Antigenic epitope targets of rhesus macaques self-curing from Schistosoma mansoni infection

Abstract

The self-cure of rhesus macaques from a schistosome infection and their subsequent strong immunity to a cercarial challenge should provide novel insights into the way these parasites can be eliminated by immunological attack. High-density arrays comprising overlapping 15-mer peptides from target proteins printed on glass slides can be used to screen sera from host species to determine antibody reactivity at the single epitope level. Careful selection of proteins, based on compositional studies, is crucial to encompass only those exposed on or secreted from the intra-mammalian stages and is intended to focus the analysis solely on targets mediating protection. We report the results of this approach using two pools of sera from hi- and lo-responder macaques undergoing self-cure, to screen arrays comprising tegument, esophageal gland, and gastrodermis proteins. We show that, overall, the target epitopes are the same in both groups, but the intensity of response is twice as strong in the high responders. In addition, apart from Sm25, tegument proteins elicit much weaker responses than those originating in the alimentary tract, as was apparent in IFNγR KO mice. We also highlight the most reactive epitopes in key proteins. Armed with this knowledge, we intend to use multi-epitope constructs in vaccination experiments, which seek to emulate the self-cure process in experimental animals and potentially in humans.

Keywords: alimentary tract proteins; antigenic targets; epitope mapping; esophageal glands; peptide array; tegument proteins.

Figure 1

Heatmaps showing the reactivities of hi- and lo-responder rhesus macaque serum pools against the four peptide arrays: 1) short alimentary tract, 2) long alimentary tract, 3) short tegument surface, and 4) long tegument surface. The intense reactivity of tegument Sm25 is very evident.

Figure 2

Bar chart summarizing the reactivity of all proteins on the four arrays, based on the data in Supplementary Table S2 . The y-axis is the cumulative Agilent peptide score above zero for each protein, ignoring protein length. The proteins in each array have been rearranged along the x-axis according to functional group by reactivity. Three transporters with high reactivity on Array 2 were segregated on the basis of SchistoCyte predictions of their tissue localization. Note that the tegument arrays were hybridized with double the concentration of serum used for the alimentary tract arrays (1:100 versus 1:200).

Figure 3

The mean reactivity of array proteins based on the data in Supplementary Table S3 , normalized for number of peptides printed. This compensates for one, two, or three amino acid offsets. The proteins are color coded by function as esophageal MEGs, gastrodermal carriers, enzymes, and tegument. The differences in group reactivity were tested for significance using a t-test. Note the log x- and y-axis scales.

Figure 4

The reactivity of hi- and lo-responder pools compared for each array, based on the data in Supplementary Table S2 . The linear relationship between the two variables was determined using Pearson’s correlation coefficient, r. The offset from the diagonal midline provides a visual indication of the extent to which each protein reacts more with the hi-responder pool.

Figure 5

Scatter plots comparing the reactivity of hi-responder rhesus macaque sera with previously published data from IFNγR KO mice (19, Farias et al., 2021), screened on the same arrays, based on data in Supplementary Table S3 , Sheet 3. The proteins were segregated by tissue of origin and function into: (A) tegument; (B) esophageal glands; (C) carriers; (D) enzymes. A strong relationship between the two data sets was revealed using Pearson’s correlation coefficient, r. (E) shows the relationship between esophageal gland proteins from hi-responder macaques and previously published data from rhesus macaques recovering from S. japonicum (18, Li et al., 2020), presented in Supplementary Table S4 . The correlation r, between the two data sets was weaker.