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image of Deciphering the Metabolic Mechanisms of Damp Retention in the Middle-Jiao Syndrome using High-throughput UPLC-Q-TOF-MS

Abstract

Background

Damp retention in the middle-jiao syndrome (DRMS), a common manifestation in Traditional Chinese Medicine (TCM), results from stagnation of damp pathogens in the middle jiao and impaired transport of food and fluids. Given the complex pathogenesis of DRMS, this study aimed to investigate its biological mechanisms using an advanced analytical approach.

Methods

A DRMS rat model was established based on three etiological factors: dietary disorders, depletion of vital qi, and excessive external dampness. Model validity was assessed small intestinal carbon propulsion rate and histopathological examination. Urine metabolomics, using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), systematically characterized metabolic profile changes and potential biomarkers.

Results

DRMS rats exhibited significantly reduced small intestinal propulsion, along with varying degrees of edema, disorganized tissue structures, and inflammatory cell infiltration in gastric, renal, and small intestinal tissues. Metabolomic analysis identified 52 differential metabolites as potential DRMS biomarkers, primarily involved in phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine and tyrosine metabolism, the citrate cycle, and cysteine and methionine metabolism pathways. Metabolic correlation networks further validated the model’s accuracy.

Discussion

The identified metabolites and pathways provide insight into the mechanisms underlying DRMS, complement existing TCM research, and offer a foundation for further studies. However, the findings are currently limited to the rat model and require human validation.

Conclusions

This study successfully established a DRMS animal model under clinically relevant TCM conditions and demonstrated the utility of metabolomics in elucidating DRMS mechanisms, providing experimental evidence for TCM syndrome characterization and advancing understanding of its etiology.

This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2026-01-12
2026-01-29
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References

  1. Kortesniemi M. Noerman S. Kårlund A. Raita J. Meuronen T. Koistinen V. Landberg R. Hanhineva K. Nutritional metabolomics: Recent developments and future needs. Curr. Opin. Chem. Biol. 2023 77 102400 10.1016/j.cbpa.2023.102400 37804582
    [Google Scholar]
  2. Lin C. Tian Q. Guo S. Xie D. Cai Y. Wang Z. Chu H. Qiu S. Tang S. Zhang A. Metabolomics for clinical biomarker discovery and therapeutic target identification. Molecules 2024 29 10 2198 10.3390/molecules29102198 38792060
    [Google Scholar]
  3. Hsu W.H. Wang S.Y. Chao Y.M. Chang K.V. Han D.S. Lin Y.L. Novel metabolic and lipidomic biomarkers of sarcopenia. J. Cachexia Sarcopenia Muscle 2024 15 5 2175 2186 10.1002/jcsm.13567 39169398
    [Google Scholar]
  4. Wang S. Song Y. Luo L. Zhang R. Guo K. Zhao Z. Untargeted LC–MS metabolomics reveals the metabolic responses in the Eriocheir sinensis gills exposed to salinity and alkalinity stress. Comp. Biochem. Physiol. C Toxicol. Pharmacol. 2024 281 109908 10.1016/j.cbpc.2024.109908 38580071
    [Google Scholar]
  5. Kim S.J. Song H.E. Lee H.Y. Yoo H.J. Mass spectrometry-based metabolomics in translational research. Adv. Exp. Med. Biol. 2021 1310 509 531 10.1007/978‑981‑33‑6064‑8_19 33834448
    [Google Scholar]
  6. Xu X. Guo W. Zhao L. Sun Y. Xu D. Yang J. Liu Y. Xie S. Wang Y. Xu Y. Exploring the in vitro anti‐inflammatory activity of gross saponins of Tribulus terrestris L. fruit by using liquid chromatography‐mass spectrometry‐based cell metabolomics approach. J. Sep. Sci. 2023 46 24 2300531 10.1002/jssc.202300531 37933967
    [Google Scholar]
  7. Zampieri M. Zimmermann M. Claassen M. Sauer U. Nontargeted metabolomics reveals the multilevel response to antibiotic perturbations. Cell Rep. 2017 19 6 1214 1228 10.1016/j.celrep.2017.04.002 28494870
    [Google Scholar]
  8. Lan Q. Li X. Fang J. Yu X. Wu Z.E. Yang C. Jian H. Li F. Comprehensive biomarker analysis of metabolomics in different syndromes in traditional Chinese medical for prediabetes mellitus. Chin. Med. 2024 19 1 114 10.1186/s13020‑024‑00983‑1 39183283
    [Google Scholar]
  9. Ye X. Wang X. Wang Y. Sun W. Chen Y. Wang D. Li Z. Li Z. A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS. J. Pharm. Biomed. Anal. 2021 206 114369 10.1016/j.jpba.2021.114369 34551376
    [Google Scholar]
  10. Chen J. Ye C. Hu X. Huang C. Yang Z. Li P. Wu A. Xue X. Lin D. Yang H. Serum metabolomics model and its metabolic characteristics in patients with different syndromes of dyslipidemia based on nuclear magnetic resonance. J. Pharm. Biomed. Anal. 2019 167 100 113 10.1016/j.jpba.2018.12.042 30763881
    [Google Scholar]
  11. Baskaran K. Moshkovich M. Hart L. Shah N. Chowdhury F. Shanmuganathan M. Britz-McKibbin P. Pai N. The role of urine metabolomics in the diagnosis and management of adult and pediatric Crohn’s disease and ulcerative colitis. Biomarkers 2025 30 1 104 113 10.1080/1354750X.2024.2438734 39642943
    [Google Scholar]
  12. Liu K. Jia B. Zhou L. Xing L. Wu L. Li Y. Lu J. Zhang L. Guan S. Ultraperformance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry-based metabolomics and lipidomics identify biomarkers for efficacy evaluation of mesalazine in a dextran sulfate sodium-induced ulcerative colitis mouse model. J. Proteome Res. 2021 20 2 1371 1381 10.1021/acs.jproteome.0c00757 33356298
    [Google Scholar]
  13. Sowa S.W. Gelderman G. Leistra A.N. Buvanendiran A. Lipp S. Pitaktong A. Vakulskas C.A. Romeo T. Baldea M. Contreras L.M. Integrative FourD omics approach profiles the target network of the carbon storage regulatory system. Nucleic Acids Res. 2017 45 4 gkx048 10.1093/nar/gkx048 28126921
    [Google Scholar]
  14. Wu L. Lai Y. Wang Y. Chen L.H. Guan Y.M. Chu R.G. Yang H.S. [Investigating effect of Faeces Bombycis on intestinal microflora in rats with syndrome of damp retention in middle-jiao by high-throughput sequencing].Zhongguo Zhongyao Zazhi 2020 45 3 623 630 32237522
    [Google Scholar]
  15. Xu W. Wang N. Ding H.R. Xu J.J. Qu Y.H. Pu D. Xiu Y.F. [Effects of Pogostemon cablin on gastrointestinal function of rats with syndrome of damp retention in middle-jiao].Zhongguo Zhongyao Zazhi 2017 42 23 4649 4655 29376266
    [Google Scholar]
  16. Liu Y. Liu P. Dai R. Wang J. Zheng Y. Shen J. Guo F. Wang L. Li H. Wei M. Analysis of plasma proteome from cases of the different traditional Chinese medicine syndromes in patients with chronic hepatitis B. J. Pharm. Biomed. Anal. 2012 59 173 178 10.1016/j.jpba.2011.10.002 22030074
    [Google Scholar]
  17. Huang G.S. Hong M.Y. Functional and molecular characterization for the damp-obstructed rat model in Chinese medicine. Am. J. Chin. Med. 2006 34 2 323 340 10.1142/S0192415X06003862 16552842
    [Google Scholar]
  18. Luo Y. Huang X. Hu H. Wang Y. Feng X. Chen S. Luo H. Intestinal microflora promotes Th2-mediated immunity through NLRP3 in damp and heat environments. Front. Immunol. 2024 15 1367053 10.3389/fimmu.2024.1367053 38756775
    [Google Scholar]
  19. Jiang Y. Tang Z. Zhu X. Xiao B. Tian H. Lei X. Peng H. Qin J. Zhang Y. Hoffman R.M. Hu X. Chen Q. Ji G. Jia L. Non-invasive omics analysis delineates molecular changes in water-only fasting and its sex-discriminating features in metabolic syndrome patients. MedComm (2020) 2023 4 6 e393 10.1002/mco2.393 37929015 PMC10622739
    [Google Scholar]
  20. Njoku K. Chiasserini D. Jones E.R. Barr C.E. O’Flynn H. Whetton A.D. Crosbie E.J. Urinary biomarkers and their potential for the non-invasive detection of endometrial cancer. Front. Oncol. 2020 10 559016 10.3389/fonc.2020.559016 33224875
    [Google Scholar]
  21. Zhou W. Hong Y. Yin A. Liu S. Chen M. Lv X. Nie X. Tan N. Zhang Z. Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes. J. Pharm. Biomed. Anal. 2020 185 113262 10.1016/j.jpba.2020.113262 32222648
    [Google Scholar]
  22. Mung D. Li L. Chemical isotope labeling liquid chromatography mass spectrometry for investigating acute dietary effects of cow milk consumption on human urine metabolome. J. Food. Drug Anal 2019 27 2 565 574 10.1016/j.jfda.2018.10.007 30987728
    [Google Scholar]
  23. Lawrence Y.A. Guard B.C. Steiner J.M. Suchodolski J.S. Lidbury J.A. Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease. PLoS One 2019 14 5 e0217797 10.1371/journal.pone.0217797 31150490
    [Google Scholar]
  24. Tzanakis K. Nattkemper T.W. Niehaus K. Albaum S.P. MetHoS: A platform for large-scale processing, storage and analysis of metabolomics data. BMC Bioinformatics 2022 23 1 267 10.1186/s12859‑022‑04793‑w 35804309
    [Google Scholar]
  25. Mendes A. Havelund J.F. Lemvig J. Schwämmle V. Færgeman N.J. MetaboLink: A web application for streamlined processing and analysis of large-scale untargeted metabolomics data. Bioinformatics 2024 40 7 btae459 10.1093/bioinformatics/btae459 39018180
    [Google Scholar]
  26. Buyukozkan M. Suhre K. Krumsiek J. SGI: Automatic clinical subgroup identification in omics datasets. Bioinformatics 2022 38 2 573 576 10.1093/bioinformatics/btab656 34529048
    [Google Scholar]
  27. Dong Y. Malitsky S. MetaboReport: From metabolomics data analysis to comprehensive reporting. Bioinformatics 2024 40 6 btae373 10.1093/bioinformatics/btae373 38885410
    [Google Scholar]
  28. Watrous J.D. Henglin M. Claggett B. Lehmann K.A. Larson M.G. Cheng S. Jain M. Visualization, quantification, and alignment of spectral drift in population scale untargeted metabolomics data. Anal. Chem. 2017 89 3 1399 1404 10.1021/acs.analchem.6b04337 28208263
    [Google Scholar]
  29. Li N. Zhu T. Dong Y. Zhao C. Chen J. Tian Y. Liu Y. Hong X. Xiong H. UPLC‐Q‐TOF‐MS‐based serum metabolomics explores the mechanism of pingwei powder in treating the damp retention in the middle‐jiao syndrome. Biomed. Chromatogr. 2025 39 7 e70143 10.1002/bmc.70143 40500971
    [Google Scholar]
  30. Patricio A. Fernandez C. Mota A. Capelo J. Dynamic versus static ultrasonic sample treatment for the solid–liquid pre-concentration of mercury from human urine. Talanta 2006 69 3 769 775 10.1016/j.talanta.2005.11.007 18970636
    [Google Scholar]
  31. Rossi B. Freni F. Carelli C. Moretti M. Galatone D. Vignali C. Morini L. Determination of Traditional and Designer Benzodiazepines in Urine through LC-MS/MS. Curr. Pharm. Des. 2022 28 32 2622 2638 10.2174/1381612828666220831103224 36045516
    [Google Scholar]
  32. Yang M. Lu Y. Jin S. Liu W. Yao M. Jiang Z. Shu Y. Postoperative Tongqi Formula ameliorates postoperative ileus via p38 MAPK signaling pathway and metabolic disorder. Heliyon 2025 11 1 e41217 10.1016/j.heliyon.2024.e41217 39811334
    [Google Scholar]
  33. Qiu J. Li J. Shang S. Zhou P. Leng J. HPLC fingerprint combined with chemometrics and multicomponent content determination for quality evaluation and control of huangma tincture. Phytochem. Anal. 2025 36 4 1002 1016 10.1002/pca.3487 39658907
    [Google Scholar]
  34. Renai L. Ancillotti C. Ulaszewska M. Garcia-Aloy M. Mattivi F. Bartoletti R. Del Bubba M. Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: Application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements. Anal. Bioanal. Chem. 2022 414 5 1841 1855 10.1007/s00216‑021‑03815‑5 35028688
    [Google Scholar]
  35. Chen Q. Liang X. Wu T. Jiang J. Jiang Y. Zhang S. Ruan Y. Zhang H. Zhang C. Chen P. Lv Y. Xin J. Shi D. Chen X. Li J. Xu Y. Integrative analysis of metabolomics and proteomics reveals amino acid metabolism disorder in sepsis. J. Transl. Med. 2022 20 1 123 10.1186/s12967‑022‑03320‑y 35287674
    [Google Scholar]
  36. Yu J. Ren J. Ren Y. Wu Y. Zeng Y. Zhang Q. Xiao X. Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes. EBioMedicine 2024 101 105008 10.1016/j.ebiom.2024.105008 38368766
    [Google Scholar]
  37. van Tetering L. Spies S. Wildeman Q.D.K. Houthuijs K.J. van Outersterp R.E. Martens J. Wevers R.A. Wishart D.S. Berden G. Oomens J. A spectroscopic test suggests that fragment ion structure annotations in MS/MS libraries are frequently incorrect. Commun. Chem. 2024 7 1 30 10.1038/s42004‑024‑01112‑7 38355930
    [Google Scholar]
  38. Kim N.Y. Jung H.Y. Kim J.K. Identification and characterisation of a novel heptapeptide mackerel by-product hydrolysate, and its potential as a functional fertiliser component. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2021 1180 122881 10.1016/j.jchromb.2021.122881 34388601
    [Google Scholar]
  39. Pang Z. Zhou G. Ewald J. Chang L. Hacariz O. Basu N. Xia J. Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat. Protoc. 2022 17 8 1735 1761 10.1038/s41596‑022‑00710‑w 35715522
    [Google Scholar]
  40. Meston D. Themelis T. Zhou Z. De Vos J. De Pra M. Steiner F. Becue I. Daeseleire E. Desmet G. Eeltink S. Development of a generic ultra-high-pressure gradient liquid-chromatography method development protocol: The analysis of residual multi-class antibiotics in food products as a case study. J. Chromatogr. A 2022 1684 463565 10.1016/j.chroma.2022.463565 36274530
    [Google Scholar]
  41. Harwood-Stamper A.J. Rowland C.A. Dunn W.B. Development of microflow ultra high performance liquid chromatography-mass spectrometry metabolomic assays for analysis of mammalian biofluids. Metabolomics 2024 20 6 120 10.1007/s11306‑024‑02187‑y 39453548
    [Google Scholar]
  42. Singh S. Kumar M. Dwivedi S. Yadav A. Sharma S. Distribution profile of iridoid glycosides and phenolic compounds in two Barleria species and their correlation with antioxidant and antibacterial activity. Front. Plant. Sci. 2023 13 1076871 10.3389/fpls.2022.1076871 36699860
    [Google Scholar]
  43. Zhang Y. Fan S. Wohlgemuth G. Fiehn O. Denoising autoencoder normalization for large-scale untargeted metabolomics by gas chromatography–mass spectrometry. Metabolites 2023 13 8 944 10.3390/metabo13080944 37623887
    [Google Scholar]
  44. Gostner J.M. Becker K. Kurz K. Fuchs D. Disturbed amino acid metabolism in hiv: Association with neuropsychiatric symptoms. Front. Psychiatry 2015 6 97 10.3389/fpsyt.2015.00097 26236243
    [Google Scholar]
  45. Zhang Y. Wang H. Yu H. Sun X. Chiral fluorescent sensor based on H 8 -BINOL for the high enantioselective recognition of d - and l -phenylalanine. RSC Advances 2022 12 19 11967 11973 10.1039/D2RA00803C 35481074
    [Google Scholar]
  46. Boča R. Štofko J. Imrich R. Ab initio study of molecular properties of l-tyrosine. J. Mol. Model. 2023 29 8 245 10.1007/s00894‑023‑05648‑8 37442864
    [Google Scholar]
  47. Shende V.V. Bauman K.D. Moore B.S. The shikimate pathway: Gateway to metabolic diversity. Nat. Prod. Rep. 2024 41 4 604 648 10.1039/D3NP00037K 38170905
    [Google Scholar]
  48. Surrer D.B. Schüsser S. König J. Fromm M.F. Gessner A. Transport of aromatic amino acids l ‐tryptophan, l ‐tyrosine, and l ‐phenylalanine by the organic anion transporting polypeptide (OATP) 3A1. FEBS J. 2024 291 21 4732 4743 10.1111/febs.17255 39206635
    [Google Scholar]
  49. Wang A. Guan B. Yu L. Liu Q. Hou Y. Li Z. Sun D. Xu H. Palmatine protects against atherosclerosis by gut microbiota and phenylalanine metabolism. Pharmacol. Res. 2024 209 107413 10.1016/j.phrs.2024.107413 39293583
    [Google Scholar]
  50. Martynyuk A.E. Glushakov A.V. Sumners C. Laipis P.J. Dennis D.M. Seubert C.N. Impaired glutamatergic synaptic transmission in the PKU brain. Mol. Genet. Metab 2005 86 34 42 (Suppl.1) 10.1016/j.ymgme.2005.06.014 16153867
    [Google Scholar]
  51. Dobrowolski S.F. Phua Y.L. Vockley J. Goetzman E. Blair H.C. Phenylketonuria oxidative stress and energy dysregulation: Emerging pathophysiological nlms provide interventional opportunity. Mol. Genet. Metab. 2022 136 2 111 117 10.1016/j.ymgme.2022.03.012 35379539
    [Google Scholar]
  52. Almohmadi N.H. Al-kuraishy H.M. Al-Gareeb A.I. Albuhadily A.K. Abdelaziz A.M. Jabir M.S. Alexiou A. Papadakis M. Batiha G.E.S. Glutamatergic dysfunction in neurodegenerative diseases focusing on Parkinson’s disease: Role of glutamate modulators. Brain Res. Bull. 2025 225 111349 10.1016/j.brainresbull.2025.111349 40252703
    [Google Scholar]
  53. Zhong J. Zhen W. Bai D. Hu X. Zhang H. Zhang R. Ito K. Zhang Y. Zhang B. Ma Y. Effects of aspirin eugenol ester on liver oxidative damage and energy metabolism in immune-stressed broilers. Antioxidants 2024 13 3 341 10.3390/antiox13030341 38539874
    [Google Scholar]
  54. Liu Y. Westerhoff H.V. Competitive, multi‐objective, and compartmented Flux Balance Analysis for addressing tissue‐specific inborn errors of metabolism. J. Inherit. Metab. Dis. 2023 46 4 573 585 10.1002/jimd.12603 36880400
    [Google Scholar]
  55. Keely S.J. Urso A. Ilyaskin A.V. Korbmacher C. Bunnett N.W. Poole D.P. Carbone S.E. Contributions of bile acids to gastrointestinal physiology as receptor agonists and modifiers of ion channels. Am. J. Physiol. Gastrointest. Liver Physiol. 2022 322 2 G201 G222 10.1152/ajpgi.00125.2021 34755536
    [Google Scholar]
  56. Salekeen R. Siam M.H.B. Sharif D.I. Lustgarten M.S. Billah M.M. Islam K.M.D. In silico insights into potential gut microbial modulation of NAD+ metabolism and longevity. J. Biochem. Mol. Toxicol. 2021 35 12 e22925 10.1002/jbt.22925 34580953
    [Google Scholar]
  57. Zhou L. Li H. Hao F. Li N. Liu X. Wang G. Wang Y. Tang H. Developmental Changes for the Hemolymph Metabolome of Silkworm (Bombyx mori L.). J. Proteome Res. 2015 14 5 2331 2347 10.1021/acs.jproteome.5b00159 25825269
    [Google Scholar]
  58. Jo S. Seo M. Nguyen T.H. Cha J.W. An Y.J. Park S. Biosynthesis-encoded lipogenic acetyl-coa measurement using NMR reveals glucose-driven lipogenesis and glutamine’s alternative roles in kidney cancer. J. Am. Chem. Soc. 2024 146 49 33753 33762 10.1021/jacs.4c11809 39611721
    [Google Scholar]
  59. Abhimanyu; Longlax, S.C.; Nishiguchi, T.; Ladki, M.; Sheikh, D.; Martinez, A.L.; Mace, E.M.; Grimm, S.L.; Caldwell, T.; Portillo Varela, A.; Sekhar, R.V.; Mandalakas, A.M.; Mlotshwa, M.; Ginidza, S.; Cirillo, J.D.; Wallis, R.S.; Netea, M.G.; van Crevel, R.; Coarfa, C.; DiNardo, A.R. TCA metabolism regulates DNA hypermethylation in LPS and Mycobacterium tuberculosis –induced immune tolerance. Proc. Natl. Acad. Sci. USA 2024 121 41 e2404841121 10.1073/pnas.2404841121 39348545
    [Google Scholar]
  60. Le A. Lane A.N. Hamaker M. Bose S. Gouw A. Barbi J. Tsukamoto T. Rojas C.J. Slusher B.S. Zhang H. Zimmerman L.J. Liebler D.C. Slebos R.J.C. Lorkiewicz P.K. Higashi R.M. Fan T.W.M. Dang C.V. Glucose-independent glutamine metabolism via TCA cycling for proliferation and survival in B cells. Cell Metab. 2012 15 1 110 121 10.1016/j.cmet.2011.12.009 22225880
    [Google Scholar]
  61. Humphries F. Shmuel-Galia L. Ketelut-Carneiro N. Li S. Wang B. Nemmara V.V. Wilson R. Jiang Z. Khalighinejad F. Muneeruddin K. Shaffer S.A. Dutta R. Ionete C. Pesiridis S. Yang S. Thompson P.R. Fitzgerald K.A. Succination inactivates gasdermin D and blocks pyroptosis. Science 2020 369 6511 1633 1637 10.1126/science.abb9818 32820063
    [Google Scholar]
  62. Hernandez-Baixauli J. Abasolo N. Palacios-Jordan H. Foguet-Romero E. Suñol D. Galofré M. Caimari A. Baselga-Escudero L. Del Bas J.M. Mulero M. Imbalances in TCA, short fatty acids and one-carbon metabolisms as important features of homeostatic disruption evidenced by a multi-omics integrative approach of lps-induced chronic inflammation in male wistar rats. Int. J. Mol. Sci. 2022 23 5 2563 10.3390/ijms23052563 35269702
    [Google Scholar]
  63. Tang Y. Wang C. Chen S. Li L. Zhong X. Zhang J. Feng Y. Wang L. Chen J. Yu M. Wang F. Wang L. Li G. He Y. Li Y. Dimethyl fumarate attenuates LPS induced septic acute kidney injury by suppression of NFκB p65 phosphorylation and macrophage activation. Int. Immunopharmacol. 2022 102 108395 10.1016/j.intimp.2021.108395 34915410
    [Google Scholar]
  64. Liu M. Liu D. Yu C. Fan H.H. Zhao X. Wang H. Zhang C. Zhang M. Bo R. He S. Wang X. Jiang H. Guo Y. Li J. Xu X. Liu Q. Caffeic acid, but not ferulic acid, inhibits macrophage pyroptosis by directly blocking gasdermin D activation. MedComm (2020) 2023 4 3 e255 10.1002/mco2.255 37090118 PMC10119582
    [Google Scholar]
  65. Dowling J.K. Afzal R. Gearing L.J. Cervantes-Silva M.P. Annett S. Davis G.M. De Santi C. Assmann N. Dettmer K. Gough D.J. Bantug G.R. Hamid F.I. Nally F.K. Duffy C.P. Gorman A.L. Liddicoat A.M. Lavelle E.C. Hess C. Oefner P.J. Finlay D.K. Davey G.P. Robson T. Curtis A.M. Hertzog P.J. Williams B.R.G. McCoy C.E. Mitochondrial arginase-2 is essential for IL-10 metabolic reprogramming of inflammatory macrophages. Nat. Commun. 2021 12 1 1460 10.1038/s41467‑021‑21617‑2 33674584
    [Google Scholar]
  66. Liu X. Si W. He L. Yang J. Peng Y. Ren J. Liu X. Jin T. Yu H. Zhang Z. Cheng X. Zhang W. Xia L. Huang Y. Wang Y. Liu S. Shan L. Zhang Y. Yang X. Li H. Liang J. Sun L. Shang Y. The existence of a nonclassical TCA cycle in the nucleus that wires the metabolic-epigenetic circuitry. Signal Transduct. Target. Ther. 2021 6 1 375 10.1038/s41392‑021‑00774‑2 34728602
    [Google Scholar]
  67. Liu Z. Apontes P. Fomenko E. Chi N. Schuster V. Kurland I. Pessin J. Chi Y. Mangiferin accelerates glycolysis and enhances mitochondrial bioenergetics. Int. J. Mol. Sci. 2018 19 1 201 10.3390/ijms19010201 29315239
    [Google Scholar]
  68. Huang C. Deng W. Xu H. Zhou C. Zhang F. Chen J. Bao Q. Zhou X. Liu M. Li J. Liu C. Short-chain fatty acids reprogram metabolic profiles with the induction of reactive oxygen species production in human colorectal adenocarcinoma cells. Comput. Struct. Biotechnol. J. 2023 21 1606 1620 10.1016/j.csbj.2023.02.022 36874158
    [Google Scholar]
  69. Dimer N.W. Ferreira B.K. Agostini J.F. Gomes M.L. Kist L.W. Malgarin F. Carvalho-Silva M. Gomes L.M. Rebelo J. Frederico M.J.S. Silva F.R.M.B. Rico E.P. Bogo M.R. Streck E.L. Ferreira G.C. Schuck P.F. Brain bioenergetics in rats with acute hyperphenylalaninemia. Neurochem. Int. 2018 117 188 203 10.1016/j.neuint.2018.01.001 29454001
    [Google Scholar]
  70. Talebi S. Eshraghi P. Nutrition in phenylketonuria. Clin. Nutr. ESPEN 2024 64 307 313 10.1016/j.clnesp.2024.09.032 39427751
    [Google Scholar]
  71. Gu Y. Wu J. Tian J. Li L. Zhang B. Zhang Y. He Y. Effects of exogenous synthetic autoinducer-2 on physiological behaviors and proteome of lactic acid bacteria. ACS Omega 2020 5 3 1326 1335 10.1021/acsomega.9b01021 32010802
    [Google Scholar]
  72. Biji C.A. Balde A. Nazeer R.A. Anti-inflammatory peptide therapeutics and the role of sulphur containing amino acids (cysteine and methionine) in inflammation suppression: A review. Inflamm. Res. 2024 73 7 1203 1221 10.1007/s00011‑024‑01893‑6 38769154
    [Google Scholar]
  73. Espe M. Adam A.C. Saito T. Skjærven K.H. Methionine: An indispensable amino acid in cellular metabolism and health of atlantic salmon. Aquacult. Nutr. 2023 2023 1 10 10.1155/2023/5706177 37927379
    [Google Scholar]
  74. Aizawa Y. Mori M. Suzuki T. Saito A. Inoue H. Shotgun proteomic investigation of methyltransferase and methylation profiles in lipopolysaccharide stimulated RAW264.7 murine macrophages. Biomed. Res. 2022 43 3 73 80 10.2220/biomedres.43.73 35718447
    [Google Scholar]
  75. Rius-Pérez S. Pérez S. Torres-Cuevas I. Martí-Andrés P. Taléns-Visconti R. Paradela A. Guerrero L. Franco L. López-Rodas G. Torres L. Corrales F. Sastre J. Blockade of the trans-sulfuration pathway in acute pancreatitis due to nitration of cystathionine β-synthase. Redox Biol. 2020 28 101324 10.1016/j.redox.2019.101324 31539805
    [Google Scholar]
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