Morgun, A. et al. Uncovering effects of antibiotics on the host and microbiota using transkingdom gene networks. Gut 64, 1732–1743 (2015).

Article  CAS  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Rodrigues, R. R. et al. Transkingdom interactions between Lactobacilli and hepatic mitochondria attenuate western diet-induced diabetes. Nat. Commun. 12, 101 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti–PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lam, K. C. et al. Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment. Cell 184, 5338–5356.e21 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Spencer, C. N. et al. Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response. Science 374, 1632–1640 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lam, K. C. et al. Transkingdom network reveals bacterial players associated with cervical cancer gene expression program. PeerJ 6, e5590 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Yambartsev, A. et al. Unexpected links reflect the noise in networks. Biol. Direct 11, 1–12 (2016).

Article  Google Scholar 

Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

Article  Google Scholar 

Rosvall, M., Axelsson, D. & Bergstrom, C. T. The map equation. Eur. Phys. J. Spec. Top. 178, 13–23 (2009).

Article  Google Scholar 

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).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Newman, M. E. Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002).

Article  CAS  PubMed  Google Scholar 

Borgatti, S. P. The Key Player Problem. Available at https://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).

Article  PubMed  PubMed Central  Google Scholar 

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).

Article  CAS  PubMed  Google Scholar 

Li, Z. et al. Microbiota and adipocyte mitochondrial damage in type 2 diabetes are linked by Mmp12+ macrophages. J. Exp. Med. 219, e20220017 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

Greer, R. L. et al. Akkermansia muciniphila mediates negative effects of IFNgamma on glucose metabolism. Nat. Commun. 7, 13329 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Balduzzi, S., Rücker, G. & Schwarzer, G. How to perform a meta-analysis with R: a practical tutorial. BMJ Ment. Health 22, 153–160 (2019).

Google Scholar 

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).

Article  MathSciNet  PubMed  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Argelaguet, R. et al. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 1–17 (2020).

Article  Google Scholar 

Argelaguet, R. et al. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets. Mol. Syst. Biol. 14, e8124 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Mo, Q. & Shen, R. iClusterPlus: integrative clustering of multiple genomic data sets. R package version 1.38.0 https://bioconductor.org/packages/iClusterPlus (2023).

Mo, Q. et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl Acad. Sci. USA 110, 4245–4250 (2013).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

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).

Article  PubMed  Google Scholar 

Liu, X. et al. Mendelian randomization analyses support causal relationships between blood metabolites and the gut microbiome. Nat. Genet. 54, 52–61 (2022).

Article  CAS  PubMed  Google Scholar 

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).

Article  PubMed  Google Scholar 

Cheung, M. W.-L. A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychol. Rev. 29, 387–396 (2019).

Article  PubMed  PubMed Central  Google Scholar 

VanderWeele, T. J. Mediation analysis: a practitioner’s guide. Annu. Rev. Public Health 37, 17–32 (2016).

Article  PubMed  Google Scholar 

Baiocchi, M., Cheng, J. & Small, D. S. Instrumental variable methods for causal inference. Stat. Med. 33, 2297–2340 (2014).

Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

Skinner, J. et al. Construct and compare gene coexpression networks with DAPfinder and DAPview. BMC Bioinforma. 12, 1–8 (2011).

Article  Google Scholar 

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).

Article  PubMed  PubMed Central  Google Scholar 

Braun, R., Cope, L. & Parmigiani, G. Identifying differential correlation in gene/pathway combinations. BMC Bioinforma. 9, 1–17 (2008).