Exosome from IFN-γ-Primed Induced Pluripotent Stem Cell-Derived Mesenchymal Stem Cells Improved Skin Inflammation and Barrier Function.

The pathogenesis of atopic dermatitis (AD) is multifactorial, including immune dysregulation and epidermal barrier defects, and a novel therapeutic modality that can simultaneously target multiple pathways is needed. We investigated the therapeutic effects of exosomes (IFN-γ-iExo) secreted from IFN-γ-primed induced pluripotent stem cell-derived mesenchymal stem cells (iMSC) in mice with Aspergillus fumigatus-induced AD. IFN-γ-iExo was epicutaneously administered to mice with AD-like skin lesions. The effects of IFN-γ-iExo treatment were investigated through clinical scores, transepidermal water loss (TEWL) measurements, and histopathology. To elucidate the therapeutic mechanism, we used an in vitro model of human keratinocyte HaCaT cells stimulated with IL-4 and IL-13 and performed extensive bioinformatics analysis of skin mRNA from mice. The expression of indoleamine 2,3-dioxygenase was higher in IFN-γ primed iMSCs than in iMSCs. In human keratinocyte HaCaT cells, treatment with IFN-γ-iExo led to decreases in the mRNA expression of thymic stromal lymphopoietin, IL-25, and IL-33 and increases in keratin 1, keratin 10, desmoglein 1, and ceramide synthase 3. IFN-γ-iExo treatment significantly improved clinical and histological outcomes in AD mice, including clinical scores, TEWL, inflammatory cell infiltration, and epidermal thickness. Bioinformatics analysis of skin mRNA from AD mice showed that IFN-γ-iExo treatment is predominantly involved in skin barrier function and T cell immune response. Treatment with IFN-γ-iExo improved the clinical and histological outcomes of AD mice, which were likely mediated by restoring proper skin barrier function and suppressing T cell-mediated immune response.

MOPA: An integrative multi-omics pathway analysis method for measuring omics activity.

Pathways are composed of proteins forming a network to represent specific biological mechanisms and are often used to measure enrichment scores based on a list of genes in means to measure their biological activity. The pathway analysis is a de facto standard downstream analysis procedure in most genomic and transcriptomic studies. Here, we present MOPA (Multi-Omics Pathway Analysis), which is a multi-omics integrative method that scores individual pathways in a sample wise manner in terms of enriched multi-omics regulatory activity, which we refer to mES (multi-omics Enrichment Score). The mES score reflects the strength of regulatory relations between multi-omics in units of pathways. In addition, MOPA is able to measure how much each omics contribute to mES that may be used to observe what kind of omics are active in a pathway within a sample group (e.g., subtype, gender), which we refer to OCR (Omics Contribution Rate). Using nine different cancer types, 93 clinical features and three types of omics (i.e., gene expression, miRNA and methylation), MOPA was used to search for clinical features that were explainable in context of multi-omics. By evaluating the performance of MOPA, we showed that it yielded higher or at least equal performance compared to previous single and multi-omics pathway analysis tools. We find that the advantage of MOPA is the ability to explain pathways in terms of omics relation using mES and OCR. As one of the results, the TGF-beta signaling pathway was captured as an important pathway that showed distinct mES and OCR values specific to the CMS4 subtype in colon adenocarcinoma. The mES and OCR metrics suggested that the mRNA and miRNA expressions were significantly different from the other subtypes, which was concordant with previous studies. The MOPA software is available at https://github.com/jaeminjj/MOPA.

Characterization of a new CCCTC-binding factor binding site as a dual regulator of Epstein-Barr virus latent infection.

Distinct viral gene expression characterizes Epstein-Barr virus (EBV) infection in EBV-producing marmoset B-cell (B95-8) and EBV-associated gastric carcinoma (SNU719) cell lines. CCCTC-binding factor (CTCF) is a structural chromatin factor that coordinates chromatin interactions in the EBV genome. Chromatin immunoprecipitation followed by sequencing against CTCF revealed 16 CTCF binding sites in the B95-8 and SNU719 EBV genomes. The biological function of one CTCF binding site (S13 locus) located on the BamHI A right transcript (BART) miRNA promoter was elucidated experimentally. Microscale thermophoresis assay showed that CTCF binds more readily to the stable form than the mutant form of the S13 locus. EBV BART miRNA clusters encode 22 miRNAs, whose roles are implicated in EBV-related cancer pathogenesis. The B95-8 EBV genome lacks a 11.8-kb EcoRI C fragment, whereas the SNU719 EBV genome is full-length. ChIP-PCR assay revealed that CTCF, RNA polymerase II, H3K4me3 histone, and H3K9me3 histone were more enriched at S13 and S16 (167-kb) loci in B95-8 than in the SNU719 EBV genome. 4C-Seq and 3C-PCR assays using B95-8 and SNU719 cells showed that the S13 locus was associated with overall EBV genomic loci including 3-kb and 167-kb region in both EBV genomes. We generated mutations in the S13 locus in bacmids with or without the 11.8-kb BART transcript unit (BART(+/-)). The S13 mutation upregulated BART miRNA expression, weakened EBV latency, and reduced EBV infectivity in the presence of EcoRI C fragment. Another 3C-PCR assay using four types of BART(+/-)·S13(wild-type(Wt)/mutant(Mt)) HEK293-EBV cells revealed that the S13 mutation decreased DNA associations between the 167-kb region and 3-kb in the EBV genome. Based on these results, CTCF bound to the S13 locus along with the 11.8-kb EcoRI C fragment is suggested to form an EBV 3-dimensional DNA loop for coordinated EBV BART miRNA expression and infectivity.

Corrigendum: MONTI: A Multi-Omics Non-Negative Tensor Decomposition Framework for Gene-Level Integrative Analysis.

[This corrects the article DOI: 10.3389/fgene.2021.682841.].

MONTI: A Multi-Omics Non-negative Tensor Decomposition Framework for Gene-Level Integrative Analysis.

Multi-omics data is frequently measured to enrich the comprehension of biological mechanisms underlying certain phenotypes. However, due to the complex relations and high dimension of multi-omics data, it is difficult to associate omics features to certain biological traits of interest. For example, the clinically valuable breast cancer subtypes are well-defined at the molecular level, but are poorly classified using gene expression data. Here, we propose a multi-omics analysis method called MONTI (Multi-Omics Non-negative Tensor decomposition for Integrative analysis), which goal is to select multi-omics features that are able to represent trait specific characteristics. Here, we demonstrate the strength of multi-omics integrated analysis in terms of cancer subtyping. The multi-omics data are first integrated in a biologically meaningful manner to form a three dimensional tensor, which is then decomposed using a non-negative tensor decomposition method. From the result, MONTI selects highly informative subtype specific multi-omics features. MONTI was applied to three case studies of 597 breast cancer, 314 colon cancer, and 305 stomach cancer cohorts. For all the case studies, we found that the subtype classification accuracy significantly improved when utilizing all available multi-omics data. MONTI was able to detect subtype specific gene sets that showed to be strongly regulated by certain omics, from which correlation between omics types could be inferred. Furthermore, various clinical attributes of nine cancer types were analyzed using MONTI, which showed that some clinical attributes could be well explained using multi-omics data. We demonstrated that integrating multi-omics data in a gene centric manner improves detecting cancer subtype specific features and other clinical features, which may be used to further understand the molecular characteristics of interest. The software and data used in this study are available at: https://github.com/inukj/MONTI.

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 (IC 50, AUC) or time-series transcriptomic data after drug treatment. However, analyzing transcriptome data upon drug treatment is challenging since more than 20,000 genes interact in complex ways. In addition, due to the difficulty of both time-series analysis and multi-omics integration, current methods can hardly perform analysis of databases with different data characteristics. One effective way is to interpret transcriptome data in terms of well-characterized biological pathways. Another way is to leverage state-of-the-art methods for multi-omics data integration. In this paper, we developed Drug Response analysis Integrating Multi-omics and time-series data (DRIM), an integrative multi-omics and time-series data analysis framework that identifies perturbed sub-pathways and regulation mechanisms upon drug treatment. The system takes drug name and cell line identification numbers or user's drug control/treat time-series gene expression data as input. Then, analysis of multi-omics data upon drug treatment is performed in two perspectives. For the multi-omics perspective analysis, IC 50-related multi-omics potential mediator genes are determined by embedding multi-omics data to gene-centric vector space using a tensor decomposition method and an autoencoder deep learning model. Then, perturbed pathway analysis of potential mediator genes is performed. For the time-series perspective analysis, time-varying perturbed sub-pathways upon drug treatment are constructed. Additionally, a network involving transcription factors (TFs), multi-omics potential mediator genes, and perturbed sub-pathways is constructed, and paths to perturbed pathways from TFs are determined by an influence maximization method. To demonstrate the utility of our system, we provide analysis results of sub-pathway regulatory mechanisms in breast cancer cell lines of different drug sensitivity. DRIM is available at: http://biohealth.snu.ac.kr/software/DRIM/.

Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data.

MOTIVATION: Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients. RESULTS: In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.