds, acting as an entry point for the microbial catabolism of complex hydrocarbons (Cerniglia, 1992; Guengerich, 2008; Haritash & Kaushik, 2009; Seo et al., 2009; Li et al., 2023). Benzoic acid emerged as a key metabolite in the system, acting as a central intermediate in the metabolic network. Its presence at different experimental time points, associated with significant variations in concentration over time, suggests that this compound functions as a link between the degradation of more complex aromatic compounds and more general metabolic pathways directed toward mineralization. This behavior is consistent with its known role as an intermediate in several aromatic degradation pathways, including benzoate degradation via CoA ligation (Harwood & Parales, 1996; Díaz, 2004; Pérez-Pantoja et al., 2012; Fuchs & Heider, 2011; Baensch et al., 2025). Multivariate analysis by PCA revealed a clear separation between time points, indicating that temporal dynamics is one of the main factors structuring the metabolomic profile of the system. The explanation of 65.54% of the total variance by the first two principal components demonstrates that the main trends in the data were captured, allowing the identification of consistent patterns of metabolic transformation. However, the observed effect among experimental replicates indicates the presence of intrinsic variability, possibly associated with biological differences in the microbial consortium or experimental variations, highlighting the need to consider this factor in more robust statistical analyses (Trygg et al., 2007; Patti et al., 2012; Fiehn et al., 2008; Bro & Smilde, 2014; Sharma et al., 2024). Complementary univariate analyses, including bar plots, heatmaps, and box plots, reinforce the interpretation of PCA by revealing distinct patterns of variation over time. This integrated approach is widely used in metabolomic studies to validate multivariate trends and identify key metabolites responsible for the separation of experimental groups (Patti et al. 2012; Xia and Wishart 2011; Lisec et al., 2006; Zhang et al., 2023). The increase in benzoic acid at specific time points suggests intermediate accumulation, possibly associated with rate-limiting steps in degradation, a behavior consistent with its role as a central intermediate in the degradation of aromatic compounds, especially in the benzoate pathway (Fuchs & Heider, 2011). On the other hand, the more stable behavior of metabolites such as benzodioic acid may indicate participation in more advanced or less dynamic stages of the process, reflecting transformations subsequent to initial oxidation and aromatic ring cleavage (Haritash & Kaushik 2009). Taken together, these patterns reinforce the importance of combining univariate and multivariate analyses for a robust interpretation of metabolic dynamics in biodegradation processes. The annotation of metabolic pathways revealed strong association with routes of aromatic compound degradation and xenobiotic metabolism, including pathways related to benzoate, toluene, styrene, naphthalene, and phenanthrene degradation. These findings are consistent with the chemical composition of petroleum and indicate that the microbial consortium has broad metabolic capacity to process different classes of hydrocarbons. The integration of this information allowed the proposal of a conceptual metabolic pathway, in which initial compounds are progressively transformed into more polar intermediates, culminating in complete mineralization (Kanehisa et al., 2017; Head et al., 2006; Rojo, 2009; Li et al., 2023; Sharma et al., 2024). Additionally, the detection of derivatized compounds, such as trimethylsilylated esters, highlights the importance of considering analytical steps in the interpretation of metabolomic data. These compounds, although not necessarily representing natural metabolites, provide indirect evidence of the presence of organic acids and other relevant intermediates in the system (Fiehn, 2008; Kind et al., 2009; Mamas et al., 2011; Lisec et al., 2006; Patti et al., 2012). Overall, the results demonstrate that petroleum hydrocarbon biodegradation is a highly integrated process, involving multiple enzymatic and metabolic steps. The identification of key metabolites, such as benzoic acid, and the characterization of associated pathways provide important insights into transformation mechanisms and potential control points of the process. As future perspectives, the integration with gene expression data and metabolic flux modeling may deepen the understanding of the mechanisms involved, contributing to the optimization of bioremediation strategies in contaminated environments (Varjani, 2017; Truskewycz et al., 2019; Sharma et al., 2024). Although no statistically significant differences were observed (p > 0.05), consistent temporal patterns suggest biologically meaningful metabolic transformations. 5. Conclusion The results of this study demonstrate that petroleum hydrocarbon biodegradation by microbial consortia is a dynamic and highly integrated process, involving multiple biotransformation steps and interconnected metabolic pathways. The metabolomic approach based on GC×GC-TOFMS enabled the identification of key metabolites and the detailed characterization of chemical transformations over time, highlighting the complexity of the system. The predominance of hydroxylated aromatic compounds and organic acids confirms the central role of oxidation reactions in the initial degradation of hydrocarbons, with emphasis on the activity of enzymes from the cytochrome P450 family. Among the identified metabolites, benzoic acid emerged as a central intermediate, connecting different degradation pathways and acting as a link between initial steps and mineralization processes. Statistical and multivariate analyses revealed the significant influence of temporal dynamics in structuring metabolomic profiles, while also indicating variability among experimental replicates, reinforcing the importance of robust analytical approaches. Metabolic pathway annotation confirmed the involvement of routes related to xenobiotic metabolism and aromatic compound degradation, reflecting the metabolic versatility of the microbial consortium. Data integration enabled the proposal of a conceptual metabolic pathway, describing the progressive conversion of complex hydrocarbons into more reactive intermediates and subsequently into final mineralization products, such as CO₂, H₂O, and biomass. These findings contribute to advancing knowledge on petroleum bioremediation mechanisms and highlight the potential of metabolomics as a strategic tool for elucidating complex environmental processes. As future perspectives, integrating additional omics approaches—such as metagenomics and transcriptomics—together with metabolic flux modeling may further enhance the understanding of the mechanisms involved and contribute to the optimization of large-scale bioremediation strategies. Overall, this study advances knowledge on the transformation of hazardous hydrocarbons in environmental systems and reinforces the potential application of microbial consortia in bioremediation processes. This study advances the understanding of microbial metabolism involved in petroleum hydrocarbon biodegradation. Referências Adahchour, M., Beens, J., Brinkman, U.A.Th., 2008. Recent developments in the application of comprehensive two-dimensional gas chromatography. 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Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry analysis of metabolites in fermenting and respiring yeast cells. Analytical Chemistry 78 (8), 2700–2709. https://doi.org/10.1021/ac052106o Mondello, L., Tranchida, P.Q., Dugo, P., Dugo, G., 2008. Comprehensive two-dimensional gas chromatography–mass spectrometry: A review. Mass Spectrometry Reviews 27 (2), 101–124. https://doi.org/10.1002/mas.20158 Orth, J.D., Thiele, I., Palsson, B.Ø., 2010. What is flux balance analysis? Nature Biotechnology 28 (3), 245–248. https://doi.org/10.1042/BCJ20210596 Ossai, I.C., Ahmed, A., Hassan, A., Hamid, F.S., 2020. Remediation of soil and water contaminated with petroleum hydrocarbon: A review. Environmental Technology & Innovation 17, 100526. https://doi.org/10.1016/j.eti.2019.100526 Patti, G.J., Yanes, O., Siuzdak, G., 2012. Metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263–269. https://doi.org/10.1038/nrm3314 Pérez-Pantoja, D., Donoso, R., Agulló, L., Córdova, M., Seeger, M., Pieper, D.H., González, B., 2012. Genomic analysis of the potential for aromatic compounds biodegradation in Burkholderiales. Environmental Microbiology 14 (5), 1091–1117. https://doi.org/10.1111/j.1462-2920.2011.02613 Rojo, F., 2009. Degradation of alkanes by bacteria. Environmental Microbiology 11 (10), 2477–2490. https://doi.org/10.1111/j.1462-2920.2009.01948.x Seo, J.-S., Keum, Y.-S., Li, Q.X., 2009. Bacterial degradation of aromatic compounds. International Journal of Environmental Research and Public Health 6 (1), 278–309. https://doi.org/10.3390/ijerph6010278 Sharma, A., Kumar, R., Singh, P., et al. (2024). Advances in microbial bioremediation of petroleum hydrocarbons: integrating omics and functional pathways. Environmental Science and Pollution Research, 31, 14567–14589. https://doi.org/10.1007/s11356-023-30479-8 Sun, Q., Ren, S.-Y., Ni, H.-G., 2022. Effects of microplastic sorption on microbial degradation of halogenated polycyclic aromatic hydrocarbons in water. Environmental Pollution 307, 120238. https://doi.org/10.1016/j.envpol.2022.120238 Tian, Y., Wang, R., Ji, M., Tian, R., Wang, R., Zhang, B., Wang, S., Liu, L., 2020. The degradation of polycyclic aromatic hydrocarbons by biological electrochemical system: A mini-review. Bioresource Technology 295, 122259. https://doi.org/10.1016/j.biortech.2019.122259 Truskewycz, A., Gundry, T.D., Khudur, L.S., Kolobaric, A., Taha, M., Aburto-Medina, A., Ball, A.S., Shahsavari, E., 2019. Petroleum hydrocarbon contamination in terrestrial ecosystems: Fate and microbial responses. Molecules 24 (18), 3400. https://doi.org/10.3390/molecules24183400 Trygg, J., Holmes, E., Lundstedt, T., 2007. Chemometrics in metabonomics. Journal of Proteome Research 6 (2), 469–479. https://doi.org/10.1021/pr060594q Tyagi, M., da Fonseca, M.M.R., de Carvalho, C.C.C.R., 2011. Bioaugmentation and biostimulation strategies to improve biodegradation of petroleum hydrocarbons. Int. Biodeterior. Biodegrad. 65, 1005–1012. https://doi.org/10.1007/s10532-010-9394-4 Varjani, S.J., 2017. Microbial degradation of petroleum hydrocarbons. Bioresour. Technol. 223, 277–286. https://doi.org/10.1016/j.biortech.2016.10.037 Vijayanand, M., Ramakrishnan, A., Subramanian, R., Issac, P.K., Nasr, M., Khoo, K.S., Rajagopal, R., Greff, B., Wan Azelee, N.I., Jeon, B.-H., Chang, S.W., Ravindran, B., 2023. Polyaromatic hydrocarbons (PAHs) in the water environment: A review on toxicity, microbial biodegradation, systematic biological advancements, and environmental fate. Environmental Research 227, 115716. https://doi.org/10.1016/j.envres.2023.115716 Wang, J., Zhang, Z., Su, Y., He, W., He, F., Song, H., 2008. Phytoremediation of petroleum polluted soil. Petroleum Science 5, 167–171. https://doi.org/10.1007/s12182-008-0026-5 Xia, J., Wishart, D.S., 2011. Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. In: Current Protocols in Bioinformatics. Wiley, Unit 14.10. https://doi.org/10.1002/0471250953.bi1410s34 Xu, X., et al., 2021. Environmental metabolomics for pollutant degradation. Sci. Total Environ. 790, 148224. https://doi.org/10.1016/j.jenvman.2025.125892 Zhang, Y., Liu, H., Chen, Z., et al. (2023). Metabolomic and multivariate analysis reveals microbial degradation pathways of petroleum hydrocarbons in marine environments. Science of the Total Environment, 875, 166552. https://doi.org/10.1016/j.scitotenv.2023.166552 Declarations Acknowledgment The authors gratefully acknowledge financial and institutional support provided through a national research and development program in the fields of geomicrobiology and petroleum biotechnology. The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged. Ethical Approval Not applicable Acknowledgment The authors gratefully acknowledge financial and institutional support provided through a national research and development program in the fields of geomicrobiology and petroleum biotechnology. The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged. Funding Shell Brasil Ltda. Consent to Participate Not applicable. This study does not involve human participants, human data, or identifiable personal information. Declaration of AI Use The authors declare that artificial intelligence tools (ChatGPT, OpenAI) were used solely to assist with language editing, grammar correction, and formatting of the manuscript. All scientific content, data, results, interpretations, and conclusions presented in this work are the original work of the authors and have not been generated or influenced by AI. The use of AI did not affect the integrity, analysis, or originality of the research. Data Availability Statement The datasets generated and analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author on reasonable request. Not applicable. This study does not involve human participants, human data, or identifiable personal information. 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. ☒ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Author Contribution DFL (Danusia Ferreira Lima) conceived and designed the study, performed the experiments, conducted data analysis, and wrote the main manuscript. AFSQ (Antonio Fernando de Souza Queiroz) contributed to data interpretation and critical revision of the manuscript. OMCO (Olívia Maria Cordeiro de Oliveira) contributed to data interpretation, supervision, and manuscript review. All authors reviewed, edited, and approved the final version of the manuscript. Additional Declarations No competing interests reported. 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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-9620783","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637756017,"identity":"9fa82d72-ec6a-41f7-bba4-42317792e5e4","order_by":0,"name":"Danusia Ferreira 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time.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/ee7ee3d22f58a017b7ac76f5.png"},{"id":109759520,"identity":"8b2ec219-34af-4179-a897-2f7c1b817b38","added_by":"auto","created_at":"2026-05-22 07:27:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92376,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix between chemical classes during petroleum biodegradation.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/0ca4d6882743820f34fc08df.png"},{"id":109759851,"identity":"d4ac033f-dd5e-4bc5-acf3-aa7fb0f18362","added_by":"auto","created_at":"2026-05-22 07:27:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83481,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of mean metabolite concentrations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/fdc33ad995d793547a055c97.png"},{"id":109760044,"identity":"d3060448-626d-4076-8525-81a0c698e556","added_by":"auto","created_at":"2026-05-22 07:28:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78849,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) biplot.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/94a195723fc910fa6ed9bb0c.png"},{"id":109481825,"identity":"f6d2f535-fbff-4ec7-b217-a7199d07fdee","added_by":"auto","created_at":"2026-05-18 15:20:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76107,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual metabolic pathway of hydrocarbon biodegradation.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/364ef54b051cdfd2ca02d2c6.png"},{"id":109799800,"identity":"72e8e883-0b3e-484c-980a-93bf81d032bd","added_by":"auto","created_at":"2026-05-22 15:34:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/2737021e-930b-4edb-b61f-9483a53c8dbd.pdf"},{"id":109481821,"identity":"0eb1f964-6033-42ec-900f-6f9b14c2ddd9","added_by":"auto","created_at":"2026-05-18 15:20:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":84561,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-9620783/v1/9280ebd189c5df3512d8cd50.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomic insights into petroleum hydrocarbon biodegradation by a microbial consortium: evidence of coordinated metabolic transformation pathways","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Metabolomics reveals metabolic pathways in hydrocarbon biodegradation\u003c/p\u003e\u003cp\u003e\u0026bull; GC\u0026times;GC-TOFMS enables high-resolution metabolite detection\u003c/p\u003e\u003cp\u003e\u0026bull; Aromatic compounds are transformed into oxygenated intermediates\u003c/p\u003e\u003cp\u003e\u0026bull; Benzoic acid acts as a central metabolic intermediate\u003c/p\u003e\u003cp\u003e\u0026bull; Microbial consortium shows coordinated metabolic activity\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eEnvironmental contamination by petroleum-derived hydrocarbons represents one of the major global challenges, impacting terrestrial and aquatic ecosystems, compromising environmental quality and human health (Majeed et al., 2021; Truskewycz et al., 2019; Ossai et al., 2020; D\u0026rsquo;Ugo et al., 2021; Mekonnen et al., 2021; Al-Hawash et al., 2018; Varjani, 2017; Zhang et al., 2023).\u003c/p\u003e \u003cp\u003eThese compounds are characterized by high persistence due to their chemical complexity, including recalcitrant fractions such as polycyclic aromatic hydrocarbons (PAHs), which exhibit low biodegradability and high toxicity (Haritash \u0026amp; Kaushik, 2009; Vijayanand et al., 2023; Feng et al., 2020; Berr\u0026iacute;os-Rol\u0026oacute;n et al., 2023; Tian et al., 2020; Wang et al., 2008; Sun et al., 2022).\u003c/p\u003e \u003cp\u003eUnderstanding the environmental fate and transformation of hazardous petroleum hydrocarbons is essential for developing efficient bioremediation strategies. In this context, bioremediation has emerged as a promising and sustainable strategy, based on the ability of microorganisms to transform and mineralize complex organic compounds into less toxic or harmless products (Das \u0026amp; Chandran, 2011; Azubuike et al., 2016; Duarte et al., 2023; Tyagi et al., 2011; Varjani, 2017).\u003c/p\u003e \u003cp\u003eDespite advances in the application of microbial consortia for petroleum degradation, the metabolic mechanisms involved in these processes are still not fully understood, especially with regard to the temporal dynamics of intermediate metabolites and the associated biochemical pathways (Head et al., 2006; Atlas \u0026amp; Hazen, 2011). The identification of these metabolites is essential to elucidate degradation routes, understand the efficiency of biological systems, and identify possible metabolic complexities that limit the complete mineralization of contaminants.\u003c/p\u003e \u003cp\u003eIn this scenario, metabolomic approaches have emerged as powerful tools to investigate the complexity of biodegradation processes, enabling the detection and characterization of intermediate and final compounds generated over time (Li et al., 2023; Gomez et al., 2020; Xu et al., 2021). In particular, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC\u0026times;GC-TOFMS) offers high separation capacity and resolution, being especially suitable for the analysis of complex mixtures such as those derived from petroleum (Mohler et al., 2006).\u003c/p\u003e \u003cp\u003eThis technique enables the identification of metabolites at low concentration levels, as well as the detection of previously undescribed compounds, contributing to a deeper understanding of the chemical transformations involved (Mondello et al., 2008; Adahchour \u0026amp; Brinkman, 2008; Koryt\u0026aacute;r et al., 2005).\u003c/p\u003e \u003cp\u003eFurthermore, the integration of metabolomic data with robust statistical analyses and functional annotation tools, such as metabolic pathway databases, allows the association of specific metabolites with biological and enzymatic processes, such as those mediated by cytochrome P450 enzymes, widely recognized for their role in the oxidation of aromatic compounds. This integrated approach enables the construction of conceptual models that describe the metabolic flux from initial compounds to final mineralization (Patti et al., 2012; Johnson et al., 2016; Kanehisa et al., 2017; Guengerich, 2008; Orth et al., 2010; Baensch et al., 2025).\u003c/p\u003e \u003cp\u003eDespite advances in biodegradation studies, the integration between metabolomic profiling and pathway-level interpretation remains limited, especially in complex systems involving petroleum hydrocarbons. In this context, this study aims to fill this gap by combining high-resolution chemical analysis using GC\u0026times;GC-TOFMS, multivariate statistics, and metabolic pathway reconstruction, providing an integrated approach to understand the dynamics of biodegradation. In addition to characterizing temporal patterns of transformation and proposing a conceptual metabolic pathway that contributes to the understanding of the mechanisms involved in the bioremediation of petroleum-contaminated environments. This study aims to investigate the metabolic organization underlying petroleum hydrocarbon biodegradation using a metabolomic approach.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Mixed microbial consortium\u003c/h2\u003e \u003cp\u003eMixed Microbial Consortium The microbial consortium used in this study consists of bacterial and filamentous fungal strains. All strains were previously demonstrated to degrade hydrocarbons and have been characterized genetically. [Details on accession numbers or patent references removed for review].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inoculum preparation\u003c/h2\u003e \u003cp\u003eEach strain of the consortium was transferred to 100 mL of BH medium (Difco\u0026trade;) using an agarose disk (1 cm \u0026Oslash;). The mixture was incubated in an orbital shaker Tecnal\u0026trade; TE-420 at 30\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, 153 rpm, for 6 days.\u003c/p\u003e \u003cp\u003eFrom the concentrated solution, aliquots were prepared and diluted in 0.9% saline solution (w/v) to reach the desired cellular concentrations: 1 \u0026times; 10⁶ CFU/mL. Cell density was determined by optical density using an LMR-96\u0026trade; microplate reader (ELISA), using a wavelength of 500 nm for fungi and 600 nm for bacteria. The final stock solution was stored at 4\u0026deg;C until use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental design and sample collection\u003c/h2\u003e \u003cp\u003eThe experiment was conducted over 0, 15, 30, and 60 days using amber glass vials with a capacity of 40 mL. For the treatment with the microbial consortium, the final volume was 10 mL, composed of 8.9 mL of BH medium, 10% consortium (1 mL), and 1% petroleum (0.1 mL), with petroleum previously diluted at a ratio of 0.8295 g in 10 mL of dichloromethane (DCM). Treated samples (medium\u0026thinsp;+\u0026thinsp;consortium\u0026thinsp;+\u0026thinsp;petroleum) were prepared in triplicate, totaling 12 samples. Initially, the vials were incubated at 153 rpm at 30\u0026deg;C, and subsequently maintained in a germination chamber at 30\u0026deg;C with a 12-hour photoperiod.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Extraction and sample preparation\u003c/h2\u003e \u003cp\u003eMetabolites were extracted from the experimental samples using standard protocols for metabolomic analysis. The samples were subjected to preparation steps including organic extraction and chemical derivatization with MSTFA (N-Methyl-N-(trimethylsilyl)-trifluoroacetamide), when necessary, to increase the detectability of compounds by mass spectrometry. Derived compounds, such as trimethylsilylated esters, were considered during data interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mass spectrometry analysis and metabolite identification\u003c/h2\u003e \u003cp\u003eMetabolomic analysis was performed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC\u0026times;GC-TOFMS), a high-resolution technique widely used for the characterization of complex mixtures such as petroleum-derived hydrocarbons.\u003c/p\u003e \u003cp\u003ePrepared samples were injected into the GC\u0026times;GC system, allowing chromatographic separation in two dimensions based on different physicochemical properties of the compounds. Detection was performed using time-of-flight mass spectrometry (TOFMS), enabling the acquisition of spectra with high scan rate and resolution, essential for metabolite identification in complex matrices.\u003c/p\u003e \u003cp\u003eCompound identification was conducted by comparison of mass spectra with commercial libraries and databases, as well as by analysis of fragmentation patterns (m/z).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eData were normalized and log-transformed prior to statistical analysis. Principal Component Analysis (PCA) was used to explore the variance structure of the data. Statistical significance was assessed using appropriate tests, and correlation matrices were constructed using Pearson coefficients. Statistical analyses were performed in a computational environment using the Google Colab platform, through the Python programming language and appropriate scientific libraries.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Normality test\u003c/h2\u003e \u003cp\u003eThe normality of metabolite concentration data was assessed using the Shapiro\u0026ndash;Wilk test, with a significance level of 5% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Metabolites with zero variance were previously identified and excluded from the analysis to avoid distortions in the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Descriptive analyses\u003c/h2\u003e \u003cp\u003eMetabolite concentrations were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Data variability was evaluated across different time points and among experimental replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Exploratory analysis and visualization\u003c/h2\u003e \u003cp\u003eData exploration included the construction of different graphical approaches, including bar plots to evaluate temporal trends, heatmaps to identify global concentration patterns, and box plots for dispersion analysis and outlier detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Multivariate analysis\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) was performed to investigate global patterns and relationships between samples and metabolites. Data were previously normalized and scaled prior to applying the technique, allowing the identification of groupings associated with experimental factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Computational libraries and tools\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using widely recognized libraries in the Python environment, including: NumPy (numerical operations); Pandas (data manipulation); SciPy (statistical tests, including Shapiro\u0026ndash;Wilk); Matplotlib and Seaborn (data visualization); Scikit-learn (multivariate analysis, including PCA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6 Spectrometric data processing\u003c/h2\u003e \u003cp\u003eRaw data obtained from GC\u0026times;GC-TOFMS were processed for peak detection, chromatographic alignment, and extraction of spectral features. Compound identification was based on spectral similarity, fragmentation patterns, and, when possible, matching with reference libraries.\u003c/p\u003e \u003cp\u003eDiagnostic ions (m/z) were used as structural markers for inferring chemical classes and possible transformation pathways of detected compounds.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metabolic annotation and pathway analysis\u003c/h2\u003e \u003cp\u003eIdentified metabolites were annotated based on metabolic databases, including KEGG, allowing their association with pathways related to hydrocarbon degradation and xenobiotic metabolism. Pathways such as benzoate, toluene, styrene, naphthalene, and phenanthrene degradation were considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Metabolite\u0026ndash;enzyme association\u003c/h2\u003e \u003cp\u003eThe association between metabolites and enzymes was inferred based on information available in metabolic databases and specialized literature. A predominance of enzymes from the cytochrome P450 family was observed, associated with oxidation reactions of aromatic compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Construction of the conceptual metabolic pathway\u003c/h2\u003e \u003cp\u003eA conceptual metabolic pathway was proposed based on the integration of identified metabolites, their enzymatic associations, and annotated metabolic pathways. This approach allowed the representation of the transformation flow from initial hydrocarbons to final mineralization products, including CO₂, H₂O, and biomass.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview of metabolites and data distribution\u003c/h2\u003e \u003cp\u003eA set of key metabolites associated with the biodegradation of petroleum hydrocarbons was identified using GC\u0026times;GC-TOFMS, including aromatic intermediate compounds and carboxylic acids. The assessment of normality using the Shapiro\u0026ndash;Wilk test (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicated that some metabolites did not follow a normal distribution (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), reflecting the intrinsic variability of complex biological systems. Although no statistically significant differences were observed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), consistent temporal patterns suggest biologically meaningful metabolic transformations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Analysis of metabolite distribution based on the Shapiro\u0026ndash;Wilk normality test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eW_Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_Value_Shapiro\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShapiro_Performed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Aacute;cido benzoico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.698263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.443237e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHidroxifluoreno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.878318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.315026e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Aacute;cido 2-Hidroxicromeno-2-oico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.750000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.771561e-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenantrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.895607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.095662e-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Aacute;cido benzodioico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.753685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.982086e-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate analysis\u003c/h2\u003e \u003cp\u003eUnivariate statistical analyses did not reveal significant differences between the experimental time points (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Although no statistically significant differences were detected, consistent temporal trends suggest biologically relevant transformations. However, clear temporal trends were observed in the metabolite profiles, as illustrated in the bar plots with standard error of the mean (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that the biodegradation process occurs in a gradual and coordinated manner. For phenanthrene, only one time point (60 days) was observed, with low mean concentration and high variability (large error bar), suggesting heterogeneity among replicates or possible instability in the degradation process. In the case of hydroxyphenanthrene, also at 15 days, the mean concentration is higher and relatively consistent, with lower variability compared to phenanthrene, indicating stable formation of this intermediate metabolite.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBenzoic acid showed expressive variations, with an increase from T0 to T15 and 60 and a slight reduction at T30, in addition to high variability among replicates, suggesting heterogeneity in the biological response. In contrast, benzodioic acid showed more discrete variations and lower dispersion, indicating greater consistency among replicates. The metabolite 2-hydroxychromene-2-oic acid did not present sufficient data for graphical analysis, highlighting limitations in data completeness. The data indicate a typical dynamic of aromatic hydrocarbon biodegradation, with the formation of intermediate (hydroxylated) metabolites followed by the accumulation of aromatic acids over time. The variability observed at some points suggests the influence of biological or experimental factors, making it important to consider replication and statistical tests to confirm the observed trends.\u003c/p\u003e \u003cp\u003eThe increase of benzoic acid at specific time points suggests its role as a key intermediate in the degradation of aromatic hydrocarbons. This accumulation may indicate a metabolic bottleneck, possibly associated with enzymatic limitations in subsequent steps of the pathway. In contrast, the more stable behavior of compounds such as benzodioic acid suggests their participation in more advanced or less dynamic stages of the degradation process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation between chemical classes\u003c/h2\u003e \u003cp\u003eThe analysis of the correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed distinct patterns of association between chemical classes throughout the biodegradation process. A strong positive correlation was observed between carboxylic acids and heterocyclic acids (r\u0026thinsp;=\u0026thinsp;0.95), suggesting that these classes are metabolically related and possibly involved in subsequent stages of degradation. In contrast, polycyclic aromatic hydrocarbons showed negative correlation with carboxylic acids (r = -0.27) and heterocyclic acids (r = -0.21), indicating an inverse behavior consistent with their conversion into more oxygenated compounds over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the strong positive correlation between polycyclic aromatic hydrocarbons and carboxylic acid esters (r\u0026thinsp;=\u0026thinsp;0.87) suggests that these compounds may share intermediate metabolic pathways or reflect parallel transformation processes. On the other hand, the negative correlation between carboxylic acid esters and carboxylic acids (r = -0.38) reinforces the hypothesis of chemical conversion between these classes, possibly associated with hydrolysis or oxidation reactions.\u003c/p\u003e \u003cp\u003eOverall, the observed patterns support a sequential degradation model, in which aromatic hydrocarbons are progressively transformed into more polar compounds, including alcohols and organic acids, reflecting the metabolic dynamics of the microbial consortium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Global metabolite patterns: heatmap analysis\u003c/h2\u003e \u003cp\u003eComplementarily, the heatmap of mean concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) confirmed these temporal trends, highlighting the increase of benzoic acid throughout the experiment and the late formation of benzodioic acid. The metabolite 2-hydroxychromene-2-oic acid showed low concentrations and missing values, which limits its interpretation. This approach allowed the identification of global patterns of accumulation and metabolic transformation over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Multivariate analysis (PCA)\u003c/h2\u003e \u003cp\u003eMultivariate analysis by PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) reinforced the existence of structural patterns in the dataset, with the first two principal components explaining 65.54% of the total variance (PC1\u0026thinsp;=\u0026thinsp;41.84%; PC2\u0026thinsp;=\u0026thinsp;23.70%). A clear separation of samples was observed according to replicates and time points, indicating that both factors significantly influence metabolomic profiles. Metabolites such as benzoic acid and 2-hydroxychromene-2-oic acid contributed strongly to variation along PC1, while benzodioic acid and phenanthrol were more associated with PC2. These results highlight the existence of a temporal metabolic dynamics associated with the biodegradation process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Integration of metabolomic data and pathway reconstruction\u003c/h2\u003e \u003cp\u003eThe integration of metabolomic data allowed the proposal of a conceptual metabolic pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), in which initial petroleum compounds are progressively transformed through hydroxylation reactions, generating intermediates such as hydroxyfluorene and phenanthrol, which are subsequently converted into organic acids, such as benzoic acid. This metabolite acts as a central intermediate, connecting initial and final stages of degradation, culminating in mineralization into CO₂, H₂O, and biomass.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of metabolite\u0026ndash;enzyme associations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed the predominance of enzymes from the cytochrome P450 family, suggesting that hydroxylation reactions play a central role in the biotransformation of the detected compounds. These enzymes are essential for increasing the reactivity of hydrocarbons, facilitating subsequent degradation steps.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentified metabolites and their associated enzymes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociated Enzymes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c3\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenzoic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroxyfluorene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhenanthrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroxyphenanthrenic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroxyphenanthrenic Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydroxyfluorene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[Cytochrome P450]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe functional characterization of the metabolites (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated that they are predominantly aromatic compounds and organic acids associated with xenobiotic metabolism, reinforcing their origin in the degradation of petroleum-derived compounds. The presence of trimethylsilylated derivatives suggests the influence of the analytical process on the detection of some compounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional and metabolic profile of the identified metabolites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain Metabolic Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBiological Function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntimicrobial, intermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxyfluorene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAromatic compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiotransformation, xenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, exposure marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Hydroxychromene-2-oic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegradation pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDerived metabolite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenanthrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAromatic compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiotransformation, xenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, exposure marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzodioic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, carboxylic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxy-phenanthrenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAromatic compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiotransformation, xenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, exposure marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenzenedioic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, carboxylic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxy-phenanthrenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAromatic compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiotransformation, xenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, exposure marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydroxyfluorene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAromatic compound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiotransformation, xenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate, exposure marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,4-Benzenedicarboxylic acid, bis(trimethylsilyl) ester\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEster (derivative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXenobiotic metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnalytical derivative, intermediate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, annotation in metabolic databases (KEGG) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed the association of metabolites with relevant hydrocarbon degradation pathways, including benzoate, toluene, styrene, naphthalene, and phenanthrene degradation, in addition to xenobiotic metabolism mediated by cytochrome P450. These results confirm the activation of specific metabolic routes involved in the biodegradation of aromatic compounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolites associated with KEGG hydrocarbon degradation pathways.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEGG_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePathway_Name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1,4-Benzenedicarboxylic acid, bis(trimethylsil...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenzoate degradation via CoA ligation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenantrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNaphthalene degradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFenantrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhenanthrene degradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHidroxifluoreno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eToluene degradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHidroxifluoreno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eko00642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStyrene degradation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results obtained in this study highlight the complexity and multiphasic nature of petroleum hydrocarbon biodegradation, emphasizing the importance of integrating metabolomic, statistical, and functional annotation analyses to understand the processes involved. The predominance of aromatic compounds and organic acids among the identified metabolites reinforces the central role of oxidation and transformation reactions of polycyclic aromatic hydrocarbons (PAHs) and alkanes during bioremediation. The observed transformation of aromatic hydrocarbons into more polar compounds suggests a reduction in environmental persistence and potential toxicity, reinforcing the applicability of microbial consortia in bioremediation strategies (Haritash \u0026amp; Kaushik, 2009; Ghosal et al., 2016; Rojo, 2009; Patti et al., 2012; Truskewycz et al., 2019; Ossai et al., 2020; Sharma et al., 2024; Zhang et al., 2023).\u003c/p\u003e \u003cp\u003eThe identification of metabolites such as hydroxyfluorene, phenanthrol, and phenanthrene derivatives indicates that the initial stage of degradation is characterized by hydroxylation reactions, which increase the polarity and reactivity of these molecules. This process is essential to enable subsequent degradation steps, such as aromatic ring cleavage and the formation of simpler compounds. In this context, the strong association observed with enzymes of the cytochrome P450 family suggests that these monooxygenases play a key role in the initial activation of compounds, acting as an entry point for the microbial catabolism of complex hydrocarbons (Cerniglia, 1992; Guengerich, 2008; Haritash \u0026amp; Kaushik, 2009; Seo et al., 2009; Li et al., 2023).\u003c/p\u003e \u003cp\u003eBenzoic acid emerged as a key metabolite in the system, acting as a central intermediate in the metabolic network. Its presence at different experimental time points, associated with significant variations in concentration over time, suggests that this compound functions as a link between the degradation of more complex aromatic compounds and more general metabolic pathways directed toward mineralization. This behavior is consistent with its known role as an intermediate in several aromatic degradation pathways, including benzoate degradation via CoA ligation (Harwood \u0026amp; Parales, 1996; D\u0026iacute;az, 2004; P\u0026eacute;rez-Pantoja et al., 2012; Fuchs \u0026amp; Heider, 2011; Baensch et al., 2025).\u003c/p\u003e \u003cp\u003eMultivariate analysis by PCA revealed a clear separation between time points, indicating that temporal dynamics is one of the main factors structuring the metabolomic profile of the system. The explanation of 65.54% of the total variance by the first two principal components demonstrates that the main trends in the data were captured, allowing the identification of consistent patterns of metabolic transformation. However, the observed effect among experimental replicates indicates the presence of intrinsic variability, possibly associated with biological differences in the microbial consortium or experimental variations, highlighting the need to consider this factor in more robust statistical analyses (Trygg et al., 2007; Patti et al., 2012; Fiehn et al., 2008; Bro \u0026amp; Smilde, 2014; Sharma et al., 2024).\u003c/p\u003e \u003cp\u003eComplementary univariate analyses, including bar plots, heatmaps, and box plots, reinforce the interpretation of PCA by revealing distinct patterns of variation over time. This integrated approach is widely used in metabolomic studies to validate multivariate trends and identify key metabolites responsible for the separation of experimental groups (Patti et al. 2012; Xia and Wishart 2011; Lisec et al., 2006; Zhang et al., 2023). The increase in benzoic acid at specific time points suggests intermediate accumulation, possibly associated with rate-limiting steps in degradation, a behavior consistent with its role as a central intermediate in the degradation of aromatic compounds, especially in the benzoate pathway (Fuchs \u0026amp; Heider, 2011). On the other hand, the more stable behavior of metabolites such as benzodioic acid may indicate participation in more advanced or less dynamic stages of the process, reflecting transformations subsequent to initial oxidation and aromatic ring cleavage (Haritash \u0026amp; Kaushik 2009). Taken together, these patterns reinforce the importance of combining univariate and multivariate analyses for a robust interpretation of metabolic dynamics in biodegradation processes.\u003c/p\u003e \u003cp\u003eThe annotation of metabolic pathways revealed strong association with routes of aromatic compound degradation and xenobiotic metabolism, including pathways related to benzoate, toluene, styrene, naphthalene, and phenanthrene degradation. These findings are consistent with the chemical composition of petroleum and indicate that the microbial consortium has broad metabolic capacity to process different classes of hydrocarbons. The integration of this information allowed the proposal of a conceptual metabolic pathway, in which initial compounds are progressively transformed into more polar intermediates, culminating in complete mineralization (Kanehisa et al., 2017; Head et al., 2006; Rojo, 2009; Li et al., 2023; Sharma et al., 2024).\u003c/p\u003e \u003cp\u003eAdditionally, the detection of derivatized compounds, such as trimethylsilylated esters, highlights the importance of considering analytical steps in the interpretation of metabolomic data. These compounds, although not necessarily representing natural metabolites, provide indirect evidence of the presence of organic acids and other relevant intermediates in the system (Fiehn, 2008; Kind et al., 2009; Mamas et al., 2011; Lisec et al., 2006; Patti et al., 2012).\u003c/p\u003e \u003cp\u003eOverall, the results demonstrate that petroleum hydrocarbon biodegradation is a highly integrated process, involving multiple enzymatic and metabolic steps. The identification of key metabolites, such as benzoic acid, and the characterization of associated pathways provide important insights into transformation mechanisms and potential control points of the process. As future perspectives, the integration with gene expression data and metabolic flux modeling may deepen the understanding of the mechanisms involved, contributing to the optimization of bioremediation strategies in contaminated environments (Varjani, 2017; Truskewycz et al., 2019; Sharma et al., 2024). Although no statistically significant differences were observed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), consistent temporal patterns suggest biologically meaningful metabolic transformations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results of this study demonstrate that petroleum hydrocarbon biodegradation by microbial consortia is a dynamic and highly integrated process, involving multiple biotransformation steps and interconnected metabolic pathways. The metabolomic approach based on GC\u0026times;GC-TOFMS enabled the identification of key metabolites and the detailed characterization of chemical transformations over time, highlighting the complexity of the system.\u003c/p\u003e \u003cp\u003eThe predominance of hydroxylated aromatic compounds and organic acids confirms the central role of oxidation reactions in the initial degradation of hydrocarbons, with emphasis on the activity of enzymes from the cytochrome P450 family. Among the identified metabolites, benzoic acid emerged as a central intermediate, connecting different degradation pathways and acting as a link between initial steps and mineralization processes.\u003c/p\u003e \u003cp\u003eStatistical and multivariate analyses revealed the significant influence of temporal dynamics in structuring metabolomic profiles, while also indicating variability among experimental replicates, reinforcing the importance of robust analytical approaches. Metabolic pathway annotation confirmed the involvement of routes related to xenobiotic metabolism and aromatic compound degradation, reflecting the metabolic versatility of the microbial consortium.\u003c/p\u003e \u003cp\u003eData integration enabled the proposal of a conceptual metabolic pathway, describing the progressive conversion of complex hydrocarbons into more reactive intermediates and subsequently into final mineralization products, such as CO₂, H₂O, and biomass. These findings contribute to advancing knowledge on petroleum bioremediation mechanisms and highlight the potential of metabolomics as a strategic tool for elucidating complex environmental processes.\u003c/p\u003e \u003cp\u003eAs future perspectives, integrating additional omics approaches\u0026mdash;such as metagenomics and transcriptomics\u0026mdash;together with metabolic flux modeling may further enhance the understanding of the mechanisms involved and contribute to the optimization of large-scale bioremediation strategies. Overall, this study advances knowledge on the transformation of hazardous hydrocarbons in environmental systems and reinforces the potential application of microbial consortia in bioremediation processes. This study advances the understanding of microbial metabolism involved in petroleum hydrocarbon biodegradation.\u003c/p\u003e"},{"header":"Referências","content":"\u003col\u003e\n\u003cli\u003eAdahchour, M., Beens, J., Brinkman, U.A.Th., 2008. Recent developments in the application of comprehensive two-dimensional gas chromatography. Journal of Chromatography A 1186 (1\u0026ndash;2), 67\u0026ndash;108. https://doi.org/10.1016/j.chroma.2007.09.088\u003c/li\u003e\n\u003cli\u003eAl-Hawash, A.B., Dragh, M.A., Li, S., Alhujaily, A., Abbood, H.A., Zhang, X., Ma, F., 2018. Principles of microbial degradation of petroleum hydrocarbons in the environment. \u003cem\u003eSci. Total Environ.\u003c/em\u003e 643, 149\u0026ndash;163. https://doi.org/10.1016/j.ejar.2018.06.001\u003c/li\u003e\n\u003cli\u003eAtlas, R.M., Hazen, T.C., 2011. Oil biodegradation and bioremediation: a tale of the two worst spills in U.S. history. \u003cem\u003eEnviron. Sci. Technol.\u003c/em\u003e 45, 6709\u0026ndash;6715. https://pubs.acs.org/doi/10.1021/es2013227\u003c/li\u003e\n\u003cli\u003eAzubuike, C.C., Chikere, C.B., Okpokwasili, G.C., 2016. Bioremediation techniques\u0026ndash;classification based on site of application: Principles, advantages, limitations and prospects. World Journal of Microbiology and Biotechnology 32, 180. https://doi.org/10.1007/s11274-016-2137-x\u003c/li\u003e\n\u003cli\u003eBaensch, C., Kr\u0026ouml;ger, A., Wilkes, H., 2025. Hydrocarbon biodegradation processes at a historic oil production site \u0026ndash; A signature metabolite study. Science of the Total Environment 178508. https://doi.org/10.1016/j.scitotenv.2025.178508\u003c/li\u003e\n\u003cli\u003eBerr\u0026iacute;os-Rol\u0026oacute;n, P.J., Cotto, M.C., M\u0026aacute;rquez, F., 2023. Polycyclic aromatic hydrocarbons (PAHs) in freshwater systems: A comprehensive review of sources, distribution, and ecotoxicological impacts. Water 15 (3), 556. https://doi.org/10.3390/toxics13040321\u003c/li\u003e\n\u003cli\u003eBro, R., Smilde, A.K., 2014. Principal component analysis. 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International Microbiology 7 (3), 173\u0026ndash;180. https://digital.csic.es/handle/10261/2134\u003c/li\u003e\n\u003cli\u003eDuarte, M., Mansilha, C., Melo, A., Sobral, D., Ferreira, R., Gomes, J.P., Rebelo, H., Veber, A., Puskar, L., Schade, U., Jord\u0026atilde;o, L., 2023. Detection of polycyclic aromatic hydrocarbons, microplastic presence and characterization of microbial communities in the soil of touristic zones at Alqueva\u0026rsquo;s edges (Alentejo, Portugal). Environmental Monitoring and Assessment 195, 512. https://link.springer.com/article/10.1007/s11356-026-37415-6\u003c/li\u003e\n\u003cli\u003eFeng, Y., Li, Z., Li, W., 2020. Polycyclic aromatic hydrocarbons (PAHs): Environmental persistence and human health risks. Advances in Experimental Medicine and Biology 1223, 123\u0026ndash;139. https://doi.org/10.1177/1934578X241311451\u003c/li\u003e\n\u003cli\u003eFiehn, O., 2008. Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. 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The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged.\u0026emsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026emsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge financial and institutional support provided through a national research and development program in the fields of geomicrobiology and petroleum biotechnology. The authors also recognize the strategic importance of support from a national energy regulatory agency under research and development funding mechanisms. Additional support from a graduate education funding agency is acknowledged.\u0026emsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShell Brasil Ltda.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not involve human participants, human data, or identifiable personal information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of AI Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that artificial intelligence tools (ChatGPT, OpenAI) were used solely to assist with language editing, grammar correction, and formatting of the manuscript. All scientific content, data, results, interpretations, and conclusions presented in this work are the original work of the authors and have not been generated or influenced by AI. The use of AI did not affect the integrity, analysis, or originality of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not involve human participants, human data, or identifiable personal information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e☐ 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.\u003c/p\u003e\n\u003cp\u003e☒ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDFL (Danusia Ferreira Lima) conceived and designed the study, performed the experiments, conducted data analysis, and wrote the main manuscript. AFSQ (Antonio Fernando de Souza Queiroz) contributed to data interpretation and critical revision of the manuscript. OMCO (Ol\u0026iacute;via Maria Cordeiro de Oliveira) contributed to data interpretation, supervision, and manuscript review. All authors reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodegradation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biod","sideBox":"Learn more about [Biodegradation](http://link.springer.com/journal/10532)","snPcode":"10532","submissionUrl":"https://submission.nature.com/new-submission/10532/3","title":"Biodegradation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Petroleum hydrocarbon biodegradation, microbial consortium, metabolomics, GC×GC-TOFMS, aromatic hydrocarbons, metabolic pathways","lastPublishedDoi":"10.21203/rs.3.rs-9620783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9620783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental contamination by petroleum hydrocarbons represents a critical global concern due to their persistence, toxicity, and potential risks to ecosystems and human health. In this study, the biodegradation of petroleum hydrocarbons by a mixed microbial consortium was investigated using a metabolomics-based approach. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC\u0026times;GC-TOFMS) enabled high-resolution profiling of metabolites formed time.\u003c/p\u003e \u003cp\u003eThe results revealed a progressive transformation of aromatic hydrocarbons into oxygenated intermediates and carboxylic acids, indicating active biodegradation processes. Although no statistically significant differences were observed (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), clear temporal trends were identified, suggesting coordinated metabolic activity. Multivariate analysis (PCA) and correlation-based approaches demonstrated structured metabolic shifts associated with hydrocarbon degradation.\u003c/p\u003e \u003cp\u003eKey metabolites, particularly benzoic acid, were identified as central intermediates linking initial oxidation steps to downstream degradation pathways. Integration with KEGG pathways indicated the involvement of enzymatic systems such as cytochrome P450 in the transformation of hazardous hydrocarbons.\u003c/p\u003e \u003cp\u003eThese findings provide important insights into the mechanisms governing petroleum hydrocarbon degradation and highlight the relevance of metabolomics for understanding contaminant transformation in environmental systems. The study contributes to advancing bioremediation strategies by elucidating metabolic pathways associated with the breakdown of hazardous organic pollutants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Metabolomic insights into petroleum hydrocarbon biodegradation by a microbial consortium: evidence of coordinated metabolic transformation pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 15:20:13","doi":"10.21203/rs.3.rs-9620783/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"107964164674337670872655853377400325229","date":"2026-05-10T03:13:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305686009413767896107790773055894746283","date":"2026-05-08T08:06:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-08T03:12:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T01:47:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T01:47:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodegradation","date":"2026-05-05T15:47:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodegradation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biod","sideBox":"Learn more about [Biodegradation](http://link.springer.com/journal/10532)","snPcode":"10532","submissionUrl":"https://submission.nature.com/new-submission/10532/3","title":"Biodegradation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15b09221-b802-4700-97eb-f2c2a77dadbc","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"107964164674337670872655853377400325229","date":"2026-05-10T03:13:40+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"305686009413767896107790773055894746283","date":"2026-05-08T08:06:48+00:00","index":11,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-08T03:12:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-08T01:47:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-08T01:47:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodegradation","date":"2026-05-05T15:47:19+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T15:20:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 15:20:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9620783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9620783","identity":"rs-9620783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}