Analysis of host factor networks during hepatitis B virus infection in primary human hepatocytes.

Background: Chronic hepatitis B virus (HBV) infection affects around 250 million people worldwide, causing approximately 887,000 deaths annually, primarily owing to cirrhosis and hepatocellular carcinoma (HCC). The current approved treatments for chronic HBV infection, such as interferon and nucleos(t)ide analogs, have certain limitations as they cannot completely eradicate covalently closed circular DNA (cccDNA). Considering that HBV replication relies on host transcription factors, focusing on host factors in the HBV genome may provide insights into new therapeutic targets against HBV.

AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic.

Intrahepatic IgA complex induces polarization of cancer-associated fibroblasts to matrix phenotypes in the tumor microenvironment of HCC.

Background And Aims: Cancer-associated fibroblasts (CAFs) play key roles in the tumor microenvironment. IgA contributes to inflammation and dismantling antitumor immunity in the human liver. In this study, we aimed to elucidate the effects of the IgA complex on CAFs in Pil Soo Sung the tumor microenvironment of HCC.

Investigation of the General Molecular Mechanisms of Gallic Acid via Analyses of Its Transcriptome Profile.

Gallic acid (GA), a phenolic compound naturally found in many plants, exhibits potential preventive and therapeutic roles. However, the underlying molecular mechanisms of its diverse biological activities remain unclear. Here, we investigated possible mechanisms of GA function through a transcriptome-based analysis using LINCS L1000, a publicly available data resource.

Genome-wide identification of overexpression and downregulation gene targets based on the sum of covariances of the outgoing reaction fluxes.

In metabolic engineering, predicting gene overexpression targets remains challenging because both endogenous and heterologous genes in a large metabolic space can be candidates, in contrast to gene knockout targets that are confined to endogenous genes. We report the development of iBridge that identifies positive and negative metabolites exerting positive and negative impacts on product formation, respectively, based on the sum of covariances of their outgoing (consuming) reaction fluxes for a target chemical. Then, "bridge" reactions converting negative metabolites to positive metabolites are identified as overexpression targets, while the opposites as downregulation targets.

Targeted and high-throughput gene knockdown in diverse bacteria using synthetic sRNAs.

Synthetic sRNAs allow knockdown of target genes at translational level, but have been restricted to a limited number of bacteria. Here, we report the development of a broad-host-range synthetic sRNA (BHR-sRNA) platform employing the RoxS scaffold and the Hfq chaperone from Bacillus subtilis. BHR-sRNA is tested in 16 bacterial species including commensal, probiotic, pathogenic, and industrial bacteria, with >50% of target gene knockdown achieved in 12 bacterial species.

Computational prediction of interactions between Paxlovid and prescription drugs.

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models.

Background: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats.

Identification and New Indication of Melanin-Concentrating Hormone Receptor 1 (MCHR1) Antagonist Derived from Machine Learning and Transcriptome-Based Drug Repositioning Approaches.

Melanin-concentrating hormone receptor 1 (MCHR1) has been a target for appetite suppressants, which are helpful in treating obesity. However, it is challenging to develop an MCHR1 antagonist because its binding site is similar to that of the human Ether-à-go-go-Related Gene (hERG) channel, whose inhibition may cause cardiotoxicity. Most drugs developed as MCHR1 antagonists have failed in clinical development due to cardiotoxicity caused by hERG inhibition.

PredMS: a random forest model for predicting metabolic stability of drug candidates in human liver microsomes.

Motivation: Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach.

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes.

A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis.

Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the initially predicted enzymatic reactions has been needed. Here, we present Deep learning-based Reaction Feasibility Checker (DeepRFC) to classify the feasibility of a given enzymatic reaction with high performance and speed.

LightBBB: computational prediction model of blood-brain-barrier penetration based on LightGBM.

Motivation: Identification of blood-brain barrier (BBB) permeability of a compound is a major challenge in neurotherapeutic drug discovery. Conventional approaches for BBB permeability measurement are expensive, time-consuming and labor-intensive. BBB permeability is associated with diverse chemical properties of compounds.

KR-39038, a Novel GRK5 Inhibitor, Attenuates Cardiac Hypertrophy and Improves Cardiac Function in Heart Failure.

G protein-coupled receptor kinase 5 (GRK5) has been considered as a potential target for the treatment of heart failure as it has been reported to be an important regulator of pathological cardiac hypertrophy. To discover novel scaffolds that selectively inhibit GRK5, we have identified a novel small molecule inhibitor of GRK5, KR-39038 [7-((3-((4-((3-aminopropyl)amino)butyl)amino)propyl) amino)-2-(2-chlorophenyl)-6-fluoroquinazolin-4(3H)-one]. KR-39038 exhibited potent inhibitory activity (IC value=0.

DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity.

Motivation: Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions.

Genomic and metabolic analysis of Komagataeibacter xylinus DSM 2325 producing bacterial cellulose nanofiber.

Bacterial cellulose nanofiber (CNF) is a polymer with a wide range of potential industrial applications. Several Komagataeibacter species, including Komagataeibacter xylinus as a model organism, produce CNF. However, the industrial application of CNF has been hampered by inefficient CNF production, necessitating metabolic engineering for the enhanced CNF production.

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers.

High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner.

Revisiting Statistical Design and Analysis in Scientific Research.

Statistics is essential to design experiments and interpret experimental results. Inappropriate use of the statistical analysis, however, often leads to a wrong conclusion. This concept article revisits basic concepts of statistics and provides a brief guideline of applying the statistical analysis for scientific research from designing experiments to analyzing and presenting the data.

Deep learning improves prediction of drug-drug and drug-food interactions.

Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), with causal mechanisms often unknown. Several computational methods have been developed to better understand drug interactions, especially for DDIs. However, these methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction.

Occurrence, evolution, and functions of DNA phosphorothioate epigenetics in bacteria.

The chemical diversity of physiological DNA modifications has expanded with the identification of phosphorothioate (PT) modification in which the nonbridging oxygen in the sugar-phosphate backbone of DNA is replaced by sulfur. Together with DndFGH as cognate restriction enzymes, DNA PT modification, which is catalyzed by the DndABCDE proteins, functions as a bacterial restriction-modification (R-M) system that protects cells against invading foreign DNA. However, the occurrence of systems across a large number of bacterial genomes and their functions other than R-M are poorly understood.

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism.

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism.

Bacterial cellulose as an example product for sustainable production and consumption.

Life cycle of bacterial cellulose. Sustainable production and consumption of bio-based products are showcased using bacterial cellulose as an example.

Systems approach to characterize the metabolism of liver cancer stem cells expressing CD133.

Liver cancer stem cells (LCSCs) have attracted attention because they cause therapeutic resistance in hepatocellular carcinoma (HCC). Understanding the metabolism of LCSCs can be a key to developing therapeutic strategy, but metabolic characteristics have not yet been studied. Here, we systematically analyzed and compared the global metabolic phenotype between LCSCs and non-LCSCs using transcriptome and metabolome data.

SPL3/4/5 Integrate Developmental Aging and Photoperiodic Signals into the FT-FD Module in Arabidopsis Flowering.

Environmental sensitivity varies across developmental phases in flowering plants. In the juvenile phase, microRNA156 (miR156)-mediated repression of SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE (SPL) transcription factors renders Arabidopsis plants incompetent to floral inductive signals, including long-day (LD) photoperiod. During the vegetative phase transition, which accompanies a reduction of miR156 and a concomitant elevation of its targets, plants acquire reproductive competence such that LD signals promote flowering.