Learning representations of microbe–metabolite interactions

Jansson, J. K. & Baker, E. S. A multi-omic future for microbiome studies. Nat. Microbiol. 1, 645 (2016).

Noecker, C. et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. MSystems 1, e00013–e00015 (2016).

Mallick, H. et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10, 3136 (2019).

Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).

Meng, C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Brief. Bioinform. 17, 628–641 (2016).

Gall, G. Le et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).

Rohart, F. et al. mixomics: an r package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).

Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333 (2014).

Argelaguet, R. et al. Multi-omics factor analysis—a framework for unsupervised integration of multi-omics data sets. Mol. Sys. Biol. 14, e8124 (2018).

Ter Braak, C. J. F. & Verdonschot, P. F. M. Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat. Sci. 57, 255–289 (1995).

Witten, D. M., Tibshirani, R. & Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009).

Bodein, A., Chapleur, O., Droit, A. & Lê Cao K. A. A generic multivariate framework for the integration of microbiome longitudinal studies with other data types. Preprint at bioRxiv https://doi.org/10.1101/585802 (2019).

Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).

Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).

Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

. & Gloor, G. B. et al. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

Tang, K., Page, J. S. & Smith, R. D. Charge competition and the linear dynamic range of detection in electrospray ionization mass spectrometry. J. Am. Soc. Mass Spectrom. 15, 1416–1423 (2004).

King, R., Bonfiglio, R., Fernandez-Metzler, C., Miller-Stein, C. & Olah, T. Mechanistic investigation of ionization suppression in electrospray ionization. J. Am. Soc. Mass Spectrom. 11, 942–950 (2000).

Matuszewski, B. K., Constanzer, M. L. & Chavez-Eng, C. M. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC–MS/MS. Anal. Chem. 75, 3019–3030 (2003).

Kalivodová, A. et al. Pls-da for compositional data with application to metabolomics. J. Chemom. 29, 21–28 (2015).

Jansson, J. K. & Baker, E. S. A multi-omic future for microbiome studies. Nat. Microbiol. 1, 16049 (2016).

Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S. & Bähler, J. Proportionality: a valid alternative to correlation for relative data. PLoS Comput. Biol. 11, e1004075 (2015).

Tipton, L. et al. Fungi stabilize connectivity in the lung and skin microbial ecosystems. Microbiome 6, 12 (2018).

Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).

Abadi, M. et al. Tensorflow: A system for large-scale machine learning. In Proc 12th Symposium on Operating Systems Design and Implementation 265–283 (USENIX Association, 2016).

Quinn, R. A. et al. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci. Adv. 4, eaau1908 (2018).

Shalapour, S. et al. Inflammation-induced IgA+ cells dismantle anti-liver cancer immunity. Nature 551, 340–345 (2017).

Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

Swenson, T. L., Karaoz, U., Swenson, J. M., Bowen, B. P. & Northen, T. R. Linking soil biology and chemistry in biological soil crust using isolate exometabolomics. Nat. Commun. 9, 19 (2018).

Baran, R. et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat. Commun. 6, 8289 (2015).

Quinn, R. A. et al. A Winogradsky-based culture system shows an association between microbial fermentation and cystic fibrosis exacerbation. ISME J. 9, 1024–1038 (2015).

Moree, W. J. et al. Interkingdom metabolic transformations captured by microbial imaging mass spectrometry. Proc. Natl Acad. Sci. USA 109, 13811–13816 (2012).

Wang, M. et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotechnol. 34, 828–837 (2016).

Maier, R. M. & Soberon-Chavez, G. Pseudomonas aeruginosa rhamnolipids: biosynthesis and potential applications. Appl. Microbiol. Biotechnol. 54, 625–633 (2000).

Wood, T. L. et al. Rhamnolipids from Pseudomonas aeruginosa disperse the biofilms of sulfate-reducing bacteria. NPJ Biofilms Microbiomes 4, 22 (2018).

Allen, L. et al. Pyocyanin production by Pseudomonas aeruginosa induces neutrophil apoptosis and impairs neutrophil-mediated host defenses in vivo. J. Immunol. 174, 3643–3649 (2005).

Quinn, R. A. et al. Chemical impacts of the microbiome across scales reveal novel conjugated bile acids. Preprint at bioRxiv https://doi.org/10.1101/654756 (2019).

Paczosa, M. K. & Mecsas, J. Klebsiella pneumoniae: going on the offense with a strong defense. Microbiol. Mol. Biol. Rev. 80, 629–661 (2016).

Tiratterra, E. et al. Role of bile acids in inflammatory bowel disease. Ann. Gastroenterol. 31, 266 (2018).

Hofmann, A. F. & Eckmann, L. How bile acids confer gut mucosal protection against bacteria. Proc. Natl Acad. Sci. USA 103, 4333–4334 (2006).

Begley, M., Gahan, C. G. M. & Hill, C. The interaction between bacteria and bile. FEMS Microbiol. Rev. 29, 625–651 (2005).

Okada, Y. et al. Propionibacterium freudenreichii component 1,4-dihydroxy-2-naphthoic acid (DHNA) attenuates dextran sodium sulphate induced colitis by modulation of bacterial flora and lymphocyte homing. Gut 55, 681–688 (2006).

Foligne, B. et al. Immunomodulation properties of multi-species fermented milks. Food Microbiol. 53, 60–69 (2016).

Ple, C. et al. Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: reverse engineering development of an anti-inflammatory cheese. Mol. Nutr. Food Res. 60, 935–948 (2016).

Colliou, N. et al. Commensal Propionibacterium strain uf1 mitigates intestinal inflammation via th17 cell regulation. J. Clin. Invest. 127, 3970–3986 (2017).

Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. Emperor: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).

Nasrabadi, N. M. Pattern recognition and machine learning. J. Electron. Imaging 16, 049901 (2007).

Pawlowsky-Glahn, V., Egozcue, J. J. & Tolosana-Delgado, R. Modeling and Analysis of Compositional Data. (John Wiley & Sons, 2015).

Tomas, M., Ilya, S., Kai, C., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (eds Burges, C. J. C. et al.) 3111–3119 (NIPS, 2013).

Koren, Y., Bell, R. & Volinsky, C. Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009).

D. P. Kingma and J. Ba. Adam: a method for stochastic optimization. Preprint at arXiv https://arxiv.org/abs/1412.6980 (2014).

Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).

Sankaran, K. & Holmes, S. P. Latent variable modeling for the microbiome. Biostatistics 20, 599–614 (2019).

Aitchison, J. & Greenacre, M. Biplots of compositional data. J. R. Stat. Soc. Ser. C Appl. Stat. 51, 375–392 (2002).

Aitchison, J. & Ng, K. W. Conditional compositional biplots: theory and application. DUGiDocs https://dugi-doc.udg.edu/handle/10256/657 (2005).

Martín-Fernández, J. A., Pawlowsky-Glahn, V., Egozcue, J. J. & Tolosona-Delgado, R. Advances in principal balances for compositional data. Math. Geosci. 50, 273–298 (2018).

Bolyen, E. et al. Reproducible, interactive, scalable, and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).