Since the goal of achieving carbon neutrality before 2060 was proposed, developing new technologies for high-value utilisation of coal, as well as for clean and efficient utilisation, has become a research hotspot in China. The gasification (Yang et al. 2021) and liquefaction (Xu et al. 2025) of coal are the core technologies for transforming coal resources from "fuel" to "raw material". They greatly expand the range of coal applications, but its large-scale development always relies on a balance between economic growth and environmental sustainability. Bioconversion of coal using microbial transformation technology can not only overcome the shortcomings of physical and chemical transformation but also have the advantages of zero pollution, low energy consumption, and simple technology. Research on the bioconversion of coal began in the 1980s, and now more and more bacteria capable of dissolving coal have been identified (Akbimbekov et al., 2022). The biodegradation technology of coal can convert coal into valuable chemicals and materials under mild conditions and offers clear advantages, such as low pollution and simple equipment (Chen et al. 2018; Akimbekov et al. 2019; Shi et al. 2023a). The types of coal that microorganisms can transform include sub-bituminous coal, lignite, and bituminous coal, etc. Experiments show that the oxidation degree and metamorphic grade of coal are the main factors affecting the degree and rate of coal biological transformation (Akimbekov et al. 2021). Since microorganisms readily degrade lignite and sub-bituminous coal, most reports on the bioconversion of coal focus on low-rank coals. At present, hundreds of microorganisms have been found that can be used for coal conversion. Various strains have been reported to degrade coal. The fungi include Rhodococcus (Jatoi et al. 2021), AD-1 strain (Sabar et al. 2020), Trichoderma citrinoviride (Feng et al. 2021), Penicillium oxalicum HM-M1 (Cheng et al. 2022). Several bacteria include Streptomyces fulvissimus K59 (Sobolczyk-Bednarek et al. 2021), Nocardia mangyaensis, Bacillus licheniformis (Shi et al. 2022), Bacillus sp. (Shi et al. 2023b), and so on. The combination of different strains can also degrade coal, such as Caenorhabditis elegans and Bacillus subtilis(Chen et al. 2023). Therefore, our group continued to report a study on the bioconversion of oxidised ShenFu coal samples with fungi under high Mn²⁺ conditions (Chen et al. 2012).

Biodegradation is an inevitable natural process of geological organic matter under suitable conditions. Exploring this process not only helps us evaluate the quality of mineral resources and exploration risks but also provides a scientific basis for the development and utilisation of emerging bioenergy technologies. In the early 1980s, Fakoussa (Fakoussa and Hofrichter 1999) first reported that bacteria could dissolve anthracite, and subsequently began studying the degradation of coal. In 1982, Gabriele and Cohen (Cohen and Gabriele 1982) discovered that two white rot bacteria, Polyporus versicolor and Poria monticolor, could liquefy lignite and turn it into small black droplets. After that, more and more bioconversion microorganisms were isolated and domesticated, and their bioconversion efficiency for low-rank coal gradually improved to 90%. The biotransformation of coal depends on the type of coal and the microorganisms used. During organic matter biodegradation, interactions occur between the metabolic components of microorganisms (Xia et al. 2025), and the transformation mechanism remains unclear (Nsa et al. 2022). Based on different experimental results, some research groups found that microbial conversion of coal can be carried out through any of the biological processes such as depolymerization, decolorization (Ralph and Catcheside 1997; Steffen et al. 2002), liquefaction (Cheng et al. 2022), and solubilization (Ahmed and Sharma 2021), in which some microbial proteins play a key role (Olawale et al. 2020; Ponnudurai et al. 2022) Meanwhile, basic metabolites, REDOX and hydrolytic enzymes include some central cation of enzyme (peroxidase) activities are also crucial for coal bioconversion efficiency (Luo et al. 2021; Cajnko et al. 2021; Bankole et al. 2022; Xu et al. 2022). In summary, the mechanism of coal bioconversion has always been the focus of coal liquefaction research.

In recent years, as metabonomics has developed, this method has been applied to the study of various biochemical processes. There are also some metabolomics studies related to coal, such as deciphering the initial products of coal (Wang et al. 2021), analysing the target metabolites and metabolic pathways of microorganisms associated with coal (Yang et al. 2023), identifying the geographic origin of coal (Xue et al. 2022), and so on. Significant progress has been made, and it has been found that the yield of coal bioconversion is related to metabolic pathways, and bioconversion is a practical pathway for the clean and mild utilisation of coal (Xiao et al. 2018; Zhao et al. 2022). Fat metabolism plays a vital role in the growth of microorganisms in extreme environments, including high salt, high cold, high temperature, low carbon source, and other strict environments, especially the metabolism of phospholipids, which is unique and essential in the process of carbon source utilization, enrichment, and transformation; this may be related to the long-term evolutionary mechanism of microorganisms. The main way it influences microorganisms is by increasing or decreasing the activity of certain enzymes, interfering with normal physiological and biochemical processes, and thus affecting the absorption and transformation of carbon sources. As the catalytic centre of the enzyme, metal ions directly affect enzyme activity, such as Mn²⁺, which is an agonist or inhibitor of the enzyme (Wang et al. 2024). Previous studies by our group also found that some metal ions can indeed improve the bioconversion efficiency of coal, but the mechanism of this promotion is unclear. In the process of bioconversion, which metabolic pathways are up-regulated or down-regulated by microorganisms, which enzymes are key, and which biochemical reactions are speed-limiting (Shen et al. 2023)? In this paper, we aim to identify key problems using metabolomics based on UPLC-Q-TOF/MS, combined with pattern recognition and network analysis methods, to provide unique insights into the bioconversion of coal.

Reagents and Materials

Methanol and acetonitrile were purchased from Merck (Darmstadt, Germany), both of which are HPLC grade. Distilled water (18.2M Ω) is produced by the Mil-li-Q ultrapure water system (Millipore, Billerica, MA, USA). All other chemicals are analytical grade and obtained from standard commercial suppliers.

Preparation of Oxidised Lignite Samples

This experiment used Shenfu lignite samples collected from Daliaoliang Coal Mine in Xinmin Town, Fugu County. Firstly, the coal samples were ground in a disc crusher and sieved through a 200 mesh sieve. This experiment used the nitric acid oxidation method to prepare oxidised coal samples: 8 mol/L nitric acid (0.25 g/mL nitric acid solution) was magnetically stirred at room temperature for 48 hours. After the oxidation reaction is completed, a vacuum filtration system is used for solid-liquid separation, and the separated oxidised lignite is repeatedly washed with distilled water until the filtrate becomes neutral. Steam-sterilise the washed oxidised brown coal at 121 ℃ for 15 minutes. Dry to constant weight at 70 ℃ and store for future use. The elemental analysis results of the original lignite and oxidised lignite samples are shown in Table 1.

* Oxygen was determined by difference.

Bioconversion of Lignite and quenching methods

The Monilia crassa Sh. Et Dodge strain used throughout the entire research process was one of the strains screened by our research team from the Shenfu coal washing wastewater in the early stage(Meng et al. 2008; Zhang et al. 2009; Meng et al. 2011; Chen et al. 2012; Wang et al. 2014), cultured in YPD liquid medium prepared with 20 g of glucose, 20 g of peptone, 10 g of yeast extract, and 1000 mL of ultrapure water. Add the spore solution of Monilia crassa Sh. Et Dodge to the culture medium at a ratio of 10⁷ spores per 50 mL of medium. Then, add 0.3 g of oxidised brown coal and shake at 160 r/min in a shake flask containing 50 mL of YPD liquid medium at 30 ℃. After 7 days of cultivation, collect the culture sample to obtain the metabolome (Li et al. 2017). Select three concentrations of Mn²⁺ (sulfate) at 1 mM, 10 mM, and 50 mM to investigate the effect of the gradient concentration of Mn²⁺ on biotransformation rate, while selecting a blank control group without adding any Mn²⁺. There are 7 biological duplicate samples.

Select methanol: acetonitrile: water = 2:2:1 as the quenching solvent, rapidly inject 1 mL of 7-day-grown culture into 5 mL of quenching solvent, shake vigorously for 30 seconds for quenching reaction, and place in a -80 ℃ refrigerator for 10 minutes. At 4 ℃, the reactants were sonicated for 5 minutes using an ultrasonic instrument to disrupt the culture medium, followed by centrifugation at 12000 rcf for 10 minutes. Take the supernatant and vacuum dry it in a high-speed vacuum concentrator for 6 hours; take the dried residue and suspend it in 500 µL pure methanol at -20 ℃, sonicate it again for 5 minutes for secondary crushing, then centrifuge it at 12000 rcf for 10 minutes at 4 ℃, collect the supernatant and concentrate and dry it under a nitrogen atmosphere at 4 ℃; Mix the two dried metabolites and store them in a -80 ℃ refrigerator for later use. The resulting residual coal is dried to constant weight at 80 ℃.

Chromatography and Mass Spectrometry

The ultra-high performance liquid chromatography is performed by Hermo Scientific ™ UltiMate ™ 3000 (Thermo Scientific, USA), chromatography column is ACQUITY UPLC BEH C18 (2.1 mm × 50 mm, 1.7 µm) column; The mobile phases are 95% water + 5% methanol containing 0.5% formic acid (A) and methanol containing 0.5% formic acid (B), respectively; The gradient elution procedure is selected as follows: 0–1 minutes, 0% (B); 1–3 minutes, 0–10% (B); 3–22 minutes, 10–99% (B); 22–28 minutes, 99% (B); 28–29 minutes, 99 − 0% (B); 29–30 minutes, 0% (B). The flow rate is 0.15 mL/min; Column temperature is 40℃; injection volume is 5 µL; Insert blank samples between samples to check for chromatographic residues.

The quadrupole time-of-flight mass spectrometer (Bruker, Germany) is equipped with an electrospray ionisation source that operates in positive ion mode (ESI+) and negative ion mode (ESI-). The nitrogen flow rate is 8 L/min, the capillary ionisation voltage is 3.5 kV, the solvent temperature is 200 ℃, and the spray gas is 4 bar. The m/z scanning range is 50-3000. The data collection rate is 0.2 seconds, and the scanning delay interval is 0.1 seconds. All analyses were conducted using sodium formate as the calibration solution to ensure accuracy and reproducibility (Liu et al. 2016).

Collecting and processing UPLC-Q-TOF/MS data using Bruker Data Analysis.

Data Mining Methods, Bioinformatics, and Statistical Analysis

Analysed UPLC-Q-TOF/MS data using MZmine 2.9, which allows for deconvolution, alignment, and data reduction to generate a table composed of quality and retention time pairs, as well as the correlation strength of all detected peaks, and further export to SIMCA-P v13.0 (Umetrics AB, Umeå, Sweden) and MetPA for multivariate data analysis. Conduct supervised PLS statistical analysis to obtain valuable information to distinguish differences in metabolic phenotypes corresponding to categories. PLS generated a load map indicating the influence of variables on carrier formation (Haridas et al., 2024).

Identify candidate biomarkers through retention behaviour, batch allocation, and online database queries. The accurate mass and structure information of candidate metabolites were matched with those obtained from YMDB (http://www.ymdb.ca/), SMPDB (http://smpdb.ca/), METLIN (http://metlin.scripps.edu/), Massbank (http://www.massbank.jp), PubChem (http://ncbi.nim.nih.gov/), and KEGG (http://www.genome.jp/kegg/) databases. The mass accuracy was 20 ppm.

Used MetPA and KEGG for metabolic pathway research to explore the most affected metabolic pathways. Import metabolites and corresponding pathways into Cytoscape (v.3.4.0) to visualise network models, and cross-network through the advanced network merging function in Cytoscape to promote further biological interpretation (Gong et al. 2017)

Effect of Mn²⁺ concentration on dissolution yield

The biological conversion rate of coal samples (based on air drying basis) is calculated using the following formula:

In the formula, is the biological conversion rate of coal powder (%); is the initial weight of coal powder added (g); is the weight of residual coal powder after biotransformation (g).

Calculate the biotransformation rates of biotransformation samples with added amounts of 1 mM, 10 mM, and 50 mM, and compare them with the blank control group by plotting. As shown in Fig. 1, exogenous Mn²⁺ significantly increases the biotransformation yield compared with the blank control group. The addition of 1 mM Mn²⁺ concentration can increase the biotransformation rate from 22% to 32%, while the addition of 10 mM Mn²⁺ concentration can increase the biotransformation rate to 58%. When the Mn²⁺ concentration was further increased to 50 mM, the biotransformation rate did not continue to grow but decreased to 45%. It can be demonstrated that Mn²⁺ has a significant activating effect on specific enzymes in the Monilia crassa Sh. Et Dodge bacterial biotransformation system of lignite. However, the higher the concentration of Mn²⁺ added, the stronger the activating effect. This may be because Mn²⁺ upregulates amino acid metabolism, transmembrane transporter activity, and oxidoreductase activity (Wang et al. 2024). When the concentration is too high, these enzymes do not achieve optimal activation. In addition, at a concentration of 10 mM Mn²⁺, the biotransformation rate of coal powder was nearly doubled. It can be inferred that some enzymes with Mn²⁺ as the active centre, such as laccase and manganese enzyme, are significantly affected in this system and play a key role in this biotransformation process.

Analysis of metabolomics data based on UPLC-Q-TOF/MS

Perform UPLC-Q-TOF/MS complete scan detection and systematic metabolomics analysis on metabolic product samples from the 7-day culture group and control group. The experiment completed data collection in both positive and negative ionisation modes to comprehensively characterise polar and non-polar metabolites in biological samples. The total ion chromatogram of the biotransformation sample (containing 10mM Mn²⁺) in positive and negative ion modes is shown in Figs. 2A and 2C, respectively. The chromatographic separation and signal response intensities are better in positive ion mode, clearly demonstrating differences in metabolic profiles between groups. This may be due to the inability of some potential markers to ionise in negative mode. Therefore, further research was conducted using positive ion mode. The MS/MS image of the biotransformation sample (containing 10mM Mn²⁺) in positive ion mode is shown in Fig. 2B.

The data were collected and analysed using Bruker Compass Data Analysis 4.3, and a standardised preprocessing process was carried out using MZmine 2.9 for systematic analysis, including key steps such as baseline correction, peak recognition, chromatographic peak alignment, and noise filtering. Finally, 8213 metabolic characteristic peaks with analytical value were extracted from the positive ion mode using the same collection method, while 741 characteristic peaks were extracted from the negative ion mode. To ensure data analysis reliability, all processed multidimensional datasets were imported into SIMCA-P 13.0 for PLS-DA analysis. A mathematical model was established to effectively distinguish metabolic differences between groups, thereby differentiating between Mn²⁺ biotransformation samples (1/10/50 mM Mn²⁺) and blank control group samples (excluding Mn²⁺).

PLS-DA is a supervised, multivariate statistical analysis method that classifies research objects based on observed or measured values of several variables. In this study, the technique used Mn²⁺ concentration as the response variable and constructed a latent variable spatial model to extract the metabolic characteristics of lignite degradation closely related to Mn²⁺. Compared with principal component analysis (PCA), PLS-DA exhibits significant advantages in the functional analysis of lignite degrading microbial communities: by introducing prior information of Mn²⁺ concentration gradient, weighted screening of lignite degradation related metabolites is carried out, targeted amplification of Mn²⁺ responsive metabolic characteristics is performed, and concentration dependent critical metabolic nodes are effectively identified, thereby accurately identifying key microbial metabolic pathways regulating lignite conversion, effectively avoiding interference from complex situations such as ion concentration window effect, nonlinear metabolic response, and sample size imbalance.

As shown in Fig. 3A, the PLS-DA score plot of the data revealed complete and significant separation between the Mn²⁺ biotransformation group and the blank control group. The blank control group samples are distributed in the first quadrant, while the Mn²⁺ biotransformation group samples are distributed in the second, third, and fourth quadrants. This completely isolated spatial distribution pattern indicates that the introduction of Mn²⁺ triggered a systematic restructuring of the microbial metabolic network, suggesting that the metabolic differences between different treatment groups were not random fluctuations, but were significantly dose-dependent and associated with Mn²⁺ concentration gradients.

As shown in Fig. 3B, the PLS-DA loading graph in positive ion mode intuitively indicates the contribution of various metabolic features to model typing via multidimensional variable projection. In a two-dimensional coordinate system, each data point represents an independent metabolite feature, and its Euclidean distance from the origin quantitatively reflects the weight influence of the variable on the PLS-DA principal components - the farther the distance, the stronger the discriminative power of the metabolite in inter-group differences. Through spatial distribution analysis, it was found that the discrete point cluster located in the edge region of the load map (i.e. far from the coordinate origin) can be identified as the key differential metabolite driving the separation of the experimental group and the control group due to its significant deviation from the core area of principal component clustering. These high-weight features exhibit a strong intergroup expression bias in the model, and their abundance changes may be directly related to metabolic pathway remodelling induced by exogenous Mn²⁺. Based on the geometric features of the load vector, the study prioritises screening metabolites corresponding to spatial extremum points as candidate biomarkers. These target compounds will enter the subsequent targeted identification process, including precise molecular weight determination, secondary mass spectrometry fragment analysis, and metabolic database matching, to clarify their chemical structure and biological functions. This screening strategy significantly improves the efficiency of identifying functional markers in complex metabolic networks by prioritising the analysis of highly discriminatory metabolites.

Screening and identification of metabolic biomarkers

This study used variable importance projection (VIP) analysis, combined with statistical validation, to screen for biomarkers. Firstly, the VIP parameter (VIP > 2) was used to screen for metabolic features that contribute significantly to the metabolomics differences between the Mn²⁺ biotransformation group and the control group. Then, an independent-samples t-test (p < 0.05) was conducted to assess statistical significance. The differential metabolites obtained can serve as candidate biomarkers for characterising the specific metabolic response of Mn²⁺ under environmental stress, and their metabolic pathway changes can effectively reflect the molecular regulatory mechanisms involved in Mn²⁺ biotransformation.

Retention time, precise molecular weight, and MS/MS data can be obtained from UPLC-Q-TOF/MS data for screening biomarkers. Using the Wilcoxon-Mann-Whitney test, 34 ions showed significant differences between the control and biotransformation groups. Online retrieval is conducted in the YMDB, METLIN, and KEGG databases using peak characteristics and identification criteria to identify unknown compounds. The mass tolerance between the identified candidate metabolites and the additional MS data for known compounds is limited to ± 20 ppm. Preliminary identification of potential biomarkers is conducted through MS-based database searches, followed by candidate selection via MS/MS analysis, and then by further database and literature searches. Through this identification process, the substance of interest was identified as m/z 496.3394.

Firstly, based on its retention time in the extraction ion chromatogram at m/z 496.3394 (Fig. 4A), identify the corresponding signal peak. Afterwards, the precise molecular weight of [M + Na]⁺ ions was determined to be 518.3213 in the spectrum (Fig. 4B). Secondly, use auxiliary software in Hystar to facilitate the determination of the elemental composition of the peak at m/z 495.3325. Innovative Formula is used to explore potential elemental compositions by matching the isotopic patterns of elemental compositions to a cluster of peaks in the spectrum, increasing confidence in identified compounds and simplifying results. The fitting effect is higher as the error value, and the stigma is lower. A series of analyses yielded only one possible element composition of C₂₄H₅₀NO₇P (Fig. 4C).

Third, the element composition is compared with the element composition registered in the database, and is identified as LysoPC(16:0). Fourth, in Fig. 5, the m/z 184.0739 and 478.3304 were observed in the MS/MS spectrum, where m/z 184.0739 represents [H₂O₃PO-CH₂CH₂N(CH₃)₃]⁺, all of which are typical fragments of the PC, 478.3304 represent [M-H₂O + H]⁺, and further supports the hypothesis that this metabolite belongs to lysoPC. All of these data are very consistent with the standard spectrum in the database. Based on all information obtained from the above process, the biomarker was identified as LysoPC(16:0).

Figure 6 proposes a potential breakthrough path based on collision-induced dissociation. The main cracking pathways include: (1) Loss of H₂O, resulting in [M-H₂O + H]⁺ (m/z 478) (2) Specific breakage produces characteristic choline fragment ions [H₂O₃POCH₂CH₂N(CH₃)₃]⁺ (m/z 184), which are key diagnostic ions for confirming the head group of phosphatidylcholine; (3) The m/z 184 ion further loses phosphoric acid (H₃PO₄/HPO₃) to generate choline quaternary ammonium ion [HOCH₂CH₂N(CH₃)₃]⁺ (m/z 104). These cleavage modes are typical of LysoPC lipids in positive-ion CID.

LysoPC (16:0) is a hemolytic phospholipid (LyP). It is a monoglyceride phospholipid, in which the phosphorylcholine moiety occupies the glycerol substituents (Shanbhag et al. 2020). Lysophosphatidylcholine can have a different combination of fatty acids having various lengths and saturation at the C-1 (sn-1) position. Fatty acids containing 16, 18, and 20 carbon atoms are the most common. In particular, lysoPC (16:0) comprises a stearic acid chain at the C-1 position. Lysophosphatidylcholine is found in algae, fungi, and various microorganisms, and 25 related enzymes have been reported in the HMDB database. For example, lysophosphatidylcholine is formed by hydrolysis of phosphatidylcholine by the lysophospholipid acyltransferase (LPCAT), which is part of the deacylation/reacylation cycle that controls its entire molecular composition (Ejsing et al. 2009). Given the apparent difference in lysoPC (16:0) between the Mn²⁺ biotransformation sample and the control, we will focus on the metabolic pathway of lysoPC (16:0) in fungi.

Based on KEGG metabolic pathway analysis, LysoPC(16:0) (KEGG compound: C04230) participates in an essential biological pathway in microorganisms, basal glycerophospholipid metabolism (map00564). This metabolic pathway starts from glycerine-p and then involves more than 30 downstream pathways, including diacetylheptaprenylglycorol/o-acetylcholine/L-serine et al (Fig. 7). In these metabolic pathways, we found that the EC (2.7.8.24) enzyme may be the key enzyme for Mn²⁺-bioconversion (Fig. 8).

EC (2.7.8.24) enzyme catalyses the biochemical reaction from A to B (R05794). In Rudder's study, it was found that the higher the Mn²⁺ concentration, the higher the EC (2.7.8.24) activity, and the greater the stimulation effect on EC (2.7.8.24) was at 10 mM. Therefore, the addition of Mn²⁺ will eventually increase the concentration of lysoPC(16:0) (Ejsing et al. 2009). Significant changes in metabolites have been found in these metabolic pathways and may be involved in these changes. Compared with the control group, this metabolic pathway was found to be affected in the Mn²⁺ biotransformation sample (Fig. 9).

The factors affecting the bioconversion are very complex. We speculate that the bioconversion may be related to some metabolic pathways in the fungus. In a metabolomics study using UPLC-Q/TOF, we identified more than 30 differential metabolites between the Mn²⁺-bioconversion sample and the controls. Further MS/MS analysis showed that lysoPC(16:0), an endogenous metabolite with the most significant difference, was identified. Subsequently, using MetaPA, we found that the high expression of lysoPC(16:0) may be due to Mn²⁺ activation of the EC (2.7.8.24) enzyme (Nugroho et al. 2022). The exogenous addition of Mn²⁺ activates the r05794 reaction of the EC enzyme, thus activating the whole metabolic pathway. Our research group is continuing its research. Future work can involve verifying this metabolic pathway.