Causal integration of multi‐omics data with prior knowledge to generate mechanistic hypotheses

Abstract

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

“…We also highlight here how we can construct a cellular signaling network of AT2 cells to elucidate critical factors involved in the state transition from AT2 to AT1 cells. By integration of a prior knowledge network (PKN) with omics, a data condition-specific signaling network can be modelled [ 130 , 131 ], and by analysis of such networks, testable hypotheses can be generated [ 132 ]. The OmniPath database provides the curated global human inter- and intracellular signaling network [ 133 ].…”

A Convergent Functional Genomics Analysis to Identify Biological Regulators Mediating Effects of Creatine Supplementation

“…Future studies on identifying biological regulators of CrM supplementation on health and disease include: (i) the changes in miRNAs content (and other regulator non-coding RNAs); for instance, the use of a small-interfering RNA against Txnip resulted in increased Cr uptake [ 72 ], meaning further work will contribute to elucidating the mechanisms of the Txnip–CRT interaction and its potential therapeutic use as a next-generation medicine [ 194 ]; (ii) the pharmacochaperones (e.g., 4-phenylbutyrate) and their safety and efficacy to treat pathologies associated with the Cr deficiency syndrome; (iii) the implementation of a systems biology approach as a necessary and unavoidable process to study other metabolic networks of high complexity, such as the Cr metabolism; (iv) the integration of (epi)genomics, transcriptomics, phosphoproteomics, and metabolomics analyses (multi-omic analysis). In this sense, a very recent tool called Causal Oriented Search of Multi-Omic Space (COSMOS) [ 195 ] was developed to extract mechanistic insights in a more consistent and robust manner. This opens up an exciting field of research with multiple applications in several human conditions.…”

Assembling Disease Networks From Causal Interaction Resources

“…Boolean models provide a simple yet powerful qualitative approach to describe how a system responds to contextual changes. The problem of assembling and contextualizing predictive Boolean models from prior knowledge and/or experimental data has been discussed and is further reviewed in section Conclusions and Perspectives (Vinayagam et al, 2011;Wang et al, 2012;Lages et al, 2018;Aghamiri et al, 2020;Dugourd et al, 2021).…”