CD4 T-cell memory responses to viral infections of humans show pronounced immunodominance independent of duration or viral persistence

Abstract

Little is known concerning immunodominance within the CD4 T-cell response to viral infections and its persistence into long-term memory. We tested CD4 T-cell reactivity against each viral protein in persons immunized with vaccinia virus (VV), either recently or more than 40 years ago, as a model self-limited viral infection. Similar tests were done with persons with herpes simplex virus 1 (HSV-1) infection as a model chronic infection. We used an indirect method capable of counting the CD4 T cells in blood reactive with each individual viral protein. Each person had a clear CD4 T-cell dominance hierarchy. The top four open reading frames accounted for about 40% of CD4 virus-specific T cells. Early and long-term memory CD4 T-cell responses to vaccinia virus were mathematically indistinguishable for antigen breadth and immunodominance. Despite the chronic intermittent presence of HSV-1 antigen, the CD4 T-cell dominance and diversity patterns for HSV-1 were identical to those observed for vaccinia virus. The immunodominant CD4 T-cell antigens included both long proteins abundantly present in virions and shorter, nonstructural proteins. Limited epitope level and direct ex vivo data were also consistent with pronounced CD4 T-cell immunodominance. We conclude that human memory CD4 T-cell responses show a pattern of pronounced immunodominance for both chronic and self-limited viral infections and that this pattern can persist over several decades in the absence of antigen.

Fig 1

CD137 upregulation identifies virus-specific CD4⁺ T cells. Direct ex vivo PBMC expression profiles of cytokines (A) and CD137 (B) from remote VV vaccinee V16 to mock and vaccinia virus (VV) antigens. Percentages of positive cells, gated on live CD3⁺ CD8⁻ CD4⁺ cells, are provided. (B) Rectangles are gates for sorting CD137^low or CD137^high CD4 T cells. (C) IFN-γ expression of bulk cultures of CD137^low- or CD137^high-sorted CD4 T cells tested to mock and VV antigens using autologous APC. Numbers represent the percentages of cells in the upper right quadrant.

Fig 2

Schema and representative raw data for oligoclonal microcultures. (A) Method of arraying VV polypeptides into 12 rows and 24 columns. Some pools contained negative controls (left). Sample [³H]thymidine proliferation data from one OCM are shown on the right. Row 2C and column 7 are positive. Confirmatory proliferation data using just the VV antigen at the row 2C-column 7 intersection, shown in red, were positive (not shown). Whole-UV-VV control at lower right. (B) Representative row and column [³H]thymidine proliferation data from an OCM with a complex pattern of reactivity, showing 4 positive-row and 5 positive-column pools. There are 20 candidate antigenic VV proteins at the intersections. Positive-control UV-VV at right. (C) Each candidate antigenic polypeptide was assayed by [³H]thymidine proliferation. There were six hits. The row 2C pool contained two positive antigens, 2C1 and 2C2.

Fig 3

CD4 T-cell reactivity to VV (A) and HSV-1 (B) proteins. Histograms represent one person each with recent (“recent vaccinia”) or remote (“remote vaccinia”) VV vaccination (V12 and V16) (A) or HSV-1 infection (B). The y axes carry the numbers of separate oligoclonal microcultures (OCM) cultured from CD137^high-sorted virus-specific PBMC-derived CD4 T cells reacting with each viral protein. The x axes carry the names or numbers of individual viral ORFs using standard nomenclature. Viral proteins are ordered from most to least dominant. Insets are breadth-dominance curves from each histogram. The x and y axes are the cumulative percentages of the CD4 T-cell breadth and the ORF level hits, respectively. Trend line natural log fits display parameters and R² with experimental data.

Fig 3

CD4 T-cell reactivity to VV (A) and HSV-1 (B) proteins. Histograms represent one person each with recent (“recent vaccinia”) or remote (“remote vaccinia”) VV vaccination (V12 and V16) (A) or HSV-1 infection (B). The y axes carry the numbers of separate oligoclonal microcultures (OCM) cultured from CD137^high-sorted virus-specific PBMC-derived CD4 T cells reacting with each viral protein. The x axes carry the names or numbers of individual viral ORFs using standard nomenclature. Viral proteins are ordered from most to least dominant. Insets are breadth-dominance curves from each histogram. The x and y axes are the cumulative percentages of the CD4 T-cell breadth and the ORF level hits, respectively. Trend line natural log fits display parameters and R² with experimental data.

Fig 4

(A) Abundance rank plots of log abundance per rank versus log rank in eight vaccinia virus recipients and four HSV-1-infected persons. The x axis represents the log values of rank; the y axis represents the log values of abundance per rank in each group. (B) Abundance rank plots of log abundance per rank versus log rank in four recent and four distant vaccinia virus recipients; the x axis represents the log value of rank; the y axis represents the log value of abundance per rank in each group. Gray lines represent the linear fit to the data with the y-intercept equalized to the data. Reactive antigens with an abundance of 3 or fewer were not included in the plots.

Fig 5

Immunodominance architecture of VV-specific CD4 responses in participant V9 measured by ex vivo IFN-γ ELISPOT. Bar labels are names of VV WR ORFs and amino acid numbers of peptides. The inset shows the breadth-dominance curves corresponding to the histogram. The x axis represents the cumulative percentage of CD4 breadth. The y axis represents the cumulative percentage of CD4 ORF level hits. Peptides are ordered as in the histogram from most to least dominant. The trend line is a natural log fit curve with parameters and R² with experimental data displayed.