Abstract
Purpose
The current outbreak of monkeypox (MPX) has created colossal concerns. However, immense research gaps have been noted in our understanding of the replication process, machinery, and genomic landscape during host cell infection. To fill this gap, differentially expressed genes (DEGs) were comprehensively analyzed during viral replication in host (MK2) cells.
Methods
We used a microarray GEO dataset which was divided into three groups: control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h. Using the dataset, DEG analysis, PPI network analysis, co-expression, and pathway analysis were conducted using bioinformatics, systems biology, and statistical approaches.
Results
We identified 250 DEGs and 24 top-ranked genes. During the DEG analysis, we identified eight up-regulated genes (LOC695323, TMEM107, LOC695427, HIST1H2AD, LOC705469, PMAIP1, HIST1H2BJ, and HIST1H3D) and 16 down-regulated genes (HOXA9, BAMBI, LMO4, PAX6, AJUBA, CREBRF, CD24, JADE1, SLC7A11, EID2, SOX4, B4GALT5, PPARGC1A, BUB3, SOS2, and CDK19). We also developed PPI networks and performed co-expression analyses using the top-ranked genes. Furthermore, five genes were listed for co-expression pattern analysis.
Conclusions
This study will help in better understanding the replication process, machinery, and genomic landscape of the virus. This will further aid the discovery and development of therapeutics against viruses.
Abbreviation: CDC, Centers for Disease Control and Prevention; DEGs, Differentially Expressed Genes; KEGG, Kyoto Encyclopedia of Genes and Genomes; MPXV, Monkeypox Virus; NCBI, National Center for Biotechnology Information; ORC, Origin Recognition Complex; ORFs, Open Reading Frames; PPI, Protein-Protein Interaction; RT-PCR, Real-Time Polymerase Chain Reaction; WHO, World Health Organization; GEO, Gene Expression Omnibus
Keywords: Monkeypox virus, Replication, MK2 cells, DEG, PPI network, Co-expression
1. Background
The recent spread of monkeypox (MPX) in non-African countries has created global concern. MPX and its infection were confirmed in England in early May 2022. Consequently, the virus has spread throughout Europe [1]. Subsequently, the virus spread rapidly in different non-epidemic countries worldwide [2]. As of December 7, 2022, the Centers for Disease Control and Prevention (CDC) USA reported 82,199 infected cases had been reported from 110 countries [3]. They also noted 65 deaths in 13 countries. The highest number of infection cases was recorded in the USA, with 28,224 known cases. In addition to the USA, the virus has spread throughout European countries; in Spain, 7317 cases were reported, 3698 cases in the UK, and 3662 cases in Germany. Previously, the viral infection was restricted to some parts of African countries, such as Central and West Africa. Previously, rare outbreaks of MPX were reported in countries outside Africa. These periodic outbreaks were associated with two main factors: import of animals and international travel [4], [5]. However, due to the rise of infection globally, the WHO announced MPX disease as a public health emergency on July 23, 2022. Similarly, the government declared a public health emergency in the USA on August 4, 2022, due to the significant increase in the number of cases in the country.
Monkeypox is a zoonotic disease, and the pathogen of the disease is the Monkeypox virus (MPXV). This virus belongs to the family Poxviridae and genus Orthopoxvirus. MPXV is a DNA virus whose genome encodes double-stranded DNA (dsDNA). The virus genome consists of approximately 197 kb linear DNA [6], [7]. The genome contained approximately 190 non-overlapping genes. These genes are non-overlapping ORFs [7], [8]. Researchers have previously illustrated the diversity of the MPXV. They have demonstrated two Clades of the MPXV, the West African (WA) Clade and Congo Basin (CB) Clade. The CB Clade is also known as the Central African Clade. Among both MPXV subtypes, the WA Clade emerged approximately 600 years ago. Subsequently, the CB Clade was separated from the WA Clade. The virus of the CB Clade is noted to be more virulent than that of the WA Clade [9]. However, researchers have observed changes in the epidemiological patterns of newly emerging viruses in recent outbreaks. The WHO has proposed a new naming system for the nomenclature of the MPXV. They presented three Clades, Clade I, Clade IIa, and Clade IIb, which were published on August 12, 2022 [1]. The virus can be transmitted from animals to humans and from humans to humans [10]. Recently, human-to-animal transmission of the virus has been reported. Seang et al. recently reported human-to-dog transmission of MPX [11].
The incubation period of the virus is 5–21 days. After infection, MPXV begins to replicate. As a member of the Poxviridae family, DNA synthesis was observed within 2 h of infection. Replication occurs within discrete juxtanuclear sites known as factories in the cytoplasm. A viral factory has been noted to develop from a single virion. Initially, the factories are enclosed by very compact endoplasmic reticulum membranes. It plays a role in replication and is the site of transcription and translation of viral mRNAs [12], [13]. However, understanding the viral infection and replication mechanisms in more detail is also necessary.
The gene expression pattern of the host during viral replication has been explored. The co-expression pattern of host and viral genes can unfold the up-regulated and down-regulated genes simultaneously. Therefore, the study of these genes is an important area of research. This helped us to reveal the viral replication mechanism using the host cell. This study could also lead to the development of new therapies against the virus. Researchers are trying to understand the gene expression pattern of the host or the co-expression pattern of the host’s gene and virus of the different viruses during infection [14], [15]. Researchers have also evaluated the gene expression patterns of the host during MPXV infection [15]. Rubins et al. attempted to understand viral gene expression programs during Poxvirus infection. To this end, a specialized microarray has been developed to understand the gene expression patterns of two viruses, MPXV and Vaccinia Virus Western Reserve (VACV). This study analyzed a transcriptome-based temporal map of two viral infections, which indicates that the two transcriptomes have distinct gene expression features for the two viruses [16]. In another study, gene expression profiles were observed in human cells infected with MPXV and VACV. The study noted the suppression of some immune-protective genes, such as IL-1α, IL-1β, TNF-α, IL-6, and CCL5 [17]. Bourquin et al. mapped MPXV, VACV, and cowpox virus gene expression. They studied MPXV-, VACV-, and Cowpox virus-infected gene expression in HeLa cells and revealed numerous infection-modulated clusters of genes. Researchers have also noted different types of genes, such as genes related to immune response and leukocyte migration [18]. Similarly, Alkhalil et al. attempted to illustrate the gene expression profile in the kidney epithelial cells of Macaca mulatta (MK2 cells) during MPXV infection using genome microarrays. They attempted to understand the differentially expressed genes at 3 h and 7 h and concluded that many genes were involved in the regulation of infection [19]. However, exploring the gene expression pattern that can provide more information about MPXV infection, is essential.
The main research objective of our study was to evaluate the differentially expressed genes (DEGs) and top-ranked DEGs during MPXV replication, in order to understand the genomic landscape of MPXV replication. The other objective of this study was to elucidate the protein–protein interaction (PPI) network and co-expression of top-ranked DEGs during MPXV replication. Finally, we attempted to understand the association between the top-ranked DEGs and biological processes. The study will help to understand the genomic pattern of MPXV replication and, provide insights for new therapeutic discoveries and their development to treat MPXV infection.
2. Methods
2.1. Acquisition of array data
The dataset was retrieved from the GEO database. The GEO database is an open resource database from NCBI. Dataset GSE21001 was used in this study. Different keywords, including “Monkeypox virus,” “Infection,” and “Microarray,” were used to search the GEO dataset [19].
2.2. Monkeypox virus and MK2 cells
All MK2 cell line expression data were retrieved from the GEO database. The dataset was separated into three categories: control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h. Our dataset contained nine human samples: three control, three MPXV-infected MK2 cells at 3 h, and three MPXV-infected MK2 cells at 7 h ( Table 1).
Table 1.
The study uses nine datasets (control, MPXV infected MK2 cell in 3 h, and MPXV infected MK2 cell in 7 h). The table also describes the characteristics of nine datasets.
| Group | Accession | Title | Source name | Infection | Cell type | Virus replication time |
|---|---|---|---|---|---|---|
| Control | GSM524843 | a- Control | Macaca mulatta kidney epithelial cells | Mock infected | MK2 | None |
| MPXV infected MK2 cell in 3 hrs | GSM524844 | a- 3 hpi | Infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 3 h |
| MPXV infected MK2 cell in 7 hrs | GSM524845 | a- 7 hpi | Infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 7 h |
| Control | GSM524846 | b- Control | Macaca mulatta kidney epithelial cells | Mock infected | MK2 | None |
| MPXV infected MK2 cell in 3 hrs | GSM524847 | b- 3 hpi | infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 3 h |
| MPXV infected MK2 cell in 7 hrs | GSM524848 | b- 7 hpi | Infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 7 h |
| Control | GSM524849 | c- Control | Macaca mulatta kidney epithelial cells | Mock infected | MK2 | None |
| MPXV infected MK2 cell in 3 hrs | GSM524850 | c- 3 hpi | Infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 3 h |
| MPXV infected MK2 cell in 7 hrs | GSM524851 | c- 7 hpi | Infected Macaca mulatta kidney epithelial cells | Monkeypox virus | MK2 | 7 h |
2.3. Data preprocessing and analysis of DEGs
The statistical tool GEO2R was used to evaluate raw gene expression data. GEO2R is a tool that utilizes GEO query and imma R packages to analyze raw gene expression data [20], [21]. After data analysis, different statistical plots were developed for data expression. The following plots were developed: volcano plots, mean difference (MD) plots, uniform manifold approximation and projection (UMAP) plots, expression density plots, venn diagrams, adjusted p-value histograms, box plots, moderated t-statistic q-q (quantile-quantile) plots, and mean-variance trend plots.
2.4. Protein–protein interaction network analysis of DEGs
From the 250 DEGs, the top-ranking DEGs were mapped, and the top-ranking protein-coding DEGs were used to compute the network using the STRING tool. The PPI networking was depicted using Cytoscape software [22]. The PPI of protein-coding DEGs was explored using the STRING database. Cytoscape developed the network and it was visualized using the Java programming language and Cytoscape application programming interface (API), and visualized using Cytoscape (version 3.8.2) [23].
The automated pipeline used in the functional annotation of modules and their visual exploration was performed using Cytoscape software. Data scripts were added to support the accurate visualization of the interaction network to generate files that can be introduced into Cytoscape for module network visualization [24], [25]. For this study, genes with a confidence score> 0.95 were used and the k-means clustering algorithm was applied to genetic data.
2.5. Construction of co-expressed gene network
Here, a network of co-expressed genes, co-expression associations of all genes in 2D (two-dimensional space) space, hierarchical clustering, scatter plot of co-expression using two genes, and expression patterns of genes were developed. To construct a co-expressed gene network, COXPRESdb v7 server was used [26]. The database provided co-expression information for 11 animal species, and the platform was used for the co-expressed gene of Macaca mulatta (Mcc-m platform). Entrez Gene IDs were inserted as inputs for developing a co-expressed gene network. Therefore, all Entrez Gene IDs were searched for the top-listed genes. In this search, all Macaca mulatta genes were used for the study.
2.6. Pathway analysis to understand insights into biological processes
Pathway enrichment analysis, which indicates a wide range of biological processes (pathological and physiological) in humans, was performed using the Reactome software [27]. No Macaca mulatta platform was found in Reactome to analyze biological processes. Therefore, biological processes in humans were used for analysis. It provides the expressed genes and the relationship between various cellular processes, such as DNA replication, metabolism, transport, and signal transduction [27]. Two main types of analysis can be performed using Reactome: gene set enrichment analysis (GSEA) with the help of gene scores excluding cutoff values and pathway enrichment using several important genes. In this study, pathway enrichment analysis was performed based on 24 top-ranked DEGs [28].
An outline of the analysis of the gene expression patterns of MPXV-infected MK2 cells is depicted in Fig. 1.
Fig. 1.
Outline of the workflow illustrates the expression data analysis of the present study using bioinformatics, systems biology, and statistical approaches.
3. Results
3.1. Data acquisition of MPXV-infected MK2 cells and DEG profiling
The gene expression profiles of the GSE21001 dataset was retrieved from the GEO database, and the dataset was further analyzed. Our dataset contained nine samples of MPXV-infected MK2 cells in three groups (one control group and two groups with MPXV-infected MK2 cells at two time points: 7 h and 3 h) (Table 1). First, a volcano plot (scatter and statistical plot) was developed using the dataset. This plot illustrated the p-value (statistical significance) of the DEGs against fold change (magnitude of change). The top 250 DEGs are listed in Table S1. These 250 DEGs were ranked. Table S1 presents the gene IDs, p-values, and F values. Table S2 presents the gene IDs of the 250 DEGs. A volcano plot of the dataset is depicted in Fig. 2. Three volcano plots were generated using our dataset: (i) the data of control vs. MPXV-infected MK2 cells at 3 h (Fig. 2a), (ii) data of control vs. MPXV-infected MK2 cells at 7 h (Fig. 2b), and (iii) data of the MPXV-infected MK2 cells at 3 h vs. MPXV-infected MK2 cells at 7 h (Fig. 2c). In the volcano plots, red and blue dots represent the up-regulated and down-regulated DEGs, respectively, which were adjusted with a p-value cut-off of 0.05.
Fig. 2.
Visualization of our DEGs data analysis outcome through the volcano plots between the control, MPXV infected MK2 cell in 3 h, and MPXV infected MK2 cell in 7 h (a) The volcano plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 3hrs (b) The volcano plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 7 h (c) The volcano plot shows the data analysis outcome of the MPXV infected MK2 cell in 3 h vs. MPXV infected MK2 cell in 7 h. In the volcano plots, red and blue dots represented the upregulated and downregulated DEGs, respectively.
Moreover, MD plots were developed to understand the log-intensity differences vs. log-intensity means and to visualize the DEGs. Here, three MD plots were generated using our dataset: (i) the data of control vs. MPXV-infected MK2 cells at 3 h (Fig. S1a); (ii) data of control vs. MPXV-infected MK2 cells at 7 h (Fig. S1b); and (iii) data of the MPXV-infected MK2 cells at 3 h vs. MPXV-infected MK2 cells at 7 h (Fig. S1c).
Furthermore, DEG data were presented using numerous statistical plots. First, a UMAP plot was developed, which helped to visualize the samples that were associated with each other (Fig. S2a). Our analysis detected the control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h. Subsequently, the analysis outcome of the dataset was represented through a venn diagram, which indicated the common DEGs among the three groups: control vs. MPXV-infected MK2 cells in 3 h, control vs. MPXV-infected MK2 cells in 7 h, and MPXV-infected MK2 cells in 3 h vs. MPXV-infected MK2 cells in 7 h. Among these groups, 110 significant DEGs were found to be common (Fig. S2b). Next, a box plot of our samples was constructed using the dataset. The plot indicates the distribution of the sample values that were used in this study (Fig. S2c). An expression density plot was developed to present the expression density of the DEG values of the three groups (Fig. S2d). This method visualizes the p-values of these tests using a histogram that indicates the pattern of the p-value histogram of the experiments [29]. This experiment indicated a decreasing trend for p-values (Fig. S2e). Furthermore, a moderated t-statistic quantile-quantile (q-q) plot was developed using the data samples of DEGs to provide a graphical view of the distribution (Fig. S2f). The plot compares the sample quantiles with the theoretical quantiles of the Student’s t-distribution. Finally, a mean–variance trend plot was drawn (Fig. S2g). This plot helps understand expression statistics and assesses the mean–variance relationship.
3.2. Top-listed DEGs and their expression pattern
Significant DEGs were observed in this study, as presented in Table 2. The p-values and F values of 24 DEGs were noted. The expression patterns of these genes at the two time points are depicted in Figs. 3a–3c. An upward trend was observed in the expression of some genes at the two time points (3 h and 7 h) compared with that of the control. The genes were LOC695323, TMEM107, LOC695427, HIST1H2AD, LOC705469, PMAIP1, HIST1H2BJ, and HIST1H3D. Similarly, a downward trend was noted in the expression of some genes at the two time points compared with that of the control. The genes were HOXA9, BAMBI, LMO4, PAX6, AJUBA, CREBRF, CD24, JADE1, SLC7A11, EID2, SOX4, B4GALT5, PPARGC1A, BUB3, SOS2, and CDK19.
Table 2.
Gene expression profile of significantly differentially expressed genes from three groups of our datasets.
| Sl. No. | Gene symbol | Gene name | P Value | F |
|---|---|---|---|---|
| 1 | LOC695323 | histone H4 | 4.26e-10 | 418.9 |
| 2 | TMEM107 | transmembrane protein 107 | 6.65e-10 | 381.5 |
| 3 | LOC695427 | histone H3.1 | 9.85e-10 | 351.3 |
| 4 | HIST1H2AD | histone cluster 1, H2ad | 1.99e-09 | 303 |
| 5 | HOXA9 | homeobox A9 | 3.01e-09 | 277.5 |
| 6 | LOC705469 | histone H2A type 1 | 3.04e-09 | 277.1 |
| 7 | BAMBI | BMP and activin membrane-bound inhibitor | 3.09e-09 | 276.1 |
| 8 | PMAIP1 | phorbol-12-myristate-13-acetate-induced protein 1 | 4.95e-09 | 249.9 |
| 9 | LMO4 | LIM domain only 4 | 7.09e-09 | 231.6 |
| 10 | PAX6 | paired box 6 | 8.94e-09 | 220.5 |
| 11 | AJUBA | ajuba LIM protein | 1.09e-08 | 211.2 |
| 12 | CREBRF | CREB3 regulatory factor | 1.14e-08 | 209.4 |
| 13 | CD24 | CD24 molecule | 1.53e-08 | 196.6 |
| 14 | JADE1 | jade family PHD finger 1 | 1.54e-08 | 196.5 |
| 15 | HIST1H2BJ | histone cluster 1, H2bj | 1.61e-08 | 194.6 |
| 16 | SLC7A11 | solute carrier family 7 (anionic amino acid transporter light chain, xc- system), member 11 | 1.67e-08 | 193 |
| 17 | EID2 | EP300 interacting inhibitor of differentiation 2 | 1.98e-08 | 186.2 |
| 18 | SOX4 | SRY-box 4 | 2.26e-08 | 181 |
| 19 | HIST1H3D | histone cluster 1, H3d | 2.68e-08 | 174.7 |
| 20 | B4GALT5 | UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 5 | 2.77e-08 | 173.4 |
| 21 | PPARGC1A | peroxisome proliferator-activated receptor gamma, coactivator 1 alpha | 4.53e-08 | 156.1 |
| 22 | BUB3 | BUB3 mitotic checkpoint protein | 4.71e-08 | 154.8 |
| 23 | SOS2 | SOS Ras/Rho guanine nucleotide exchange factor 2 | 5.81e-08 | 148 |
| 24 | CDK19 | cyclin-dependent kinase 19 | 6.21e-08 | 145.9 |
Fig. 3.
Some top-ranked DEGs and their expression pattern (a) The expression pattern of LOC695323, TMEM107, LOC695427, HIST1H2AD, HOXA9, OC705469, BAMBI, PMAIP1 (b) The expression pattern of LMO4, PAX6, AJUBA, CREBRF, CD24, JADE1, HIST1H2BJ, SLC7A11 (c) The expression pattern of EID2, SOX4, HIST1H3D, B4GALT5, PPARGC1A, BUB3, SOS2, CDK19.
3.3. PPI network construction of significant protein-coding DEGs
To investigate the protein–protein interactions of the identified DEGs, the PPI network was plotted using the DEGs from Table 2. The PPI network is depicted in Fig. 4a, which indicates 17 edges and 32 nodes. Furthermore, 250 genes were used to construct the PPI network, as depicted in Fig. 4b. The densely interlinked regions of the PPI network were identified at the lower section close to the middle region of the protein network. Consequently, a significant cluster was formed in the PPI network.
Fig. 4.
Visualization of the PPI networks developed from the 250 DEGs and top-ranked DEGs (a) Visualization of the PPI networks developed from the top-ranked DEGs (b) Visualization of the PPI networks developed from the 250 DEGs. The PPI networks were developed using Cytoscape software.
3.4. Co-expressed gene network of top-ranked DEGs
To further explore the co-expression analysis, a co-expressed gene network was developed. The input Entrez Gene IDs that were used to develop a co-expressed gene network are listed in Table S3.
A circular view of the co-expressed gene network is depicted in Fig. S3a. Moreover, 24 edges were identified in the network, among which eight nodes were interconnected. Subsequently, a Graphviz mode of the circular view of a co-expressed gene network was developed, which helped us understand the genes with Entrez Gene IDs (Fig. S3b). To understand the interlinked genes of the co-expression analysis, a concentric view of the co-expressed gene network was plotted (Fig. S3c). Additionally, the concentric view of the co-expressed gene network was plotted using the Graphviz mode to better understand the interlinking genes with Entrez Gene IDs (Fig. S3d). Both the co-expressed gene networks indicated a cluster with interlinking genes. A central gene was identified in this cluster, HIST1H2AD, and six genes were associated with this cluster. Some additional genes were found in the cluster, including histone H4 (Entrez Gene ID:697721), histone H2A type 1-B/E (Entrez Gene ID: 697977), and histone H2A type 1-H (Entrez Gene ID: 704994). We identified the top-listed DEGs that were associated with the KEGG map (Table S4).
3.5. Global view and hierarchical clustering of co-expressed gene network of top-listed DEGs
A global view of the co-expressed gene network was created in a 2D space. This allows researchers to intuitively understand the associations among the gene sets of interest in different species and visualize the co-expression associations of all genes in 2D space using UMAP. A global view of the 2D form of the co-expressed gene network is depicted in Fig. 5a. A global view in 2D space with quarried and co-expressed genes was plotted (Fig. 5b). Furthermore, a hierarchical clustering of the co-expressed gene network of the top-listed DEGs was constructed. This was represented using a heatmap (Fig. 5c). The hierarchical clustering of the co-expressed gene network indicated a dense cluster with five genes: LOC695427, LOC705469, HIST1H2AD, HIST1H3D, and HIST1H2BJ. It also indicated that HIST1H2AD was a significant gene and the other genes were associated with this gene.
Fig. 5.
The figure shows the global view and hierarchical clustering of the co-expressed gene network of top-ranked DEGs (a) Global view in 2D from the co-expressed gene network. The global view in the 2D form of co-expression associations of all genes from top-ranked DEGs was developed using UMAP (b) The global view in the 2D space of the co-expressed gene network with quarry genes and co-expressed genes (c) Hierarchical clustering of co-expressed gene network of top-ranked DEGs. The hierarchical clustering was visualized through a heatmap. Here, we have used the average linkage method.
3.6. Co-expression pattern analysis using two associated genes
The co-expression pattern of two associated genes was generated and their z-score was calculated. The z-scores provide a semi-quantitative estimation and relation of the expression levels of expressed genes [30]. A co-expression analysis of the two associated genes and their z-scores was performed. In this study, HIST1H2AD was used on one end and one of the four genes (LOC695427, LOC705469, HIST1H3D, and HIST1H2BJ) on the other end. The co-expressions of HIST1H2AD and LOC695427, HIST1H2AD and LOC705469, HIST1H2AD and HIST1H3D, and HIST1H2AD and HIST1H2BJ are depicted in Fig. S4a–S4d and their z-scores were 7.25, 5.78, 5.73, and 5.74, respectively.
3.7. Expression pattern of significant genes
Herein, the expression patterns of five significant genes, which were called the signature genes of this process, were identified (HIST1H2AD, LOC695427, LOC705469, HIST1H3D, and HIST1H2BJ). The expression pattern was analyzed using a previous gene expression dataset from the ArrayExpress database. Two expression patterns were evaluated: the normalized expression pattern of every sample and the grouped experiments, which are individually depicted in scatter and box plots. The normalized expression pattern of HIST1H2AD in each sample is depicted in Fig. S5a. The expressed sample of the scatter plot was between 2.5 and 7.0, and the dense cluster of the scatter plot was between 3.5 and 5.0. Furthermore, the normalized expression pattern of HIST1H2AD in grouped experiments is depicted in Fig. S5b. The 28 box plots are indicated in this figure. The median values of the 28 box plots were 4.0–4.5.
The normalized expression pattern of LOC695427 in every sample is depicted in Fig. S6a. Notably, the expressed sample of the scatter plot was between 3.0 and 7.0, and the dense cluster of the scatter plot was between 4.0 and 5.5. The normalized expression pattern of LOC695427 in the grouped experiments is depicted in Fig. S6b, and 28 box plots are represented in this figure. The median values of most of the box plots were 4.5–5.0.
The normalized expression pattern of LOC705469 in every sample is depicted in Fig. S7a. The expressed sample of the scatter plot was between 3.6 and 5.6, and the dense cluster of the scatter plot was between 4.0 and 4.6. Additionally, the normalized expression pattern of the grouped experiments is depicted in Fig. S7b, and 28 box plots are represented in this figure. The median values of most of the box plots were between 4.2 and 4.6.
Similarly, the normalized expression pattern of HIST1H3D in every sample is illustrated in Fig. S8a. The expressed sample of the scatter plot was between 4.0 and 7.0, and the dense cluster of the scatter plot was between 4.0 and 5.0. Also, the normalized expression pattern in the grouped experiments is depicted in Fig. S8b, and 28 box plots are illustrated in this figure. The median values of most of the box plots were between 4.5 and 5.0.
The normalized expression pattern of HIST1H2BJ in each sample is depicted in Fig. S9a. The expressed sample of the scatter plot was between 3.0 and 7.0, and the dense cluster of the scatter plot was between 5.0 and 6.5.
Additionally, the normalized expression pattern in the grouped experiments is depicted in Fig. S9b, and 28 box plots are illustrated in this figure. The median values of most of the box plots were between 5.0 and 5.5.
Notably, a crucial question was whether the five genes that were discovered during the analysis were associated with the replication process. Having conducted an extensive literature survey, the association of these five signature genes (HIST1H2AD, LOC695427, LOC705469, HIST1H3D, and HIST1H2BJ) was elucidated with gene replication in previous studies (Table S5).
3.8. Pathway analysis for insights into biological processes
Furthermore, a pathway analysis was conducted to gain insights about the top-ranked 24 DEGs and biological processes. The mapped biological processes associated with the 24 DEGs are indicated in Table S6. An overview of the analyzed pathway is depicted in Fig. S10a. It indicated the top-ranked genes involved in different biological processes, such as DNA repair, DNA replication, gene expression (transcription), and signal transduction. Notably, deeper insights were obtained from every biological process. Here, we present two examples of the profound insights, such as DNA replication and repair. First, we found that pre-initiation, assembly of the pre-replicative complex, and assembly of the ORC at the origin of replication were found to be involved in the DNA replication process (Fig. S10b). Second, base excision repair, AP site formation, depyrimidination, and depurination were revealed to be involved in the DNA repair process (Fig. S10b). Furthermore, a landscape map of the biological processes associated with the top 24 DEGs was developed (Fig. S11). The biological processes associated with the signature genes of the DEGs were identified (Table S7).
4. Discussion
Viral replication is a significant research topic, providing important information regarding replication mechanisms. On entering the body, a virus relies on the molecular machinery of the host cell to replicate. Therefore, understanding the host cell replication machinery is essential [31]. Herein, we attempted to understand host cell gene expression and co-expression patterns during MPXV replication. Additionally, PPI networking patterns of protein-coding genes during MPXV replication were depicted. We mapped the expression pattern at two time points, 3 h and 7 h, to identify the significantly expressed genes. Furthermore, we studied gene expression patterns in the kidney epithelial cells of MPXV-infected Macaca mulatta (MK2 cells). We identified the gene ID, p-value, and F value of the top 250 DEGs. From these genes, we identified the 24 top-ranking genes. Subsequently, we constructed a PPI network using the top 24 protein-coding genes and the 250 DEGs. Furthermore, a co-expression map was developed to understand the association between the 24 top-ranking genes and the other genes. From the co-expression pattern analysis, we chose five signature genes and explored further to understand their role in replication. The expression patterns of the five signature genes were analyzed. However, some researchers have attempted to understand the gene expression patterns of viruses. Rubins et al. analyzed gene expression patterns using microarray analysis and prepared a transcriptional map of MPXV and vaccinia genomes. In addition, they performed a comparative investigation of the gene expression of the two viruses [16]. In another study, Rubins et al. determined the gene expression patterns of MPXV and VACV. They discovered MPXV induced-suppression of expression of some innate immune system genes, such as IL-6, IL-1α, IL-1β, TNF-α, and CCL5 [17]. Alkhalil et al. reported the gene expression pattern of MPXV-infected MK2 cells at 3 h and 7 h using microarray analysis. They identified both up-regulated and down-regulated genes. They also found the functional distribution of DEGs. They mapped the interfaces of host–pathogen interactions using combined microarray and statistical analysis [19]. However, we performed DEGs, PPI networking, co-expression, and pathway analyses using the same dataset. Our study used a blended mode of analysis of three areas: bioinformatics, systems biology, and statistical approaches. We performed a comprehensive analysis to determine the final hypothesis of the study.
We evaluated the DEGs of MPXV-infected MK2 cells from nine datasets: control, MPXV-infected MK2 cells at 3 h, and MPXV-infected MK2 cells at 7 h (Table 1). Dissanayake et al. performed a comparative transcriptomic investigation of influenza virus and rhinovirus (RV). The researchers of this study performed DEG analysis, and finally, DEGs were determined using RV, influenza A virus, and influenza B virus. Finally, the top 20 up-regulated DEGs were identified [32]. Similarly, Li et al. performed a transcriptome study to elucidate the transcript patterns of C6/36 cells infected with dengue virus 2. A total of 1239 DEGs were identified. Among these genes, 1133 were up-regulated and 106 down-regulated [33]. However, in the present study, we identified the top 250 DEGs in MPXV-infected MK2 cells (Table S1). From these genes, 24 top-ranking genes DEGs were identified. We found eight up-regulated genes and 16 down-regulated genes from the top-ranked genes. Furthermore, five signature genes (HIST1H2AD, LOC695427, LOC705469, HIST1H3D, and HIST1H2BJ) associated with replication could act as MPXV replication machinery. Our study revealed a significant genomic landscape for MPXV replication. Therefore, the findings of this study lay the foundation for further research on the replication of MPXV-infected host cells. However, further functional verification is required for these five signature genes.
Moreover, several reports have stated that viruses hijack host cell machinery to replicate; on entering, viruses hijack the host cell metabolism. Thaker et al. illustrated the hijacking of cellular metabolism after virus entry into the host cell and during replication [34]. Similarly, Syed et al. illustrated the hijacking of host lipid metabolism by hepatitis C virus on entering the host cell [35]. Furthermore, viruses hijack the signaling pathway of the cell. Diehl and Schaal demonstrated that the host cell PI3K/Akt signaling pathway, which might be crucial for many processes, such as apoptosis, autophagy, RNA processing, and translation, was hijacked by the virus [36]. Our study also indicated the process of hijacking the host cell machinery. We identified one escape process during biological process identification: RNA polymerase I promoter escape. This could be a process of hijacking the host cell machinery for replication.
The epidemiological and transmission patterns of a virus are associated with replication. Recent studies have examined the replication and clinical manifestation of MPXV. Hornuss et al. analyzed the replication patterns, transmission characteristics, and clinical manifestations of MPXV in four cases in Germany [37]. Lapa et al. analyzed semen samples from patients with MPXV. Researchers have attempted to monitor the duration of viral shedding. They studied MPXV replication through real-time polymerase chain reaction (RT-PCR). Six days after symptom onset, the collected semen was inoculated in Vero E6 cells. MPXV replication was confirmed using RT-PCR [38]. Similarly, Moschese et al. collected samples from urethral and anal swabs of patients who attended a sexual health clinic in Milan, Italy, in July 2022. They determined the replication of the virus using MPXV-specific RT-PCR [39]. In recent studies, researchers investigated MPXV replication using RT-PCR or other analyses for diagnostic purposes and attempted to understand viral shedding. However, recent studies have not elucidated the genomic landscape of replicating MPXV. In this study, we performed in depth analyses of DEGs in MPXV-infected host cells (MK2 cells) and co-expression patterns under the replication pattern, machinery, and genomic landscape. Therefore, our study is novel and remarkable in the present scenario of MPXV outbreak in non-epidemic countries.
5. Conclusions
The lack of effective treatment is evident in non-epidemic countries with recent MPXV infections. Using bioinformatics, systems biology, and statistical approaches, we identified 24 top-ranked DEGs. In addition, five signature genes were identified. These genes may act as regulatory genes for MPXV replication in host cells. Therefore, these findings help to determine the MPXV replication machinery and genomic landscape of MPXV replication. Furthermore, the findings will help to increase our understanding of the complex host–virus interaction, replication, and disease progression during MPXV infection. Future studies on this topic will be aided by these baseline data. Moreover, our integration of top-ranked DEGs, PPI networks of the 250 DEGs that were analyzed, 24 top-ranked DEGs, and co-expression networks are promising therapeutic targets for developing new therapeutic molecules against MPXV.
Ethics approval and consent to participate
Not required.
Consent for publication
Not required.
CRediT authorship contribution statement
Conceptualization, writing-original draft preparation, methodology, investigation was performed by C.C; Validation and formal analysis done by M.B; Reviewing and editing was done by K.D; Supervision and project administration was done by C.C; Fund acquisition by S-S. L. All authors have read and agreed to the published version of the manuscript.
Competing interests
The authors declare that they have no competing interests.
Acknowledgments
This study was supported by Hallym University Research Fund and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF- 2020R1I1A3074575).
Funding
No fund received.
Footnotes
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jiph.2023.01.015.
Appendix A. Supplementary material
Supplementary material
.
Table S1. The top 250 DEGs are listed in our analysis. The table also illustrates P value, F, gene symbol, and gene title.
.
Table S2. The top 250 DEGs are listed in this table. The table also illustrates adj.Pvalue, Pvalue, gene symbol, and gene title.
.
Table S3. The top-rank 24 DEGs are listed in this table. The table also illustrates gene symbol, gene name, and EntrezGene ID.
.
Table S4.The top-rank DEGs linked with the KEGG map.
.
Table S5. The critical genes noted in the study and their associations with replication were reported in previous studies. Other researchers also noted that these genes were associated with replication machinery.
.
Table S6. The mapped biological processes are related to our top-rank 24 DEGs. The table also illustrates pathway identifier, pathway name, total entities, entities ratio, reactions ratio, mapped entities, etc.
.
Table S7. Identified some biological processes related to signature genes of DEGs.
.
Fig. S1. The depiction of MD (mean difference) plots using our three DEGs data the control, MPXV infected MK2 cell in 3 h., MPXV infected MK2 cell in 7 h (a) The MD plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 3 h (b) The MD plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 7 h (c) The MD plot shows the data analysis outcome of the MPXV infected MK2 cell in 3hvs. MPXV infected MK2 cell in 7 h.
.
Fig. S2. Visualization of our DEGs data using different statistical analyses. The outcome of the statistical analysis shows through the different charts and graphs (a) Visualization of our DEGs data through the UMAP plot (b) The DEGs data analysis outcome of the dataset was represented through a Venn diagram (c) The DEGs data analysis outcome of the dataset was represented through a box plot which shows the distribution of our chosen sample values used in this study (d) The plot depicted the expression density of values of the three groups' DEGs (e) The plot depicted the p-value histogram of the values of three groups' DEGs (f) The plot shows the outcome of the moderated t-statistic q-q(quantile-quantile) plot (g) The depiction of the mean-variance trend plot using the three groups' DEGs.
.
Fig. S3. Co-expressed gene network shows top-rank DEGs (a) circular view of co-expressed gene network of top-ranked DEGs (b) Co-expressed gene network of top-ranked DEGs with Entrez Gene IDs (c) The plotted the co-expressed gene network with a concentric view. It shows the interlinked regions more clearly (d) The plotted the co-expressed gene network with the concentric view. The plotted co-expressed gene network shows the Entrez Gene IDs.
.
Fig. S4. The outcome of the co-expression pattern analysis using two associated genes (a) the co-expression pattern of HIST1H2AD and LOC695427, (b) The co-expression pattern of HIST1H2AD and LOC705469, (c) The co-expression pattern of HIST1H2AD and HIST1H3D (d) The co-expression pattern of HIST1H2AD and HIST1H2BJ.
.
Fig.S5. Expression pattern of HIST1H2AD gene (a) Normalized expression pattern of the HIST1H2AD gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H2AD gene represented through the box plot.
.
Fig. S6. Expression pattern of LOC695427 gene (a) Normalized expression pattern of the LOC695427 gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the LOC695427 gene represented through the box plot.
.
Fig. S7. Expression pattern of LOC705469 gene (a) Normalized expression pattern of the LOC705469 gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the LOC705469 gene represented through the box plot.
.
Fig. S8. Expression pattern of HIST1H3D gene (a) Normalized expression pattern of the HIST1H3D gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H3D gene represented through the box plot.
.
Fig. S9. Expression pattern of HIST1H2BJ gene (a) Normalized expression pattern of the HIST1H2BJ gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H2BJ gene represented through the box plot.
.
Fig. 10. The outcome of pathway analysis to understand insights into biological processes (a) Different biological processes associated with the top-rank DEGs (b) Two examples of association of more profound insights into biological processes (DNA repair and DNA replication).
.
Fig. S11. A landscape map illustrates the biological processes related to our top-ranked 24 DEGs.
.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material
Table S1. The top 250 DEGs are listed in our analysis. The table also illustrates P value, F, gene symbol, and gene title.
Table S2. The top 250 DEGs are listed in this table. The table also illustrates adj.Pvalue, Pvalue, gene symbol, and gene title.
Table S3. The top-rank 24 DEGs are listed in this table. The table also illustrates gene symbol, gene name, and EntrezGene ID.
Table S4.The top-rank DEGs linked with the KEGG map.
Table S5. The critical genes noted in the study and their associations with replication were reported in previous studies. Other researchers also noted that these genes were associated with replication machinery.
Table S6. The mapped biological processes are related to our top-rank 24 DEGs. The table also illustrates pathway identifier, pathway name, total entities, entities ratio, reactions ratio, mapped entities, etc.
Table S7. Identified some biological processes related to signature genes of DEGs.
Fig. S1. The depiction of MD (mean difference) plots using our three DEGs data the control, MPXV infected MK2 cell in 3 h., MPXV infected MK2 cell in 7 h (a) The MD plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 3 h (b) The MD plot shows the data analysis outcome of control vs. MPXV infected MK2 cell in 7 h (c) The MD plot shows the data analysis outcome of the MPXV infected MK2 cell in 3hvs. MPXV infected MK2 cell in 7 h.
Fig. S2. Visualization of our DEGs data using different statistical analyses. The outcome of the statistical analysis shows through the different charts and graphs (a) Visualization of our DEGs data through the UMAP plot (b) The DEGs data analysis outcome of the dataset was represented through a Venn diagram (c) The DEGs data analysis outcome of the dataset was represented through a box plot which shows the distribution of our chosen sample values used in this study (d) The plot depicted the expression density of values of the three groups' DEGs (e) The plot depicted the p-value histogram of the values of three groups' DEGs (f) The plot shows the outcome of the moderated t-statistic q-q(quantile-quantile) plot (g) The depiction of the mean-variance trend plot using the three groups' DEGs.
Fig. S3. Co-expressed gene network shows top-rank DEGs (a) circular view of co-expressed gene network of top-ranked DEGs (b) Co-expressed gene network of top-ranked DEGs with Entrez Gene IDs (c) The plotted the co-expressed gene network with a concentric view. It shows the interlinked regions more clearly (d) The plotted the co-expressed gene network with the concentric view. The plotted co-expressed gene network shows the Entrez Gene IDs.
Fig. S4. The outcome of the co-expression pattern analysis using two associated genes (a) the co-expression pattern of HIST1H2AD and LOC695427, (b) The co-expression pattern of HIST1H2AD and LOC705469, (c) The co-expression pattern of HIST1H2AD and HIST1H3D (d) The co-expression pattern of HIST1H2AD and HIST1H2BJ.
Fig.S5. Expression pattern of HIST1H2AD gene (a) Normalized expression pattern of the HIST1H2AD gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H2AD gene represented through the box plot.
Fig. S6. Expression pattern of LOC695427 gene (a) Normalized expression pattern of the LOC695427 gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the LOC695427 gene represented through the box plot.
Fig. S7. Expression pattern of LOC705469 gene (a) Normalized expression pattern of the LOC705469 gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the LOC705469 gene represented through the box plot.
Fig. S8. Expression pattern of HIST1H3D gene (a) Normalized expression pattern of the HIST1H3D gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H3D gene represented through the box plot.
Fig. S9. Expression pattern of HIST1H2BJ gene (a) Normalized expression pattern of the HIST1H2BJ gene represented through a scatter plot (b) The normalized expression pattern of grouped experiments of the HIST1H2BJ gene represented through the box plot.
Fig. 10. The outcome of pathway analysis to understand insights into biological processes (a) Different biological processes associated with the top-rank DEGs (b) Two examples of association of more profound insights into biological processes (DNA repair and DNA replication).
Fig. S11. A landscape map illustrates the biological processes related to our top-ranked 24 DEGs.