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[Preprint]. 2023 Dec 12:2023.12.11.571123.
doi: 10.1101/2023.12.11.571123.

Transposable elements may enhance antiviral resistance in HIV-1 elite controllers

Manvendra Singh  1   2 , Sabrina M Leddy  1 , Luis Pedro Iñiguez  3 , Matthew L Bendall  4 , Douglas F Nixon  4 , Cédric Feschotte  1
Affiliations

Transposable elements may enhance antiviral resistance in HIV-1 elite controllers

Manvendra Singh et al. bioRxiv. .

Abstract

Less than 0.5% of people living with HIV-1 are elite controllers (ECs) - individuals who have a replication-competent viral reservoir in their CD4+ T cells but maintain undetectable plasma viremia without the help of antiretroviral therapy. While the EC CD4+ T cell transcriptome has been investigated for gene expression signatures associated with disease progression (or, in this case, a lack thereof), the expression and regulatory activity of transposable elements (TEs) in ECs has not been explored. Yet previous studies have established that TEs can directly impact the immune response to pathogens, including HIV-1. Thus, we hypothesize that the regulatory activities of TEs could contribute to the natural resistance of ECs against HIV-1. We perform a TE-centric analysis of previously published multi-omics data derived from EC individuals and other populations. We find that the CD4+ T cell transcriptome and retrotranscriptome of ECs are distinct from healthy controls, treated patients, and viremic progressors. However, there is a substantial level of transcriptomic heterogeneity among ECs. We categorize individuals with distinct chromatin accessibility and expression profiles into four clusters within the EC group, each possessing unique repertoires of TEs and antiviral factors. Notably, several TE families with known immuno-regulatory activity are differentially expressed among ECs. Their transcript levels in ECs positively correlate with their chromatin accessibility and negatively correlate with the expression of their KRAB zinc-finger (KZNF) repressors. This coordinated variation is seen at the level of individual TE loci likely acting or, in some cases, known to act as cis-regulatory elements for nearby genes involved in the immune response and HIV-1 restriction. Based on these results, we propose that the EC phenotype is driven in part by the reduced availability of specific KZNF proteins to repress TE-derived cis-regulatory elements for antiviral genes, thereby heightening their basal level of resistance to HIV-1 infection. Our study reveals considerable heterogeneity in the CD4+ T cell transcriptome of ECs, including variable expression of TEs and their KZNF controllers, that must be taken into consideration to decipher the mechanisms enabling HIV-1 control.

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Figures

Figure 1.
Figure 1.. Differential (retro)transcriptomic profiles in ECs vs. HCs
A. Volcano plot illustrating the differentially expressed TEs and genes between ECs (n=4) and HCs (n=5). Coloration is based on increased or decreased expression of genes (orange and green, respectively) and TEs (red and purple, respectively). Total detected genes and TE loci are plotted by log2-transformed fold change. Statistical significance given in the form of sqrt -log10 adjusted p-value, calculated by Wilcoxon rank sum test with Bonferroni correction. Data source: Gonzalo-Gil et al., 2019. B. Heatmap displaying the expression of the top differentially expressed genes in CD4+ T cells of ECs (n=4; red bar) vs. HCs (n=5; blue bar). Heatmap coloration is based on the z-score distribution from −2 to 2 (gold to purple; low to high expression). Data source: Gonzalo-Gil et al., 2019. C. Heatmap displaying the expression of the top differentially expressed TE families in CD4+ T cells of EC (n=4; red bar) vs. HCs (n=5; blue bar). Heatmap coloration is based on the z-score distribution from −2 to 2 (gold to purple; low to high expression). Data source: Gonzalo-Gil et al., 2019.
Figure 2.
Figure 2.. Differential (retro)transcriptomic & immune cell profiles in ECs vs. VPs
A. PCA triplot from PBMCs of ECs (red) and VPs (blue), based on the most variably expressed genes and TE families. Data source: Zhang et al., 2018. B. Heatmap of z-scaled expression (log2 TPM) from select gene/TE sets between ECs and VPs. On the y-axis, immune genes are in black, leukocyte surface markers are in blue, and TEs are in red. Data source: Zhang et al., 2018. C. Heatmap displaying the z-scaled expression (log2 TPM) of genes and TEs distinguishing EC and VP RNA-seq samples. Every row denotes a gene or TE element. Data source: Zhang et al., 2018. D. Box plots for leukocyte population of interest, identified via deconvolution analysis of PBMC RNA-seq data. Statistical significance determined by Wilcoxon rank-sum tests. Data source: Zhang et al., 2018.
Figure 3.
Figure 3.. EC CD4+ T cells can be grouped into four distinct clusters
A. UMAP plot of the four clusters of EC CD4+ T cell subtypes (N=128) using KNN graph construction on bulk RNA-seq data, based on the Euclidean distance in PCA space (see Methods). Every point is a CD4+ T cell RNA-seq sample, colored by cluster assignment. Data sources: Jiang et al., 2020 and Boritz et al., 2016. B. Stacked barplot displaying the composition of CD4+ T cell subtypes (naïve, CM, TM, EM, total) in each of the four clusters. Data sources: Jiang et al., 2020 and Boritz et al., 2016. C. Gene ontology biological process (GO NR-BP; ●) and KEGG pathway (KEGG; ▲) delineation of the four EC clusters using WebGestalt. Data derived from differential expression analysis of the EC clusters, using the significant DEGs (p-value < 0.05) as each cluster’s respective gene list. For each of the four EC clusters, the highest ranked GO terms and KEGG pathways by adjusted p-value are shown. ‘Enrichment ratio’ refers to the number of observed genes divided by the number of expected genes from each GO or KEGG category in the cluster’s gene list. Data sources: Jiang et al., 2020 and Boritz et al., 2016. D. Dot plot illustrating the scaled expression of selected genes related to HIV-1 replication in the four EC clusters. Coloration represents the log2-transformed expression scaled to their transcriptome and averaged across the cluster’s samples, from lower (blue) to higher (red) expression. The size of the dots is directly proportional to the percent of samples expressing the given gene in a given cluster. Data sources: Jiang et al., 2020 and Boritz et al., 2016.
Figure 4:
Figure 4:. Induction of innate immune gene expression by proximal LTRs.
A. Barplots showing the expression (log2 TPM) of selected differentially expressed innate immune genes in HCs (n=5) and ECs (n=4). P-value is calculated by paired student t-test. RNA- & ATAC-seq data source: Gonzalo-Gil et al., 2019. B. Integrative genome visualization (IGV) of normalized ATAC-Seq signal around the selected DEGs in Figure 5A between HC (n=4) and EC (n=4) CD4+ T cells. ATAC-seq peaks of interest are shaded in light gray. The proximal TE integrants are shown below the IGV graph, under which the encoded TF binding over the corresponding TE integrant(s) is also shown. RNA- & ATAC-seq data source: Gonzalo-Gil et al., 2019.
Figure 5.
Figure 5.. Characterization of TE expression in specific EC clusters and its correlation to KZNF repression
A. Dot plot illustrating the intensity and abundance of TE family expression (log2 CPM+1) across the four EC clusters. Coloration is scaled from lower (black) to higher (red) expression. Point size is directly proportional to the percent of samples expressing the TE family in the given cluster. Data sources: Jiang et al., 2020 and Boritz et al., 2016. B. Line plots showing the distribution of averaged, normalized ATAC-seq signal over all loci in the selected TE families for the samples in each cluster. The ATAC-seq signal counts are calculated and normalized as mappable reads per million per 100 bp bins, in a +/− 5 kb genomic window at the elements’ left boundary in the human genome. For the L1 elements, only those that are intact and full-length are considered, denoted as ‘hot’ L1s for their recent transpositional activity. Lines are color coded by cluster. Data source: Jiang et al., 2020. C. Scatter plot showing scaled, log2-transformed expression of previously implicated TE families and KZNFs in the analyzed CD4+ T cells subtype EC RNA-seq data, cluster classified in Figure 5A. Linear regression analysis (black line) indicates the correlation between TE families and their targeting KZNF’s expression in EC samples. The rho value is obtained from pairwise-ranked correlation analysis. Data sources: Jiang et al., 2020 and Boritz et al., 2016.
Figure 6.
Figure 6.
The interplay of KRAB-ZNFs and TEs regulates the expression of proximal antiviral genes
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