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. 2023 Feb 14;11(1):e0251622.
doi: 10.1128/spectrum.02516-22. Epub 2023 Jan 5.

Human Endogenous Retrovirus (HERV) Transcriptome Is Dynamically Modulated during SARS-CoV-2 Infection and Allows Discrimination of COVID-19 Clinical Stages

Nicole Grandi  1 , Maria Chiara Erbì  1 , Sante Scognamiglio  1 , Enzo Tramontano  1   2
Affiliations

Human Endogenous Retrovirus (HERV) Transcriptome Is Dynamically Modulated during SARS-CoV-2 Infection and Allows Discrimination of COVID-19 Clinical Stages

Nicole Grandi et al. Microbiol Spectr. .

Abstract

SARS-CoV-2 infection is known to trigger an important inflammatory response, which has a major role in COVID-19 pathogenesis. In infectious and inflammatory contexts, the modulation of human endogenous retroviruses (HERV) has been broadly reported, being able to further sustain innate immune responses due to the expression of immunogenic viral transcripts, including double-stranded DNA (dsRNA), and eventually, immunogenic proteins. To gain insights on this poorly characterized interplay, we performed a high-throughput expression analysis of ~3,300 specific HERV loci in the peripheral blood mononuclear cells (PBMCs) of 10 healthy controls and 16 individuals being either convalescent after the infection (6) or retesting positive after convalescence (10) (Gene Expression Onmibus [GEO] data set GSE166253). Results showed that the exposure to SARS-CoV-2 infection modulates HERV expression according to the disease stage and reflecting COVID-19 immune signatures. The differential expression analysis between healthy control (HC) and COVID-19 patients allowed us to identify a total of 282 differentially expressed HERV loci (deHERV) in the individuals exposed to SARS-CoV-2 infection, independently from the clinical form. In addition, 278 and 60 deHERV loci that were specifically modulated in individuals convalescent after COVID19 infection (C) and patients that retested positive to SARS-CoV-2 after convalescence (RTP) as individually compared to HC, respectively, as well as 164 deHERV loci between C and RTP patients were identified. The identified HERV loci belonged to 36 different HERV groups, including members of all three classes. The present study provides an exhaustive picture of the HERV transcriptome in PBMCs and its dynamic variation in the presence of COVID-19, revealing specific modulation patterns according to the infection stage that can be relevant to the disease clinical manifestation and outcome. IMPORTANCE We report here the first high-throughput analysis of HERV loci expression along SARS-CoV-2 infection, as performed with peripheral blood mononuclear cells (PBMCs). Such cells are not directly infected by the virus but have a crucial role in the plethora of inflammatory and immune events that constitute a major hallmark of COVID-19 pathogenesis. Results provide a novel and exhaustive picture of HERV expression in PBMCs, revealing specific modulation patterns according to the disease condition and the concomitant immune activation. To our knowledge, this is the first set of deHERVs whose expression is dynamically modulated across COVID-19 stages, confirming a tight interplay between HERV and cellular immunity and revealing specific transcriptional signatures that can have an impact on the disease clinical manifestation and outcome.

Keywords: COVID-19; HERV; RNA-seq; SARS-CoV-2; human endogenous retroviruses; transcriptome.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

FIG 1
FIG 1
HERV sample-to-sample distance (A) and PCA plot (B). (A) Heatmaps of the overall similarity between samples: the correlation distance measure was used in clustering columns based on the rlog-normalized HERV expression data. Samples are annotated by condition: red, healthy controls; green, convalescent after recover from SARS-CoV-2 infection; blue, retesting positive after convalescence. The three clusters highlight specific HERV transcriptional signatures induced by SARS-CoV-2 exposure and infection. Distance values are shades of blue, as represented in the color key and histogram legends. (B) Principal-component analysis as performed on rlog-normalized HERV loci expression data. It is possible to see the division between nonexposed controls and individuals exposed to SARS-CoV-2 infection according to the PC1 (45% of variance) as well as the division according to the presence of SARS-CoV-2 active infection by the PC2 (13% of variance).
FIG 2
FIG 2
(A and B) Heatmaps based on the top 500 HERVs as sorted by the highest mean (A) or variance (B) of expression. Hierarchical clustering of the top 500 HERV insertions with the highest average (A) or variance (B) of rlog-normalized counts. The top 500 HERV loci are in rows, and the samples are in columns. rlog-normalized counts are color-scaled from blue (minimum) to red (maximum). The correlation distance measure was used in clustering columns. Samples are annotated by condition: red, healthy controls; green, convalescent after recover from SARS-CoV-2 infection; blue, retesting positive after convalescence.
FIG 3
FIG 3
Results of HERV differential expression analysis on the total data set. (A) Volcano plot in which each point represents an individual HERV locus, which spread according to the magnitude (log2 fold change, x axis) and statistical significance (log10 -adjusted P values, y axis) of its modulation in SARS-CoV-2-exposed individuals (RTP and C) compared to healthy controls (HC). Red points indicate significantly modulated HERVs. (B) Summary of the number of deHERVs as divided by group: both upregulated and downregulated members are reported.
FIG 4
FIG 4
Details of HERV modulation in the overall analysis and in the different subcomparisons. (A) Left: diagram representing the results of the overall differential expression analysis (RTP and C versus HC) in terms of number of HERVs found to be differentially expressed (being either up- or downregulated), expressed but not differentially expressed (no-DE), and not expressed at all. Right: genomic context of integration of the deHERV sequences, which were found either in the intergenic position or within a gene; the latter were further distinguished since the colocalized gene was itself DE and, in this case, if it was coding a protein or not. (B) Venn diagrams of the different differential expression subcomparisons. Top: results and overlaps of the DE analysis as performed in C versus HC, RTP versus HC, and RTP versus C; bottom: detail of the number of deHERVs showing a concordant modulation in C and RTP individually compared to HC.
FIG 5
FIG 5
The 31 key deHERVs showing a significant modulation in all DE analyses. (A) Distribution among the different HERV groups. (B) Heatmap of expression based on the calculation of transcripts per million kilobases values (TPM); the concomitant modulation of eventual colocalized de-genes, when present, is indicated with red and green dots, meaning colocalization with an upregulated or downregulated gene(s), respectively. Samples predicted to be low responders based on the analysis of the 44 key innate immune genes are marked with an asterisk (*).
FIG 6
FIG 6
Boxplot of expression levels for the key deHERVs modulated in all conditions that are colocalized with de-genes. The expression levels (as transcripts per million kilobases values [TPM]) of the 7 out of 31 key deHERVs integrated within cellular genes that were themselves modulated are plotted along with those of the colocalized genes under the different conditions, to assess the possible reciprocal influence. Plots marked with an asterisk are the deHERVs with the highest expression (log10 TPM, >10 for at least one condition). Statistics are based on t test.
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