| Autor: |
Newman NK; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Macovsky M; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Rodrigues RR; Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.; Microbiome and Genetics Core, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., Bruce AM; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Pederson JW; Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR., Patil SS; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Padiadpu J; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Dzutsev AK; Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., Shulzhenko N; Carlson College of Veterinary Medicine, Oregon State University, Corvallis, OR., Trinchieri G; Cancer Immunobiology Section, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., Brown K; College of Pharmacy, Oregon State University, Corvallis, OR, USA., Morgun A; College of Pharmacy, Oregon State University, Corvallis, OR, USA. |
| Abstrakt: |
Technological advances have generated tremendous amounts of high-throughput omics data. Integrating data from multiple cohorts and diverse omics types from new and previously published studies can offer a holistic view of a biological system and aid in deciphering its critical players and key mechanisms. In this protocol, we describe how to use Transkingdom Network Analysis (TkNA), a unique causal-inference analytical framework that can perform meta-analysis of cohorts and detect master regulators among measured parameters that govern pathological or physiological responses of host-microbiota (or any multi-omic data) interactions in a particular condition or disease. TkNA first reconstructs the network that represents a statistical model capturing the complex relationships between the different omics of the biological system. Here, it selects differential features and their per-group correlations by identifying robust and reproducible patterns of fold change direction and sign of correlation across several cohorts. Next, a causality-sensitive metric, statistical thresholds, and a set of topological criteria are used to select the final edges that form the transkingdom network. The second part of the analysis involves interrogating the network. Using the network's local and global topology metrics, it detects nodes that are responsible for control of given subnetwork or control of communication between kingdoms and/or subnetworks. The underlying basis of the TkNA approach involves fundamental principles including laws of causality, graph theory and information theory. Hence, TkNA can be used for causal inference via network analysis of any host and/or microbiota multi-omics data. This quick and easy-to-run protocol requires very basic familiarity with the Unix command-line environment. |
