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. 2011;6(8):e22365.
doi: 10.1371/journal.pone.0022365. Epub 2011 Aug 4.

High-resolution description of antibody heavy-chain repertoires in humans

Ramy Arnaout  1 , William Lee, Patrick Cahill, Tracey Honan, Todd Sparrow, Michael Weiand, Chad Nusbaum, Klaus Rajewsky, Sergei B Koralov
Affiliations

High-resolution description of antibody heavy-chain repertoires in humans

Ramy Arnaout et al. PLoS One. 2011.

Abstract

Antibodies' protective, pathological, and therapeutic properties result from their considerable diversity. This diversity is almost limitless in potential, but actual diversity is still poorly understood. Here we use deep sequencing to characterize the diversity of the heavy-chain CDR3 region, the most important contributor to antibody binding specificity, and the constituent V, D, and J segments that comprise it. We find that, during the stepwise D-J and then V-DJ recombination events, the choice of D and J segments exert some bias on each other; however, we find the choice of the V segment is essentially independent of both. V, D, and J segments are utilized with different frequencies, resulting in a highly skewed representation of VDJ combinations in the repertoire. Nevertheless, the pattern of segment usage was almost identical between two different individuals. The pattern of V, D, and J segment usage and recombination was insufficient to explain overlap that was observed between the two individuals' CDR3 repertoires. Finally, we find that while there are a near-infinite number of heavy-chain CDR3s in principle, there are about 3-9 million in the blood of an adult human being.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. A five-step annotation methodology was used.
Step 1: New sequence was obtained through pyrosequencing. Step 2: Using a word-counting strategy, the best-match V was determined. When there was a tie for best match, or when the best match was nevertheless a poor match, sequences were removed from subsequent V-D-J usage analysis. The best-match V was then aligned to the new sequence using megablast. Steps 3: The region that matched V was removed (grayed out in the figure) and the same word-counting strategy was applied to the remaining sequence to determine the best-match J sequence. As with V, ties and poor matches were removed; the best-match J was aligned using megablast. Step 4: The region that matched J was removed (gray) and D was assigned and aligned to the remainder as in Steps 2 and 3. Step 5: Using the alignments, exonucleotide chewback and nucleotide addition were determined.
Figure 2
Figure 2. V, D, and J segment use in human is highly uneven.
Lines indicate cumulative distributions (measured on right-hand side y-axis) (a)–(c). Not all V segments are labeled on the x-axis. V, D, and J segments appear at similar frequencies in two different human subjects (d)–(f). Each point corresponds to a single segment. Insets show cumulative distributions. VDJ combinations also appear unevenly, with the 100 most frequent VDJ combinations responsible for 50 percent of all recombination events (g).
Figure 3
Figure 3. V, D, and J segment use in mouse is also highly uneven.
Lines indicate cumulative distributions (measured on right-hand side y-axis) (a)–(c). Out of space considerations only V segments present at ≥1% are shown. Insets show cumulative distributions. VDJ combinations also appear unevenly, with the ∼200 most frequent VDJ combinations responsible for 50 percent of all recombination events (d).
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