Parvovirus B19 Replication and Expression in Differentiating Erythroid Progenitor Cells

Abstract

The pathogenic Parvovirus B19 (B19V) is characterized by a strict adaptation to erythroid progenitor cells (EPCs), a heterogeneous population of differentiating cells with diverse phenotypic and functional properties. In our work, we studied the dynamics of B19V infection in EPCs in dependence on the cell differentiation stage, in terms of distribution of infected cells, synthesis of viral nucleic acids and production of infectious virus. EPCs at early differentiation stage led to an abortive infection, without viral genome replication and a very low transcriptional activity. EPCs at later stages were permissive, with highest levels of viral replicative activity at day 9 (+3.0 Log from 2 to 48 hpi) and lower levels at day 18 (+1.5 Log from 2 to 48 hpi). B19V DNA increment was in accordance with the percentage of cells positive to flow-FISH assay (41.4% at day 9, 1.1% at day 18). Quantitation of total RNA indicated a close association of genome replication and transcription with viral RNA accumulation within infected cells related to viral DNA increase during the course of infection. Analysis of the different classes of mRNAs revealed two distinct pattern of genome expression profile with a fine regulation in the frequency utilization of RNA processing signals: an early phase, when cleavage at the proximal site leading to a higher relative production of mRNA for NS protein, and a late phase, when cleavage at the distal site was more frequent leading to higher relative abundance of mRNA for VP and 11 kDA proteins. Infectious virus was released from cells at day 6-15, but not at day 18. Our results, providing a detailed description of B19V replication and expression profile in differentiating EPCs, highlight the very tight adaptation of B19V to a specific cellular target defined both by its erythroid lineage and its differentiation stage.

Fig 1. Outline of parvovirus B19 genome structure and organization.

Top, ORF distribution within B19V internal coding region and related coding sequences. Center, functional map of B19V genome and distribution of regulatory signals: grey boxes, inverted terminal regions; P₆, promoter region; pAp1, pAp2, pAd: proximal and distal cleavage-polyadenylation sites; D1, A1-1/2, D2, A2-1/2, donor and alternative acceptor sites for introns 1 and 2, respectively. Below, a simplified map of B19V transcripts. Bottom, position of primers used in the PCR array (Table 1).

Fig 2. Phenotypic characteristics of differentiating EPCs.

The PBMC-derived cell population was evaluated by cytofluorimetric analysis for the presence and abundance of erythroid differentiation markers (A: CD36; B: CD71; C: CD235a) and B19V receptor moieties (D: Globoside; E: α5β1 integrin). Each graph reports, at three days intervals, the percentage of cells positive for each indicated marker and the relative Mean Fluorescence Intensity (rel mfi), calculated as the mean geometric fluorescence for each sample, normalized to the average value within the fraction of positive cells. Reported curves are 3^rd order polynomial nonlinear fits, obtained from n total independent experimental data in the range 2–18 days of culture: for CD36, n = 49; for CD71, n = 35; for CD235a, n = 20; for Globoside, n = 32; for α5β1 integrin, n = 21. Source data are shown in S1 Dataset.

Fig 3. B19V replication in EPCs.

EPCs were infected at different days from isolation with B19V, at the multiplicity of 10³ geq/cell. The amount of B19V DNA was determined by qPCR, and the Log increase in viral genome copies measured between 2 hours post-infection (hpi) and 24 or 48 hpi. Columns indicate the mean values obtained from independent experiments (n = 3–6), bars indicate the standard error of means. By one-way analysis of variance, statistical significance was obtained for mean values in both the 24 and 48 hpi series (p<0.001). By Tukey’s multiple comparison test, the following groups were significantly different (***, p<0.001): day 3 vs. days 6–9 and vs. days 12–18; days 6–9 vs. days 12–18. Source data are shown in S1 Dataset.

Fig 4. B19V replication and distribution in EPCs.

EPCs were infected at different days from isolation with B19V, at the multiplicity of 10³ geq/cell, and analyzed at 2, 24 and 48 hpi by qPCR and flow-FISH. (A) Amount of B19V DNA determined by qPCR, Log DNA geq/10⁴ cells. (B) Fraction of positive cells determined by the flow-FISH assay. (C) Correlation of data obtained by the two different assays. X: samples at 2 hpi; ◯: samples at 24 hpi; ●: samples at 48 hpi; --- : linear regression fit. Source data are shown in S1 Dataset.

Fig 5. B19V replication and distribution in EPCs.

The panel reports the dot plot graphs of flow-FISH assay in EPCs at different days of differentiation, at 24 and 48 hpi. The fraction of B19V positive cells (upper right quadrant) was evaluated by determining the number of cells above a fixed threshold along the X-axis (FITC) in the infected cell samples and subtracting the number of cells above threshold in respective uninfected negative controls.

Fig 6. B19V replication and expression in a time course of infection in EPCs.

Cells infected at different days from isolation at a moi of 10³ geq/cell were collected at the indicate time points post infection (2–48 hpi) and the amounts of viral nucleic acids determined by qPCR and qRT-PCR, for the different samples series. (A) Amount of B19V DNA determined by qPCR, Log DNA geq/10⁴ cells. (B) Amount of B19V total RNA determined by qRT-PCR, Log RNA target copies/10⁴ cells. Source data are shown in S1 Dataset.

Fig 7. B19V replication and expression in a time course of infection in EPCs.

The dynamics of accumulation of viral DNA and of the major classes of viral mRNAs in infected EPCs is reported for each sample series at different days from isolation, and for the different time points within the course of infection. The amount of the different classes of viral mRNAs was determined by qRT-PCR, using the selected primer pair combinations shown in Table 1, followed by normalization within each combination set and to the amount of total viral RNA. Source data are shown in S1 Dataset.

Fig 8. Transcript processing in a time course of infection in EPCs.

Composite columns indicate the frequency of processing events at intron 1, intron 2 or pAp/pAd sites, and the resulting overall composition of the major classes of viral mRNAs. Cumulative data are averaged for each time point in the course of infection, for the days 6–18 experimental series. Source data are shown in S1 Dataset.

Fig 9. Virus release from infected EPCs and infectivity.

(A) Variation of viral DNA amounts released in the supernatants (spn) of B19V-infected EPCs, at different time points post-infections for EPCs at different days from isolation. Log of viral DNA target copies (Log DNA geq/100 μL) is reported for the different samples series. (B) Infectivity of virus released from EPCs. Supernatants recovered at 12 hpi and 48 hpi from B19V-infected EPCs at different days of culture (day 3–18) were used to infect a new population of EPCs at day 9 of culture, and the amount of intracellular viral DNA at 2 and 48 hpi determined by qPCR assay. DNA amounts are reported as variation bars: lower limits of bars are the amount of viral DNA detected at 2 hpi, upper limits are the amount detected at 48 hpi (Log DNA geq/10⁴ cells). By one-way analysis of variance, statistical significance was not present for the 12 hpi series, but achieved for the 48 hpi series (p<0.001). By Tukey’s multiple comparison test, the following groups within the 48 hpi series were significantly different (p<0.001): day 3 vs. days 6–15; days 6–15 vs. day 18. Source data are shown in S1 Dataset.

Fig 10. A model for B19V replication and expression.

Viral genome can be present in four consecutive states, connected by three state transitions: input ssDNA, initial dsDNA, replicating rfDNA, and product ssDNA. Two functional profiles are identified as “early” (from dsDNA) and “late” (from rfDNA), characterized by a differential abundance and relative composition of transcriptome (compare map in Fig 1). Each profile is involved in regulative loops on genome state transitions. Erythroid specific factors are critical in regulating state transitions and are dependent on the differentiation and physiological state of the cell.