Genes Related to Antiviral Activity, Cell Migration, and Lysis Are Differentially Expressed in CD4⁺T Cells in Human T Cell Leukemia Virus Type 1-Associated Myelopathy/Tropical Spastic Paraparesis Patients

Abstract

Human T cell leukemia virus type 1 (HTLV-1) preferentially infects CD4(+) T cells and these cells play a central role in HTLV-1 infection. In this study, we investigated the global gene expression profile of circulating CD4(+) T cells from the distinct clinical status of HTLV-1-infected individuals in regard to TAX expression levels. CD4(+) T cells were isolated from asymptomatic HTLV-1 carrier (HAC) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients in order to identify genes involved in HAM/TSP development using a microarray technique. Hierarchical clustering analysis showed that healthy control (CT) and HTLV-1-infected samples clustered separately. We also observed that the HAC and HAM/TSP groups clustered separately regardless of TAX expression. The gene expression profile of CD4(+) T cells was compared among the CT, HAC, and HAM/TSP groups. The paxillin (Pxn), chemokine (C-X-C motif ) receptor 4 (Cxcr4), interleukin 27 (IL27), and granzyme A (Gzma) genes were differentially expressed between the HAC and HAM/TSP groups, regardless of TAX expression. The perforin 1 (Prf1) and forkhead box P3 (Foxp3) genes were increased in the HAM/TSP group and presented a positive correlation to the expression of TAX and the proviral load (PVL). The frequency of CD4(+)FOXP3(+) regulatory T cells (Treg) was higher in HTLV-1-infected individuals. Foxp3 gene expression was positively correlated with cell lysis-related genes (Gzma, Gzmb, and Prf1). These findings suggest that CD4(+) T cell activity is distinct between the HAC and HAM/TSP groups.

TMEM120A and B: Nuclear Envelope Transmembrane Proteins Important for Adipocyte Differentiation

“…The original NCBI annotation for TMEM120A labeled it TMPIT for t rans m embrane p rotein i nduced by T NFα because it appeared in a transcriptome list of genes upregulated by TNFα in endothelial cells, however, the details of this study were not published. Tmem120A also appeared in a list of genes differentially expressed in HTLV-1 infected CD4 + T cells [ 49 ] and a list of genes changing at the E10.5 stage of limb development [ 50 ]. While the former is harder to explain in the context of fat, it is possible that the latter reflects the beginnings of fat differentiation.…”

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP)

“…The Gene Expression Omnibus (GEO) repository database was explored to find the datasets related to ACs, ATLL, and HAM/TSP. A total of six microarray datasets including GSE29312 [ 11 ], GSE29332 [ 11 ], GSE55851 [ 12 ], GSE38537 [ 13 ], GSE33615 [ 14 ], and GSE82160 [ 15 ] were found for analysis and validation. The characteristic of each dataset is described in Table 1 .…”

Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One

“…To validate the accuracy of our two-gene model in the discrimination of ATLL, HAM/TSP, and control samples, this model was applied to the other datasets not used in the discovery set: (Kress et al, 2010) (GSE17718), (Hamamura et al, 2007) (GSE6034), (Pinto et al, 2014) (GSE38537), (Yamagishi et al, 2012) (GSE33615), (Olière et al, 2010) (GSE57259), and GSE19080. After downloading and pre-processing these datasets, ROC curve analysis was applied to measure the discriminant power of CD40LG and GBP2 in classifying HLTV-1 clinical manifestations.…”

“…Six microarray expression datasets were retrieved from GEO: GSE17718 (Kress et al, 2010), GSE6034 (Hamamura et al, 2007), GSE38537 (Pinto et al, 2014), GSE33615 (Fujikawa et al, 2016), GSE57259 (Araya et al, 2014), and GSE19080 (no citation available at GEO/NCBI). To confirm the gene signature performance, we performed the gene model comparison in the validation dataset independently, without using the thresholds yielded by the decision tree model estimated during the discovery phase.…”