{"paper_id":"c2d785b0-2f6d-43e6-aca7-87bab4c9e6e1","body_text":"Metabonomic study of Mn 2+ in promoting the biotransformation of Shenfu oxidised Lignite by pattern recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabonomic study of Mn 2+ in promoting the biotransformation of Shenfu oxidised Lignite by pattern recognition Qiulin Li, Fuxin Chen, Jiacheng Fu, Qingfeng Wang, Xiang Han, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8313358/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the biotransformation technology of coal has received increasing attention due to its significant economic and environmental advantages in the energy field. However, the specific conversion mechanism is challenging to study in depth, partly because it is difficult to identify intermediates in the biological conversion of coal. In this study, we reported a metabolomics method based on UPLC-Q-TOF/MS to analyse which metabolic pathway is most important in biotransformation under high Mn 2+ conditions. During the biological transformation process, a total of 30 potential differential metabolites were screened, including free fatty acids, phospholipids, sphingolipids, glycerides, and others. The most significant difference, lysoPC(16:0), was identified by MS/MS and standard sample analysis between Mn 2+ -bioconversion and control subjects. MetPA suggests that the high expression of lysoPC(16:0) may be closely related to microbial glycerophospholipid metabolism. Based on UPLC-Q-TOF/MS bioconversion metabolomics, combined with pattern recognition and network analysis, this approach provides a powerful tool for identifying potential biomarkers. It represents a new strategy for studying the mechanisms underlying biomass under strict conditions. ShenFu Lignite Bioconversion Metabolomics lysoPC(16:0) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Key points Studied the biotransformation process of coal under high manganese conditions. LysoPC (16:0) has been identified as the most important biomarker. LysoPC (16:0) may be closely related to microbial glycerophospholipid metabolism. Introduction 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 Mn 2+ 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 Mn 2+ , 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. Materials and Methods 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 . Table 1 Element analysis of raw lignite and oxidised lignite (%) Sample C H N O* Raw lignite 65.24 4.697 0.987 29.076 Oxidized lignite 54.57 3.484 4.634 37.312 * 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 10 7 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 Mn 2+ (sulfate) at 1 mM, 10 mM, and 50 mM to investigate the effect of the gradient concentration of Mn 2+ on biotransformation rate, while selecting a blank control group without adding any Mn 2+ . 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 ) Results Effect of Mn 2+ concentration on dissolution yield The biological conversion rate of coal samples (based on air drying basis) is calculated using the following formula: $$\\:{\\eta\\:}_{0}=\\frac{{W}_{0}-{W}_{1}}{{W}_{0}}\\times\\:100\\%$$ In the formula, \\(\\:{\\eta\\:}_{0}\\) is the biological conversion rate of coal powder (%); \\(\\:{W}_{0}\\) is the initial weight of coal powder added (g); \\(\\:{W}_{1}\\) 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 Mn 2+ significantly increases the biotransformation yield compared with the blank control group. The addition of 1 mM Mn 2+ concentration can increase the biotransformation rate from 22% to 32%, while the addition of 10 mM Mn 2+ concentration can increase the biotransformation rate to 58%. When the Mn 2+ concentration was further increased to 50 mM, the biotransformation rate did not continue to grow but decreased to 45%. It can be demonstrated that Mn 2+ 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 Mn 2+ added, the stronger the activating effect. This may be because Mn 2+ 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 Mn 2+ , the biotransformation rate of coal powder was nearly doubled. It can be inferred that some enzymes with Mn 2+ 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 Mn 2+ ) in positive and negative ion modes is shown in Figs. 2 A and 2 C, 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 Mn 2+ ) in positive ion mode is shown in Fig. 2 B. 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 Mn 2+ biotransformation samples (1/10/50 mM Mn 2+ ) and blank control group samples (excluding Mn 2+ ). 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 Mn 2+ concentration as the response variable and constructed a latent variable spatial model to extract the metabolic characteristics of lignite degradation closely related to Mn 2+ . 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 Mn 2+ concentration gradient, weighted screening of lignite degradation related metabolites is carried out, targeted amplification of Mn 2+ 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. 3 A, the PLS-DA score plot of the data revealed complete and significant separation between the Mn 2+ biotransformation group and the blank control group. The blank control group samples are distributed in the first quadrant, while the Mn 2+ biotransformation group samples are distributed in the second, third, and fourth quadrants. This completely isolated spatial distribution pattern indicates that the introduction of Mn 2+ 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 Mn 2+ concentration gradients. As shown in Fig. 3 B, 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 Mn 2+ . 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 Mn 2+ 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 Mn 2+ under environmental stress, and their metabolic pathway changes can effectively reflect the molecular regulatory mechanisms involved in Mn 2+ 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. 4 A), identify the corresponding signal peak. Afterwards, the precise molecular weight of [M + Na] + ions was determined to be 518.3213 in the spectrum (Fig. 4 B). 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 C 24 H 50 NO 7 P (Fig. 4 C). 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 [H 2 O 3 PO-CH 2 CH 2 N(CH 3 ) 3 ] + , all of which are typical fragments of the PC, 478.3304 represent [M-H 2 O + 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 H 2 O, resulting in [M-H 2 O + H] + (m/z 478) (2) Specific breakage produces characteristic choline fragment ions [H 2 O 3 POCH 2 CH 2 N(CH 3 ) 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 (H 3 PO 4 /HPO 3 ) to generate choline quaternary ammonium ion [HOCH 2 CH 2 N(CH 3 ) 3 ] + (m/z 104). These cleavage modes are typical of LysoPC lipids in positive-ion CID. Discussion 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 Mn 2+ 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 Mn 2+ -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 Mn 2+ 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 Mn 2+ 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 Mn 2+ biotransformation sample (Fig. 9 ). Conclusions 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 Mn 2+ -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 Mn 2+ activation of the EC (2.7.8.24) enzyme (Nugroho et al. 2022 ). The exogenous addition of Mn 2+ 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. Declarations Ethical Approval Not applicable Funding This study was funded by grants from the Key Project of National Natural Science Foundation of China (U24A20552), Natural Science Foundation of China (No.32202164, No.52174208), Shaanxi Natural Science Foundation (2021JQ556), China Postdoctoral Science Foundation (No.2020M673610XB), and Xi'an Science and Technology Plan Project (23NYGG-0068). Author Contribution Qiulin Li: Investigation, Writing-review & editing. Fuxin Chen: Methodology, Data curation, Writing-original draft. Jiacheng Fu: Project administration, Methodology. Qingfeng Wang: Investigation, Methodology. Xiang Han: Writing-original draft, Data curation. Xiangrong Liu: Conceptualization, Formal analysis. Gang Li: Methodology, Data curation. Anning Zhou: Project administration, Formal analysis. Nan Zhang: Conceptualization, Formal analysis. Qiuhong Wang: Conceptualization, Investigation. Acknowledgement This study was funded by grants from the Key Project of National Natural Science Foundation of China (U24A20552), Natural Science Foundation of China (No.32202164, No.52174208), Shaanxi Natural Science Foundation (2021JQ556), China Postdoctoral Science Foundation (No.2020M673610XB), Xi'an Science and Technology Plan Project (23NYGG-0068). Data Availability All the data supporting this article are available from the corresponding author upon request. References Ahmed A, Sharma A (2021) Fungal Solubilisation and Subsequent Microbial Methanation of Coal Processing Wastes. Appl Biochem Biotechnol 193:3970–3982. https://doi.org/10.1007/s12010-021-03681-y Akimbekov N, Digel I, Abdieva G, Ualieva P, Tastambek K (2021) Lignite biosolubilization and bioconversion by Bacillus sp. : the collation of analytical data. Biofuels 12:247–258. https://doi.org/10.1080/17597269.2020.1753936 Akimbekov N, Digel I, Qiao X, Tastambek K, Zhubanova A (2019) Lignite Biosolubilization by Bacillus sp. RKB 2 and Characterization of its Products. Geomicrobiology 1–7. https://doi.org/10.1080/01490451.2019.1695022 Akimbekov NS, Digel I, Tastambek KT, Marat AK, Turaliyeva MA, Kaiyrmanova GK (2022) Biotechnology of Microorganisms from Coal Environments: From Environmental Remediation to Energy Production. Biology 11:1306. https://doi.org/10.3390/biology11091306 Bankole PO, Omoni VT, Tennison-Omovoh CA, Adebajo SO, Mulla SI, Adekunle AA, Semple KT (2022) Novel laccase from Xylaria polymorpha and its efficiency in the biotransformation of pharmaceuticals: Optimization of operational conditions, comparative effect of redox-mediators and toxicity studies. Colloids Surf B 217:112675. https://doi.org/10.1016/j.colsurfb.2022.112675 Cajnko MM, Oblak J, Grilc M, Likozar B (2021) Enzymatic bioconversion process of lignin: mechanisms, reactions and kinetics. Bioresour Technol 340:125655. https://doi.org/10.1016/j.biortech.2021.125655 Chen F, Gong P, Cai H, Zhou A (2012) Study on the activation effect of metal ions in coal biotransformation process. J Xi'an Univ Sci Technol 32:514–517. https://doi.org/10.13800/j.cnki.xakjdxxb.2012.04.024 Chen F, He H, Zhao SM, Yao JH, Sun Q, Huang GH, Xiao D, Tang LF, Leng YW, Tao XX (2018) Analysis of Microbial Community Succession during Methane Production from Baiyinhua Lignite. Energy Fuels 32:10311–10320. https://doi.org/10.1021/acs.energyfuels.8b01181 Chen L, Liu J, Cui Y, Ren H, Zhao H, Zhao N, Song Y, Guan J, Niu J, Li G, Wang B, He H (2023) Degradation of humic acid from Yima coal by the combination of elegant silver mold and Bacillus subtilis . J. China Coal Soc 48:4224–4232. https://doi.org/10.13225/j.cnki.jccs.2022.1581 Cheng J, Ma L, Zhao W, Liu Y, Zou Y (2022) Study on the biotransformation of Pingzhuang lignite by endogenous oxalic acid fungus HM-M1 . Coal Sci Technol 50:317–324. https://doi.org/10.13199/j.cnki.cst.2020-0803 Cohen MS, Gabriele PD (1982) Degradation of Coal by the Fungi Polyporus versicolor and Poria monticola . Appl Environ Microbiol 44:23–27. https://doi.org/10.1128/aem.44.1.23-27.1982 Ejsing CS, Sampaio JL, Surendranath V, Duchoslav E, Ekroos K, Klemm RW, Simons K, Shevchenko A (2009) Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci USA 106:2136–2141. https://doi.org/10.1073/pnas.0811700106 Fakoussa RM, Hofrichter M (1999) Biotechnology and microbiology of coal degradation. Appl Microbiol Biotechnol 52:25–40. https://doi.org/10.1007/s002530051483 Feng X, Sun J, Xie Y (2021) Degradation of Shanxi lignite by Trichoderma citrinoviride . Fuel 291:120204. https://doi.org/10.1016/j.fuel.2021.120204 Gong P, Chang X, Chen X, Bai X, Wen H, Pi S, Yang W, Wang L, Chen F (2017) Metabolomics study of cadmium-induced diabetic nephropathy and protective effect of caffeic acid phenethyl ester using UPLC-Q-TOF-MS combined with pattern recognition. Environ Toxicol Pharmacol 54:80–92. https://doi.org/10.1016/j.etap.2017.06.021 Haridas PC, Ravichandran R, Shaikh N, Kishore P, Kumar PS, Banerjee K, Sekhar CN (2024) Authentication of the species identity of squid rings using UHPLC-Q-orbitrap MS/MS-based lipidome fingerprinting and chemoinformatics. Food Chem 442:138525. https://doi.org/10.1016/j.foodchem.2024.138525 Jatoi AS, Aziz S, Soomro SA (2021) Effect of native microorganism Rhodococcus spp. SL-9 for dibenzothiophene degradation and its application towards clean coal approach. Clean Eng Technol 3:100126. https://doi.org/10.1016/j.clet.2021.100126 Li J, Liu X, Pi S, Ma M, Liao G (2017) Optimization of Microbial Degradation Process Conditions for Linfen Lignite in Shanxi Province. Coal Convers 40:65–72. https://doi.org/10.19726/j.cnki.ebcc.2017.02.012 Liu J, Wang B, Tai C, Wu L, Zhao H, Guan J, Chen L (2016) An Effective Method to Detect Volatile Intermediates Generated in the Bioconversion of Coal to Methane by Gas Chromatography-Mass Spectrometry after In-Situ Extraction Using Headspace Solid-Phase Micro-Extraction under Strict Anaerobic Conditions. PLoS ONE 11:e0163949. https://doi.org/10.1371/journal.pone.0163949 Luo JM, Zhu WC, Cao ST, Lu ZY, Zhang MH, Song B, Shen YB, Wang M (2021) Improving Biotransformation Efficiency of Arthrobacter simplex by Enhancement of Cell Stress Tolerance and Enzyme Activity. J Agric Food Chem 69:704–716. https://doi.org/10.1021/acs.jafc.0c06592 Meng Q, Zhou A, Wang Y, Zhang Y, Qu J, Wang G (2008) Study on Screening of Strains for Bioconversion of Shenfu Coal. Chem Bioeng 25:30–33 Meng Q, Wang Y, Zhou A (2011) Synergistic effect of photo-biological co-conversion of Shenfu coal. J China Univ Min Technol 40:438–442 Nsa IY, Akinyemi BT, Bello-Akinosho M, Ezechukwu SN, Bayode TB, Igbinigie EE, Adeleke RA (2022) Development of a saprophytic fungal inoculum for the biodegradation of sub-bituminous coal. SN Appl Sci 4:53. https://doi.org/10.1007/s42452-021-04925-6 Nugroho ADW, Van OB, Bachtiar SA, Boeren S, Kleerebezem M, Bachmann H (2022) Manganese Modulates Metabolic Activity and Redox Homeostasis in Translationally Blocked Lactococcus cremoris , Impacting Metabolic Persistence, Cell Culturability, and Flavor Formation. Microbiol Spectr 10:e0270821. https://doi.org/10.1128/spectrum.02708-21 Olawale JT, Edeki OG, Cowan AK (2020) Bacterial degradation of coal discard and geologically weathered coal. JCST 7:405–416. https://doi.org/10.1007/s40789-020-00306-3 Ponnudurai V, Rajarathinam R, Muthuvelu KS, Velmurugan S, Nalajala RK, Arumugam L (2022) Investigation on the utilization of coal washery rejects by different microbial sources for biogenic methane production. Chemosphere 287:132165. https://doi.org/10.1016/j.chemosphere.2021.132165 Ralph JP, Catcheside DEA (1997) Transformations of low rank coal by Phanerochaete chrysosporium and other wood-rot fungi . Fuel Process Technol 52:79–93. https://doi.org/10.1016/S0378-3820(97)00018-0 Sabar MA, Ali MI, Fatima N, Malik AY, Jamal A, Liaquat R, He H, Liu FJ, Guo H, Urynowicz M, Huang Z (2020) Evaluation of humic acids produced from Pakistani subbituminous coal by chemical and fungal treatments. Fuel 278:118301. https://doi.org/10.1016/j.fuel.2020.118301 Shanbhag K, Mhetre A, Khandelwal N, Kamat SS (2020) The Lysophosphatidylserines-An Emerging Class of Signalling Lysophospholipids. J Membr Biol 253:381–397. https://doi.org/10.1007/s00232-020-00133-2 Shen W, Li X, Shi C, Yang J, Zhao S, Yang Z, Wang D (2023) Influences of Four Kinds of Surfactants on Biodegradations of Tar-Rich Coal in the Ordos Basin by Bacillus bicheniformis. Microorganisms 11:2397. https://doi.org/10.3390/microorganisms11102397 Shi C, Liu X, Wu H, Zhao S, Yang Z (2023a) Enhancements of mixed surfactants on Wucaiwan coal biodegradation by Nocardia mangyaensis . Energy 266:126501. https://doi.org/10.1016/j.energy.2022.126501 Shi C, Liu X, Zhao S, Yang Z, Lu X, Tong M (2022) Sequential degradations of Dananhu lignites by Nocardia mangyaensis and Bacillus licheniformis . Fuel 318:123623. https://doi.org/10.1016/j.fuel.2022.123623 Shi C, Liu X, Zhao SS, Yang Z (2023b) Additions of esterase and rhamnolipid promote the degradation of Inner Mongolia coal by Pseudomonas japonica . Fuel 340:127640. https://doi.org/10.1016/j.fuel.2023.127640 Sobolczyk-Bednarek J, Choińska-Pulit A, Łaba W (2021) Biosolubilization of low-rank coal by the newly isolated strain Streptomyces fulvissimus K59 . Fuel 301:121082. https://doi.org/10.1016/j.fuel.2021.121082 Steffen KT, Hatakka A, Hofrichter M (2002) Degradation of humic acids by the litter-decomposing basidiomycete Collybia dryophila . Appl Environ Microbiol 68:3442–3448. https://doi.org/10.1128/AEM.68.7.3442-3448.2002 Wang B, Ndayisenga F, Zhang G, Yu Z (2021) Deciphering the initial products of coal during methanogenic bioconversion: Based on an untargeted metabolomics approach. Gcb Bioenergy 13:967–978. https://doi.org/10.1111/gcbb.12824 Wang Y, Meng Q, Zhou A, Li X (2014) The research of screened strains Shenfu coal bioconversible method. Appl Chem Ind 43:1576–1579. https://doi.org/10.16581/j.cnki.issn1671-3206.2014.09.035 Wang Y, Bai Y, Su J, Xu L, Ren M, Cao M (2024) Manganese(IV) reduction coupled with ammonium oxidation mediated by a single strain aromatoleum evansii MAY27 : Performance, metabolomics, and mechanism. Bioresour Technol 409:131235. https://doi.org/10.1016/j.biortech.2024.131235 Xia Q, Shi L, Ma D, Xia W, Xu T (2025) Microorganism interaction in microbial community, metabolic behavior and biodegradation effect on bitumen. Bioresour Technol 430:132555. https://doi.org/10.1016/j.biortech.2025.132555 Xiao L, Li Y, Liao Y, Ma H, Wu J, Zhang Y, Yao J (2018) Bioconversion of lignite humic acid by white-rot fungi and characterization of products. 3 Biotech 8:258. https://doi.org/10.1007/s13205-018-1281-4 Xu H, Zhou X, Zhen N, Yin X, Liu T, Tang Y, Liu J, Liu L, Ma F (2025) Comparative analysis of covalent bond cleavage and free radical behavior during the heating process in direct coal liquefaction of naomaohu and shangwan coals. Fuel Process Technol 275:108255. https://doi.org/10.1016/j.fuproc.2025.108255 Xu SJ, Chen XY, Wang XF, Sun HZ, Hou ZJ, Cheng JS, Yuan YJ (2022) Artificial microbial consortium producing oxidases enhanced the biotransformation efficiencies of multi-antibiotics. J Hazard Mater 439:129674. https://doi.org/10.1016/j.jhazmat.2022.129674 Xue W, Cao W, Li L, Fu Y, Zeng Z (2022) Identification of coal geographic origin by FTIR coupled with metabolomics analysis. Infrared Phys Techn 125:104224. https://doi.org/10.1016/j.infrared.2022.104224 Yang J, Liu X, Yang Z, Zhao S (2023) Biodegradation of Dananhu low-rank coal by Planomicrobium huatugouensis : Target metabolites possessing degradation abilities and their biodegradation pathways. Energy 276:127642. https://doi.org/10.1016/j.energy.2023.127642 Yang P, Zhao S, Zhang Q, Hu J, Liu R, Huang Z, Gao Y (2021) Synergistic effect of the cotton stalk and high-ash coal on gas production during co-pyrolysis/gasification. Bioresour Technol 336:125336. https://doi.org/10.1016/j.biortech.2021.125336 Zhang Y, Meng Q, Zhou A (2009) Bioconversion of Shenfu Photo-Oxidation Coal Humic Acid. Chem Bioeng 26:79–82 Zhao W, Su X, Zhang Y, Xia D, Hou S, Zhou Y, Fu H, Wang L, Yin X (2022) Microbial electrolysis enhanced bioconversion of coal to methane compared with anaerobic digestion: Insights into differences in metabolic pathways. Energy Convers Manage 259:115553. https://doi.org/10.1016/j.enconman.2022.115553 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8313358\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":571025767,\"identity\":\"f873e812-d4f3-4e54-9c8d-1c4ff88ea4a6\",\"order_by\":0,\"name\":\"Qiulin Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiulin\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":571025768,\"identity\":\"0ac0c19b-f788-4303-adfe-c12261b0a3fd\",\"order_by\":1,\"name\":\"Fuxin Chen\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACCSCuYGDgYWBmPvggoUJCTp4oLWdAWtjZkg0enLEwNmwgUgsDAz+PmeTDtopEhgMEdPDPbj724GDbYRlzZh5jg8R5EgmMDcwPH93AZ8mdY+kGB9vSeCyb2QofJG6TyGNnYDM2zsGjxUAix0z6Y5sNj8Fh5s0GQC3FjA08bNL4teR/kzjYJgHUwmAmkThHIrHhAEEtOWxALSBbWIBaGojQInEjzUziwLk0oBZgICcckzA2bCbgF/4Zyc8kDpQdtjc4f/jgwx81dXLy7M0PH+PTggUwk6Z8FIyCUTAKRgEWAAB+v0mXAX++KQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Fuxin\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":571025769,\"identity\":\"17582505-460a-4ade-b311-2a72a3a8edf4\",\"order_by\":2,\"name\":\"Jiacheng Fu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institute of Digital Technology and Economics\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiacheng\",\"middleName\":\"\",\"lastName\":\"Fu\",\"suffix\":\"\"},{\"id\":571025771,\"identity\":\"a0422045-bb87-4e81-ba25-939d6fb58813\",\"order_by\":3,\"name\":\"Qingfeng Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an Jiaotong University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingfeng\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":571025773,\"identity\":\"cfeb73ac-d1f0-4edb-a323-61f2798bea21\",\"order_by\":4,\"name\":\"Xiang Han\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiang\",\"middleName\":\"\",\"lastName\":\"Han\",\"suffix\":\"\"},{\"id\":571025777,\"identity\":\"752d49f1-201a-4271-86f8-46dfd0b988a0\",\"order_by\":5,\"name\":\"Xiangrong Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiangrong\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":571025778,\"identity\":\"d1d3c81e-8bbf-4b84-90d1-eb3ba389acb8\",\"order_by\":6,\"name\":\"Gang Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Gang\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":571025780,\"identity\":\"95af45c4-4db0-4612-bf2c-debea4fa92a2\",\"order_by\":7,\"name\":\"Anning Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anning\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":571025783,\"identity\":\"94a5db21-de6f-45ad-b57e-62f3bd5af71c\",\"order_by\":8,\"name\":\"Nan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":571025786,\"identity\":\"074f80b1-c576-499f-ac08-735ea2532f46\",\"order_by\":9,\"name\":\"Qiuhong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xi'an University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qiuhong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-12-09 05:38:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8313358/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8313358/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":99862137,\"identity\":\"69ab12d6-e291-4463-834f-f5f9b40afa5d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:34\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1587158,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CleanManuscript.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/55bf48cf976852f800d6205a.docx\"},{\"id\":99862142,\"identity\":\"8cd58c2e-dbeb-46d6-8344-24d735ab3d47\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"json\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":10514,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ed04674ee39c4f168311dedd8d83446d.json\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/c11a1b8251aa7b7b03c5d7b3.json\"},{\"id\":100357278,\"identity\":\"297a586f-d334-4d26-9397-e9d40e3c0ec7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:19:33\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":129906,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ed04674ee39c4f168311dedd8d83446d1enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/3d0b1e202f8bc500d4741cf8.xml\"},{\"id\":100358048,\"identity\":\"4ba17004-0344-4d33-89fb-bee428da311d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:36\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":41052,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/48fcecaab63a9bb1fc7ab293.jpeg\"},{\"id\":100358035,\"identity\":\"4d0e3508-1966-496d-9954-0fa8b7818916\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:35\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":168524,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/a7b011f77caa4b8781987235.jpeg\"},{\"id\":99862144,\"identity\":\"2849326a-5589-4ed8-9b4e-815c110f0fbf\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":600278,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/e04d21024cb4745b316715e9.jpeg\"},{\"id\":99862147,\"identity\":\"bfda0834-826b-4593-9ac8-5c1013befe0c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":87384,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/dcd0d127bdaa03d826b329c4.jpeg\"},{\"id\":99862150,\"identity\":\"352d570a-5d18-48a2-a414-c1ba6e79df7c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":77742,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/cc10ee2259e28f47ede49e97.jpeg\"},{\"id\":99862154,\"identity\":\"404e2b13-f536-41f8-a143-4eb4119829c8\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":44284,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage6.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/bb8a143e6f38dc8e619b21f6.jpeg\"},{\"id\":99862160,\"identity\":\"5a969154-e5e8-4da4-a4a2-f79fe5953ad2\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":494792,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage7.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/d1b7edd7b7d42f5d6d656f51.jpeg\"},{\"id\":99862156,\"identity\":\"eb60ab92-3e88-4b4d-b09b-5abe52074312\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"jpeg\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":293732,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage8.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/d1e86584111851bb3ae40bbe.jpeg\"},{\"id\":100357876,\"identity\":\"c67d3f7f-c9c5-4cce-9eb9-e0ba9c15be7d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:27\",\"extension\":\"jpeg\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":55972,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage9.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/a53acfbba5e6499f72568846.jpeg\"},{\"id\":99862164,\"identity\":\"9f3f92d7-d1f3-4b63-b591-e18d4ccd516f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":7665,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/fbf10a2514c5ca17779056af.png\"},{\"id\":99862161,\"identity\":\"d0a5cc83-84cc-41a7-9556-81665ac7e1a6\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":26141,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/4b6a87c6cb24c7a885e053c2.png\"},{\"id\":99862158,\"identity\":\"b0690187-a6b4-4754-b190-2244138a7417\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":106278,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/0df7d12c22eb92fc26134cfc.png\"},{\"id\":99862163,\"identity\":\"58d0a73c-6fb1-45e5-abb0-54d119f50752\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":14148,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/da92da0b453d0ba597b5bd61.png\"},{\"id\":100357890,\"identity\":\"3160fbbc-0b87-48c7-8786-22271f4ee99d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:27\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":11885,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/fe3e7be0e37768e1b68d8f81.png\"},{\"id\":99862159,\"identity\":\"34d7fa63-c21b-4127-aa61-abce6986154a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":4631,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/668c05c66138600bf839a579.png\"},{\"id\":100357955,\"identity\":\"6948fcd8-be4c-45c9-8770-0e6563ab3913\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:31\",\"extension\":\"png\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":38394,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/bbb4ebee8b5be8ba750ef98d.png\"},{\"id\":100358162,\"identity\":\"3aca98ad-ef9d-4d25-b66d-ce609422e05f\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:41\",\"extension\":\"png\",\"order_by\":19,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":23940,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/1becebd2c3d70adf1e452d45.png\"},{\"id\":99862155,\"identity\":\"dbeb6e61-cadd-4323-b562-0037ae12f5af\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":20,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":6853,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/e1e67eeb843f69c13dd92dfe.png\"},{\"id\":100358115,\"identity\":\"9265ab1c-56b0-49eb-98ca-f3289bca9dae\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:39\",\"extension\":\"xml\",\"order_by\":21,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":128197,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ed04674ee39c4f168311dedd8d83446d1structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/ac8ef05dae759e2a28af4b9d.xml\"},{\"id\":100358097,\"identity\":\"f15edf96-e175-45f8-b9c1-1d8c63380f0a\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:38\",\"extension\":\"html\",\"order_by\":22,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":137778,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/5f4f1c1333c21b6290ca0e2c.html\"},{\"id\":99862135,\"identity\":\"d8b77f40-35a5-40b6-8d69-19f68dbe414c\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:34\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":122264,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e on bioconversion under three different concentrations\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/4bd51aef36bee409364b9ad0.png\"},{\"id\":100358082,\"identity\":\"53733518-54e9-4ce9-9d05-29001bdc81ea\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:20:38\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":345408,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTypical positive ionisation mode (A) TIC chromatograms of biotransformation sample (containing 10 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e) in positive ion mode, (B) MS/MS chromatograms of the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-bioconversion samples analysed under UPLC-Q-TOF/MS conditions, and (C) TIC chromatogram of biotransformation sample (containing 10 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e) in negative ion mode.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/22ea8a61a44ec372eef77e4f.png\"},{\"id\":99862136,\"identity\":\"daeeed51-5edb-4d25-9a76-3fb0fcf978cd\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:34\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":307897,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifference analysis of the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-bioconversion sample and control subjects. (A) Score chart obtained from PLS-DA analysis. (B) Loading diagram of PLS-DA in the positive model\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/e1f483b4aed19b2b21f933ee.png\"},{\"id\":99862139,\"identity\":\"ee464a39-53e4-4204-9abe-072c27e918e3\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:34\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":207979,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIdentification of LysoPC(16:0) as a differentiable metabolite in the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-bioconversion sample.(A) The ion chromatogram from 10.1-10.9 min. (B) MS spectrum from 10.1-10.9 min. (C) The matched formula confirmed by MS and SmartFormular\\u003csup\\u003eTM\\u003c/sup\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/43c9222d68f846546c701dbc.png\"},{\"id\":99862145,\"identity\":\"d45e9b7e-0972-40b5-9fc1-0a030d22f377\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":164561,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMS/MS spectrum from m/z 496.3394\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/73aebb769daee8707ef9ae8a.png\"},{\"id\":99862141,\"identity\":\"95af750d-3585-4bd2-8e14-245fbe0b3b9e\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":125371,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe chemical structure of LysoPC(16:0) and proposed fragmentation pathway based on collisionally induced dissociation (CID).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/2668b7237925de4bbc5f8a85.png\"},{\"id\":100357249,\"identity\":\"01db0232-e605-4cf7-9a17-b5d4c96a087b\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 07:19:31\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1483503,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEnzymes involved in Lypco\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/51f870ca8e5bfd4f6dc64a0b.png\"},{\"id\":99862143,\"identity\":\"fccfc382-3e1e-4fba-acfc-292c4252e6a0\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":826938,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEC-catalysed biochemical reaction\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/b7da90b10dc7f46b936a14f9.png\"},{\"id\":99862153,\"identity\":\"4c498e1e-cdde-4682-b3d0-66174585e3df\",\"added_by\":\"auto\",\"created_at\":\"2026-01-09 07:11:35\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":144972,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePossible pathways for Mn\\u003csup\\u003e2+\\u003c/sup\\u003e to improve the yield of bioconversion\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/26314b3ecc16964291ad2c24.png\"},{\"id\":100421673,\"identity\":\"fb48734b-51d9-497c-bc5c-a0507502e55d\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 13:41:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4148896,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8313358/v1/52581c58-6d85-4d87-ba01-7241d44052ff.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Metabonomic study of Mn 2+ in promoting the biotransformation of Shenfu oxidised Lignite by pattern recognition\",\"fulltext\":[{\"header\":\"Key points\",\"content\":\"\\u003cul\\u003e\\n\\u003cli\\u003eStudied the biotransformation process of coal under high manganese conditions.\\u003c/li\\u003e\\n\\u003cli\\u003eLysoPC (16:0) has been identified as the most important biomarker.\\u003c/li\\u003e\\n\\u003cli\\u003eLysoPC (16:0) may be closely related to microbial glycerophospholipid metabolism.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\"},{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSince 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. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) and liquefaction (Xu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) 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. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Akimbekov et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Shi et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2023a\\u003c/span\\u003e). 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. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). 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 \\u003cem\\u003efungi\\u003c/em\\u003e include \\u003cem\\u003eRhodococcus\\u003c/em\\u003e (Jatoi et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), \\u003cem\\u003eAD-1 strain\\u003c/em\\u003e (Sabar et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), \\u003cem\\u003eTrichoderma citrinoviride\\u003c/em\\u003e (Feng et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), \\u003cem\\u003ePenicillium oxalicum HM-M1\\u003c/em\\u003e (Cheng et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Several bacteria include Streptomyces fulvissimus K59 (Sobolczyk-Bednarek et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), Nocardia mangyaensis, Bacillus licheniformis (Shi et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), Bacillus sp. (Shi et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2023b\\u003c/span\\u003e), and so on. The combination of different strains can also degrade coal, such as \\u003cem\\u003eCaenorhabditis elegans\\u003c/em\\u003e and \\u003cem\\u003eBacillus subtilis\\u003c/em\\u003e(Chen et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Therefore, our group continued to report a study on the bioconversion of oxidised ShenFu coal samples with \\u003cem\\u003efungi\\u003c/em\\u003e under high Mn\\u003csup\\u003e2+\\u003c/sup\\u003e conditions (Chen et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBiodegradation 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 \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e) first reported that \\u003cem\\u003ebacteria\\u003c/em\\u003e could dissolve anthracite, and subsequently began studying the degradation of coal. In 1982, Gabriele and Cohen (Cohen and Gabriele \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e1982\\u003c/span\\u003e) discovered that two \\u003cem\\u003ewhite rot bacteria\\u003c/em\\u003e, \\u003cem\\u003ePolyporus versicolor\\u003c/em\\u003e and \\u003cem\\u003ePoria monticolor\\u003c/em\\u003e, 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. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), and the transformation mechanism remains unclear (Nsa et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). 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 \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e; Steffen et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), liquefaction (Cheng et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), and solubilization (Ahmed and Sharma \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), in which some microbial proteins play a key role (Olawale et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Ponnudurai et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) 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. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Cajnko et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Bankole et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Xu et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In summary, the mechanism of coal bioconversion has always been the focus of coal liquefaction research.\\u003c/p\\u003e \\u003cp\\u003eIn 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. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), analysing the target metabolites and metabolic pathways of microorganisms associated with coal (Yang et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), identifying the geographic origin of coal (Xue et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), 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. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zhao et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e, which is an agonist or inhibitor of the enzyme (Wang et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). 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. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)? 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.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003eReagents and Materials\\u003c/p\\u003e \\u003cp\\u003eMethanol 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.\\u003c/p\\u003e \\u003cp\\u003ePreparation of Oxidised Lignite Samples\\u003c/p\\u003e \\u003cp\\u003eThis 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\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\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\\u003eElement analysis of raw lignite and oxidised lignite (%)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSample\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eO*\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRaw lignite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65.24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.697\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.987\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e29.076\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOxidized lignite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e54.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.484\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.634\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37.312\\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\\u003e* Oxygen was determined by difference.\\u003c/p\\u003e \\u003cp\\u003eBioconversion of Lignite and quenching methods\\u003c/p\\u003e \\u003cp\\u003eThe \\u003cem\\u003eMonilia crassa Sh. Et Dodge strain\\u003c/em\\u003e 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. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Meng et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Chen et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), 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 10\\u003csup\\u003e7\\u003c/sup\\u003e 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. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Select three concentrations of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e (sulfate) at 1 mM, 10 mM, and 50 mM to investigate the effect of the gradient concentration of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e on biotransformation rate, while selecting a blank control group without adding any Mn\\u003csup\\u003e2+\\u003c/sup\\u003e. There are 7 biological duplicate samples.\\u003c/p\\u003e \\u003cp\\u003eSelect methanol: acetonitrile: water\\u0026thinsp;=\\u0026thinsp;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 \\u0026micro;L 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 ℃.\\u003c/p\\u003e \\u003cp\\u003eChromatography and Mass Spectrometry\\u003c/p\\u003e \\u003cp\\u003eThe ultra-high performance liquid chromatography is performed by Hermo Scientific \\u0026trade; UltiMate \\u0026trade; 3000 (Thermo Scientific, USA), chromatography column is ACQUITY UPLC BEH C18 (2.1 mm \\u0026times; 50 mm, 1.7 \\u0026micro;m) column; The mobile phases are 95% water\\u0026thinsp;+\\u0026thinsp;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\\u0026ndash;1 minutes, 0% (B); 1\\u0026ndash;3 minutes, 0\\u0026ndash;10% (B); 3\\u0026ndash;22 minutes, 10\\u0026ndash;99% (B); 22\\u0026ndash;28 minutes, 99% (B); 28\\u0026ndash;29 minutes, 99\\u0026thinsp;\\u0026minus;\\u0026thinsp;0% (B); 29\\u0026ndash;30 minutes, 0% (B). The flow rate is 0.15 mL/min; Column temperature is 40℃; injection volume is 5 \\u0026micro;L; Insert blank samples between samples to check for chromatographic residues.\\u003c/p\\u003e \\u003cp\\u003eThe 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. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCollecting and processing UPLC-Q-TOF/MS data using Bruker Data Analysis.\\u003c/p\\u003e \\u003cp\\u003eData Mining Methods, Bioinformatics, and Statistical Analysis\\u003c/p\\u003e \\u003cp\\u003eAnalysed 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, Ume\\u0026aring;, 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., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIdentify 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 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.ymdb.ca/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.ymdb.ca/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), SMPDB (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://smpdb.ca/\\u003c/span\\u003e\\u003cspan address=\\\"http://smpdb.ca/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), METLIN (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://metlin.scripps.edu/\\u003c/span\\u003e\\u003cspan address=\\\"http://metlin.scripps.edu/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), Massbank (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.massbank.jp\\u003c/span\\u003e\\u003cspan address=\\\"http://www.massbank.jp\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), PubChem (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://ncbi.nim.nih.gov/\\u003c/span\\u003e\\u003cspan address=\\\"http://ncbi.nim.nih.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), and KEGG (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.genome.jp/kegg/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.genome.jp/kegg/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) databases. The mass accuracy was 20 ppm.\\u003c/p\\u003e \\u003cp\\u003eUsed 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. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e)\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eEffect of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration on dissolution yield\\u003c/p\\u003e \\u003cp\\u003eThe biological conversion rate of coal samples (based on air drying basis) is calculated using the following formula:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{\\\\eta\\\\:}_{0}=\\\\frac{{W}_{0}-{W}_{1}}{{W}_{0}}\\\\times\\\\:100\\\\%$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn the formula, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\eta\\\\:}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the biological conversion rate of coal powder (%); \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{W}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the initial weight of coal powder added (g); \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{W}_{1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the weight of residual coal powder after biotransformation (g).\\u003c/p\\u003e \\u003cp\\u003eCalculate 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.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, exogenous Mn\\u003csup\\u003e2+\\u003c/sup\\u003e significantly increases the biotransformation yield compared with the blank control group. The addition of 1 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration can increase the biotransformation rate from 22% to 32%, while the addition of 10 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration can increase the biotransformation rate to 58%. When the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration was further increased to 50 mM, the biotransformation rate did not continue to grow but decreased to 45%. It can be demonstrated that Mn\\u003csup\\u003e2+\\u003c/sup\\u003e has a significant activating effect on specific enzymes in the \\u003cem\\u003eMonilia crassa Sh. Et Dodge bacterial\\u003c/em\\u003e biotransformation system of lignite. However, the higher the concentration of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e added, the stronger the activating effect. This may be because Mn\\u003csup\\u003e2+\\u003c/sup\\u003e upregulates amino acid metabolism, transmembrane transporter activity, and oxidoreductase activity (Wang et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). When the concentration is too high, these enzymes do not achieve optimal activation. In addition, at a concentration of 10 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e, the biotransformation rate of coal powder was nearly doubled. It can be inferred that some enzymes with Mn\\u003csup\\u003e2+\\u003c/sup\\u003e 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnalysis of metabolomics data based on UPLC-Q-TOF/MS\\u003c/p\\u003e \\u003cp\\u003ePerform 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e) in positive and negative ion modes is shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC, 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e) in positive ion mode is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation samples (1/10/50 mM Mn\\u003csup\\u003e2+\\u003c/sup\\u003e) and blank control group samples (excluding Mn\\u003csup\\u003e2+\\u003c/sup\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePLS-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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration as the response variable and constructed a latent variable spatial model to extract the metabolic characteristics of lignite degradation closely related to Mn\\u003csup\\u003e2+\\u003c/sup\\u003e. 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration gradient, weighted screening of lignite degradation related metabolites is carried out, targeted amplification of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e 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.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA, the PLS-DA score plot of the data revealed complete and significant separation between the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation group and the blank control group. The blank control group samples are distributed in the first quadrant, while the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation group samples are distributed in the second, third, and fourth quadrants. This completely isolated spatial distribution pattern indicates that the introduction of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e concentration gradients.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB, 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e. 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.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eScreening and identification of metabolic biomarkers\\u003c/p\\u003e \\u003cp\\u003eThis study used variable importance projection (VIP) analysis, combined with statistical validation, to screen for biomarkers. Firstly, the VIP parameter (VIP\\u0026thinsp;\\u0026gt;\\u0026thinsp;2) was used to screen for metabolic features that contribute significantly to the metabolomics differences between the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation group and the control group. Then, an independent-samples t-test (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) was conducted to assess statistical significance. The differential metabolites obtained can serve as candidate biomarkers for characterising the specific metabolic response of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e under environmental stress, and their metabolic pathway changes can effectively reflect the molecular regulatory mechanisms involved in Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation.\\u003c/p\\u003e \\u003cp\\u003eRetention 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 \\u0026plusmn;\\u0026thinsp;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.\\u003c/p\\u003e \\u003cp\\u003eFirstly, based on its retention time in the extraction ion chromatogram at m/z 496.3394 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA), identify the corresponding signal peak. Afterwards, the precise molecular weight of [M\\u0026thinsp;+\\u0026thinsp;Na]\\u003csup\\u003e+\\u003c/sup\\u003e ions was determined to be 518.3213 in the spectrum (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). 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 C\\u003csub\\u003e24\\u003c/sub\\u003eH\\u003csub\\u003e50\\u003c/sub\\u003eNO\\u003csub\\u003e7\\u003c/sub\\u003eP (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThird, the element composition is compared with the element composition registered in the database, and is identified as LysoPC(16:0). Fourth, in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, the m/z 184.0739 and 478.3304 were observed in the MS/MS spectrum, where m/z 184.0739 represents [H\\u003csub\\u003e2\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003ePO-CH\\u003csub\\u003e2\\u003c/sub\\u003eCH\\u003csub\\u003e2\\u003c/sub\\u003eN(CH\\u003csub\\u003e3\\u003c/sub\\u003e)\\u003csub\\u003e3\\u003c/sub\\u003e]\\u003csup\\u003e+\\u003c/sup\\u003e, all of which are typical fragments of the PC, 478.3304 represent [M-H\\u003csub\\u003e2\\u003c/sub\\u003eO\\u0026thinsp;+\\u0026thinsp;H]\\u003csup\\u003e+\\u003c/sup\\u003e, 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).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e proposes a potential breakthrough path based on collision-induced dissociation. The main cracking pathways include: (1) Loss of H\\u003csub\\u003e2\\u003c/sub\\u003eO, resulting in [M-H\\u003csub\\u003e2\\u003c/sub\\u003eO\\u0026thinsp;+\\u0026thinsp;H]\\u003csup\\u003e+\\u003c/sup\\u003e (m/z 478) (2) Specific breakage produces characteristic choline fragment ions [H\\u003csub\\u003e2\\u003c/sub\\u003eO\\u003csub\\u003e3\\u003c/sub\\u003ePOCH\\u003csub\\u003e2\\u003c/sub\\u003eCH\\u003csub\\u003e2\\u003c/sub\\u003eN(CH\\u003csub\\u003e3\\u003c/sub\\u003e)\\u003csub\\u003e3\\u003c/sub\\u003e]\\u003csup\\u003e+\\u003c/sup\\u003e (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 (H\\u003csub\\u003e3\\u003c/sub\\u003ePO\\u003csub\\u003e4\\u003c/sub\\u003e/HPO\\u003csub\\u003e3\\u003c/sub\\u003e) to generate choline quaternary ammonium ion [HOCH\\u003csub\\u003e2\\u003c/sub\\u003eCH\\u003csub\\u003e2\\u003c/sub\\u003eN(CH\\u003csub\\u003e3\\u003c/sub\\u003e)\\u003csub\\u003e3\\u003c/sub\\u003e]\\u003csup\\u003e+\\u003c/sup\\u003e (m/z 104). These cleavage modes are typical of LysoPC lipids in positive-ion CID.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eLysoPC (16:0) is a hemolytic phospholipid (LyP). It is a monoglyceride phospholipid, in which the phosphorylcholine moiety occupies the glycerol substituents (Shanbhag et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). 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 \\u003cem\\u003ealgae\\u003c/em\\u003e, \\u003cem\\u003efungi\\u003c/em\\u003e, 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\\u003cem\\u003e)\\u003c/em\\u003e, which is part of the deacylation/reacylation cycle that controls its entire molecular composition (Ejsing et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Given the apparent difference in lysoPC (16:0) between the Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation sample and the control, we will focus on the metabolic pathway of lysoPC (16:0) in \\u003cem\\u003efungi\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eBased 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.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). In these metabolic pathways, we found that the EC (2.7.8.24) enzyme may be the key enzyme for Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-bioconversion (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eEC (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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e will eventually increase the concentration of lysoPC(16:0) (Ejsing et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e biotransformation sample (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThe 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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-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 Mn\\u003csup\\u003e2+\\u003c/sup\\u003e activation of the EC (2.7.8.24) enzyme (Nugroho et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The exogenous addition of Mn\\u003csup\\u003e2+\\u003c/sup\\u003e 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.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eEthical Approval\\u003c/h2\\u003e \\u003cp\\u003eNot applicable\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis study was funded by grants from the Key Project of National Natural Science Foundation of China (U24A20552), Natural Science Foundation of China (No.32202164, No.52174208), Shaanxi Natural Science Foundation (2021JQ556), China Postdoctoral Science Foundation (No.2020M673610XB), and Xi'an Science and Technology Plan Project (23NYGG-0068).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eQiulin Li: Investigation, Writing-review \\u0026amp; editing. Fuxin Chen: Methodology, Data curation, Writing-original draft. Jiacheng Fu: Project administration, Methodology. Qingfeng Wang: Investigation, Methodology. Xiang Han: Writing-original draft, Data curation. Xiangrong Liu: Conceptualization, Formal analysis. Gang Li: Methodology, Data curation. Anning Zhou: Project administration, Formal analysis. Nan Zhang: Conceptualization, Formal analysis. Qiuhong Wang: Conceptualization, Investigation.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThis study was funded by grants from the Key Project of National Natural Science Foundation of China (U24A20552), Natural Science Foundation of China (No.32202164, No.52174208), Shaanxi Natural Science Foundation (2021JQ556), China Postdoctoral Science Foundation (No.2020M673610XB), Xi'an Science and Technology Plan Project (23NYGG-0068).\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll the data supporting this article are available from the corresponding author upon request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAhmed A, Sharma A (2021) \\u003cem\\u003eFungal\\u003c/em\\u003e Solubilisation and Subsequent \\u003cem\\u003eMicrobial\\u003c/em\\u003e Methanation of Coal Processing Wastes. Appl Biochem Biotechnol 193:3970\\u0026ndash;3982. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s12010-021-03681-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s12010-021-03681-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAkimbekov N, Digel I, Abdieva G, Ualieva P, Tastambek K (2021) Lignite biosolubilization and bioconversion by \\u003cem\\u003eBacillus sp.\\u003c/em\\u003e: the collation of analytical data. Biofuels 12:247\\u0026ndash;258. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/17597269.2020.1753936\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/17597269.2020.1753936\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAkimbekov N, Digel I, Qiao X, Tastambek K, Zhubanova A (2019) Lignite Biosolubilization by \\u003cem\\u003eBacillus sp. RKB 2\\u003c/em\\u003e and Characterization of its Products. Geomicrobiology 1\\u0026ndash;7. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/01490451.2019.1695022\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/01490451.2019.1695022\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAkimbekov NS, Digel I, Tastambek KT, Marat AK, Turaliyeva MA, Kaiyrmanova GK (2022) Biotechnology of \\u003cem\\u003eMicroorganisms\\u003c/em\\u003e from Coal Environments: From Environmental Remediation to Energy Production. Biology 11:1306. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/biology11091306\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/biology11091306\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBankole PO, Omoni VT, Tennison-Omovoh CA, Adebajo SO, Mulla SI, Adekunle AA, Semple KT (2022) Novel laccase from \\u003cem\\u003eXylaria polymorpha\\u003c/em\\u003e and its efficiency in the biotransformation of pharmaceuticals: Optimization of operational conditions, comparative effect of redox-mediators and toxicity studies. Colloids Surf B 217:112675. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.colsurfb.2022.112675\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.colsurfb.2022.112675\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCajnko MM, Oblak J, Grilc M, Likozar B (2021) Enzymatic bioconversion process of lignin: mechanisms, reactions and kinetics. Bioresour Technol 340:125655. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.biortech.2021.125655\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biortech.2021.125655\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen F, Gong P, Cai H, Zhou A (2012) Study on the activation effect of metal ions in coal biotransformation process. J Xi'an Univ Sci Technol 32:514\\u0026ndash;517. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.13800/j.cnki.xakjdxxb.2012.04.024\\u003c/span\\u003e\\u003cspan address=\\\"10.13800/j.cnki.xakjdxxb.2012.04.024\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen F, He H, Zhao SM, Yao JH, Sun Q, Huang GH, Xiao D, Tang LF, Leng YW, Tao XX (2018) Analysis of \\u003cem\\u003eMicrobial Community\\u003c/em\\u003e Succession during Methane Production from Baiyinhua Lignite. Energy Fuels 32:10311\\u0026ndash;10320. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acs.energyfuels.8b01181\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.energyfuels.8b01181\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen L, Liu J, Cui Y, Ren H, Zhao H, Zhao N, Song Y, Guan J, Niu J, Li G, Wang B, He H (2023) Degradation of humic acid from Yima coal by the combination of \\u003cem\\u003eelegant silver mold\\u003c/em\\u003e and \\u003cem\\u003eBacillus subtilis\\u003c/em\\u003e. J. China Coal Soc 48:4224\\u0026ndash;4232. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.13225/j.cnki.jccs.2022.1581\\u003c/span\\u003e\\u003cspan address=\\\"10.13225/j.cnki.jccs.2022.1581\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCheng J, Ma L, Zhao W, Liu Y, Zou Y (2022) Study on the biotransformation of Pingzhuang lignite by endogenous \\u003cem\\u003eoxalic acid fungus HM-M1\\u003c/em\\u003e. Coal Sci Technol 50:317\\u0026ndash;324. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.13199/j.cnki.cst.2020-0803\\u003c/span\\u003e\\u003cspan address=\\\"10.13199/j.cnki.cst.2020-0803\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCohen MS, Gabriele PD (1982) Degradation of Coal by the \\u003cem\\u003eFungi Polyporus versicolor\\u003c/em\\u003e and \\u003cem\\u003ePoria monticola\\u003c/em\\u003e. Appl Environ Microbiol 44:23\\u0026ndash;27. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/aem.44.1.23-27.1982\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/aem.44.1.23-27.1982\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEjsing CS, Sampaio JL, Surendranath V, Duchoslav E, Ekroos K, Klemm RW, Simons K, Shevchenko A (2009) Global analysis of the yeast lipidome by quantitative shotgun mass spectrometry. Proc Natl Acad Sci USA 106:2136\\u0026ndash;2141. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.0811700106\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.0811700106\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFakoussa RM, Hofrichter M (1999) Biotechnology and microbiology of coal degradation. Appl Microbiol Biotechnol 52:25\\u0026ndash;40. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s002530051483\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s002530051483\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeng X, Sun J, Xie Y (2021) Degradation of Shanxi lignite by \\u003cem\\u003eTrichoderma citrinoviride\\u003c/em\\u003e. Fuel 291:120204. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuel.2021.120204\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuel.2021.120204\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGong P, Chang X, Chen X, Bai X, Wen H, Pi S, Yang W, Wang L, Chen F (2017) Metabolomics study of cadmium-induced diabetic nephropathy and protective effect of caffeic acid phenethyl ester using UPLC-Q-TOF-MS combined with pattern recognition. Environ Toxicol Pharmacol 54:80\\u0026ndash;92. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.etap.2017.06.021\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.etap.2017.06.021\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHaridas PC, Ravichandran R, Shaikh N, Kishore P, Kumar PS, Banerjee K, Sekhar CN (2024) Authentication of the species identity of \\u003cem\\u003esquid rings\\u003c/em\\u003e using UHPLC-Q-orbitrap MS/MS-based lipidome fingerprinting and chemoinformatics. Food Chem 442:138525. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.foodchem.2024.138525\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.foodchem.2024.138525\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJatoi AS, Aziz S, Soomro SA (2021) Effect of native microorganism \\u003cem\\u003eRhodococcus spp. SL-9\\u003c/em\\u003e for dibenzothiophene degradation and its application towards clean coal approach. Clean Eng Technol 3:100126. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.clet.2021.100126\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.clet.2021.100126\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi J, Liu X, Pi S, Ma M, Liao G (2017) Optimization of Microbial Degradation Process Conditions for Linfen Lignite in Shanxi Province. Coal Convers 40:65\\u0026ndash;72. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.19726/j.cnki.ebcc.2017.02.012\\u003c/span\\u003e\\u003cspan address=\\\"10.19726/j.cnki.ebcc.2017.02.012\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu J, Wang B, Tai C, Wu L, Zhao H, Guan J, Chen L (2016) An Effective Method to Detect Volatile Intermediates Generated in the Bioconversion of Coal to Methane by Gas Chromatography-Mass Spectrometry after In-Situ Extraction Using Headspace Solid-Phase Micro-Extraction under Strict Anaerobic Conditions. PLoS ONE 11:e0163949. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0163949\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0163949\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLuo JM, Zhu WC, Cao ST, Lu ZY, Zhang MH, Song B, Shen YB, Wang M (2021) Improving Biotransformation Efficiency of \\u003cem\\u003eArthrobacter simplex\\u003c/em\\u003e by Enhancement of Cell Stress Tolerance and Enzyme Activity. J Agric Food Chem 69:704\\u0026ndash;716. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/acs.jafc.0c06592\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.jafc.0c06592\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMeng Q, Zhou A, Wang Y, Zhang Y, Qu J, Wang G (2008) Study on Screening of Strains for Bioconversion of Shenfu Coal. Chem Bioeng 25:30\\u0026ndash;33\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMeng Q, Wang Y, Zhou A (2011) Synergistic effect of photo-biological co-conversion of Shenfu coal. J China Univ Min Technol 40:438\\u0026ndash;442\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNsa IY, Akinyemi BT, Bello-Akinosho M, Ezechukwu SN, Bayode TB, Igbinigie EE, Adeleke RA (2022) Development of a \\u003cem\\u003esaprophytic fungal\\u003c/em\\u003e inoculum for the biodegradation of sub-bituminous coal. SN Appl Sci 4:53. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s42452-021-04925-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s42452-021-04925-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNugroho ADW, Van OB, Bachtiar SA, Boeren S, Kleerebezem M, Bachmann H (2022) Manganese Modulates Metabolic Activity and Redox Homeostasis in Translationally Blocked \\u003cem\\u003eLactococcus cremoris\\u003c/em\\u003e, Impacting Metabolic Persistence, Cell Culturability, and Flavor Formation. Microbiol Spectr 10:e0270821. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/spectrum.02708-21\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/spectrum.02708-21\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOlawale JT, Edeki OG, Cowan AK (2020) Bacterial degradation of coal discard and geologically weathered coal. JCST 7:405\\u0026ndash;416. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s40789-020-00306-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s40789-020-00306-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePonnudurai V, Rajarathinam R, Muthuvelu KS, Velmurugan S, Nalajala RK, Arumugam L (2022) Investigation on the utilization of coal washery rejects by different microbial sources for biogenic methane production. Chemosphere 287:132165. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.chemosphere.2021.132165\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.chemosphere.2021.132165\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRalph JP, Catcheside DEA (1997) Transformations of low rank coal by \\u003cem\\u003ePhanerochaete chrysosporium\\u003c/em\\u003e and other \\u003cem\\u003ewood-rot fungi\\u003c/em\\u003e. Fuel Process Technol 52:79\\u0026ndash;93. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0378-3820(97)00018-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0378-3820(97)00018-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSabar MA, Ali MI, Fatima N, Malik AY, Jamal A, Liaquat R, He H, Liu FJ, Guo H, Urynowicz M, Huang Z (2020) Evaluation of humic acids produced from Pakistani subbituminous coal by chemical and fungal treatments. Fuel 278:118301. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuel.2020.118301\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuel.2020.118301\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShanbhag K, Mhetre A, Khandelwal N, Kamat SS (2020) The Lysophosphatidylserines-An Emerging Class of Signalling Lysophospholipids. J Membr Biol 253:381\\u0026ndash;397. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00232-020-00133-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00232-020-00133-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShen W, Li X, Shi C, Yang J, Zhao S, Yang Z, Wang D (2023) Influences of Four Kinds of Surfactants on Biodegradations of Tar-Rich Coal in the Ordos Basin by Bacillus bicheniformis. Microorganisms 11:2397. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/microorganisms11102397\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/microorganisms11102397\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi C, Liu X, Wu H, Zhao S, Yang Z (2023a) Enhancements of mixed surfactants on Wucaiwan coal biodegradation by \\u003cem\\u003eNocardia mangyaensis\\u003c/em\\u003e. Energy 266:126501. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.energy.2022.126501\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.energy.2022.126501\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi C, Liu X, Zhao S, Yang Z, Lu X, Tong M (2022) Sequential degradations of Dananhu lignites by \\u003cem\\u003eNocardia mangyaensis\\u003c/em\\u003e and \\u003cem\\u003eBacillus licheniformis\\u003c/em\\u003e. Fuel 318:123623. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuel.2022.123623\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuel.2022.123623\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi C, Liu X, Zhao SS, Yang Z (2023b) Additions of esterase and rhamnolipid promote the degradation of Inner Mongolia coal by \\u003cem\\u003ePseudomonas japonica\\u003c/em\\u003e. Fuel 340:127640. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuel.2023.127640\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuel.2023.127640\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSobolczyk-Bednarek J, Choińska-Pulit A, Łaba W (2021) Biosolubilization of low-rank coal by the newly isolated strain Streptomyces \\u003cem\\u003efulvissimus K59\\u003c/em\\u003e. Fuel 301:121082. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuel.2021.121082\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuel.2021.121082\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSteffen KT, Hatakka A, Hofrichter M (2002) Degradation of humic acids by the litter-decomposing basidiomycete \\u003cem\\u003eCollybia dryophila\\u003c/em\\u003e. Appl Environ Microbiol 68:3442\\u0026ndash;3448. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/AEM.68.7.3442-3448.2002\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/AEM.68.7.3442-3448.2002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang B, Ndayisenga F, Zhang G, Yu Z (2021) Deciphering the initial products of coal during methanogenic bioconversion: Based on an untargeted metabolomics approach. Gcb Bioenergy 13:967\\u0026ndash;978. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/gcbb.12824\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/gcbb.12824\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang Y, Meng Q, Zhou A, Li X (2014) The research of screened strains Shenfu coal bioconversible method. Appl Chem Ind 43:1576\\u0026ndash;1579. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.16581/j.cnki.issn1671-3206.2014.09.035\\u003c/span\\u003e\\u003cspan address=\\\"10.16581/j.cnki.issn1671-3206.2014.09.035\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang Y, Bai Y, Su J, Xu L, Ren M, Cao M (2024) Manganese(IV) reduction coupled with ammonium oxidation mediated by a single strain \\u003cem\\u003earomatoleum evansii MAY27\\u003c/em\\u003e: Performance, metabolomics, and mechanism. Bioresour Technol 409:131235. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.biortech.2024.131235\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biortech.2024.131235\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXia Q, Shi L, Ma D, Xia W, Xu T (2025) Microorganism interaction in microbial community, metabolic behavior and biodegradation effect on bitumen. Bioresour Technol 430:132555. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.biortech.2025.132555\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biortech.2025.132555\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXiao L, Li Y, Liao Y, Ma H, Wu J, Zhang Y, Yao J (2018) Bioconversion of lignite humic acid by \\u003cem\\u003ewhite-rot fungi\\u003c/em\\u003e and characterization of products. 3 Biotech 8:258. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13205-018-1281-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13205-018-1281-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXu H, Zhou X, Zhen N, Yin X, Liu T, Tang Y, Liu J, Liu L, Ma F (2025) Comparative analysis of covalent bond cleavage and free radical behavior during the heating process in direct coal liquefaction of naomaohu and shangwan coals. Fuel Process Technol 275:108255. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.fuproc.2025.108255\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.fuproc.2025.108255\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXu SJ, Chen XY, Wang XF, Sun HZ, Hou ZJ, Cheng JS, Yuan YJ (2022) Artificial microbial consortium producing oxidases enhanced the biotransformation efficiencies of multi-antibiotics. J Hazard Mater 439:129674. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jhazmat.2022.129674\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jhazmat.2022.129674\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXue W, Cao W, Li L, Fu Y, Zeng Z (2022) Identification of coal geographic origin by FTIR coupled with metabolomics analysis. Infrared Phys Techn 125:104224. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.infrared.2022.104224\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.infrared.2022.104224\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang J, Liu X, Yang Z, Zhao S (2023) Biodegradation of Dananhu low-rank coal by \\u003cem\\u003ePlanomicrobium huatugouensis\\u003c/em\\u003e: Target metabolites possessing degradation abilities and their biodegradation pathways. Energy 276:127642. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.energy.2023.127642\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.energy.2023.127642\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang P, Zhao S, Zhang Q, Hu J, Liu R, Huang Z, Gao Y (2021) Synergistic effect of the cotton stalk and high-ash coal on gas production during co-pyrolysis/gasification. Bioresour Technol 336:125336. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.biortech.2021.125336\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biortech.2021.125336\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang Y, Meng Q, Zhou A (2009) Bioconversion of Shenfu Photo-Oxidation Coal Humic Acid. Chem Bioeng 26:79\\u0026ndash;82\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao W, Su X, Zhang Y, Xia D, Hou S, Zhou Y, Fu H, Wang L, Yin X (2022) Microbial electrolysis enhanced bioconversion of coal to methane compared with anaerobic digestion: Insights into differences in metabolic pathways. Energy Convers Manage 259:115553. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.enconman.2022.115553\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.enconman.2022.115553\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"ShenFu Lignite, Bioconversion, Metabolomics, lysoPC(16:0)\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8313358/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8313358/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eIn recent years, the biotransformation technology of coal has received increasing attention due to its significant economic and environmental advantages in the energy field. However, the specific conversion mechanism is challenging to study in depth, partly because it is difficult to identify intermediates in the biological conversion of coal. In this study, we reported a metabolomics method based on UPLC-Q-TOF/MS to analyse which metabolic pathway is most important in biotransformation under high Mn\\u003csup\\u003e2+\\u003c/sup\\u003e conditions. During the biological transformation process, a total of 30 potential differential metabolites were screened, including free fatty acids, phospholipids, sphingolipids, glycerides, and others. The most significant difference, lysoPC(16:0), was identified by MS/MS and standard sample analysis between Mn\\u003csup\\u003e2+\\u003c/sup\\u003e-bioconversion and control subjects. MetPA suggests that the high expression of lysoPC(16:0) may be closely related to microbial glycerophospholipid metabolism. Based on UPLC-Q-TOF/MS bioconversion metabolomics, combined with pattern recognition and network analysis, this approach provides a powerful tool for identifying potential biomarkers. It represents a new strategy for studying the mechanisms underlying biomass under strict conditions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Metabonomic study of Mn 2+ in promoting the biotransformation of Shenfu oxidised Lignite by pattern recognition\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-09 07:11:29\",\"doi\":\"10.21203/rs.3.rs-8313358/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b55a71ca-33f4-4a35-b4e8-66d03ed4d177\",\"owner\":[],\"postedDate\":\"January 9th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-15T09:39:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-09 07:11:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8313358\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8313358\",\"identity\":\"rs-8313358\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}