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. 2019 Apr 3;13(4):e0007262.
doi: 10.1371/journal.pntd.0007262. eCollection 2019 Apr.

Application of long read sequencing to determine expressed antigen diversity in Trypanosoma brucei infections

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Application of long read sequencing to determine expressed antigen diversity in Trypanosoma brucei infections

Siddharth Jayaraman et al. PLoS Negl Trop Dis. .

Abstract

Antigenic variation is employed by many pathogens to evade the host immune response, and Trypanosoma brucei has evolved a complex system to achieve this phenotype, involving sequential use of variant surface glycoprotein (VSG) genes encoded from a large repertoire of ~2,000 genes. T. brucei express multiple, sometimes closely related, VSGs in a population at any one time, and the ability to resolve and analyse this diversity has been limited. We applied long read sequencing (PacBio) to VSG amplicons generated from blood extracted from batches of mice sacrificed at time points (days 3, 6, 10 and 12) post-infection with T. brucei TREU927. The data showed that long read sequencing is reliable for resolving variant differences between VSGs, and demonstrated that there is significant expressed diversity (449 VSGs detected across 20 mice) and across the timeframe of study there was a clear semi-reproducible pattern of expressed diversity (median of 27 VSGs per sample at day 3 post infection (p.i.), 82 VSGs at day 6 p.i., 187 VSGs at day 10 p.i. and 132 VSGs by day 12 p.i.). There was also consistent detection of one VSG dominating expression across replicates at days 3 and 6, and emergence of a second dominant VSG across replicates by day 12. The innovative application of ecological diversity analysis to VSG reads enabled characterisation of hierarchical VSG expression in the dataset, and resulted in a novel method for analysing such patterns of variation. Additionally, the long read approach allowed detection of mosaic VSG expression from very few reads-the earliest in infection that such events have been detected. Therefore, our results indicate that long read analysis is a reliable tool for resolving diverse gene expression profiles, and provides novel insights into the complexity and nature of VSG expression in trypanosomes, revealing significantly higher diversity than previously shown and the ability to identify mosaic gene formation early during the infection process.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1
(A) Parasitaemia measurements for the batches of mice sacrificed at days 3, 6, 10 and 12 days post-infection. A summary of analysis of Pacbio data generated from these samples is shown, including main decision steps (black boxes represent data filtering steps; data within black boxes was included), (B) read length distribution, (C) alignment of reads to VSG, (D) correlation of read number with number of VSGs identified.
Fig 2
Fig 2
Analysis of Pacbio sequence data: (A) Number of reads aligning to VSGs per number of full passes, with proportion of those reads comprising predicted ORF in yellow; red line shows percentage of reads at each threshold of full pass number that contained a predicted VSG ORF. (B) Percentage of reads at each position in the N-Terminal domain that contained a mutation with respect to the reference genome sequence for VSG Tb08.27P2.380; alignment coverage is shown by the black line, insertions by green dot, deletions by red dot and mismatch by blue dot. (C) Focused representation of data in 2B, with only mutations with respect to the genome reference sequence <2% at each position in the N-Terminal domain VSG Tb08.27P2.380 shown; insertions shown by green dot, deletions by red dot and mismatch by blue dot. (D) Percentage error rate plotted against number of full passes; red line indicates number of nucleotide positions for those aligning to VSG Tb08.27P2.380 that contained an error with respect to genome reference sequence against number of full passes.
Fig 3
Fig 3. VSGs per sample.
(A) Average number of donor VSGs mapped at each time point, plotted with and without VSGs identified from single reads of at least one full pass (‘singletons’), (B) Proportion of reads that map to identified donor VSGs for each mouse and each timepoint; reads that map to the 10 most abundant identified donors (across the whole dataset from 20 mice) are shown; reads that map to donor genes other than these ten are represented as ‘others’.
Fig 4
Fig 4. VSGs are expressed in a semi-predictable order.
(A) Expressed VSG sequences were clustered by sequence similarity (Materials and Methods and S1 Appendix) and ordered on the x-axis according to the cluster they were assigned to. The average similarity of each sequence to others from the same population was calculated (‘ordinariness’, a measure of how common that sequence is) and is plotted for each sequence on the y-axis. Grey lines indicate the profiles of individual mice, while coloured lines indicate the average for that particular cluster, coloured according to the cluster. (B) Diversity analysis showing the effective number of distinct VSG profiles found on day 3, 6, 10 and 12 for each mouse (dots) on that day, with the average across the days represented by the solid line.
Fig 5
Fig 5. Mosaic gene identification.
(A) Average number of donor VSGs (y-axis) per position across scaled VSG reads (gene length scaled to 100 (x-axis); data derived from 296,937 VSG amplicons)–blue line shows data for all reads, red line shows data for all data without reads that map to Tb08.27P2.380. (B) Example mosaic gene candidates, represented by read identifier, and displayed to scale from 5’ to 3’ (N-terminal domain only), with colour of each segment indicating most likely donor VSG from the TREU927 VSG repertoire (VSG gene shown in key; where relevant sequences are identical for more than one potential reference gene donor; all possibilities are shown in key).

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