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).
Table 1.
Primers used in the qPCR and qRT-PCR assays for the detection and quantitative evaluation of B19V nucleic acids.
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 3rd 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.
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.