ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data.

Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization.

Distant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval.

GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.

Motivation: Asthma is a heterogeneous disease where various subtypes are established and molecular biomarkers of the subtypes are yet to be discovered. Recent availability of multi-omics data paved a way to discover molecular biomarkers for the subtypes. However, multi-omics biomarker discovery is challenging because of the complex interplay between different omics layers.

Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure.

Background: Proteomics and genomics studies have contributed to understanding the pathogenesis of chronic obstructive pulmonary disease (COPD), but previous studies have limitations. Here, using a machine learning (ML) algorithm, we attempted to identify pathways in cultured bronchial epithelial cells of COPD patients that were significantly affected when the cells were exposed to a cigarette smoke extract (CSE).

Methods: Small airway epithelial cells were collected from patients with COPD and those without COPD who underwent bronchoscopy.

Characterization of radiation-resistance mechanism in Spirosoma montaniterrae DY10 in terms of transcriptional regulatory system.

To respond to the external environmental changes for survival, bacteria regulates expression of a number of genes including transcription factors (TFs). To characterize complex biological phenomena, a biological system-level approach is necessary. Here we utilized six computational biology methods to infer regulatory network and to characterize underlying biologically mechanisms relevant to radiation-resistance.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps.

Transplantation of neuron-inducing grafts embedding positively charged gold nanoparticles for the treatment of spinal cord injury.

In this study, we aimed to investigate the recovery after traumatic spinal cord injury (SCI) by inducing cellular differentiation of transplanted neural stem cells (NSCs) into neurons. We dissociated NSCs from the spinal cords of Fisher 344 rat embryos. An injectable gel crosslinked with glycol chitosan and oxidized hyaluronate was used as a vehicle for NSC transplantation.

Lobeglitazone Exerts Anti-Inflammatory Effect in Lipopolysaccharide-Induced Bone-Marrow Derived Macrophages.

The purpose of this study is to elucidate the anti-inflammatory effect of lobeglitazone (LOBE) in lipopolysaccharide (LPS)-induced bone-marrow derived macrophages (BMDMs). We induced nitric oxide (NO) production and pro-inflammatory gene expression through LPS treatment in BMDMs. The changes of NO release and expression of pro-inflammatory mediators by LOBE were assessed via NO quantification assay and a real-time quantitative polymerase chain reaction (RT-qPCR), respectively.

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data.

DRIM: A Web-Based System for Investigating Drug Response at the Molecular Level by Condition-Specific Multi-Omics Data Integration.

Pharmacogenomics is the study of how genes affect a person's response to drugs. Thus, understanding the effect of drug at the molecular level can be helpful in both drug discovery and personalized medicine. Over the years, transcriptome data upon drug treatment has been collected and several databases compiled before drug treatment cancer cell multi-omics data with drug sensitivity ( , AUC) or time-series transcriptomic data after drug treatment.

Impact of mutations in DNA methylation modification genes on genome-wide methylation landscapes and downstream gene activations in pan-cancer.

Background: In cancer, mutations of DNA methylation modification genes have crucial roles for epigenetic modifications genome-wide, which lead to the activation or suppression of important genes including tumor suppressor genes. Mutations on the epigenetic modifiers could affect the enzyme activity, which would result in the difference in genome-wide methylation profiles and, activation of downstream genes. Therefore, we investigated the effect of mutations on DNA methylation modification genes such as DNMT1, DNMT3A, MBD1, MBD4, TET1, TET2 and TET3 through a pan-cancer analysis.

PropaNet: Time-Varying Condition-Specific Transcriptional Network Construction by Network Propagation.

Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data.

Terfenadine metabolism of human cytochrome P450 2J2 containing genetic variations (G312R, P351L and P115L).

The human cytochrome P450 2J2 is involved in several metabolic reactions, including the oxidation of important therapeutics and epoxidation of endogenous arachidonic acid. At least ten genetic variations of P450 2J2 have been identified, but their effects on enzymatic activity have not been clearly characterized. Here, we evaluated the functional effects of three genetic variations of P450 2J2 (G312R, P351L, and P115L).

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions.

Transcription factors (TFs) are major trans-acting factors in transcriptional regulation. Therefore, elucidating TF-target interactions is a key step toward understanding the regulatory circuitry underlying complex traits such as human diseases. We previously published a reference TF-target interaction database for humans-TRRUST (Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining)-which was constructed using sentence-based text mining, followed by manual curation.

Enhanced Purification of Recombinant Rat NADPH-P450 Reductase by Using a Hexahistidine-Tag.

NADPH-P450 reductase (NPR) transfers electrons from NADPH to cytochrome P450 and heme oxygenase enzymes to support their catalytic activities. This protein is localized within the endoplasmic reticulum membrane and utilizes FMN, FAD, and NADPH as cofactors. Although NPR is essential toward enabling the biochemical and pharmacological analyses of P450 enzymes, its production as a recombinant purified protein requires a series of tedious efforts and a high cost due to the use of NADP in the affinity chromatography process.

Structural insights into the binding of lauric acid to CYP107L2 from Streptomyces avermitilis.

Streptomyces avermitilis is an actinobacterium known to produce clinically useful macrolides including avermectins. CYP107L2 from S. avermitilis shares a high sequence similarity with the PikC (CYP107L1) from S.