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Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats - PubMed

  • ️Thu Jan 01 2015

Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats

Scott Sherrill-Mix et al. Retrovirology. 2015.

Abstract

Background: HIV infection has been reported to alter cellular gene activity, but published studies have commonly assayed transformed cell lines and lab-adapted HIV strains, yielding inconsistent results. Here we carried out a deep RNA-Seq analysis of primary human T cells infected with the low passage HIV isolate HIV89.6.

Results: Seventeen percent of cellular genes showed altered activity 48 h after infection. In a meta-analysis including four other studies, our data differed from studies of HIV infection in cell lines but showed more parallels with infections of primary cells. We found a global trend toward retention of introns after infection, suggestive of a novel cellular response to infection. HIV89.6 infection was also associated with activation of several human endogenous retroviruses (HERVs) and retrotransposons, of interest as possible novel antigens that could serve as vaccine targets. The most highly activated group of HERVs was a subset of the ERV-9. Analysis showed that activation was associated with a particular variant of ERV-9 long terminal repeats that contains an indel near the U3-R border. These data also allowed quantification of >70 splice forms of the HIV89.6 RNA and specified the main types of chimeric HIV89.6-host RNAs. Comparison to over 100,000 integration site sequences from the same infected cell populations allowed quantification of authentic versus artifactual chimeric reads, showing that 5' read-in, splicing out of HIV89.6 from the D4 donor and 3' read-through were the most common HIV89.6-host cell chimeric RNA forms.

Conclusions: Analysis of RNA abundance after infection of primary T cells with the low passage HIV89.6 isolate disclosed multiple novel features of HIV-host interactions, notably intron retention and induction of transcription of retrotransposons and endogenous retroviruses.

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Figures

Fig. 1
Fig. 1

Comparisons among studies quantifying cellular gene expression after HIV infection. For each pair of studies, the association between up- and downregulation calls was measured for genes identified by both studies as differentially expressed (above the diagonal). As another comparison, we also measured the agreement between studies for which genes were called differentially expressed regardless of direction (below the diagonal). The color scale shows the conservative (i.e. closest to 1) boundary of the confidence interval of the odds ratio with blue indicating a positive association and red a negative association between studies. For confidence intervals overlapping 1, the value was set to 1. Therefore all colored squares indicate significant associations

Fig. 2
Fig. 2

Comparisons of the effect of HIV infection on cellular gene expression to additional studies comparing transcription in subsets of immune cells. The MSigDB database was used to extract 185 sets of differentially expressed genes from pairs of transcriptional profiling studies of immune cell subsets involving CD4+ T cells. For each pair of studies, we used Fisher’s exact test to measure the association between up- and downregulation calls for genes identified as differentially expressed in both our HIV study and the comparator immune subsets. a The transcriptional profiles with strongest associations with changes observed in our study of HIV89.6 infection of primary T cells. Blue indicates a positive association between changes seen in HIV-infected cells and the first immune subset (text colored blue) while red indicates a positive association with the second immune subset (text colored red). The color scale shows the conservative (i.e. closest to 1) boundary of the confidence interval of the odds ratio. For confidence intervals overlapping 1, the value was set to 1. Therefore all colored squares indicate significant associations. b As in a, but showing the transcriptional profiles most strongly associated with changes observed in lymph node biopsies from acutely infected patients [30]

Fig. 3
Fig. 3

Changes in the abundance of intronic regions with HIV infection. Expression of intronic and exonic regions was quantified as the proportion of reads mapping within the intron/exon out of the total reads mapping to the transcription units overlapping that intron/exon. a Comparison of the ratios of expression between infected and uninfected replicates in exclusively intronic or exonic regions of transcription units. b Reproducibility of intron retention between replicates. Each point quantifies the change in expression with HIV infection for a specific intronic region. The x-axis shows changes in gene activity accompanying infection for one set of replicates (Infected-1 and Infected-2 vs. Uninfected-1) and the y-axis shows the same data for different replicates (Infected-3 vs. Uninfected-2). c Reproducibility of intron retention between studies. The plot is arranged as in b but with all data from our study combined on the x-axis and corresponding data from Chang et al. [25] on the y-axis

Fig. 4
Fig. 4

Repeat categories enriched upon infection with HIV. a The association of repeat regions differentially expressed after HIV89.6 infection of primary T cells observed for varying thresholds of differential expression. The threshold used to call a gene differentially expressed based on the Bayesian posterior median was varied and Fisher’s exact test was used to assess whether any genomic repeats had a significant association with this differential expression. Note that only ERV-9 (annotated as HERV9-int in the RepeatMasker database) and it’s corresponding long terminal repeat ERV-9/LTR12C were significantly associated with large changes in expression. b Enrichment of repeat categories in regions differentially expressed (Bayesian 95 % credible interval >2× change) between HIV-infected and control CD4+ T cells. The repeated sequences are ordered on the x-axis by the extent of induction within each class with circles indicating repeats annotated as hominid specific and squares marking all other repeats, the y-axis shows the p value for upregulation after infection. The dashed line indicates a Bonferroni corrected p value of 0.05. c The proportion of human mapped reads that align within classes of genomic repeats for data from primary CD4+ T cells from this study and SupT1 cells from Chang et al. [25]. A single read mapping multiple times to a given category was only counted once

Fig. 5
Fig. 5

Characteristics of ERV-9/LTR12C sequences associated with induction upon infection of primary T cells with HIV89.6. a An alignment of the 3′ end of the U3 region of repeats annotated as ERV-9/LTR12C. Each row is a section of the long terminal repeat sequence and each column a base in that sequence colored by nucleotide identity. For clarity, positions appearing in less than 2 % of sequences are omitted. Two distinct classes are visible with a short form and long forms containing varying numbers of repeated sequence. Mutations away from the consensus can also be seen. b The proportion of LTR12C regions with significant increases in read abundance after infection with HIV and their 95 % confidence intervals separated by the length class of the LTR, presence in a gene, presence of a TATA box and the number of GATA2 motifs in the U3 region. These variables were selected by stepwise regression regression comparing differential expression of LTR12C to the length class of the LTR, the number of mutations away from consensus, the number of NFY, GATA2 and MZF1 motifs and the presence of a TATA box motif within 50 bp of the transcription start site. Variables are labeled with the estimated change in log odds ratio (β) and their Wald test p values

Fig. 6
Fig. 6

Transcription and splicing of the HIV89.6 RNA. a Junctions between HIV splice donors and acceptors observed in the RNA-Seq data. Acceptors are shown as the columns and donors as the rows with the coloring indicating the frequency of each pairing. b The relative abundance of 78 HIV89.6 transcripts as determined by a combination of PacBio sequencing [6] and Illumina sequencing. Message structures were generated by targeted long read single molecule sequencing, which allowed association of multiple splice junctions in single sequence reads. The Illumina short read sequencing allowed normalization of message abundances between size classes. The inferred HIV message population is shown colored by relative abundance

Fig. 7
Fig. 7

Analysis of chimeric RNA sequences containing both human and HIV sequences. Counts of the location in the HIV genome of the HIV-human junctions in filtered chimeric reads. Due to abundant sequencing artifacts (Additional file 7), reads were filtered to exclude reads where the human and HIV portions contained overlapping complementarity at the sequence junction (a sign of potential artifactual formation) and to exclude reads where the viral portion did not start at a known splice site or 5′ or 3′ border of the HIV genome

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