Decoding pathogenesis factors involved in the progression of ATLL or HAM/TSP after infection by HTLV-1 through a systems virology study Mohadeseh Zarei Ghobadi University of Isfahan Rahman Emamzadeh ( [email protected] ) University of Isfahan Majid Teymoori-Rad Tehran University of Medical Sciences Sayed-Hamidreza Mozhgani Alborz University of Medical Sciences Research Article Keywords: HTLV-1, ACs, ATLL, HAM/TSP, differentially co-expressed modules, pathogenesis Posted Date: July 16th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-705476/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract Background Human T-cell Leukemia Virus type-1 (HTLV-1) is a retrovirus that causes two diseases including Adult T- cell Leukemia/Lymphoma (ATLL cancer) and HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP, a neurodegenerative disease) after a long latency period as an asymptomatic carrier (AC). There are no obvious explanations about how AC carriers develop each of the mentioned diseases. Finding the discriminative molecular factors and pathways may clarify the destiny of the infection. Methods To shed light on the involved molecular players and activated pathways in each state, differentially co- expressed modules (DiffCoEx) algorithm was employed to identify the highly correlated genes which were co-expressed differently between normal and ACs, ACs and ATLL, as well as ACs and HAM/TSP samples. Through differential pathway analysis, the dysregulated pathways and the specic disease- genes-pathways were gured out. Moreover, the common genes between the member of DiffCoEx and differentially expressed genes were found and the specic genes in ATLL and HAM/TSP were considered as possible biomarkers. Results The dysregulated genes in the ATLL were mostly enriched in immune and cancer-related pathways while the ones in the HAM/TSP were enriched in immune, inammation, and neurological pathways. The differential pathway analysis claried the differences between the gene players in the common activated pathways. Eventually, the nal analysis revealed the involvement of specic dysregulated genes including KIRREL2, RAB36, and KANK1 in HAM/TSP as well as LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 in ATLL. Conclusion The identied potential prognostic biomarkers and therapeutic targets are proposed as the most important platers in developing ATLL or HAM/TSP. Moreover, the proposed signaling network claries the differences between the functional players in the activated pathways in ACs, ATLL, and HAM/TSP. 1. Introduction Human T-cell Leukemia Virus type-1 (HTLV-1) belongs to the family Retroviridae and subfamily Orthoretrovirinae [1]. Adult T-cell Leukemia/Lymphoma (ATLL cancer) and Tropical Spastic Page 2/21 Paraparesis/HTLV-1 Associated Myelopathy (TSP/HAM) are two major diseases that may be developed after HTLV-1 infection and elapsing an asymptomatic carrier (AC) state. Although HTLV-1 is not a widespread virus all over the world, it is an endemic pathogen in sub-Saharan Africa, East north of Iran, the Caribbean region, Japan, and South America [2]. Approximately, 90% of the infected HTLV-1 are asymptomatic carriers (ACs) [3] with the capability of the silent transmission of the virus through blood contact, sexual intercourse, breastfeeding, etc [4]. Three common receptors on various cells including neuropilin, glucose transporter 1 (GLUT-1), and heperan sulfate proteoglycan are the main receptors for HTLV-1 [5]. The HTLV-1 proviral DNA is found in the immune cell types comprising dendritic cells, monocytes, CD8 + T-cells, B cells, and in higher extent in CD4 + T-cells [6, 7]. Two oncogenic proteins named as the HTLV-1 basic leucine zipper protein (HBZ) and the transactivator protein (Tax) are frequently expressed by the HTLV-1 genome. However, the tax gene expresses in only around 40% of ATLL patients [8]. Tax has a critical function in the viral pathogenesis of HAM/TSP patients through promoting the proliferation of infected cells by activating NFκB and AP-1 pathways, avoiding apoptosis, and activating cytotoxic T lymphocyte (CTL) response [3, 9]. On the other hand, HBZ prohibits NF-κB pathway, promotes tumor progression, and boosts T cell proliferation and lymphoma [10, 11]. One of the benecial approaches to nd the patterns of co-regulated genes is gene co-expression analysis [12]. Two common approaches are utilized to determine the mechanistic diversity between two conditions including identifying differential gene expression and differential gene co-expression. Through co-expression analysis, the highly correlated genes (a module) can be identied. Moreover, the differential co-expressed analysis determines the specic co-regulated genes in each condition. DiffCoEx is a sensitive and ecient method to nd the gene co-expression differences grouped in various modules between multiple conditions [13]. The major advantage of this method is identifying signicant differential co-expressed gene groups even in the attendance of the within-group correlation across two conditions. Since the pathogenesis mechanism of HTLV-1 as a virus-caused cancer and virus-caused neurologic disease have not been yet fully ascertained, the identication of differential co-regulated genes that are implicated in ACs, ATLL, and HAM/TSP can determine new functional players in each condition. To this end, we integrated various gene expression datasets and then found the differential co-expressed genes between every two conditions employing DiffCoEx approach. The outcomes disclosed the gene players and activated pathways for the development of each disease after infection. Figure 1 indicates the workow of the suggested procedure to nd the most important genes and pathways implicated in each condition. 2. Methods 2.1 Data collection, merging, and preprocessing Page 3/21 To nd the relevant datasets correspond to our study purpose, Gene Expression Omnibus (GEO) repository database was searched. A total of four microarray datasets including GSE29312, GSE29332, GSE33615, and GSE55851 were selected to be involved in our analysis. In total, these datasets contain 58 normal samples, 43 ACs samples, 62 ATLL samples, and 20 HAM/TSP samples. Since datasets belong to different platforms, removeBatchEffect function in the R limma package was utilized to remove the batch effect across different datasets [14]. The expression data of samples related to each condition were merged, individually. A total of 15565 common genes were used for further analysis. The merged data were quantile-normalized and log2-transformed. Moreover, to validate the identied modules, the ACs and HAM/TSP samples from GSE38537 and ATLL samples from GSE43017 were used. The more details related to each dataset is explained in Table 1. Table 1 Characteristics of datasets involved in the analysis and validation. Datasets for main analyses Dataset Platform Number of Samples GSE29312 Illumina HumanHT-12 V3.0 expression beadchip Normal: 9 ( GPL6947) ACs: 20 HAM/TSP: 10 GSE29332 Illumina HumanWG-6 v3.0 expression beadchip Normal: 8 ( GPL6884) ACs: 17 HAM/TSP: 10 GSE55851 Agilent-026652 Whole Human Genome Microarray 4x44K v2 Normal: 3 ( GPL10332) ACs: 6 ATLL: 12 GSE33615 Agilent-014850 Whole Human Genome Microarray 4x44K Normal: 21 G4112F ATLL: 52 ( GPL4133) Datasets for validation GSE38537 Agilent-014850 Whole Human Genome Microarray 4x44K ACs: 4 G4112F HAM/TSP: 4 ( GPL6480) GSE43017 Affymetrix Human Genome U133 Plus 2.0 Array ATLL: 7 ( GPL570) 2.2 Determination of differential co-expressed modules Page 4/21 To nd the differential co-expressed modules among two conditions, the DiffCoEx algorithm was employed [13]. DiffCoEx principally uses Weighted Gene Co-expression Analysis (WGCNA) [12] and contains ve steps as follows: i) An adjacency matrix is constructed for each condition in which their elements are gene pair-wise correlations. We used Pearson coecient to measure correlations. ii) A matrix of adjacency difference (dij) is determined by calculating the signed squared correlation coecients. The signicant co-expression changes between two genes are specied via higher dij values. The soft threshold parameter β is determined so that the network follows a scale-free topology [15]. Then, dij values reach the power of β. iii) The topological overlap dissimilarity matrix (dissTOM) is constructed to detect the shared genes with common neighbors in the graph which is constituted based on the adjacency matrix containing the differential correlation network. The topological overlap between co- expressed genes in the resulted network belonging to a module in only one condition contains all the genes within that module. iv) The hierarchical clustering is performed with ashClust [16]. Then, dynamicTreeCut function is used to extract gene groups (modules) from the obtained dendrogram and mergeCloseModules function is employed to merge close modules. Each module is specied with a color. v) The statistical signicance of differential co-expression is evaluated using the dispersion statistic to quantify the correlation alteration between two conditions. In this study, we identied differential co- expressed modules between normal and ACs, ACs and ATLL, ACs and HAM/TSP. Moreover, through this approach, module-to-module co-expression changes were examined through evaluating the signicance of the correlation alterations among the genes from each possible module pair, employing a similar