Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions

Nolan K. Newman, Matthew S. Macovsky, Richard R. Rodrigues, Amanda M. Bruce, Jacob W. Pederson, Jyothi Padiadpu, Jigui Shan, Joshua Williams, Sankalp S. Patil, Amiran K. Dzutsev, Natalia Shulzhenko, Giorgio Trinchieri, Kevin Brown, Andrey Morgun

Published: 2024-03-12 DOI: 10.1038/s41596-024-00960-w

Transkingdom Network Analysis

TkNA

causal inference

multi-omics interactions

host-microbiota interactions

AI 解读

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

查看全部

Sections

Figures

References

Supplementary information

Mo, Q. & Shen, R. iClusterPlus: integrative clustering of multiple genomic data sets. R package version 1.38.0 http://bioconductor.org/packages/iClusterPlus (2023).
Morgun, A. et al. Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks. Gut 64, 1732–1743 (2015).
Rodrigues, R. R. et al. Antibiotic-induced alterations in gut microbiota are associated with changes in glucose metabolism in healthy mice. Front. Microbiol. 8, 2306 (2017).
Rodrigues, R. R. et al. Transkingdom interactions between Lactobacilli and hepatic mitochondria attenuate western diet-induced diabetes. Nat. Commun. 12, 101 (2021).
Shulzhenko, N. et al. CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont. Clin. Immunol. 197, 139–153 (2018).
Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).
Lam, K. C. et al. Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment. Cell 184, 5338–5356.e21 (2021).
McCulloch, J. A. et al. Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1. Nat. Med. 28, 545–556 (2022).
Spencer, C. N. et al. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374, 1632–1640 (2021).
Lam, K. C. et al. Transkingdom network reveals bacterial players associated with cervical cancer gene expression program. PeerJ 6, e5590 (2018).
Yambartsev, A. et al. Unexpected links reflect the noise in networks. Biol. Direct 11, 1–12 (2016).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).
Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).
Padiadpu, J. et al. Multi-omic network analysis identified betacellulin as a novel target of omega-3 fatty acid attenuation of western diet-induced nonalcoholic steatohepatitis. EMBO Mol. Med. 15, e18367 (2023).
Newman, M. E. Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002).
Borgatti, S. P. The Key Player Problem. Available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1149843 (2003).
Freeman, L. C. in Social Networks: Critical Concepts in Sociology (ed. Scott, J.) 238–263 (Routledge, 2002).
Dong, X. et al. Reverse enGENEering of regulatory networks from big data: a roadmap for biologists. Bioinform. Biol. Insights 9, 61–74 (2015).
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798–813 (2013).
Li, Z. et al. Microbiota and adipocyte mitochondrial damage in type 2 diabetes are linked by Mmp12+ macrophages. J. Exp. Med. 219, e20220017 (2022).
Shulzhenko, N. et al. Crosstalk between B lymphocytes, microbiota and the intestinal epithelium governs immunity versus metabolism in the gut. Nat. Med. 17, 1585–1593 (2011).
Mine, K. L. et al. Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer. Nat. Commun. 4, 1806 (2013).
Greer, R. L. et al. Akkermansia muciniphila mediates negative effects of IFNgamma on glucose metabolism. Nat. Commun. 7, 13329 (2016).
Kahalehili, H. M. et al. Dietary indole-3-carbinol activates AhR in the gut, alters Th17-microbe interactions, and exacerbates insulitis in NOD mice. Front. Immunol. 11, 606441 (2021).
Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. BMJ Ment. Health 22, 153–160 (2019).
Schwarzer, G., Carpenter, J. R. & Rücker, G. Meta-analysis with R (Springer, 2015).
Sera, F., Armstrong, B., Blangiardo, M. & Gasparrini, A. An extended mixed‐effects framework for meta‐analysis. Stat. Med. 38, 5429–5444 (2019).
Rohart, F., Gautier, B., Singh, A. & Lê Cao, K.-A. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752 (2017).
Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 1–17 (2020).
Argelaguet, R. et al. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
Mo, Q. et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl Acad. Sci. USA 110, 4245–4250 (2013).
Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).
Davey Smith, G. & Ebrahim, S. in Biosocial Surveys (National Academies Press, 2008).
Yang, Q., Lin, S. L., Kwok, M. K., Leung, G. M. & Schooling, C. M. The roles of 27 genera of human gut microbiota in ischemic heart disease, type 2 diabetes mellitus, and their risk factors: a Mendelian randomization study. Am. J. Epidemiol. 187, 1916–1922 (2018).
Liu, X. et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat. Genet. 54, 52–61 (2022).
Wang, C., Hu, J., Blaser, M. J. & Li, H. Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data. Bioinformatics 36, 347–355 (2020).
Cheung, M. W.-L. A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychol. Rev. 29, 387–396 (2019).
VanderWeele, T. J. Mediation analysis: a practitioner’s guide. Annu. Rev. Public Health 37, 17–32 (2016).
Baiocchi, M., Cheng, J. & Small, D. S. Instrumental variable methods for causal inference. Stat. Med. 33, 2297–2340 (2014).
Skinner, J. et al. Construct and compare gene coexpression networks with DAPfinder and DAPview. BMC Bioinforma. 12, 1–8 (2011).
Thomas, L. D., Vyshenska, D., Shulzhenko, N., Yambartsev, A. & Morgun, A. Differentially correlated genes in co-expression networks control phenotype transitions. F1000Res. 5, 2740 (2016).
Braun, R., Cope, L. & Parmigiani, G. Identifying differential correlation in gene/pathway combinations. BMC Bioinforma. 9, 1–17 (2008).
Chunikhina, E. et al. The C-SHIFT algorithm for normalizing covariances. IEEE/ACM Trans. Comput. Biol. Bioinform. 20, 720–730 (2022).
Välikangas, T., Suomi, T. & Elo, L. L. A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief. Bioinform. 19, 1–11 (2018).
Zhang, Y. et al. Improvements in metabolic syndrome by xanthohumol derivatives are linked to altered gut microbiota and bile acid metabolism. Mol. Nutr. Food Res. 64, e1900789 (2020).
Padiadpu, J. et al. Early transcriptome changes associated with western diet induced NASH in Ldlr−/− mice points to activation of hepatic macrophages and an acute phase response. Front. Nutr. 10, 1147602 (2023).
Danelishvili, L. et al. Mycobacterium tuberculosis proteome response to antituberculosis compounds reveals metabolic “escape” pathways that prolong bacterial survival. Antimicrob. Agents Chemother. 61, e00430-17 (2017).
Newman, N. et al. Reducing gut microbiome-driven adipose tissue inflammation alleviates metabolic syndrome. Microbiome 11, 208 (2023).
Bader, G. D. & Hogue, C. W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinforma. 4, 1–27 (2003).
Wang, J. et al. ClusterViz: a cytoscape APP for cluster analysis of biological network. IEEE/ACM Trans. Comput. Biol. Bioinform. 12, 815–822 (2014).
Newman, M. Networks (Oxford University Press, 2018).
Zhao, Y. et al. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository. J. Transl. Med. 19, 1–15 (2021).
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).
Trapnell, C. et al. Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms. Nat. Biotechnol. 28, 511–515 (2010).
Bolstad, B. M., Irizarry, R. A., Åstrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).
Yang, Y. H., Dudoit, S., Luu, P. & Speed, T. P. Normalization for cDNA microarry data. In Microarrays: Optical Technologies and Informatics Vol. 4266, 141–152 (SPIE, 2001).
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, 1–9 (2010).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Sanders, H. L. Marine benthic diversity: a comparative study. Am. Nat. 102, 243–282 (1968).
Joseph, N., Paulson, C., Corrada Bravo, H. & Pop, M. Robust methods for differential abundance analysis in marker gene surveys. Nat. Methods 10, 1200–1202 (2013).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Paulson, J. N., Pop, M. & Bravo, H. C. metagenomeSeq: statistical analysis for sparse high-throughput sequencing. Bioconductor Package 1 http://www.cbcb.umd.edu/software/metagenomeSeq (2013).
Bijlsma, S. et al. Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Anal. Chem. 78, 567–574 (2006).
Sysi-Aho, M., Katajamaa, M., Yetukuri, L. & Orešič, M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinforma. 8, 1–17 (2007).
Warrack, B. M. et al. Normalization strategies for metabonomic analysis of urine samples. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 877, 547–552 (2009).
Saccenti, E. Correlation patterns in experimental data are affected by normalization procedures: consequences for data analysis and network inference. J. Proteome Res. 16, 619–634 (2017).
Dieterle, F., Ross, A., Schlotterbeck, G. & Senn, H. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal. Chem. 78, 4281–4290 (2006).
Wulff, J. E. & Mitchell, M. W. A comparison of various normalization methods for LC/MS metabolomics data. Adv. Biosci. Biotechnol. 9, 339 (2018).
Karpievitch, Y. V. et al. Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition. Bioinformatics 25, 2573–2580 (2009).
Ernest, B., Gooding, J. R., Campagna, S. R., Saxton, A. M. & Voy, B. H. MetabR: an R script for linear model analysis of quantitative metabolomic data. BMC Res. notes 5, 1–10 (2012).
Karpievitch, Y., Stuart, T. & Mohamed, S. ProteoMM: multi-dataset model-based differential expression proteomics analysis platform. R package version 1 http://bioconductor.org/packages/ProteoMM (2023).
Röst, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).
Röst, H. L., Schmitt, U., Aebersold, R. & Malmström, L. pyOpenMS: a Python‐based interface to the OpenMS mass‐spectrometry algorithm library. Proteomics 14, 74–77 (2014).
Callister, S. J. et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5, 277–286 (2006).
Huber, W., Von Heydebreck, A., Sültmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96–S104 (2002).
Graw, S. et al. proteiNorm–a user-friendly tool for normalization and analysis of TMT and label-free protein quantification. ACS Omega 5, 25625–25633 (2020).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Lynn, D. J. et al. InnateDB: facilitating systems‐level analyses of the mammalian innate immune response. Mol. Syst. Biol. 4, 218 (2008).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. 102, 15545–15550 (2005).
Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692 (2022).
Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).
Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions

Supplementary information

Supplementary Information

推荐阅读