Quantifying changes in the T cell receptor repertoire during thymic development

One of the feats of adaptive immunity is its ability to recognize foreign pathogens while sparing the self. During maturation in the thymus, T cells are selected through the binding properties of their antigen-specific T-cell receptor (TCR), through the elimination of both weakly (positive selection) and strongly (negative selection) self-reactive receptors. However, the impact of thymic selection on the TCR repertoire is poorly understood. Here, we use transgenic Nur77-mice expressing a T-cell activation reporter to study the repertoires of thymic T cells at various stages of their development, including cells that do not pass selection. We combine high-throughput repertoire sequencing with statistical inference techniques to characterize the selection of the TCR in these distinct subsets. We find small but significant differences in the TCR repertoire parameters between the maturation stages, which recapitulate known differentiation pathways leading to the CD4+ and CD8+ subtypes. These differences can be simulated by simple models of selection acting linearly on the sequence features. We find no evidence of specific sequences or sequence motifs or features that are suppressed by negative selection. These results favour a collective or statistical model for T-cell self non-self discrimination, where negative selection biases the repertoire away from self recognition, rather than ensuring lack of self-reactivity at the single-cell level

Data from: Phage display peptide libraries: deviations from randomness and correctives

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study we implemented Next Generation Sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon overrepresentation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study we demonstrate that the UAG overrepresentation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synthesis-efficiency and can be corrected using compensating oligonucleotide-mixtures calibrated by Mass Spectroscopy. Construction of libraries implementing these correctives results in markedly improved libraries that display random distribution of amino acids, thus ensuring that enriched peptides obtained in biopanning represent a genuine selection event, a fundamental assumption for phage display applications

2nd_generation_library

The archive includes the raw (fastq) and filtered (fasta) sequences of the 2nd generation random library described in the main text

1st_generation_library

The archive includes the raw (fastq) and filtered (fasta) sequences of the 1st generation random library described in the main text

3rd_generation_library

The archive includes the raw (fastq) and filtered (fasta) sequences of the 3rd generation random library described in the main text

Deep Panning: Steps towards Probing the IgOme

Background: Polyclonal serum consists of vast collections of antibodies, products of differentiated B-cells. The spectrum of antibody specificities is dynamic and varies with age, physiology, and exposure to pathological insults. The complete repertoire of antibody specificities in blood, the IgOme, is therefore an extraordinarily rich source of information–a molecular record of previous encounters as well as a status report of current immune activity. The ability to profile antibody specificities of polyclonal serum at exceptionally high resolution has been an important and serious challenge which can now be overcome. Methodology/Principal Findings: Here we illustrate the application of Deep Panning, a method that combines the flexibility of combinatorial phage display of random peptides with the power of high-throughput deep sequencing. Deep Panning is first applied to evaluate the quality and diversity of naïve random peptide libraries. The production of very large data sets, hundreds of thousands of peptides, has revealed unexpected properties of combinatorial random peptide libraries and indicates correctives to ensure the quality of the libraries generated. Next, Deep Panning is used to analyze a model monoclonal antibody in addition to allowing one to follow the dynamics of biopanning and peptide selection. Finally Deep Panning is applied to profile polyclonal sera derived from HIV infected individuals. Conclusions/Significance: The ability to generate and characterize hundreds of thousands of affinity-selected peptide

Deep Panning with mAb GV4H3.

<p>(<b>A</b>) Mapitope prediction of the GV4H3 epitope on HIV gp120. The top 20 peptides of Capture #2 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041469#pone-0041469-t001" target="_blank">Table 1</a>) were used as the dataset for Mapitope prediction of the GV4H3 epitope. The single predicted cluster comprises two discontinuous segments of the antigen (green and blue) brought to flank the core of the epitope (residues 221–226, pink). (<b>B-E</b>) MEME analysis of the GV4H3 derived peptides. The 20 top most frequent peptides of Capture #3 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041469#pone-0041469-t001" target="_blank">Table 1</a>) generated a major motif “AGWAV”. This motif (<b>B</b>) and three additional motifs are identified when all 4,823 peptides are analyzed. The “VGF” motif (<b>C</b>) is a simpler version of the major motif. The two additional minor motifs (<b>D</b> and <b>E</b>) do not have obvious similarity to the epitope of the mAb. The “ADGIGGG” motif clearly corresponds with the most frequent peptide ADGIVGW (see text). The numbers in red represent the number of unique peptides that define each motif.</p

Pie charts depicting the proportion of unique peptides in phage display libraries.

<p>A total of 155,241 inserts were read for the random phage display peptide library (<b>A</b>). 24% of the peptides contained at least one UAA or UGA stop codon (red plus dark red). 58% of the peptides were unique containing a UAG stop codon (light green) of these some exist in multiple copies (3% of the total, dark green). 15% of the peptides were completely devoid of stop codons (blue, less than 1% had 2–5 copies). Pie Chart (<b>B</b>) depicts the same set of peptides devoid of all those that had detectable frameshifted inserts (37,223 inserts leaving 118,018 functional peptides of which ca 1% contained stop codons UAA and UGA nonetheless). A second library was constructed in DH5alpha <i>supE144</i> cells (<b>C</b>). Values below 1% are not given.</p

BLASTP analysis of HIVIG-captured peptides against viral coat proteins.

<p>Of the 223 top unique HIVIG-captured peptides, 18 (8%) scored hits in BLASTP analysis against the HIV-1_HXB2 gp160 (blue). Repeating this procedure with the same protein but scrambled gives an average value of 5.5 hits when performed 1,000 times (2.5%±2.2 s.d., red). The differences between native and scrambled coat proteins BLASTP results of 11 other RNA viruses were not found to be significant. <i>*P</i><0.01.</p

Three rounds of panning with mAb GV4H3.

<p>GV4H3 mAb was used to bio-pan the 7 mer random peptide library 3 consecutive rounds of panning (Capture #1 through #3) and compared with the naïve library. For each sample the 20 top most frequent peptides are given along with the number of times they appear. The number of unique versus total peptides is shown as well. Numbers in parentheses represent the percent value of the total peptides for each category. Bold sequences indicate peptides that are carried over from Capture #1. Bold and Italic sequences indicate peptides carried over from Capture #2.</p