Toxoplasmosis is one of the major public health problems world-wide, and is caused by an apicomplexian protozoan parasite Toxoplasma gondii . T. gondii is unique in nature, because it can infect all type of nucleated cells of warm-blooded animals and humans . Major transmission routes of the parasite can be oocysts contaminated foods, water and vegetables , ingestion of undercooked meat and meat products  and congenital transmission from T. gondii infected pregnant women   and blood transfusion/organ transplantation  . Generally, the primary infection is asymptomatic or with mild flu-like symptoms in immune competent host, but can be fatal and severe in immune compromised individual and fetus bearing pregnant women .
T. gondii is critical in a sense that it can evade the immune system after acute infection and thereby undergoes developmental switching from fast replicating tachyzoite to slow replicating dormant bradyzoite preferentially in skeletal muscle and brain  . This transformation from one parasite stage to another establishes chronic infection. As the parasite can infect any cells, it can affect other organs including heart , eyes , kidney  and liver . Liver is an important organ where the entire metabolism e.g. carbohydrate, lipids, proteins, detoxification of xenobiotics and drugs takes place . Many studies show that the parasite causes a number of pathological changes in liver including hepatitis, granuloma hepatomegaly and necrosis . Furthermore, Ustun and colleagues has shown that T. gondii infection can be associated with liver cirrhosis . These data nevertheless confirm the existence of hepatic toxoplasmosis. It has to be stressed that understanding of host parasite interaction is important for disease pathogenesis and designing new drugs. Recently, He J-J and colleagues has performed transcriptome and proteome analysis of T. gondii infected mouse liver to understand the impact of infection on host liver both at RNA and protein level  . The authors identified differentially regulated genes (n = 2758) and proteins (n = 301) including up-and down-regulated genes and proteins in T. gondii infected mouse liver compared to non-infected one  . However, one of the major limitations of the studies was to overlook the correlation of gene expression at both mRNA and protein level. This is important, because the entire transcript at RNA level does not participate in protein synthesis through translation which suggests the presence of epigenetic regulation at transcription or translation level. Therefore, this study has been designed to integrate both transcriptome and proteome data set of T. gondii infected mouse liver for identifying the genes those are regulated as same fashion in both transcription and translation. Herein, we have integrated whole transcriptome shotgun sequencing (RNAseq) and tandem mass spectrometry (LC-MS/MS) proteomics of T. gondii infected mouse liver to get better understanding of host-parasite interaction and molecular mechanism of disease pathogenesis. The integrated analysis of transcriptomics and proteomics data merges large number of RNA transcripts with relatively less number of proteins to find out common candidates by cross matching the sequence identifier. The outcome of this integrated technology helps further analysis on gene ontology to discover accurate biological role of selected and common transcript/proteins.
A large number of cellular processes are mediated by physical interactions among proteins such as, signal transduction, enzyme activity, and post-translational modification. A protein-protein interaction (PPI) network has a small number of highly connected protein nodes (known as hubs) and many poorly connected nodes. These hub proteins are the most important for survival and reproduction of that particular organism. They may function as effector molecules for disease pathogenesis, diagnostic marker and drug target. As homology modeling of these hub proteins were also analyzed in this study, the information might be useful to get a rough idea where the alpha carbons of key residues sit the folded protein. They can guide site-directed mutagenesis experiments, or hypotheses about structure-function relationships. Even within the pharmaceutical industry homology modeling can be valuable in structure-based drug discovery and drug design.
All of these analyses were done on integrated transcriptomic and proteomic data. So, findings of our study may provide useful insights of pathogenesis of T. gondii infection and host-parasite interaction.
2. Methods and Materials
2.1. Retrieval of Data Set
Raw data files of transcriptome and proteome analyses of T. gondii infected mouse liver were retrieved separately from online database deposited from the study of He J-J and colleagues  . Briefly, in these studies BLALB/c mice and PYS strain were used as host and T. gondii parasite respectively. Three mice were either infected with T. gondii tachyzoite (n = 200) and/or were treated with PBS for non-infected control. Liver tissues were collected from 6 days post infected and control mice for experimental analysis. RNA sequencing and LC-MS analysis were performed with the infected and non-infected liver tissue to determine gene expression pattern at RNA level and protein profiling at protein level. From the raw data set, differentially regulated genes and proteins files were sorted and used for further analysis in this study. Outline of the whole procedure is shown in Figure 1.
2.2. Analysis of Differentially Expressed Genes and Proteins
Venn analysis was performed with differentially expressed genes and proteins to
Figure 1. A schematic representation of the workflow for analyzing transcriptome and proteome data. Abbreviations: DEG, Differentially Expressed Gene; DEP, differentially expressed b proteins; DAVID, Database for Annotation, Visualization and Integrated Discovery; PPI, Protein-Protein Interaction; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins; CytoNCA, A Cytoscape apps for network centrality analysis; MCODE, a cytoscape apps for molecular complex detection.
find out the common transcripts those are translated into proteins using Venn diagram online software of the VIB/UGent (http://bioinformatics.psb.ugent.be/webtools/Venn/). Transcript those translated into proteins was further analyzed in pursues software (http://www.coxdocs.org/doku.php?id=perseus:start) to find out up and down-regulated DEGs/DEPs. The program provided a scatter diagram which clearly showed these two groups separately.
GO analysis was performed on 159 DEGs/DEPs to study the global biological changes in liver function after T. gondii infection by using DAVID (Data-base for Annotation, Visualization, and Integrated Discovery) tool. The tool was used for gene ontology search to investigate biological processes, cellular components and molecular functions. GO terms having an EASE score (a modified Fisher Exact P-Value) less than 0.05 and Count (number of DEGs/DEPs involved in any GO terms) equal or greater than 5 were considered.
Differentially expressed up and down regulated genes/proteins were also analyzed through Cytoscape 64-bit program for identification of their association on different pathways including biological process, cellular process and molecular function.
2.3. Analysis of Protein-Protein Interaction
Protein-protein interaction network was also prepared by using both STRING and Cytoscape program. Protein-protein interactions were generated in STRING and subsequently the interaction network was imported into Cytoscape software for visualization and further analysis. STRING parameters were kept as it is (minimum required interaction score (medium confidence) was 0.4 and as maximum number of interactors, only query proteins being displayed. Average local clustering coefficient of this PPI was 0.754 and PPI enrichment p-value was less than 1.0e−16. These proteins were found to interact among themselves than expected.
2.4. Analysis of mRNA-Protein Stability
Data containing protein and mRNA copy numbers, half-lives, transcription rate and translation rate constants in mouse fibroblasts (NIH3T3) was downloaded from online repository . In this study they used amino acids and a nucleoside analogue 4-thiouridine (4sU) for labelling to measure simultaneously protein and mRNA turnover in a population of exponentially growing non-synchronized NIH3T3 mouse fibroblasts. They quantified proteins by liquid chromatography and online tandem mass spectrometry (LC-MS/MS) and identified 84,676 peptide sequences. Later they assigned them to 6445 unique proteins (false discovery rate, 1% at the peptide and protein level). After pulse labelling, they fractionated RNA samples into the newly synthesized and pre-existing fractions and analyzed them by mRNA sequencing and quantified by mapping reads to their exonic region. They calculated mRNA half-lives based on the ratios of newly synthesized RNA/total RNA ratio and the pre-existing RNA/total RNA ratio. On the other hand, proteins were five times more stable than mRNAs. Notably, they did not find any correlation between protein and mRNA half-lives (R2 = 0.02). So, to investigate the experimental noise they performed a second independent large-scale experiment and measured mRNA and protein levels and their half-lives again. The overall correlation of half-lives and levels between both replicates was good. They deposited the raw data to publicly available online database.
Proteotranscriptomics approach has not been utilized to its full potential to study the host-pathogen interactions. Here, we integrated transcriptomic and proteomic approaches to identify factors involved in the pathogenesis of Toxoplasma gondii (T. gondii) infected mouse liver. Recently, He J-J et al. studied transcriptome and proteome of mouse liver before and after T. gondii infection using RNA sequencing  and liquid chromatography-mass spectrometric (LC-MS/MS) technique . The authors deposited the raw data to publicly available online database. Herein, raw data files of transcriptome and proteome of T. gondii infected mouse liver were retrieved separately and used for further analysis to integrate both approaches for searching novel factors involving disease pathogenesis and drug target.
3.1. Identification of Common Differentially Expressed Genes/Proteins
From transcriptome analysis of T. gondii infected mouse liver, 18,000 transcripts were identified at RNA level . Among them 2758 transcripts were differentially expressed. On the other hand, proteome analysis of T. gondii infected mouse liver revealed 3700 proteins of which 301 were differentially expressed .
After analyzing 2758 differentially expressed genes (DEGs) and 301 differentially expressed proteins (DEPs), common DEGs and DEPs expressed in both transcriptome and proteome level were identified by merging two datasets in Perseus software. Results revealed that 159 DEGs/DEPs were expressed at both RNA and protein level as shown in Figure 2(a). Our data also showed that 2599
Figure 2. (a) Venn diagram of differentially expressed genes/proteins of T. gondii infected mouse liver. Transcriptomic analysis of T. gondii infected mice liver identified 2758 DEGs and proteomic analysis of infected mouse liver identified 301 DEPs in T. gondii infected mice liver. After analysis 159 DEGs/DEPs were found at both RNA and protein level. (b) Scatter diagram of common 159 differentially regulated genes/proteins. Perseus software was used to create this figure using transcriptomic Log2 and proteomic Log2 data of 159 DEGs/DEPs.
DEGs were only found at RNA level whereas 142 DEPs were found at protein level. List of common 159 DEGs/DEPs are shown in supplementary TableS1.
3.2. Identification of Up and Down-Regulated Genes/Proteins
Among these 159 differentially expressed genes/proteins, 86 were found to be up-regulated while 72 were down-regulated (Supplementary TableS2 and TableS3, respectively) using Perseus software. These DEGs/DEPs were used to create a scatter plot with four quadrants (Figure 2(b)) where quadrant I revealed 86 DEGs/DEPs (orange) which had increased level of expression that indicates higher transcriptomic and proteomic log2 value. In quadrant III, there were 72 DEGs/DEPs (purple) which had low transcriptomic and proteomic log2 value. Quadrant II represented one single DEGs/DEPs (blue) which was considered as mixed expression and deviated from its principle. From this figure it was also found that no DEGs/DEPs had fallen into quadrant IV.
3.3. Gene Ontology (GO) Analysis of Up-Regulated Genes/Proteins
GO analysis was performed on 159 DEGs/DEPs to study the global biological changes in liver function after T. gondii infection by using DAVID. This reveals that 86 up-regulated genes/proteins were associated with 17 biological processes, 16 cellular components and 10 molecular functions (Supplementary TableS4). Different GO terms and its participating DEGs/DEPs number were used to prepare Figures 3(a)-(c). Among 17 biological processes top four processes (Immune System Process, Innate Immune Response, Cellular Response to Interferon-beta and Adaptive Immune Response) have significant FDR value and largest number of genes enriched. From GO analysis it was also found out that the up-regulated gene products mainly enriched in the extracellular exosome (44), blood micro-particle (14), extracellular space (25) and extracellular region (24). These were the top 4 terms of GO cellular component having significant FDR value. Cytoplasm and membrane were two other cellular components having large number of DEGs/DEPs enriched (38 and 37 respectively) but their FDR value was high. So, they were not considered as enriched terms. Similarly, Figure 3(c) revealed that “GTPase activity” (FDR = 0.0029), “GTP binding” (FDR = 0.034), “Serine-type endopeptidase inhibitor activity” (FDR = 0.196) and “Peptidase inhibitor activity” (FDR = 0.196) were the most enriched term with 9, 10, 6 and 6 genes hits respectively.
3.4. Gene Ontology (GO) Analysis of Down-Regulated Genes/Proteins
72 down regulated DEGs/DEPs were found to be associated with 7 biological pathways, 10 cellular components and 15 molecular functions (Figures 4(a)-(c)). GO terms along with their associated genes were listed in supplementary TableS5. Among 7 biological pathways, oxidation-reduction process, metabolic process, lipid metabolic process and transport were the top four terms having
Figure 3. (a) Gene ontology for biological process analysis of up-regulated genes/proteins. (b) Gene ontology for cellular component (CC) annotation of up-regulated genes/proteins. (c) Gene ontology molecular function (MF) annotation of up-regulated genes/proteins.
the largest number of genes enriched. Down-regulated cellular component and associated differentially expressed genes/proteins were shown in Figure 4(b). This figure revealed that the down-regulated gene products mainly located in the Organelle Membrane (12), Mitochondria (26), Endoplasmic Reticulum Membrane (17) and Intracellular Membrane-bounded Organelle (17). These were the top four terms of GO cellular component having significant FDR value.
Figure 4. (a) Gene ontology for biological process analysis of down-regulated genes/proteins. (b) Gene ontology for cellular component (CC) annotation of down-regulated genes/proteins. (c) Gene ontology for molecular function (MF) annotation of down-regulated genes/proteins.
From this analysis it was also found out that Oxidoreductase activity, Monooxygenase activity, Heme binding and Catalytic activity were the most enriched term with 24, 12, 13 and 16 genes hits respectively (Figure 4(c)).
3.5. Gene Ontology Analysis of Unique Differentially Expressed Transcript (DETs) and Differentially Expressed Proteins (DEPs)
From the Figure2(a) it was found that 2599 differentially expressed transcripts didn’t take part in translation. That’s why these 2599 DETs and 142 DEPs were considered as unique. According to DAVID tool, 318 biological processes were found to be associated with 2599 DETs. Top 20 GO terms were shown in supplementary Figure1(a). 203 DETs fell into the category of “Oxidation-reduction process”, whose FDR value was 5.26E−40. The category of “Immune system process” (FDR = 3.86E−24), “Inflammatory response” (FDR = 1.09E−18), “Metabolic process” (FDR = 6.39E−18) were associated with 120, 103, 123 DETs respectively. These were the significantly enriched biological processes with the largest number of DETs hits. Similarly, GO annotation also revealed that 18 biological processes were associated with 142 differentially expressed proteins (Supplementary FigureS1(b)). According to the supplementary FigureS1(b), DEPs were associated with “Protein folding” (FDR = 0.011936) whereas, 14 DEPs with “Translation” (FDR = 0.026348). These were the significantly enriched GO terms with the largest number of DEPs hits. 22 DEPs fell into the category of “Transport” (FDR = 52.771). In spite of associating with large number of DEPs this GO term was not considered as significant due to its FDR value.
3.6. Protein-Protein Interaction (PPI) Analysis of Common DEPs
From gene ontology annotation it was found that immune system process is the most significantly enriched up-regulated biological process and oxidation-reduction process is the most significant down-regulated biological process in T. gondii infection. Protein-protein interaction (PPI) network of DEPs associated with these biological processes (immune system process and oxidation-reduction process) was generated by STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database. This data then imported into Cytoscape (https://cytoscape.org/) software to visualize the protein interaction relationship network and to analyze significant clusters and hub proteins. Data showed that Immune system processes were mediated by 26 DEPs in mouse. According to DAVID output this was the top-most biological process having a significant FDR value (5.75E−21) which was up-regulated after T. gondii infection. Although GO analysis showed that 26 up-regulated proteins were involved in immune system process, after generating a PPI in STRING it was found that one protein, Pml, does not interact with any other protein. So, after filtering out disconnected protein, a PPI network containing 76 interactions for a total of 25 DEPs was generated as shown in Figure 5(a). Such enrichment (25 DEPs had 76
Figure 5. (a) Protein-protein interaction (PPI) among up-regulated proteins involved in immune system process. Nodes represent proteins (brown color) while, blue arrow represents interaction between proteins. (b) Protein-protein interaction (PPI) among down-regulated proteins involved in oxidation-reduction process. Nodes (purple color) represent proteins while, blue arrow represents interaction between proteins.
interactions) suggested that the proteins were at least partially biologically connected, as a group. 25 DEPs which were involved in this PPI, were Ifit3, Serping1, Fgb, Fga, Samhd1, C3, Serpina3g, Cd74, Lcn2, Irgm1, Gbp2b, Tap1, S100a9, Tap2, Zbp1, Psmb9, Anxa1, Psmb8, Cfh, Cd14, H2-K1, Fgg, Ifit1 and H2-Aa. Gene ontology analysis of 72 down-regulated DEPs revealed that the top most biological process was oxidation-reduction process having a significant FDR value (2.60E−17). Analysis result showed that 26 DEPs were involved in this process. A protein-protein interaction (PPI) among these 26 proteins was generated in STRING database and imported the data into Cytoscape software for visualization. Among these 26 DEGs/DEPs, Me1 did not interact with other proteins. Figure 5(b) was designed after filtering out this disconnected protein. Default parameters were used as before. A PPI network containing 79 interactions for a total of 25 DEPs was generated in Cytoscape software. Average local clustering coefficient of this interaction is 0.615 and PPI enrichment p-value is less than 1.0e−16. This indicated that these proteins interacted among themselves than expected. 25 DEPs of this PPI were Fmo3, Cyp3a16, Cyp2b9, Cyp8b1, Cyp3a11, Hsd17b6, Bbox1, Cyp2c29, Pah, Cat, Ido2, Fads2, Ndufb6, Ndufb5, Hsd3b3, Ehhadh, Uqcrfs1, Cyp4a10, Cyp2c50, Cyp3a25, Cyp2c54, Cyp1a2, Acadsb, Hao2, and Aldh3a2.
3.7. Determination of mRNA-Protein Stability Using Half Life
Snapshot analysis of mRNA and protein levels alone cannot provide sufficient information to understand the dynamic nature of gene expression comprehensively. Of note the impact of mRNA and protein half-lives on their respective abundance may provide important insight into the regulatory pattern. We took advantage of the mRNA and protein half-lives datasets in moue fibroblast cells from Bjorn Schwanhausser et al. In brief, the authors quantify cellular mRNA and protein expression levels and turnover in parallel in a population of unperturbed moue fibroblast cells by Pulse labelling to determine mRNA and protein half-lives .
Among the 159 common DEGs/DEPs which were identified in this current study, only 51 DEGs/DEPs were found to be matched from the half-life retrieved data. Again, among these 51 DEGs/DEPs, 31 had known stability according to retrieved data. These 31 DEGs/DEPs and their stability were enlisted in Table 1. Using these 31 DEGs/DEPs a scatter plot was generated for better understanding by using ggplot package of R programming language which was shown in Figure 6. Identification of the stability was based on half-lives of a particular gene on its mRNA and protein level. Intestinally, the hub-protein Tap1 showed higher mRNA and protein half-lives implying that hub-proteins may have higher mRNA and protein stability to carry out biological functions.
Study of host-pathogen interaction is important for understanding the disease pathogenesis and drug treatment. Recently, He J-J and colleagues did transcriptome and proteome analysis separately on T. gondii PYS strain infected BALB/c
Table 1. List of common DEGs/DEPs along with their stability.
Figure 6. mRNA-Protein stability confirm by half-life: A scatter plot showing stability of common DEGs/DEPs. 15 genes fell into quadrant I whose encoded mRNAs and proteins both were stable. Quadrant II had 11 genes whose encoded mRNAs were stable and proteins were unstable. 3 genes fell into quadrant III whose encoded mRNAs and proteins both were unstable and lastly, quadrant IV had 2 genes whose encoded mRNAs were unstable but proteins were stable.
mice liver. In this study, raw date of transcriptome and proteome from T. gondii infected and uninfected mouse liver tissues were further analyzed through integrated proteotranscriptomic approaches. It has to be stressed that liver is an important organ that perform multiple task in the body. For example, 1) It can act as a storage site of glycogen, vitamins and minerals; 2) It can act as a house of metabolism of carbohydrate, proteins fat and drugs; 3) It helps for the synthesis and excretion of bile acid, bilirubin, cholesterol, etc. Besides, after acute infection T. gondii can undergoes stage conversion from fast replicating tachyzoite to slow replicating bradyzoite not only in brain and skeletal muscle but also in liver, eyes etc. Thus, T. gondiimay causes liver cirrhosis and inflammation. Therefore, study of transcriptome and proteome of T. gondii infected mouse liver might help to understand the mechanism on the impact of the parasite on liver function. Some previous studies on serological, biochemical and direct detection of T. gondii have revealed that T. gondii infection plays an important role in liver pathologies  . However, mechanism of how T. gondii establishes hepatic infection remains unclear.
Transcriptome of T. gondii infected and non-infected mouse liver revealed 2758 differentially regulated genes and 301 differentially regulated proteins, among them 159 differentially expressed genes (DEGs) were identified as common and those were translated into differentially expressed proteins (DEPs). This data suggests that all of the differentially expressed genes are not completely translated into proteins. The possible reasons could be: 1) There might be some unstable RNA that does not participate in protein synthesis 2) Among these, even some protein may degrade quickly therefore not found at the time of translation.
After infection T. gondii developed strategies to manipulate host immune system, energy metabolism, and signaling by inducing host cell gene transcription . Gene ontology analysis of these 159 DEGs/DEPs showed that immune response, extracellular exosome, blood micro-particle, extracellular space, GTPase activity, peptidase inhibitor activity and identical protein binding activity become up-regulated while, oxidation-reduction, fatty acid metabolism, lipid metabolism, organelle membrane, mitochondria, oxidoreductase activity, heme binding and monooxygenase activity become down-regulated during the early stage of T. gondii infection. In a previous study on proteomic profiling on mouse liver during acute toxoplasmosis it was suggested that endosome and proteolysis activity become upregulated after T. gondii infection  while, our results demonstrated that extracellular exosome and peptidase inhibitor activity become up-regulated in this parasitic infection. This discrepancy between the results may be attributed to the use of different tools. Regardless, up-regulation of immune response related terms and down-regulation of different metabolic pathways are, overall, consistent with previous transcriptomic and proteomic reports in previous studies   .
Understanding PPI is essential for understanding molecular and cellular mechanism, in healthy and diseased states of an organism. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies such as drug or vaccine development. Here, proteins involved in most significantly enriched up-regulated (Immune system process) and down-regulated (Oxidation-reduction process) biological process were used to generate PPI.
Investigation of the modular properties of protein–protein interaction (PPI) networks can facilitate further discovery of the underlying molecular interaction mechanisms that drive cell response under specific conditions, such as drug treatment . In some previous studies interaction networks were used to predict gene function, to identify novel disease-related genes and to understand the overlapping association across disease phenotypes  . Recently, computational approaches have been used to build topological clusters as functional modules in PPI networks .
MCODE was used as discovery tools to further analyze the network and extract sub network from the whole PPI. Based on the topological structure three densely connected clusters were found from the whole PPI of proteins involved in immune system process (Figure 5). Cluster 1 proteins had high score that includes 9 nodes and 34 edges. Nodes are following-Ifit3, Ifit1, Iigp1, Zbp1, Psmb8, Psmb9, Tap1, Gbp2b, Irgm1proteins. Based on the importance connectivity of these 9 proteins only three (Psmb8, Psmb9 and Tap1) were identified as hub protein by CytoNCA application. Psmb8 has established well known association with the etiology and pathology of toxoplasmosis .
The proteasome is a multi-catalytic proteinase complex which is characterized by its ability to cleave peptides with Arginine, Phenyl alanine, Tyrosine, Leucine, and Glutamate adjacent to the leaving group at neutral or slightly basic pH. The proteasome has an ATP-dependent proteolytic activity. Psmb8 (proteasome subunit beta type-8) subunit belongs to the peptidase T1B family (276 amino acid) and is required for adipocyte differentiation. This subunit is involved in antigen processing to generate class I binding peptides and in the inflammatory response pathway. A previous study showed that T. gondii infection induce the expression of Psmb8 mRNA in antigen presenting cell (APC) and increase the capacity of APC to induce the production of IFN-γby antigen-specific CD8+ T cells . In vitro infection of a dendritic cell (DC) cell line with T. gondii can also induce the expression of Psmb8and resulted in enhanced proteasome proteolytic activity. In their study they also observed that mice lacking Psmb8were also shown to be highly susceptible to infection with T. gondii and showed a reduced number of functional CD8+ T cells.
Another subunit, Psmb 9, belongs to the peptidase T1B family (219 amino acid). It is involved in antigen processing to generate class I binding peptides and contributes to (nuclear factor kappa-beta 1A (NFKB1A) degradation and subsequently NFKB1 generation. Luisa Möhle and colleagues identified a strong increase of mRNA expression for the immunoproteasome subunits Psmb 9 (LMP 2) and Psmb 8 (LMP7) in whole-brain RNA from T.gondii infected mice .
TAP 1 (Antigen peptide transporter 1) belongs to the ABC transporter superfamily (ABCB family) and MHC peptide exporter subfamily (724 amino acid). They are involved in the transport of antigens from the cytoplasm to the endoplasmic reticulum for association with major histocompatibility complex I (MHC class I) molecules. It also acts as a molecular scaffold for the final stage of MHC class I folding, namely the binding of peptide. Nascent MHC class I molecules associate with TAP via tapasin based on similarity. Goldszmid RS and colleagues revealed that TAP 1 plays an important role in the induction of interferon-gamma-producing natural killer (NK) cells and demonstrate that NK cell licensing can influence host resistance to T. gondii infection through its effect on cytokine production in addition to its role in cytotoxicity. So, it can be said that TAP-1 indirectly regulates CD4+ T cell priming in Toxoplasma gondii infection by controlling NK cell IFN-gamma production . Furthermore, six other proteins from cluster one including Ifit3, Ifit1, Iigp1, Zbp1, Gbp2b and Irgm1 were also associated with up-regulating immune response after T. gondii infection. Though in my present study Ifit3 and Ifit1 were found to be significantly upregulated during acute T. gondii infection, there is no such study which show the association of Ifit3 and Ifit1with T. gondii infection. Association of rest of the 4 proteins with toxoplasmosis was found previously.
Z-DNA binding protein 1 (ZBP1) is one of the most abundant host transcripts during acute and chronic T. gondii infection. In one study, Pittman KJ and colleagues determined that ZBP1 functions to control T. gondii growth thus minimizing the infection (Pittman et al., 2016). Another study showed that Zbp1 can initiate innate immune responses by binding cytosolic DNA (Wang et al., 2008). However, there are few reports regarding the contribution of Zbp1 to NF-κ-beta activation in the spleen of mice infected by T. gondii .
IIGP1 (Interferon-inducible GTPase1) is essential for interferon-γ-induced cell-autonomous immunity against T. gondii in mice, but the mechanism of resistance is poorly understood. In a study Sascha Martens and colleagues showed that IIGP1 accumulate at live T. gondii containing vacuoles in a GTP-dependent manner. After maturation-like process vesiculation of the parasitophorous vacuole membrane occurs. This culminates in disruption of the parasitophorous vacuole and finally of the parasite itself. They also found that over-expression of IIGP1 leads to accelerated vacuolar disruption while, targeted deletion of the IIGP1 gene results in partial loss of the IFN-γ-mediated T. gondii growth restriction in mouse astrocytes .
GBP2 (guanylate binding proteins) is also a GTPase protein which inhibits replication of Toxoplasma gondii. Some previous studies showed that murine GBP2 (mGBP2) is highly expressed in several cell types, including T and B cells after stimulation and revealed that mGBP2-deficient mice has a marked immune susceptibility to T. gondii . So, it can be said that mGBP2 is an essential immune effector molecule mediating anti-parasitic resistance. IRGM1 proteins (immunity-related GTPases) provide an early defense mechanism in mice against the protozoal pathogen, T. gondii. IRGM knocked-out mice show striking susceptibility to T. gondii infection  . Zbp1 can initiate innate immune responses by binding cytosolic DNA . However, there are few reports regarding the contribution of Zbp1 to NF-κB activation in the spleen of mice infected by T. gondii.
By similar manner, 2 sub networks (significant clusters) were extracted using MCODE application from whole PPI of proteins which were involved in downregulating oxidation-reduction process. Among 9 proteins (CYP3A16, CYP2C29, CYP1A2, CYP2C54, CYP3A25, CYP3A11, CYP2C50, CYP4A10, CYP2B9) of highly scored cluster 1 three proteins were identified as hub proteins by CytoNCA algorithm, which are following-CYP1A2, CYP4A10 and CYP3A25). Surprisingly, all of the proteins are members of cytochrome P450 superfamily of enzymes. The former application (MCODE) analyzes network based on topological structure, while the latter (CytoNCA) provides an estimate of functions based on gene annotations. Some previous studies also revealed the association of these down-regulated proteins with acute T. gondii infection in mouse liver .
Sub networks and hub proteins provide novel hypotheses for pathways involved in disease  . They have some special biological properties, for example, they tend to be more essential than non-hub proteins  , they are found to play a central role in modular organization of the protein interaction network   and some studies indicate that hub proteins may also be evolutionarily conserved to a larger extent than non-hubs .
Investigation of the modular properties of protein-protein interaction (PPI) networks can facilitate further discovery of the underlying molecular interaction mechanisms that drive cell response under specific conditions, such as drug treatment . In some previous studies interaction networks were used to predict gene function, to identify novel disease-related genes and to understand the overlapping association across disease phenotypes  . Recently, computational approaches have been used to build topological clusters as functional modules in PPI networks .
The resulting hub proteins and functional clusters of my current study might be useful to reveal the pathological mechanism of T. gondii induced toxoplasmosis in host. Further investigation into these hub proteins elucidating their role in regulating immune system and oxidation-reduction process can be an interesting avenue of exploration and can be of help while designing drugs. They can also be used as bio-marker in the diagnostic field.
In this current study, structure prediction of these hub proteins was also done by homology modeling. Homology modeling has become a useful tool for the prediction of protein structure when only sequence data are available. Structural information is often more valuable than sequence alone for determining protein function, dynamics and interactions with ligands or other proteins. The “low-resolution” structure provided by homology modeling contains sufficient information about the spatial arrangement of important residues in the protein and may guide the design of new experiments, for example site-directed mutagenesis. Even within the pharmaceutical industry homology modeling can be valuable in structure-based drug discovery and drug design.
The resulting hub proteins and functional clusters of the current study might be useful to reveal the pathological mechanism of T. gondii-induced toxoplasmosis in the host. Further investigation into these hub proteins elucidating their role in regulating immune system and oxidation-reduction process can be an interesting avenue of exploration and can be of help while designing drugs. They can also be used as bio-marker in the diagnostic field.
In summary, the current study showed that how multi-OMICS integration approach can efficiently be used to elucidate the intricate molecular pathways that may contribute to the onset, development and progression of pathological condition during Toxoplasma gondii infection. The multi-OMICS integration of transcriptome and proteome lead to the identification of the effector molecules (hub proteins) that may potentially be of great importance and subsequently utilized in the in discovery of next-generation biomarker and the designing of efficient and rapid diagnosis of Toxoplasma gondii infections. Since the identified hub-proteins showed differential expression pattern depending on the infection status, these proteins can be considered as ideal candidates for therapeutic and diagnostic targets. Further studies to pinpoint testing of identified effector molecules are to be performed. In vitro and in vivo study of these molecules would be interesting to confirm their functional activities and efficacy. Limitation of the study: Since Toxoplasma gondii is an intracellular protozoan parasite and causes mostly in asymptomatic infection in human. Due to primary asymptomatic infections and followed by self-recovery, proteome data from T. gondii infected clinical patients are not available and difficult to generate. Therefore, studying of T. gondii in mouse model could be insightful information for scientific arena.
No specific funding was allocated for this study.
Figure S1. (a): Gene ontology for biological processes annotation of 2599 unique differentially expressed transcripts (DETs), (b): Gene ontology for biological processes annotation of 142 unique differentially expressed proteins (DEPs).
Table S1. List of 159 common differentially expressed genes/proteins.
Table S2. List of 86 common up-regulated genes/proteins.
Table S3. List of common 72 down-regulated genes/proteins.
Table S4. List of up-regulated genes/proteins and associated GO terms.
Table S5. List of down-regulated genes/proteins and associated GO terms.
 Flegr, J., Prandota, J., Sovičková, M. and Israili, Z.H. (2014) Toxoplasmosis—A Global Threat. Correlation of Latent Toxoplasmosis with Specific Disease Burden in a Set of 88 Countries. PLoS ONE, 9, e90203.
 Pappas, G., Roussos, N. and Falagas, M.E. (2009) Toxoplasmosis Snapshots: Global Status of Toxoplasma gondii Seroprevalence and Implications for Pregnancy and Congenital Toxoplasmosis. International Journal for Parasitology, 39, 1385-1394.
 Andiappan, H., et al. (2014) Toxoplasma Infection in Pregnant Women: A Current Status in Songklanagarind Hospital, Southern Thailand. Parasites & Vectors, 7, Article No. 239.
 Dubey, J.P., Lindsay, D.S. and Speer, C.A. (1998) Structures of Toxoplasma gondii Tachyzoites, Bradyzoites, and Sporozoites and Biology and Development of Tissue Cysts. Clinical Microbiology Reviews, 11, 267.
 Tenter, A.M., Heckeroth, A.R. and Weiss, L.M. (2000) Toxoplasma gondii: From Animals to Humans. International Journal for Parasitology, 30, 1217-1258.
 Manlio, et al. (2008) Temporal and Spatial Distribution of Toxoplasma gondii Differentiation into Bradyzoites and Tissue Cyst Formation in Vivo. Infection and Immunity, 76, 3491-3501.
 Khurana, S. and Batra, N. (2016) Toxoplasmosis in Organ Transplant Recipients: Evaluation, Implication, and Prevention. Tropical Parasitology, 6, 123-128.
 Alvarado-Esquivel, C., Torres-Berumen, J.L., Estrada-Martínez, S., Liesenfeld, O. and Mercado-Suarez, M.F. (2011) Toxoplasma gondii Infection and Liver Disease: A Case-Control Study in a Northern Mexican Population. Parasites & Vectors, 4, 75.
 Alamri, Z.Z. (2018) The Role of Liver in Metabolism: An Updated Review with Physiological Emphasis. International Journal of Basic & Clinical Pharmacology, 7, 2271-2276.
 He, J.-J. (2016) Transcriptomic Analysis of Mouse Liver Reveals a Potential Hepato-Enteric Pathogenic Mechanism in Acute Toxoplasma gondii Infection. Parasites & Vectors, 9, 427.
 He, J.-J., Ma, J., Elsheikha, H.M., Song, H.-Q., Zhou, D.-H. and Zhu, X.-Q. (2016) Proteomic Profiling of Mouse Liver Following Acute Toxoplasma gondii Infection. PLoS ONE, 11, e0152022.
 Blader, I.J. and Saeij, J.P. (2009) Communication between Toxoplasma gondii and Its Host: Impact on Parasite Growth, Development, Immune Evasion, and Virulence. Apmis, 117, 458-476.
 Nelson, M.M., et al. (2008) Modulation of the Host Cell Proteome by the Intracellular Apicomplexan Parasite Toxoplasma gondii. Infection and Immunity, 76, 828-844.
 Zhou, D.-H., et al. (2013) Changes in the Proteomic Profiles of Mouse Brain after Infection with Cyst-Forming Toxoplasma gondii. Parasites & Vectors, 6, Article No. 96.
 Zhou, C.X., Elsheikha, H.M., Zhou, D.H., Liu, Q., Zhu, X.Q. and Suo, X. (2016) Dual Identification and Analysis of Differentially Expressed Transcripts of Porcine PK-15 Cells and Toxoplasma gondii during in Vitro Infection. Frontiers in Microbiology, 7, 1-13.
 Iskar, M., et al. (2013) Characterization of Drug-Induced Transcriptional Modules: Towards Drug Repositioning and Functional Understanding. Molecular Systems Biology, 9, 662.
 Lee, I., Lehner, B., Vavouri, T., Shin, J., Fraser, A.G. and Marcotte, E.M. (2010) Predicting Genetic Modifier Loci Using Functional Gene Networks. Genome Research, 20, 1143-1153.
 Lu, Y., et al. (2019) Identification of Differentially Expressed Genes and Signaling Pathways Using Bioinformatics in Interstitial Lung Disease due to Tyrosine Kinase Inhibitors Targeting the Epidermal Growth Factor Receptor. Investigational New Drugs, 37, 384-400.
 Tu, L., et al. (2009) Critical Role for the Immunoproteasome Subunit LMP7 in the Resistance of Mice to Toxoplasma gondii Infection. European Journal of Immunology, 39, 3385-3394.
 Möhle, L., et al. (2016) Chronic Toxoplasma gondii Infection Enhances β-Amyloid Phagocytosis and Clearance by Recruited Monocytes. Acta Neuropathologica Communications, 4, 25.
 Goldszmid, R.S., et al. (2007) TAP-1 Indirectly Regulates CD4+ T Cell Priming in Toxoplasma gondii Infection by Controlling NK Cell IFN-γ Production. Journal of Experimental Medicine, 204, 2591-2602.
 He, J.J., et al. (2016) Transcriptomic Analysis of Global Changes in Cytokine Expression in Mouse Spleens Following Acute Toxoplasma gondii Infection. Parasitology Research, 115, 703-712.
 Degrandi, D., et al. (2013) Murine Guanylate Binding Protein 2 (mGBP2) Controls Toxoplasma gondii Replication. Proceedings of the National Academy of Sciences of the United States of America, 110, 294-299.
 Collazo, C.M., et al. (2001) Inactivation of LRG-47 and IRG-47 Reveals a Family of Interferon γ-Inducible Genes with Essential, Pathogen-Specific Roles in Resistance to Infection. Journal of Experimental Medicine, 194, 181-187.
 Taylor, G.A., et al. (2000) Pathogen-Specific Loss of Host Resistance in Mice Lacking the IFN-γ-Inducible Gene IGTP. Proceedings of the National Academy of Sciences of the United States of America, 97, 751-755.
 Wang, Z.C., et al. (2008) Regulation of Innate Immune Responses by DAI (DLM-1/ ZBP1) and Other DNA-Sensing Molecules. Proceedings of the National Academy of Sciences of the United States of America, 105, 5477.
 Ideker, T., Ozier, O., Schwikowski, B. and Siegel, A.F. (2002) Discovering Regulatory and Signalling Circuits in Molecular Interaction Networks. Bioinformatics, 18, 233-240.
 Lee, D.S., Park, J., Kay, K.A., Christakis, N.A., Oltvai, Z.N. and Barabási, A.L. (2008) The Implications of Human Metabolic Network Topology for Disease Comorbidity. Proceedings of the National Academy of Sciences of the United States of America, 105, 9880-9885.