Biosynthetic enzyme analysis identifies a protective role for TLR4-acting gut microbial sulfonolipids in inflammatory bowel disease.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
10
05
2023
accepted:
18
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
The trillions of microorganisms inhabiting the human gut are intricately linked to human health. While specific microbes have been associated with diseases, microbial abundance alone cannot reveal the molecular mechanisms involved. One such important mechanism is the biosynthesis of functional metabolites. Here, we develop a biosynthetic enzyme-guided disease correlation approach to uncover microbial functional metabolites linked to disease. Applying this approach, we negatively correlate the expression of gut microbial sulfonolipid (SoL) biosynthetic enzymes to inflammatory bowel disease (IBD). Targeted chemoinformatics and metabolomics then confirm that SoL abundance is significantly decreased in IBD patient data and samples. In a mouse model of IBD, we further validate that SoL abundance is decreased while inflammation is increased in diseased mice. We show that SoLs consistently contribute to the immunoregulatory activity of different SoL-producing human microbes. We further reveal that sulfobacins A and B, representative SoLs, act on Toll-like receptor 4 (TLR4) and block lipopolysaccharide (LPS) binding, suppressing both LPS-induced inflammation and macrophage M1 polarization. Together, these results suggest that SoLs mediate a protective effect against IBD through TLR4 signaling and showcase a widely applicable biosynthetic enzyme-guided disease correlation approach to directly link the biosynthesis of gut microbial functional metabolites to human health.
Identifiants
pubmed: 39477928
doi: 10.1038/s41467-024-53670-y
pii: 10.1038/s41467-024-53670-y
doi:
Substances chimiques
Toll-Like Receptor 4
0
Lipopolysaccharides
0
TLR4 protein, human
0
Tlr4 protein, mouse
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9371Subventions
Organisme : Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
ID : 1R35GM150565
Organisme : National Science Foundation (NSF)
ID : 2239561
Informations de copyright
© 2024. The Author(s).
Références
Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).
pubmed: 26206939
pmcid: 4641445
doi: 10.1126/science.1254766
Haiser, H. J. & Turnbaugh, P. J. Developing a metagenomic view of xenobiotic metabolism. Pharmacol. Res. 69, 21–31 (2013).
pubmed: 22902524
doi: 10.1016/j.phrs.2012.07.009
Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P. & Forano, E. Microbial degradation of complex carbohydrates in the gut. Gut. Microbes 3, 289–306 (2012).
pubmed: 22572875
pmcid: 3463488
doi: 10.4161/gmic.19897
Dai, H. et al. Recent advances in gut microbiota-associated natural products: structures, bioactivities, and mechanisms. Nat. Prod. Rep. 40, 1078–1093 (2023).
pubmed: 37013809
doi: 10.1039/D2NP00075J
Lavelle, A. & Sokol, H. Gut microbiota-derived metabolites as key actors in inflammatory bowel disease. Nat. Rev. Gastroenterol. Hepatol. 17, 223–237 (2020).
pubmed: 32076145
doi: 10.1038/s41575-019-0258-z
Skelly, A. N., Sato, Y., Kearney, S. & Honda, K. Mining the microbiota for microbial and metabolite-based immunotherapies. Nat. Rev. Immunol. 19, 305–323 (2019).
pubmed: 30858494
doi: 10.1038/s41577-019-0144-5
Spanogiannopoulos, P., Bess, E. N., Carmody, R. N. & Turnbaugh, P. J. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14, 273–287 (2016).
pubmed: 26972811
pmcid: 5243131
doi: 10.1038/nrmicro.2016.17
Cao, Y. et al. Commensal microbiota from patients with inflammatory bowel disease produce genotoxic metabolites. Science 378, eabm3233 (2022).
pubmed: 36302024
pmcid: 9993714
doi: 10.1126/science.abm3233
Yao, L. et al. A biosynthetic pathway for the selective sulfonation of steroidal metabolites by human gut bacteria. Nat. Microbiol. 7, 1404–1418 (2022).
pubmed: 35982310
pmcid: 10327491
doi: 10.1038/s41564-022-01176-y
Fischbach, M. A. Microbiome: Focus on causation and mechanism. Cell 174, 785–790 (2018).
pubmed: 30096310
pmcid: 6094951
doi: 10.1016/j.cell.2018.07.038
Chaudhari, S. N., McCurry, M. D. & Devlin, A. S. Chains of evidence from correlations to causal molecules in microbiome-linked diseases. Nat. Chem. Biol. 17, 1046–1056 (2021).
pubmed: 34552222
pmcid: 8480537
doi: 10.1038/s41589-021-00861-z
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: Short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
pubmed: 27259147
doi: 10.1016/j.cell.2016.05.041
Chiurchiù, V., Leuti, A. & Maccarrone, M. Bioactive lipids and chronic inflammation: Managing the fire within. Front. Immunol. 9, 38 (2018).
pubmed: 29434586
pmcid: 5797284
doi: 10.3389/fimmu.2018.00038
Bae, M. et al. Akkermansia muciniphila phospholipid induces homeostatic immune responses. Nature 608, 168–173 (2022).
pubmed: 35896748
pmcid: 9328018
doi: 10.1038/s41586-022-04985-7
MacEyka, M. & Spiegel, S. Sphingolipid metabolites in inflammatory disease. Nature 510, 58–67 (2014).
pubmed: 24899305
pmcid: 4320971
doi: 10.1038/nature13475
Walker, A. et al. Sulfonolipids as novel metabolite markers of Alistipes and Odoribacter affected by high-fat diets. Sci. Rep. 7, 11047 (2017).
pubmed: 28887494
pmcid: 5591296
doi: 10.1038/s41598-017-10369-z
Pitta, T. P., Leadbetter, E. R. & Godchaux, W. Increase of ornithine amino lipid content in a sulfonolipid-deficient mutant of Cytophaga johnsonae. J. Bacteriol. 171, 952–957 (1989).
pubmed: 2914878
pmcid: 209687
doi: 10.1128/jb.171.2.952-957.1989
Durack, J. & Lynch, S. V. The gut microbiome: relationships with disease and opportunities for therapy. J. Exp. Med. 216, 20–40 (2018).
pubmed: 30322864
doi: 10.1084/jem.20180448
Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).
pubmed: 31142855
pmcid: 6650278
doi: 10.1038/s41586-019-1237-9
Dziarski, R., Park, S. Y., Kashyap, D. R., Dowd, S. E. & Gupta, D. Pglyrp-regulated gut microflora Prevotella falsenii, Parabacteroides distasonis and Bacteroides eggerthii enhance and Alistipes finegoldii attenuates colitis in mice. PLOS ONE 11, e0146162 (2016).
pubmed: 26727498
doi: 10.1371/journal.pone.0146162
Lima, S. F. et al. Transferable immunoglobulin A–coated Odoribacter splanchnicus in responders to fecal microbiota transplantation for ulcerative colitis limits colonic inflammation. Gastroenterology 162, 166–178 (2022).
pubmed: 34606847
doi: 10.1053/j.gastro.2021.09.061
Hou, L. et al. Identification and biosynthesis of pro-inflammatory sulfonolipids from an opportunistic pathogen Chryseobacterium gleum. ACS Chem. Biol. 17, 1197–1206 (2022).
pubmed: 35476918
doi: 10.1021/acschembio.2c00141
Pasternak, B. A. et al. Lipopolysaccharide exposure is linked to activation of the acute phase response and growth failure in pediatric Crohnʼs disease and murine colitis. Inflamm. Bowel Dis. 16, 856–869 (2010).
pubmed: 19924809
doi: 10.1002/ibd.21132
Pastor Rojo, O. et al. Serum lipopolysaccharide-binding protein in endotoxemic patients with inflammatory bowel disease. Inflamm. Bowel Dis. 13, 269–277 (2007).
pubmed: 17206721
doi: 10.1002/ibd.20019
Im, E., Riegler, F. M., Pothoulakis, C. & Rhee, S. H. Elevated lipopolysaccharide in the colon evokes intestinal inflammation, aggravated in immune modulator-impaired mice. Am. J. Physiol.-Gastrointest. Liver Physiol. 303, G490–G497 (2012).
pubmed: 22723263
doi: 10.1152/ajpgi.00120.2012
Stephens, M. & von der Weid, P.-Y. Lipopolysaccharides modulate intestinal epithelial permeability and inflammation in a species-specific manner. Gut. Microbes 11, 421–432 (2020).
pubmed: 31203717
doi: 10.1080/19490976.2019.1629235
Chaleckis, R., Meister, I., Zhang, P. & Wheelock, C. E. Challenges, progress and promises of metabolite annotation for LC–MS-based metabolomics. Curr. Opin. Biotechnol. 55, 44–50 (2019).
pubmed: 30138778
doi: 10.1016/j.copbio.2018.07.010
Cui, L., Lu, H. & Lee, Y. H. Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases. Mass Spec. Rev. 37, 772–792 (2018).
doi: 10.1002/mas.21562
Schrimpe-Rutledge, A. C., Codreanu, S. G., Sherrod, S. D. & McLean, J. A. Untargeted metabolomics strategies—challenges and emerging directions. J. Am. Soc. Mass Spectrom. 27, 1897–1905 (2016).
pubmed: 27624161
pmcid: 5110944
doi: 10.1007/s13361-016-1469-y
Proctor, L. M. et al. The integrative human microbiome project. Nature 569, 641–648 (2019).
doi: 10.1038/s41586-019-1238-8
Vatanen, T. et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat. Microbiol. 4, 470–479 (2019).
pubmed: 30559407
doi: 10.1038/s41564-018-0321-5
Katz, K. et al. The sequence read archive: a decade more of explosive growth. Nucleic Acids Res. 50, D387–D390 (2022).
pubmed: 34850094
doi: 10.1093/nar/gkab1053
Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2021).
pubmed: 32690973
doi: 10.1038/s41587-020-0603-3
Liu, Y. et al. Identification and characterization of the biosynthetic pathway of the sulfonolipid capnine. Biochemistry 61, 2861–2869 (2022).
pubmed: 35414181
doi: 10.1021/acs.biochem.2c00102
Vences‐Guzmán, M. Á. et al. Identification of the Flavobacterium johnsoniae cysteate‐fatty acyl transferase required for capnine synthesis and for efficient gliding motility. Environ. Microbiol. 23, 2448–2460 (2021).
pubmed: 33626217
doi: 10.1111/1462-2920.15445
Radka, C. D., Miller, D. J., Frank, M. W. & Rock, C. O. Biochemical characterization of the first step in sulfonolipid biosynthesis in Alistipes finegoldii. J. Biol. Chem. 298, 1–13 (2022).
doi: 10.1016/j.jbc.2022.102195
Radka, C. D., Frank, M. W., Rock, C. O. & Yao, J. Fatty acid activation and utilization by Alistipes finegoldii, a representative Bacteroidetes resident of the human gut microbiome. Mol. Microbiol. 113, 807–825 (2020).
pubmed: 31876062
doi: 10.1111/mmi.14445
Kamiyama, T. et al. Sulfobacins A and B, novel von Willebrand factor receptor antagonists: I. Production, isolation, characterization and biological activities. J. Antibiotics 48, 924–928 (1995).
doi: 10.7164/antibiotics.48.924
Schirmer, M. et al. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat. Microbiol. 3, 337–346 (2018).
pubmed: 29311644
doi: 10.1038/s41564-017-0089-z
Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
pubmed: 30531976
doi: 10.1038/s41564-018-0306-4
Tsugawa, H. et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 38, 1159–1163 (2020).
pubmed: 32541957
doi: 10.1038/s41587-020-0531-2
Guo, J., Shen, S., Xing, S., Yu, H. & Huan, T. ISFrag: De novo recognition of in-source fragments for liquid chromatography–mass spectrometry data. Anal. Chem. 93, 10243–10250 (2021).
pubmed: 34270210
doi: 10.1021/acs.analchem.1c01644
Folz, J. et al. Human metabolome variation along the upper intestinal tract. Nat. Metab. 5, 777–788 (2023).
pubmed: 37165176
doi: 10.1038/s42255-023-00777-z
Witting, M. & Böcker, S. Current status of retention time prediction in metabolite identification. J. Sep. Sci. 43, 1746–1754 (2020).
pubmed: 32144942
doi: 10.1002/jssc.202000060
García, C. A., Gil-de-la-Fuente, A., Barbas, C. & Otero, A. Probabilistic metabolite annotation using retention time prediction and meta-learned projections. J. Cheminformatics 14, 33 (2022).
doi: 10.1186/s13321-022-00613-8
Sun, L. et al. A simple method for HPLC retention time prediction: linear calibration using two reference substances. Chin. Med. 12, 16 (2017).
pubmed: 28642805
doi: 10.1186/s13020-017-0137-x
Wang, Y. et al. A simple method for peak alignment using relative retention time related to an inherent peak in liquid chromatography-mass spectrometry-based metabolomics. J. Chromatographic Sci. 57, 9–16 (2019).
doi: 10.1093/chromsci/bmy074
Hale, L. P., Gottfried, M. R. & Swidsinski, A. Piroxicam treatment of IL-10-deficient mice enhances colonic epithelial apoptosis and mucosal exposure to intestinal bacteria. Inflamm. Bowel Dis. 11, 1060–1069 (2005).
pubmed: 16306768
doi: 10.1097/01.MIB.0000187582.90423.bc
Berg, D. J. et al. Rapid development of colitis in NSAID-treated IL-10-deficient mice. Gastroenterology 123, 1527–1542 (2002).
pubmed: 12404228
doi: 10.1053/gast.2002.1231527
Cario, E. Toll-like receptors in inflammatory bowel diseases: a decade later. Inflamm. Bowel Dis. 16, 1583–1597 (2010).
pubmed: 20803699
doi: 10.1002/ibd.21282
Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).
pubmed: 32839597
doi: 10.1038/s41592-020-0933-6
Kawasaki, T. & Kawai, T. Toll-like receptor signaling pathways. Front. Immunol. 5, 1–8 (2014).
doi: 10.3389/fimmu.2014.00461
Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).
pubmed: 20404851
doi: 10.1038/ni.1863
Maeda, J. et al. Inhibitory effects of sulfobacin B on DNA polymerase and inflammation. Int. J. Mol. Med. 26, 521–527 (2010).
Shimazu, R. et al. MD-2, a molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4. J. Exp. Med. 189, 1777–1782 (1999).
pubmed: 10359581
pmcid: 2193086
doi: 10.1084/jem.189.11.1777
Miyake, K. Roles for accessory molecules in microbial recognition by Toll-like receptors. J. Endotoxin Res. 12, 195–204 (2006).
pubmed: 16953972
Park, B. S. et al. The structural basis of lipopolysaccharide recognition by the TLR4–MD-2 complex. Nature 458, 1191–1195 (2009).
pubmed: 19252480
doi: 10.1038/nature07830
Su, L. et al. Sulfatides are endogenous ligands for the TLR4–MD-2 complex. Proc. Natl Acad. Sci. 118, 1–12 (2021).
doi: 10.1073/pnas.2105316118
Luk, J. M., Kumar, A., Tsang, R. & Staunton, D. Biotinylated lipopolysaccharide binds to endotoxin receptor in endothelial and monocytic cells. Anal. Biochem. 232, 217–224 (1995).
pubmed: 8747478
doi: 10.1006/abio.1995.0010
Mosser, D. M. & Edwards, J. P. Exploring the full spectrum of macrophage activation. Nat. Rev. Immunol. 8, 958–969 (2008).
pubmed: 19029990
doi: 10.1038/nri2448
Zhang, Y. et al. ECM1 is an essential factor for the determination of M1 macrophage polarization in IBD in response to LPS stimulation. Proc. Natl Acad. Sci. USA 117, 3083–3092 (2020).
pubmed: 31980528
doi: 10.1073/pnas.1912774117
Zhang, X. & Mosser, D. Macrophage activation by endogenous danger signals. J. Pathol. 214, 161–178 (2008).
pubmed: 18161744
doi: 10.1002/path.2284
Parker, B. J., Wearsch, P. A., Veloo, A. C. M. & Rodriguez-Palacios, A. The genus Alistipes: Gut bacteria with emerging implications to inflammation, cancer, and mental health. Front. Immunol. 11, 906 (2020).
pubmed: 32582143
doi: 10.3389/fimmu.2020.00906
Chang, P. V., Hao, L., Offermanns, S. & Medzhitov, R. The microbial metabolite butyrate regulates intestinal macrophage function via histone deacetylase inhibition. Proc. Natl Acad. Sci. USA 111, 2247–2252 (2014).
pubmed: 24390544
doi: 10.1073/pnas.1322269111
Trapecar, M. et al. Gut-liver physiomimetics reveal paradoxical modulation of IBD-related inflammation by short-chain fatty acids. Cell Syst. 10, 223–239.e9 (2020).
pubmed: 32191873
doi: 10.1016/j.cels.2020.02.008
Bhattarai, Y. et al. Bacterially derived tryptamine increases mucus release by activating a host receptor in a mouse model of inflammatory bowel disease. iScience 23, 101798 (2020).
pubmed: 33299969
doi: 10.1016/j.isci.2020.101798
Dodd, D. et al. A gut bacterial pathway metabolizes aromatic amino acids into nine circulating metabolites. Nature 551, 648–652 (2017).
pubmed: 29168502
pmcid: 5850949
doi: 10.1038/nature24661
Paik, D. et al. Human gut bacteria produce Τ
pubmed: 35296854
doi: 10.1038/s41586-022-04480-z
Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021).
pubmed: 34325466
doi: 10.1038/s41586-021-03832-5
Li, W. et al. A bacterial bile acid metabolite modulates Treg activity through the nuclear hormone receptor NR4A1. Cell Host Microbe 29, 1366–1377.e9 (2021).
pubmed: 34416161
pmcid: 9064000
doi: 10.1016/j.chom.2021.07.013
Rakoff-Nahoum, S. & Medzhitov, R. Toll-like receptors and cancer. Nat. Rev. Cancer 9, 57–63 (2009).
pubmed: 19052556
doi: 10.1038/nrc2541
Moreira Lopes, T. C., Mosser, D. M. & Gonçalves, R. Macrophage polarization in intestinal inflammation and gut homeostasis. Inflamm. Res. 69, 1163–1172 (2020).
pubmed: 32886145
doi: 10.1007/s00011-020-01398-y
Bhattarai, Y., Muniz Pedrogo, D. A. & Kashyap, P. C. Irritable bowel syndrome: a gut microbiota-related disorder? Am. J. Physiol.-Gastrointest. Liver Physiol. 312, G52–G62 (2016).
pubmed: 27881403
pmcid: 5283907
doi: 10.1152/ajpgi.00338.2016
Wang, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).
pubmed: 23023125
doi: 10.1038/nature11450
Thomann, A. K. et al. Depression and fatigue in active IBD from a microbiome perspective-a Bayesian approach to faecal metagenomics. BMC Med. 20, 366 (2022).
pubmed: 36244970
doi: 10.1186/s12916-022-02550-7
Mars, R. A. T. et al. Longitudinal multi-omics reveals subset-specific mechanisms underlying irritable bowel syndrome. Cell 182, 1460–1473.e17 (2020).
pubmed: 32916129
doi: 10.1016/j.cell.2020.08.007
Mukherjee, S. et al. Twenty-five years of Genomes OnLine Database (GOLD): data updates and new features in v.9. Nucleic Acids Res. 51, D957–D963 (2023).
pubmed: 36318257
doi: 10.1093/nar/gkac974
Blin, K. et al. antiSMASH 6.0: improving cluster detection and comparison capabilities. Nucleic Acids Res. 49, W29–W35 (2021).
pubmed: 33978755
doi: 10.1093/nar/gkab335
Blin, K. et al. The antiSMASH database version 2: a comprehensive resource on secondary metabolite biosynthetic gene clusters. Nucleic Acids Res. 47, D625–D630 (2019).
pubmed: 30395294
doi: 10.1093/nar/gky1060
Terlouw, B. R. et al. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters. Nucleic Acids Res. 51, D603–D610 (2023).
pubmed: 36399496
doi: 10.1093/nar/gkac1049
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).
pubmed: 33828273
pmcid: 8026399
doi: 10.1038/s41592-021-01101-x
Mirdita, M., Steinegger, M., Breitwieser, F., Söding, J. & Levy Karin, E. Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics 37, 3029–3031 (2021).
pubmed: 33734313
pmcid: 8479651
doi: 10.1093/bioinformatics/btab184
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics 38, 5315–5316 (2022).
pubmed: 36218463
pmcid: 9710552
doi: 10.1093/bioinformatics/btac672
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
pubmed: 33885785
doi: 10.1093/nar/gkab301
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
R. Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2021).
Oksanen, J. et al. Vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan (2020).
Van Rossum, G. & Drake, F. L. Python 3 Reference Manual. (CreateSpace, Scotts Valley, CA, 2009).
McKinney, W. Data structures for statistical computing in python. in Proceedings of the 9th Python in Science Conference 51–56 https://doi.org/10.25080/Majora-92bf1922-00a (2010).
Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B (Methodol.) 57, 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x
Erben, U. et al. A guide to histomorphological evaluation of intestinal inflammation in mouse models. Int J. Clin. Exp. Pathol. 7, 4557–4576 (2014).
pubmed: 25197329
pmcid: 4152019
Ellermann, M. et al. Endocannabinoids Inhibit the Induction of Virulence in Enteric Pathogens. Cell 183, 650–665.e15 (2020).
pubmed: 33031742
pmcid: 7606741
doi: 10.1016/j.cell.2020.09.022
Schmid, R. et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat. Biotechnol. 41, 447–449 (2023).
pubmed: 36859716
pmcid: 10496610
doi: 10.1038/s41587-023-01690-2
Nothias, L.-F. et al. Bioactivity-based molecular networking for the discovery of drug leads in natural product bioassay-guided fractionation. J. Nat. Prod. 81, 758–767 (2018).
pubmed: 29498278
doi: 10.1021/acs.jnatprod.7b00737
Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658
doi: 10.1101/gr.1239303
Morris, G. M. et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput Chem. 30, 2785–2791 (2009).
pubmed: 19399780
pmcid: 2760638
doi: 10.1002/jcc.21256
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Computational Chem. 31, 455–461 (2010).
doi: 10.1002/jcc.21334
Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 61, 3891–3898 (2021).
pubmed: 34278794
doi: 10.1021/acs.jcim.1c00203
Schrödinger, L. L. C. The PyMOL molecular graphics system, version 2.0. (2015).
Li, J. MassIVE MSV000091884 - GNPS - Bioactive molecular networking for sulfonolipids - MZmine Processing for FBMN: This dataset contains the raw data used for MZmine processing and Feature-based molecular networking of semi-purified fractions obtained from A. timonensis used for bioactive molecular networking to reveal contribution of sulfonolipids to observed biological activity. MassIVE https://doi.org/10.25345/C5028PP9T (2023).
doi: 10.25345/C5028PP9T
Zhang, J. ZHANGJianArya/SoL: initial release for citation. Zenodo https://doi.org/10.5281/ZENODO.13896673 (2024).
doi: 10.5281/ZENODO.13896673