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Article
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Comparison of Oxidative and Hypoxic Stress Responsive
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Genes from Meta-Analysis of Public Transcriptomes
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Takayuki Suzuki, Yoko Ono and Hidemasa Bono *
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Program of Biomedical Science, Graduate School of Integrated Sciences for Life, Hiroshima University, 3-10-
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23 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-0046, Japan;
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* Correspondence: bonohu@hiroshima-u.ac.jp; Tel.: +81-82-424-4013
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Abstract: Analysis of RNA-sequencing (RNA-seq) data is an effective means to analyze the gene
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expression levels under specific conditions and discover new biological knowledge. More than
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74000 experimental series with RNA-seq have been stored in public databases as of October 20, 2021.
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Since this huge amount of expression data accumulated from past studies is a promising source of
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new biological insights, we focused on a meta-analysis of 1783 runs of RNA-seq data under the
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conditions of two types of stresses: oxidative stress (OS) and hypoxia. The collected RNA-seq data
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of OS were organized as the OS dataset to retrieve and analyze differentially expressed genes
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(DEGs). The OS-induced DEGs were compared with the hypoxia-induced DEGs retrieved from a
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previous study. The results from the meta-analysis of OS transcriptomes revealed two genes, CRIP1
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and CRIP3, which were particularly downregulated, suggesting a relationship between OS and zinc
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homeostasis. The comparison between meta-analysis of OS and hypoxia showed that several genes
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were differentially expressed under both stress conditions, and it was inferred that the downregu-
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lation of cell cycle-related genes is a mutual biological process in both OS and hypoxia.
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Keywords: oxidative stress; RNA-seq; meta-analysis; hypoxia
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1. Introduction
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Oxidative stress (OS) is characterized by an imbalance between oxidants and antiox-
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idants, caused by an increase in the levels of reactive oxygen species (ROS) in a biological
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system. ROS comprise free radicals that can damage cellular molecules and disrupt ho-
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meostasis when antioxidants are downregulated, or ROS levels are upregulated. Chronic
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OS has been observed in various diseases such as Parkinson’s disease, hepatitis, and can-
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cer [1–5].
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Due to its strong relationship with human health, the mechanisms of OS have been
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extensively investigated to provide biological and medical knowledge. These include a
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the mechanism of DNA damage by the highly reactive hydroxyl radicals, the role of OS
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in carcinogenesis appearance, and the increase in OS-inducible inflammatory cells by ac-
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tivation of specific transcription factors such as NF-E2 related factor-2 (NRF2) [6,7]. The
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past studies also have resulted in 435 genes in Homo sapiens annotated with the term
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“GO:0006979 response to oxidative stress” in gene ontology (GO). On the other hand, the
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broadness of OS-inducible factors and the dynamics of ROS in biological systems make
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the OS studies challenging and complicated [8]. In spite of attempts to list up and catego-
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rize the OS-related compounds, contributing factors for OS involve enormous range of
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both external and internal sources [1] and distinguishing oxidative and non-oxidative
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sources is challenging. Therefore, the present study focused on analyzing the common
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feature among various sources of OS from the perspective of changes in gene expression.
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As for another underdeveloped area of OS studies, a clear line between other types of
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stresses and OS has not been defined. It necessary to compare the OS and other stresses
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such as hypoxia, which is also an oxygen-related stress condition.
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Hypoxia is characterized by reduced oxygen availability in tissues and is known to
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increase ROS levels through changes in signaling cascades and protein expression [9]. A
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previous study has successfully attained the collective intelligence of public hypoxic tran-
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scriptomes by analyzing 944 runs of RNA-seq data [10]. This approach, a statistical anal-
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ysis of combined results from multiple studies, called meta-analysis has attracted atten-
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tions. It is because the data-driven nature of meta-analysis makes it possible to discover
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new findings that are difficult to achieve with traditional hypothesis-driven research
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methods [11]. The dataset and results obtained in the meta-analysis of hypoxia are valua-
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ble sources for both of hypothesis- and data-driven research.
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To discover novel areas by utilizing valuable open sources, we collected OS tran-
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scriptomes of human cultured cells from public databases and performed a meta-analysis.
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This study aimed to investigate the key genes and characteristics for not only specifically
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OS, but also comparison between OS and hypoxia, by analyzing the differentially ex-
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pressed genes (DEGs) from the meta-analysis of both of OS and hypoxia, based on 1783
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RNA-seq data (839 from this study and 944 from our previous study of meta-analysis in
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hypoxia [10]). These investigated genes, the OS curated dataset for OS, and the method
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described in this study to compare the results of multiple meta-analyses are expected to
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be valuable sources for promoting future studies.
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2. Materials and Methods
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2.1 Curation of Public Gene Expression Data
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As a first step to access and view the integrated expression metadata from public
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databases, we initially used a graphical web tool, All Of gene Expression (AOE) [12]. AOE
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provides integrated information about gene expression data integrated from Gene Expres-
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sion Omnibus (GEO) [13], ArrayExpress [14], Genomic Expression Archive [15], and
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RNA-seq data only archived in the Sequence Read Archive (SRA) [16]. Extensive key-
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words, including “oxidative stress”, rotenone, paraquat, hydrogen peroxide (H2O2), UV,
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lipopolysaccharide, arsenite, and deoxynivalenol, were searched in GEO to collect a list of
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experiment data series related to the RNA-seq data of OS in humans. Then, we manually
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curated the adequate data with three main criteria: relation to the definition of oxidative
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stress, relation to an increase in the ROS level, and availability of the corresponding con-
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trol data (normal state) to pair the OS data.
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2.2 RNA-seq data retrieval, processing, and quantification
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We used ikra for RNA-seq data retrieval, processing, and quantification. Ikra is an
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automated pipeline program for RNA-seq data of Homo sapiens and Mus musculus [17].
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Ikra automates the following processes: conversion of the collected SRA format data to
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FASTQ formatted files using fasterq-dump (version.2.9.6) [18], quality control and trim-
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ming of transcript reads with trim-galore (version 0.6.6) [19], and quantification of the
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transcripts in a unit of transcripts per million (TPM) by salmon (version 1.4.0) [20] with
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reference transcript sets in GENCODE Release 31 (GRCh38.p12).
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2.3 Calculation of ON_ratio and ON_score
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We calculated the ratio of expression value of each gene in all pairs between Oxida-
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tive stress and Normal state (termed as ON_ratio) [10,11]. The ON_ratio was calculated
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using equation (1):
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ON_ratio =
!!""#
#!$%&'()!*+(+,"#
(1)
T corresponds to the expression value quantified in TPM. A small number (1 in this case)
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was added to the expression value to avoid the calculation of zero. ON_ratio values
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helped classifying each gene into three groups: upregulated, downregulated, and un-
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changed. When the ON_ratio was greater than the threshold, the gene was considered
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upregulated, and when the ON_ratio was less than the threshold, the gene was treated
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as downregulated, otherwise the gene was categorized as unchanged. We adopted 5 and
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10-fold thresholds for upregulation and 0.2 and 0.1-fold thresholds for downregulation
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after testing several thresholds.
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To take all the collected RNA-seq data pairs into account, we calculated an Oxida-
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tive stress-Normal state score (termed as ON_score [11]) based on ON_ratio values using
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the equation (2):
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ON_score = count numberupregulated – count numberdownregulated
(2)
ON_ratio and ON_score were previously introduced in the meta-analysis of OS tran-
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scriptome in insects [11] and the meta-analysis of hypoxic transcriptome [10] (termed as
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HN-ratio and HN-score in the meta-analysis of hypoxia).
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2.4 Analysis and comparison of gene sets
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Differentially expressed gene sets were analyzed by using the web tool, Metascape
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[21], which performs gene set enrichment analysis. We examined the corresponding terms
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and p-values obtained using the gene set enrichment analysis. We also used a web Venn
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diagram tool [22] to search and visualize the matched genes among different gene sets.
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3. Results
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3.1. Data curation/collection of oxidative stress transcriptome data
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We collected 839 RNA-seq data and curated them as the OS dataset with 386 pairs of
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OS and normal state transcriptome data. As OS is caused by various factors, sources of OS
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in the OS dataset include hydrogen peroxide (H2O2), UV, rotenone, lipopolysaccharide,
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arsenite, radiation, NRF2 knockdown/KO, BRD4 KO, deoxynivalenol, palmitate, cad-
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mium, methylmercury, zinc dimethyldithiocarbamate, aging, paraquat, and others (Table
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1). The proportion of the data pairs of hydrogen peroxide, UV, and rotenone against the
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total 386 pairs was as follows: 25%, 15%, and 12%, respectively. The percentage of the
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samples derived from cancer cells was 18% (71 pairs out of 386 pairs). Other metadata
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about the OS dataset such as each SRA project ID, SRR ID, cell type, concentration of treat-
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ment, hours of treatment, and library type of sequencing are shown in figshare [23].
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Table 1. The number of data pairs retrieved co for each source of OS.
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Source of OS
Number of
data pairs
Hydrogen peroxide (H2O2)
98 (25%)
Ultra-Violet rays (UV)
59 (15%)
Rotenone
45 (12%)
Lipopolysaccharide (LPS)
38 (10%)
Arsenite
33 (9%)
Infra-Red rays (Radiation)
24 (6%)
NRF2 knockdown/KO, BRD4 KO
22 (6%)
Deoxynivalenol
10 (3%)
Palmitate/high fat/high glucose
10 (3%)
Cadmium, Methylmercury,
Zinc dimethyldithiocarbamate
8 (2%)
Aging
6 (2%)
Paraquat
5 (1%)
Others (Senescence, Menadione, etiostat, etc)
28 (7%)
Total
386
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Figure 1. Schematic views of narrowing down the genes in oxidative/hypoxic transcriptome meta-
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analysis. (a) the 19,704 coding genes indexed for the reference genome were filtered by ON_score
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and by excluding Gene Ontology (GO) annotated genes to retrieve the 20 most differentially ex-
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pressed genes (DEGs). (b) The number of genes downregulated in oxidative stress and hypoxia
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was then obtained as per the schematic in the figure.
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3.2. Verifying the Characteristics of DEGs by the OS dataset
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A schematic view of the analysis is shown in (Figure 1). The most upregulated 493
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genes and the most downregulated 492 genes, in a total of 985 genes (5% of the total cod-
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ing genes in GENCODE Release 31 (GRCh38.p12)), were retrieved by ON_score 10 as
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DEGs. We performed gene set enrichment analysis using metascape to visualize the char-
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acteristics of the DEGs. The analysis showed that the 493 most upregulated genes by OS
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were related to “GO:0009617: response to bacterium” and “M5885: NABA matrisome as-
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sociated” (Figure 2a). The 492 most downregulated genes by OS were related to
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“GO:0000280 nuclear division” and “R-HAS-69278: Cell Cycle, Mitotic” (Figure 2b). We
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then found that 32 out of 985 genes were common to genes annotated with GO:0006979
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(response to oxidative stress). The most upregulated genes common with GO annotation
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were IL6, PTGS2, and MMP3, and the most downregulated genes common with GO an-
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notation were CDK1, SELENOP, and KLF2 (Figure 2c). The same procedure to verify the
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DEGs retrieved by ON_score 5 was also performed [23]. The use of ON_score 5 reveals a
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gene set that includes genes not as differentially expressed as ON_score 10. This shows
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the broader characteristics of the OS. We used ON_score 5 in the analysis of 3.4 comparison
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of meta-analysis results by OS and hypoxia.
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3.3. Evaluation of DEGs by oxidative stress
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To evaluate the genes exceptionally expressed by OS, the parameter of ON_score 10
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was applied to retrieve 985 DEGs. 32 genes that were already annotated with the
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GO:0006979 (response to oxidative stress) were excluded from the DEGs, thus revealing
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OS-related genes have not yet attracted attention (Figure 1a). The most upregulated 10
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genes and the most downregulated 10 genes were retrieved and analyzed (Figure 1a, Fig-
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ure 3). Five out of the 10 most downregulated genes (H2BC14, PIMREG, KIF20A, CDC20,
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and H2AC14) were related to cell cycle. Two of them (H2BC14 and H2AC14) encode the
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core components of histones. In addition, two genes encoding zinc binding domains
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(CRIP1 and CRIP3) are included in the list of the 10 most downregulated genes. In contrast,
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the most 3 upregulated genes were CCL20, CXCL8, and CXCL1, encoding C-C motif chem-
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okine-20, interleukin-8, and growth-regulated alpha protein respectively. Genes that re-
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spond to inflammation were included in the most upregulated genes.
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(a)
(b)
(c)
Figure 2. Verifying the characteristics of differentially expressed genes (DEGs): Enrichment analysis for (a) the 493 most
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upregulated genes by oxidative stress (OS) and (b) the 492 most downregulated genes by OS. (c) ON_score for 32 genes
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that were identified as DEGs and annotated as GO:0006979 (response to oxidative stress).
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Figure 3. ON_score for the ten most upregulated and downregulated genes after extraction of annotated genes with
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GO:0006979 (response to oxidative stress).
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3.4. Comparison of the meta-analysis results by OS and hypoxia
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A schematic description of the retrieval and analysis of the downregulated genes in
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both OS and hypoxia is shown in Figure 1b. We collected 985 DEGs of OS and hypoxia
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using the ON_score and HN-score. Each DEGs were divided into two gene sets: 493 most
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upregulated genes and 492 most downregulated genes. The four gene sets derived from
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the two types of stress conditions were compared using Venn diagrams to show the com-
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mon differentially expressed genes (Figure 4a). We found that 44 genes were upregulated
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in both stress conditions (termed as HN_up ON_up), 50 genes were downregulated in
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both stress conditions (termed as HN_down ON_down), 11 genes were upregulated in
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hypoxia but downregulated in OS (termed as HN_up ON_down), and 8 genes were up-
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regulated in OS but downregulated in hypoxia (termed as HN_down ON_up). The num-
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ber of genes upregulated or downregulated in both stress conditions was greater than the
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number of genes upregulated or downregulated under either one of the stress conditions.
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The characteristics of each gene set in common were analyzed by performing gene
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set enrichment analysis using metascape. “R-HAS-69278: Cell Cycle, Mitotic” and
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“GO:1903047: mitotic cell cycle process” are the most enriched terms with log10(p-value)
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of -19.21 and -18.93 for HN_down ON_down (Figure 4b). HN_up ON_up is related to the
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terms, “M145: PID P53 Downstream pathway” and “M166: PID ATF2 pathway” (Figure
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4c). HN_up ON_down and HN_down ON_up included 11 genes and 8 genes respectively.
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A list of genes in each gene set is shown in figshare [23].
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Figure 4. Comparison of results from meta-analysis in oxidative stress (OS) and hypoxia. (a) visualization of comparison
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among gene sets. HN_up: the most 493 upregulated genes by hypoxia, HN_down: the most 492 downregulated genes by
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hypoxia. ON_up: the most 493 upregulated genes by OS, ON_down: the most 492 downregulated genes by OS. Enrich-
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ment analysis for (b) 50 genes downregulated in both stresses and (c) 44 genes upregulated in both stresses.
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4. Discussion
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In this study, we curated the 386 pairs of OS-related RNA-seq data collected from
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public databases. The collected data were systematically processed and analyzed to iden-
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tify the DEGs related to OS. Gene set enrichment analysis was performed to identify and
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confirm the characteristics of the DEGs. In addition, we implemented a new approach to
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analyze the relationship between the two types of stresses, OS and hypoxia, by comparing
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the results of both meta-analyses [10]. We compared the genes upregulated and downreg-
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ulated by hypoxia and OS to obtain four new gene sets, HN_up ON_up, HN_down
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ON_down, HN_up ON_down, and HN_down ON_up. Each gene set was analyzed using
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gene set enrichment analysis.
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Meta-analysis of the OS dataset revealed two interesting genes encoding cysteine-
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rich proteins (CRIP1 and CRIP3) that were the 10th and 5th most downregulated by OS,
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respectively. Each encoded protein contains zinc-binding domains, and the protein en-
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coded by CRIP1 is considered to act as a zinc transporter and absorption [24,25]. Previous
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studies have reported several roles for zinc in antioxidant defense systems. For example,
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zinc inhibits the enzyme nicotinamide adenine dinucleotide phosphate oxidase (NADPH-
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Oxidase) and promotes the synthesis of metallothionein which contributes to the reduc-
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tion of ROS [26]. Zinc is also known as a component of the enzyme superoxide dismutase
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(SOD) which acts to reduce and maintain ROS levels in cells [26]. On the other hand, ex-
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cess zinc exhibits other types of toxicities leading to the symptoms such as nausea, vom-
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iting, fever, and headaches [27]. Therefore, zinc homeostasis is one of the key biological
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systems for preventing various types of stresses. As the proteins encoded by CRIP1 and
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CRIP3 contain zinc-binding domains, we can assume that they participate in the regula-
211
tion of zinc homeostasis. Based on this hypothesis and the results of this study, we suggest
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that the regulation of zinc homeostasis is impaired in OS due to decreased expression of
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(a) (c)
(b)
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CRIP1 and CRIP3. Since zinc deficiency is known to be a cause of OS [3,28], we speculate
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that the downregulation of CRIP1 and CRIP3 is affected by OS-induced pathways that
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contribute to the reduced availability of zinc in cells. Uncovering the functions of CRIP1
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and CRIP3 could be a way to clarify some of the relationships between OS and zinc ho-
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meostasis, which may promote the development or the prevention of OS and zinc home-
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ostasis-related diseases such as atherosclerosis [29], Parkinson’s disease [30], cancer, and
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hepatitis virus infection [31,32].
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The comparison of the meta-analysis results by two types of stresses, OS and hypoxia,
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revealed gene sets that were found as differentially expressed in both stresses. Particularly
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the gene set downregulated in both stresses showed distinct characteristic with cell cycle
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(Figure 4b). This result supports the previous biological findings that DNA damage in-
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duced by increased ROS levels cause cell cycle arrest or apoptosis [33,34]. In addition, an
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increase in ROS production in mitochondria is known to be a common event in both OS
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and hypoxia [35]; therefore, the downregulation of cell cycle-related genes was an ex-
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pected result. Furthermore, meta-analysis of the OS dataset revealed five cell cycle-related
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genes: H2BC14, PIMREG, KIF20A, CDC20, and H2AC14 that were respectively 2nd, 3rd, 6th,
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7th, and 9th most downregulated by OS, supporting the above observation by showing that
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DEGs associated with OS are related to the cell cycle. As these ten OS-induced downreg-
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ulated genes were not included in the genes common to hypoxia, further research is
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needed to clarify whether the expression of these genes is unique to OS or shared by types
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of stresses other than hypoxia.
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The results of this study may play a role in elucidating the causative mechanisms and
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development of treatments for such diseases as atherosclerosis (OS and zinc homeostasis
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related), chronic kidney disease, and metabolic syndrome (both OS and hypoxia related)
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[36,37] by further studies searching on the functions of the important genes revealed. As
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the number of public expression data increases, the more accurate and detailed infor-
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mation about genes that respond to OS can be obtained by updating the OS dataset in the
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future. We have also shown the possibility of revealing information about the relation-
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ships between the types of stresses by comparing the results from the meta-analysis. Thus,
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the use of collective intelligence including the results of this study, which will continue to
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be produced in the future, makes it possible to efficiently promote studies on the search
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for key pathways, for causes of diseases, and treatments of diseases.
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Author Contributions: Conceptualization, T.S., H.B.; methodology, T.S., Y.O, and H.B..; software,
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T.S, Y.O., H.B.; validation, T.S., Y.O, H.B; formal analysis, T.S., Y.O., H.B.; investigation, T.S.; re-
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sources, T.S., H.B.; data curation, T.S.; writing—original draft preparation, T.S.; writing—review
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and editing, Y.O., H.B.; visualization, T.S.; supervision, H.B.; project administration, H.B.; funding
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acquisition, H.B. All authors have read and agreed to the published version of the manuscript.
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Funding: This research was supported by the Center of Innovation for Bio-Digital Transformation
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(BioDX), an open innovation platform for industry-academia co-creation (COI-NEXT), and the Ja-
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pan Science and Technology Agency (JST, COI-NEXT, JPMJPF2010). This study was also supported
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by the ROIS-DS-JOINT (009RP2021).
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Institutional Review Board Statement: Not applicable
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Informed Consent Statement: Not applicable
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Data Availability Statement: The data presented in this study are publicly available in figshare [23].
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Acknowledgments: Computations were performed on the computers at Hiroshima University Ge-
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nome Editing Innovation Center.
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Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the
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design of the study; in the collection, analyses, or interpretation of data; in the writing of the manu-
263
script, or in the decision to publish the results.
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