Ectopic expression of HIV-1 Tat modifies gene expression in cultured B cells: implications for the development of B-cell lymphomas in HIV-1-infected patients

An increased frequency of B-cell lymphomas is observed in human immunodeficiency virus-1 (HIV-1)-infected patients, although HIV-1 does not infect B cells. Development of B-cell lymphomas may be potentially due to the action of the HIV-1 Tat protein, which is actively released from HIV-1-infected cells, on uninfected B cells. The exact mechanism of Tat-induced B-cell lymphomagenesis has not yet been precisely identified. Here, we ectopically expressed either Tat or its TatC22G mutant devoid of transactivation activity in the RPMI 8866 lymphoblastoid B cell line and performed a genome-wide analysis of host gene expression. Stable expression of both Tat and TatC22G led to substantial modifications of the host transcriptome, including pronounced changes in antiviral response and cell cycle pathways. We did not find any strong action of Tat on cell proliferation, but during prolonged culturing, Tat-expressing cells were displaced by non-expressing cells, indicating that Tat expression slightly inhibited cell growth. We also found an increased frequency of chromosome aberrations in cells expressing Tat. Thus, Tat can modify gene expression in cultured B cells, leading to subtle modifications in cellular growth and chromosome instability, which could promote lymphomagenesis over time.


INTRODUCTION
In the second part of the 20 th century, human immunodeficiency virus-1 (HIV-1) has rapidly spread throughout the world and caused high mortality due to its high evolution rate. HIV-1 preferentially infects CD4 + T cells, macrophages, and microglial cells, leading to a damaged immune system and the development of acquired immunodeficiency syndrome (AIDS). Combined antiretroviral therapy (cART) stops the virus from making copies of itself in the body and may slow down the development of AIDS (Autran et al., 1997;HIV-CAUSAL Collaboration et al., 2010). However, even after the introduction of cART, individuals infected with HIV-1 are at significantly higher risk of developing non-AIDS-related comorbidities, including the development of neurocognitive disorders (Mateen et al., 2012;Marino et al., 2020), cardiovascular diseases (Wang et al., 2015;Jiang et al., 2018), adipose tissue senescence (Gorwood et al., 2020), and cancer (Shmakova, Germini & Vassetzky, 2020).
Despite the improved control of HIV-1 infection achieved by cART, B-cell lymphomas are still elevated in HIV-1-infected patients and are the most frequent cause of death in these patients (Noy, 2020;Shmakova, Germini & Vassetzky, 2020;Hübel, 2020). However, even more surprising is the fact that HIV-1-infected patients have an increased incidence of specific B-cell lymphomas, namely Burkitt lymphoma and diffuse large B-cell lymphoma (Gloghini, Dolcetti & Carbone, 2013;Besson et al., 2017;Atallah-Yunes, Murphy & Noy, 2020). Only a few articles report that B cells can be infected with HIV-1 (Fritsch et al., 1998;Lazzi et al., 2002;Katano et al., 2007), and it seems that this is an extremely rare/artifactual event. One of the most likely mechanisms of the development of HIV-1associated B-cell lymphomas in HIV-1-infected patients may be an action of viral proteins on uninfected B cells (Dolcetti et al., 2016).
HIV-1 produces a small nuclear transcriptional activator protein known as transactivator of transcription (Tat) which regulates viral transcription (Ali et al., 2021). In the absence of Tat, HIV-1 proviral promoter is incompetent for elongation: shortly after transcription initiation, RNA Pol II is stalled due to the presence of inactive form of positive transcription elongation factor b (P-TEFb) composed of CDK9, cyclin T1 or T2, and inhibitory 7SK small nuclear ribonucleoprotein complex (containing 7SK RNA and HEXIM1) (Kao et al., 1987;Sedore et al., 2007;D'Orso & Frankel, 2010). Tat is able to relieve this repressed state by binding to the TAR-like sequence in 7SK snRNA and displacing HEXIM1 from cyclin T1, which disrupts the 7SK-P-TEFb negative transcriptional regulatory complex and releases active P-TEFb (Barboric et al., 2007;Sedore et al., 2007;Muniz et al., 2010;Pham et al., 2018). This ability of Tat depends on Tat C22 residue within the activation domain (Barboric et al., 2007). Tat then recruits the active P-TEFb complex (consisting of CDK9, cyclin T1 or T2) and other transcriptional coactivators to the TAR RNA element at the 5′ end of stalled nascent HIV-1 transcripts to relieve stalled RNA Pol II (Nekhai & Jeang, 2006;He et al., 2010). Simultaneously, HIV-1 Tat modulates cellular processes by interacting with different cellular structures, particularly nuclear components (Musinova et al., 2016;Ali et al., 2021).
The mechanisms of Tat-dependent lymphomagenesis in HIV-1-infected patients have been described only partially. Incubation of B cells from healthy donors with recombinant HIV-1 Tat ex vivo led to the convergence of chromosomal loci that are usually involved in t (8;14) translocation, which is common in Burkitt lymphoma (Germini et al., 2017). These data indicate that exogenous viral proteins can induce global rearrangement of nuclear organization and that these changes can promote lymphomagenesis. Additionally, HIV-1 Tat can modify the chromatin organization and gene expression of host cells, particularly T cells (Marban et al., 2011;Reeder et al., 2015) and macrophages (Carvallo et al., 2017), and it seems that Tat can induce a chain of similar events in B cells. Indeed, Tat can affect the expression of several genes in B cells, including AICDA, a gene that encodes the activation-induced cytidine deaminase that participates in immunoglobulin gene maturation (Sall et al., 2019;Akbay et al., 2021). Additionally, HIV-1 Tat enhances c-MYC transcription by binding to the c-MYC promoter, which can contribute to a more aggressive lymphoma phenotype (Lazzi et al., 2002;Alves de Souza Rios et al., 2021). Thus, HIV-1 Tat present in blood may affect gene expression in B cells, and these changes can promote lymphomagenesis. However, the effect of Tat on B cells has never been studied on a genome-wide level. Here, we ectopically expressed HIV-1 Tat in the lymphoblastoid B cell line (RPMI 8866) and analyzed host gene expression by RNA-seq. We found that the expression of HIV-1 Tat led to substantial modifications of gene expression and induced cellular antiviral reactions. Ectopic Tat expression also resulted in modification of cellular proliferation and genome stability, thus promoting changes that could facilitate lymphomagenesis.
Plasmids for EGFP, Tat-EGFP and TatC22G-EGFP expression and lentiviral particles were constructed by Evrogen (Moscow, Russia). EGFP-, Tat-EGFP-or TatC22G-EGFPexpressing cells were collected using a FACSAria SORP cell sorter (BD Biosciences, San Jose, CA, USA). The excitation wavelength for EGFP was 488 nm, and the emission was detected by a 505LP and 515/20BP set of filters. Sorting was performed with an 85 mm nozzle and the corresponding custom pressure parameters. The sorted cells were grown, then frozen in the complete medium in the presence of DMSO, and stored in liquid nitrogen. To achieve better reproducibility of experiments, cells were used for no more than one month after thawing (excluding the experiments on long-term culture).

Cell lysate preparation, SDS-PAGE and western blotting
Cells were collected by centrifugation for 10 min at 800 g. Cell pellets were washed with PBS and resuspended in NETN buffer (150 mM NaCl, 1 mM EDTA, 50 mM Tris pH 7.5, 0.5% NP 40, protease inhibitor cocktail), sonicated, incubated on ice for 30 min and centrifuged at 4 C at 12,000 g for 10 min. Protein quantification was performed using the Pierce TM BCA Protein Assay Kit (Thermo Scientific, Waltham, MA, USA) on a NanoDrop 2000C (Thermo Scientific, Waltham, MA, USA). After measuring the concentration, cell lysates were supplemented with Laemmli buffer and 0.1 M DTT and then heated at 95 C for 10 min.

RNA extraction and sequencing
Cells were collected and stored in RNALater (Qiagen, Hilden, Germany). Total RNA was extracted using a RNeasy Mini RNA isolation kit (Qiagen, Hilden, Germany) following the Qiagen protocol, with the following modifications: (1) lysis time was increased up to 40 min; (2) on-column DNase I treatment was performed. RNA sample quality was assessed using a capillary electrophoresis Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA), and all samples had RIN >8. The cDNA libraries were constructed using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB) following the manufacturer's recommendations. RNA was fragmented for 5 min. Thereafter, the constructed libraries were sequenced on an Illumina HiSeq 2000 with a single-end 51 bp read length. Basecalling was performed using bcl2fastq v2.17.1.14.

RNA-seq data processing and analysis
Read quality control was performed using FastQC (version 0.11.7) (http://www. bioinformatics.babraham.ac.uk/projects/fastqc/). Due to the good quality of the reads, no filtering or adapter removal was performed. The reads were then aligned to the human genome assembly GRCh38.p10 using HISAT2 (version 2.0.5) (Kim, Langmead & Salzberg, 2015). Read counting was performed in strand-specific mode by the htseq-count script from Python library HTSeq (version 0.12.4) (Anders, Pyl & Huber, 2015) using GENCODE v26 gene annotation (ALL). Genes with no counts for all samples, as well as highly expressed ribosomal genes, were filtered out, resulting in the expression set of 32,120 genes across 12 samples.
Principal component analysis (PCA) was performed on the rlog-transformed count data, and the first two principal components were extracted with the corresponding percentage of explained variance. Differential expression analysis was performed with the R package DESeq2 (version 1.30.1) (Love, Huber & Anders, 2014). We declared the gene to be differentially expressed if padj (p value adjusted by the Benjamini-Hochberg procedure) was smaller than 0.05 and the fold change was larger than 1.5 in any direction (Table S1). The statistical power for protein-coding genes (with median 80 aligned reads) was calculated with R package RNASeqPower (version 1.30.0) and was 0.91. Each group of samples consisted of three biological replicates.
Overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA) (Subramanian et al., 2005) were performed with the R package clusterProfiler (version 3.18.1) (Yu et al., 2012). GSEA was performed on a preranked list of genes ordered by the stat column of DESeq2 results. The KEGG (release 99) (Kanehisa & Goto, 2000) and GO biological process (Ashburner et al., 2000; The Gene Ontology Consortium, 2017) databases were used as the sources of gene sets for ORA and GSEA. GO annotation was obtained from the R package org.Hs.eg.db (version 3.12.0). An adjusted p value cutoff of 0.05 was used to select statistically significant categories. REVIGO (Supek et al., 2011) with a cutoff parameter of 0.4 was used to remove redundant GO terms.
To assess the possible additional activation of EBV genes due to Tat protein expression in RPMI Tat cells, the same pipeline for differential expression analyses was used. The EBV gene annotation file in GTF format was obtained from GenBank file NC_007605.1 using a custom Python script. Raw reads were aligned to combined human and viral genomes. The EBV GTF annotation file was used to obtain counts for viral genes, and previously obtained human gene counts were used to estimate size factors for all samples. Subsequent differential expression analysis was performed for viral genes.

RNA extraction and qRT-PCR assays
Total RNA from RPMI 8866 cells was isolated using the RNeasy Mini Kit (Qiagen, Hilden, Germany). The RNA concentration was measured with a NanoPhotometer (Implen, Westlake Village, CA, USA). Reverse transcription was performed with an iScript Advanced cDNA Synthesis Kit (BioRad, Hercules, CA, USA) according to the manufacturer's instructions, and qPCR was performed in technical triplicates using a SYBR Green kit (Syntol) in a CFX96 Real-Time PCR Detection System (BioRad, Hercules, CA, USA). Melting curve analyses were performed to verify the amplification specificity. Experiments were performed in biological triplicates, and error bars represent the SEM as indicated in all figure legends. The HPRT, YWHAZ and UBC2 genes were used as references. The primers used for qRT-PCR analysis are listed in Table S2.

Analysis of the cell cycle
Cells were incubated in the presence of 1 mg/ml EdU for 15 min at 37 C, washed in PBS, fixed with 3.7% paraformaldehyde for 10 min and permeabilized in 0.5% Triton X-100. EdU was labeled using a Click-iT EdU Cell Proliferation Kit for Imaging, Alexa Fluor 555 dye (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer's instructions. DNA was stained with 1 mg/ml Hoechst 33342 (Thermo Fisher Scientific, Waltham, MA, USA). Cells were analyzed using a FACSAria SORP cell sorter (BD Biosciences, San Jose, CA, USA). The detection parameters were as follows: Ex. 405 nm, Em. 450/50 BP for Hoechst 33342 and Ex. 561 nm, Em. 585/15 BP for EdU-Alexa Fluor 555.
For Ki-67 staining, cells were fixed with 1% paraformaldehyde for 10 min, washed in PBS, permeabilized in 0.01% Triton X-100 for 10 min, washed in PBS and stained with anti-Ki-67 PE-conjugated antibodies (BD Pharmingen, San Diego, CA, USA) according to the manufacturer's instructions. Cells were analyzed using a FACSAria SORP cell sorter (BD Biosciences, San Jose, CA, USA). PE fluorescence was detected at Ex. 561 nm, Em. 585/15 BP.

Chromosome preparations, FISH and cytogenetic analysis
For metaphase chromosome preparations, colcemid (Invitrogen, Waltham, MA, USA) was added to cultivation media at a final concentration of 0.1 mg/ml. Cells were collected 3 h after the addition of colcemid. Hypotonic treatment (0.075 M KCl) was performed for 15 min at 37 C. Cells were fixed with an ice-cold mix of methanol and glacial acetic acid (3:1). Metaphase slides were made according to standard procedures and used for FISH one day after preparation. FISH was performed according to the manufacturer's recommendation with a mix of DNA probes specific to whole human chromosomes 1, 2 and 4 (Metasystems, Altlußheim, Germany). The DNA probes to human chromosomes 1, 2 and 4 were labeled by fluorochromes of red, green and both colors, respectively.
We used Fisher's exact test to estimate the statistical significance of differences in the level of chromosomal aberrations. Differences were considered statistically significant at a significance level of p < 0.01.

Generation of cell lines for the analysis of HIV-1 Tat action in B cells
To analyze the effect of HIV-1 Tat on B cells, we developed RPMI 8866-based cell lines stably expressing Tat protein fused with EGFP (hereafter referred to as RPMI Tat ) (Fig. 1A). As controls, we constructed cell lines expressing either EGFP (RPMI EGFP ) or TatC22G-EGFP, a mutant Tat protein deprived of transactivation capacity (RPMI Cys ).
Stable lines were obtained by transduction of cells with pseudoviral particles, and cells expressing the proteins of interest were selected using a fluorescence-activated sorter (FACS) (Fig. 1B). Flow cytometry demonstrated high purity and homogeneity of the obtained cell lines. Of note, there was an admixture of nonfluorescent cells in the RPMI Tat and RPMI Cys cell lines. Tat-EGFP and TatC22G-EGFP expression was confirmed by western blotting (Fig. 1C). Tat-EGFP and TatC22G-EGFP were partially proteolysed, and as a result, additional bands at~27 kDa were visible (nonprocessed images are presented in  S1). As the EGFP tag could have interfered with Tat activity, we analyzed the transactivation capacity of Tat-EGFP and TatC22G-EGFP using an in vitro assay based on the fast-maturing fluorescent protein TurboRFP. TurboRFP expression in HeLa cells was controlled by a fragment of the HIV-1 3′ LTR (Kurnaeva et al., 2022). We transduced EGFP, Tat-EGFP, or TatC22G-EGFP into these HeLa cells and found that the expression of Tat-EGFP substantially increased TurboRFP fluorescence compared to EGFP, which did not cause an increase in TurboRFP expression (Fig. 1D). TatC22G had a~17 fold decreased transactivation activity as compared to Tat (the median fluorescence intensity of TurboRFP in Tat-expressing cells was 34601, the median fluorescence intensity in TatC22G-expressing cells was 2009, and the median fluorescence intensity in EGFP-expressing cells was 309 in the representative experiment shown in Fig. 1D). Hence, the transactivation activity of the Tat protein was not perturbed by its fusion with EGFP.

HIV Tat protein affects the gene expression profile of RPMI 8866 cells
To determine the genes regulated by HIV-1 Tat, total RNA from RPMI 8866, RPMI EGFP , RPMI Tat and RPMI Cys cell lines was collected, and RNA-seq was performed. Three biological replicates were sequenced for each cell line. From 16 to 24 million 51 nt sequencing reads were generated for the RPMI Tat , RPMI Cys , RPMI EGFP and RPMI samples. On average, 67.17% of reads in every sample were uniquely aligned to the reference genome GRCh38, and 78.93% of them were nonambiguously mapped to the GENCODE gene annotation (Fig. S2). To assess variability between replicates and between cell lines, rlog-transformed filtered count data were visualized by principal component analysis (Fig. S3A). Biological replicates proved to be highly alike by clustering tightly according to sample type. RPMI Tat samples tended to cluster with RPMI Cys samples, implying similar effects of Tat-EGFP and TatC22G-EGFP on gene expression. This finding was also confirmed by Spearman correlation of normalized gene expression profiles (Fig. S3B).
To identify changes in gene expression induced by Tat protein in RPMI 8866 cells, we performed differential expression analysis for each cell line. We qualified differentially expressed genes (DEGs) with p.adj < 0.05 and fold change >1.5 in any direction.
Comparison of the control RPMI EGFP against the RPMI cell line demonstrated that the impact of EGFP on gene expression could be neglected, as the analysis revealed only 17 upregulated and 36 downregulated DEGs, implying that the gene expression profiles of RPMI EGFP and RPMI samples were highly similar ( Fig. 2A). Therefore, we further used RPMI cells as the control and their gene expression level as the baseline to identify genes regulated by Tat or TatC22G proteins.
Out of 32,120 genes with detectable expression, 1,038 genes were differentially expressed between RPMI Tat and RPMI cells. A comparable number of DEGs (902 genes) were identified in the comparison RPMI Cys vs the control ( Fig. 2A). Among the detected DEGs, slightly more genes were downregulated (60% or 55%) than upregulated in the presence of Tat or TatC22G protein, respectively. The majority of genes whose expression was affected were protein-coding genes. We validated the expression of several DEGs using quantitative real-time polymerase chain reaction (qRT-PCR) assays, thus confirming the reliability of RNA-seq (Fig. 2B). RPMI 8866 is an EBV-positive B-lymphoblastoid cell line derived from a patient with chronic myelogenous leukemia (McCune, Fu & Kunkel, 1981). RPMI 8866 cells express EBNA1, -2, -3A, -3B, -3C, and LMP-1, -2A, and -2B proteins and several noncoding RNAs. To explore the possibility that changes in gene expression were a result of Tat-induced changes in the expression of EBV genes, we performed differential gene expression analysis of the EBV transcriptome and found no evidence of Tat or TatC22G protein impact on the expression of viral genes in RPMI Tat or RPMI Cys cells (Fig. S4); thus we concluded that we observed a direct effect of Tat or TatC22G on the host cell.
Next, we performed gene set enrichment analysis (GSEA) and overrepresentation analysis (ORA) to search for activated or suppressed functional gene groups and molecular pathways as defined by the GO Biological Process (GO BP) and KEGG databases.
In addition to differentially expressed protein coding genes, the expression levels of 154 long non-protein coding RNAs (lncRNAs) were altered by HIV-1 Tat expression (54 and 100 lncRNAs were upregulated and downregulated, respectively). Differentially expressed lncRNAs accounted for 14.8% of all DEGs between RPMI Tat and control cells. These lncRNAs are poorly characterized, but two genes of lncRNAs among the identified DEGs are well known. The MALAT1 lncRNA involved in transcriptional regulation and alternative splicing (West et al., 2014) was upregulated 2.44-fold in RPMI Tat over the control (the elevated expression was additionally confirmed by qRT-PCR, Fig. S5A). MALAT1 is upregulated in HIV-1-infected CD4 + T cells (Qu et al., 2019) and in the peripheral blood of HIV-1-infected patients (Jin et al., 2016). NEAT1 lncRNA forms the core structural component of paraspeckle bodies and is known for its contribution to HIV replication (Zhang et al., 2013). We observed a 1.66-fold increase in NEAT1 expression in the RPMI Tat B cell line (see also Fig. S5B), indicating that NEAT1 may play an important role in the development of cell responses induced by the Tat protein.
We also analyzed other DE lncRNAs. The complete list of DE lncRNAs is presented in Table S4, and, since the level of some of these lncRNAs was extremely low, we additionally analyzed ten most expressed DE lncRNAs ( differentially expressed in RPMI Tat cells, are also differentially expressed in different tumors and might be involved in development of these tumors. The most known examples are MALAT1 and NEAT1 expressed in different neoplasms, including B-cell lymphomas and leukemias (Table S5).

Comparison of Tat-and TatC22G-induced effects
HIV-1 Tat can modify host gene expression by several nonrelated mechanisms. Tat can transactivate host genes by binding to TAR-like sequences in their nascent mRNAs, e.g., IL6 mRNA (Ambrosino et al., 1997) or TNF-β mRNA (Buonaguro et al., 1994). Tat can also associate with chromatin and control RNA polymerase II recruitment and pause release (Reeder et al., 2015). Additionally, HIV-1 Tat interacts with hundreds of nuclear proteins (Gautier et al., 2009), and this interaction may also modify the expression of host genes. Transactivation of transcription by the mutant TatC22G protein was significantly weaker than that by Tat (Fig. 1D), but it could still bind to chromatin and interact with other proteins. Both Tat and TatC22G proteins modulate the expression of similar and overlapping sets of genes (Fig. 4A). Comparison of the transcriptome profiles of the RPMI Tat and RPMI Cys cell lines revealed 201 protein-coding DEGs (p.adj < 0.05; fold change >1.5 in any direction).
Overrepresentation analysis against the KEGG database also demonstrated little gene set enrichment, reflecting relatively small differences between RPMI Cys and RPMI Tat cells  4B). Notably, the Cytokine-cytokine receptor interaction pathway (hsa04060) downregulated by HIV-1 Tat was further suppressed by mutant TatC22G protein in RPMI Cys cells compared to RPMI Tat cells.

The pathological effects of Tat expression
Our RNA-seq analysis identified several pathways affected by Tat. We next experimentally analyzed key characteristics of RPMI 8866, RPMI EGFP , RPMI Tat , and RPMI Cys cell lines to identify how Tat affected B cells. Electron microscopy demonstrated that the expression of EGFP, Tat-EGFP or TatC22G-EGFP did not lead to any substantial changes in cellular organization (Fig. 5A). To ascertain whether changes in the expression of several genes involved in the G 1 /S phase transition and cell proliferation (Fig. 2C) affected cell cycle progression in Tat-expressing cells, we analyzed the proportion of G 1 /G 0 , S, and G 2 /M cells in the total population of RPMI Tat cells using flow cytometry. We could not detect any significant changes in the proportions of cells at different stages of the cell cycle using this approach (Figs. 5B, 5C). We also detected S-phase cells using incorporation of EdU and did not observe any difference between different cell lines (Fig. 5D). Finally, we analyzed fractions of cycling (non-G 0 ) cells using antibodies against the proliferation marker Ki-67, and again, no difference was found (Fig. 5E).
Cell death can also influence the dynamics of cell populations; therefore we analyzed apoptosis in RPMI Tat cells. We did not observe any statistically significant changes in the content of sub-G 1 cells (dead cells), cells with reduced mitochondrial potential (TMRE staining, which allowed for marker cells during very early stages of apoptosis), proportion of annexin V-positive, or caspase 3-positive cells (markers of apoptotic cells) (Fig. 5F).
Thus, stable expression of Tat did not produce strong effects that could be detected using standard analysis of cellular proliferation and apoptosis. At the same time, subtle, but persistent changes (if they exist) could potentially influence the dynamics of cell growth. Therefore, we cultured cells for three months and found that the percentage of cells expressing Tat-EGFP was gradually reduced (Figs. 6A, 6B). Such replacement of Tat-expressing cells by non-expressing cells was not detected in cells expressing either TatC22G-EGFP or EGFP, clearly indicating that this effect was a consequence of Tat protein per se.
To detect possible mutagenic effects of HIV-1 Tat expression, we performed cytogenetic analysis. We scored chromosome aberrations involving chromosomes 1, 2 and 4 revealed by fluorescence in situ hybridization with whole chromosome paints (Fig. 6C). The images of metaphases were automatically acquired, and then metaphases were analyzed for the presence of structural chromosomal aberrations, including translocations, dicentrics, acentrics, and deletions. Numerical chromosomal abnormalities, such as polyploidy and aneuploidy, were also taken into consideration. We observed a reproducible increase in the frequency of translocations as well as in the total yield of structural aberrations in cells expressing either Tat-EGFP or TatC22G-EGFP ( Fig. 6D; Table S6).    ., 2017)), or a model with the expression of HIV-1 controlled by an inducible promoter (for example, (Reeder et al., 2015)). These experimental systems are more likely to simulate an acute infection situation, but they do not allow for the study of weak long-term effects of protein and compensatory reactions of the cell. In the current study, we obtained the B lymphoid cell line RPMI 8866 stably expressing HIV-1 Tat fused with EGFP and used these cells to simulate a prolonged systemic effect of the presence of Tat in blood of chronically infected patients. The proportion of EGFP-positive cells was decreased during prolonged cultivation of RPMI Tat but not RPMI EGFP and RPMI Cys cells (a representative experiment). (C) Representative image of a metaphase plate with detected chromosomes 1 (red), 2 (green) and 4 (orange). The chromosome with a translocation is marked with an arrow. (D) Cells expressing Tat-EGFP or TatC22G-EGFP contained a significantly higher proportion of chromosomes with aberrations (see Table S6 for a detailed description of chromosome aberrations To detect possible changes provoked by chronic exposure of B-cells to Tat, we performed a genome-wide analysis of cellular gene expression (RNA-seq). Tat protein induces differential expression of approximately 1,000 genes (p.adj < 0.05 and fold change of ≥1.5 in any direction). To predict the effects of these changes, the analysis of metabolic and signaling pathways (GO BP and KEGG analysis) was performed. GO BP analysis demonstrated the activation of pathways involved in cellular antiviral reactions, and suppression of different metabolic pathways and proliferation. KEGG analysis also demonstrated activation of innate immunity pathways involved in antiviral reactions: toll-like receptor signaling and NOD-like receptor signaling pathways. We observed an increase in JAK1 and STAT1 expression, indicating that Tat may affect these pathways via the JAK-STAT signaling. Additionally, we found the reduction of Cytokine-cytokine receptor interaction pathway. Downregulation of this pathway (and downregulation of metabolic pathways and proliferation) may result from direct HIV-1 Tat action (proviral reactions). It should be noted however that not all cytokines and their receptors were downregulated. HIV-1 Tat induces the expression of several proinflammatory cytokines, mainly IL-6 in several cell types (Ambrosino et al., 1997;Nookala & Kumar, 2014;Ben Haij et al., 2015). Our results also confirm the upregulation of pro-inflammatory IL6 gene in Tat-expressing B cells. Thus, the gene expression pattern we observed in Tat-expressing B cells probably resulted from a combination of Tat action per se and cellular antiviral reactions.
We also analyzed the lncRNA expression. Differentially expressed lncRNAs accounted for 14.8% of all DEGs between RPMI Tat and the control cells. While functions of the majority of identified lncRNA are poorly studied, some of them (e.g., NEAT1 and MALAT1) play a role in development of B-cell neoplasms (Table S5). In Tat-expressing RPMI 8866 cells, both of these lncRNAs were upregulated. It is possible that these or some other DE lncRNAs can be involved in the development of B-cell lymphomas in HIV-1infected patients, but this requires further study.
Another important observation on the mode of action of Tat in host cells came from the comparison of the action of Tat and its TatC22G mutant with the reduced transactivation activity. In our experiments, 313 protein-coding genes were regulated by Tat only (115 upregulated and 198 downregulated, respectively), 228 genes were regulated by TatC22G only (117 upregulated and 111 downregulated genes), while the most genes (435) were regulated both by Tat and TatC22G (185 upregulated and 250 downregulated genes) (Fig. 4). HIV-1 Tat can modify cellular processes via different mechanisms. It can transactivate host genes by binding to TAR-like sequences (for example, this mechanism seems to regulate the transcription of IL6 in T cells (Ambrosino et al., 1997) and TNF-b in B cells (Buonaguro et al., 1994)). Tat can also interact with chromatin and RNA-polymerase II (Reeder et al., 2015) as well as with hundreds of other cellular and nuclear proteins (Gautier et al., 2009). Therefore, to distinguish between the two potential modes of action of Tat, we compared the effects of HIV-1 Tat and that with the C22G mutation possessing a significantly decreased transactivation activity. We hypothesized that genes differentially expressed between RPMI Tat and RPMI Cys cells would be associated with the transactivator activity of this protein while genes similarly regulated would rather be affected by protein-protein interactions involving Tat. The RNA-seq results demonstrated that most of the effects of EGFP-Tat and EGFP-TatC22G were similar, and hence, the main mechanism of prolonged Tat action on B-cells seems to be due to Tat interaction with host proteins.
We next analyzed some physiological parameters of RPMI Tat cells. We could not find any manifested changes in cellular morphology, proliferation, or apoptosis. At the same time, we observed an effect of prolonged cultivation, i.e., a decrease in EGFP-Tatexpressing cells, indicating that Tat inhibited cell growth. Thus, although there were no strong effects of EGFP-Tat expression, a weak effect, which manifested itself only in situations of long observation, was induced. Additionally, we found that chromosome aberrations occurred more frequently in cell lines expressing EGFP-Tat and EGFP-TatC22G. These observations are consistent with published data obtained using an ex vivo model (El-Amine et al., 2018). Modification of cell cycle progression and chromosome aberrations may both promote lymphomagenesis.
Thus, the presence of HIV-1 Tat can in the long run modify the cellular physiology and genome stability of cultured B cells and, as a result, may promote oncogenic transformation. The precise mechanisms of these effects will be the subject of our future work.