Drug Search and Design Considering Cell Specificity of Chemically Induced Gene Expression Profiles for Disease-Associated Tissues.

The use of omics data, including gene expression profiles, has recently gained increasing attention in drug discovery. Omics-based drug searches and designs are often based on the correlations between chemically induced and disease-induced gene expression profiles; however, the cell specificity has not been considered. In this study, we designed a novel computational method for drug search and design using cell-specific correlations between drugs and diseases.

Therapeutic target prediction for orphan diseases integrating genome-wide and transcriptome-wide association studies.

Therapeutic target identification is challenging in drug discovery, particularly for rare and orphan diseases. Here, we propose a disease signature, TRESOR, which characterizes the functional mechanisms of each disease through genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data, and develop machine learning methods for predicting inhibitory and activatory therapeutic targets for various diseases from target perturbation signatures (i.e.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology.

Background: Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.

Objective: We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).

Methods: We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data.

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data.

De novo drug design based on patient gene expression profiles via deep learning.

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules.

Regulome-based characterization of drug activity across the human diseasome.

Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery.

Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity.

Small compound-based direct cell conversion with combinatorial optimization of pathway regulations.

Motivation: Direct cell conversion, direct reprogramming (DR), is an innovative technology that directly converts source cells to target cells without bypassing induced pluripotent stem cells. The use of small compounds (e.g.

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures.

Motivation: A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results: In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases.

TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach.

Motivation: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells).

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses.

Background: Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical-disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.

Results: With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses.

The novel driver gene ASAP2 is a potential druggable target in pancreatic cancer.

Targeting mutated oncogenes is an effective approach for treating cancer. The 4 main driver genes of pancreatic ductal adenocarcinoma (PDAC) are KRAS, TP53, CDKN2A, and SMAD4, collectively called the "big 4" of PDAC, however they remain challenging therapeutic targets. In this study, ArfGAP with SH3 domain, ankyrin repeat and PH domain 2 (ASAP2), one of the ArfGAP family, was identified as a novel driver gene in PDAC.

Network-based characterization of disease-disease relationships in terms of drugs and therapeutic targets.

Motivation: Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized.

Omics-based Identification of Glycan Structures as Biomarkers for a Variety of Diseases.

Glycans play important roles in cell communication, protein interaction, and immunity, and structural changes in glycans are associated with the regulation of a range of biological pathways involved in disease. However, our understanding of the detailed relationships between specific diseases and glycans is very limited. In this study, we proposed an omics-based method to investigate the correlations between glycans and a wide range of human diseases.

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm.

Motivation: Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.

Results: Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases.

The Use of Large-Scale Chemically-Induced Transcriptome Data Acquired from LINCS to Study Small Molecules.

Identification of the modes of action of bioactive compounds is an important issue in chemical systems biology. In this chapter we review a recently developed data-driven approach using large-scale chemically induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures to elucidate the modes of action of bioactive compounds. First, we present a method for pathway enrichment analyses of regulated genes to reveal biological pathways activated by compounds.

KampoDB, database of predicted targets and functional annotations of natural medicines.

Natural medicines (i.e., herbal medicines, traditional formulas) are useful for treatment of multifactorial and chronic diseases.

Evaluation of an S-system root-finding method for estimating parameters in a metabolic reaction model.

In a mathematical model, estimation of parameters from time-series data of metabolic concentrations in cells is a challenging task. However, it seems that a promising approach for such estimation has not yet been established. Biochemical Systems Theory (BST) is a powerful methodology to construct a power-law type model for a given metabolic reaction system and to then characterize it efficiently.

Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures.

Genome-wide identification of all target proteins of drug candidate compounds is a challenging issue in drug discovery. Moreover, emerging phenotypic effects, including therapeutic and adverse effects, are heavily dependent on the inhibition or activation of target proteins. Here we propose a novel computational method for predicting inhibitory and activatory targets of drug candidate compounds.

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines.

PENDISC: a simple method for constructing a mathematical model from time-series data of metabolite concentrations.

The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC (Parameter Estimation in a N on- DImensionalized S-system with Constraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system.

Estimation of kinetic parameters in an S-system equation model for a metabolic reaction system using the Newton-Raphson method.

Metabolic reaction systems can be modeled easily in terms of S-system type equations if their metabolic maps are available. This study therefore proposes a method for estimating parameters in decoupled S-system equations on the basis of the Newton-Raphson method and elucidates the performance of this estimation method. Parameter estimation from the time-course data of metabolite concentrations reveals that the parameters estimated are highly accurate, indicating that the estimation algorithm has been constructed correctly.