1. Trams-omics for pharmacology

There are few approved chemical compound drugs  whose pharmacological effects in the targeted tissues are fully understood as a mechanistic multi-layered network. We apply the trans-omic approach to understand the drug actions as a multi-layered network. Using such a drug-responding network as a 'map', we further perform a proof-of-concept study  to rationally find partner drugs that interact with the anchor drug for the disease of interest in a pharmacodynamical manner (cooperate or antagonize other drugs at the same site of action).

Figure 1 | Characterizing drug action mechanism by trans-omic analysis.

Top: Preparation of drug-treated biological samples from target tissues.

Middle: Comprehensive measurements for time-series data acquisition from distinct omic layers.

Bottom: Reconstruction of the drug responsive multi-layered network for elucidating the entire action mechanism of the drug.

2. Immunometabolic reprogramming

Switching of intracellular metabolic pathways has been commonly found behind broad biological functions such as immunity and energy homeostasis. We focus on these phenomena, recently termed as 'metabolic reprogramming', and study its mechanism in trans-omic manners. We reconstruct the whole picture of multi-layered networks that realize metabolic reprogramming of immune cells in collaboration with experts in immunology. 

Figure 2 | A trans-omics analysis of metabolic reprogramming in immune cells.

Top : Immune cells that switch metabolic pathways in response to environmental cues.

Middle : Comprehensive measurements for time-series data acquisition from distinct omic layers.

Bottom : Reconstruction of multi-layered biochemical networks underlying immunometabolic reprogramming.

3. Development of technologies for next-generation trans-omics

We develop next-generation trans-omics technologies to integrate omic layers changing in different time scales for example comparatively fast omic layers (metabolome, metabolic flux, and phosphoproteome) and slower layers (epigenome, transcriptome, and expressing proteome) (Fig. 3). Furthermore, in collaboration with other laboratories, we incorporate large-scale data such as lipidome that have not been utilized in current trans-omics studies (Fig. 4).  It is expected that incorporation of lipidome might allow trans-omic approaches to be applied to the analysis of a broader range of diseases and biological phenomena that cannot be handled with current trans-omics technologies.

Figure 3 | Integration of lipidome data with other omic data.

Currently, our trans-omics technologies cover integration of two ‘fast’ omic layers, metabolome and phosphoproteome.
We develop next generation trans-omics technologies to integrate also ‘slow’ gene expression-related layers so that one could apply trans-omic approaches to broader biological phenomena.

Figure 4 | Integration of lipidome data with other omic data.

Extending trans-omics, which is currently based on phosphorproteome and hydrophilic metabolites (left half), by integrating with lipidome data that constitute the signaling and metabolism layers (right half).

Figure 5 | Omic layers in rate.

Each reaction rate (terms represented by ‘v’) is a function of amounts of molecules that belong to same or different omic layers. Causalities across omic layers are reconstructed based on reaction kinetics.