Dynamic Evolution of Toll-Like Receptor Multigene Families in Echinoderms
Bibliographic record
Abstract
The genome sequence of the purple sea urchin, Strongylocentrotus purpuratus, a large and long-lived invertebrate, provides a new perspective on animal immunity. Analysis of this genome uncovered a highly complex immune system in which the gene families that encode homologs of the pattern recognition receptors that form the core of vertebrate innate immunity are encoded in large multigene families. The sea urchin genome contains 253 Toll-like receptor (TLR) sequences, more than 200 Nod-like receptors and 1095 scavenger receptor cysteine-rich domains, a 10-fold expansion relative to vertebrates. Given their stereotypic protein structure and simple intron-exon architecture, the TLRs are the most tractable of these families for more detailed analysis. A role for these receptors in immune defense is suggested by their similarity to TLRs in other organisms, sequence diversity, and expression in immunologically active tissues, including phagocytes. The complexity of the sea urchin TLR multigene families is largely derived from expansions independent of those in vertebrates and protostomes, although a small family of TLRs with structure similar to that of Drosophila Toll can be traced to an ancient eumetazoan ancestor. Several other echinoderm sequences are now available, including Lytechinus variegatus, as well as partial sequences from two other sea urchin species. Here, we present an analysis of the invertebrate deuterostome TLRs with emphasis on the echinoderms. Representatives of most of the S. purpuratus TLR subfamilies and homologs of the mccTLR sequences are found in L. variegatus, although the L. variegatus TLR gene family is notably smaller (68 TLR sequences). The phylogeny of these genes within sea urchins highlights lineage-specific expansions at higher resolution than is evident at the phylum level. These analyses identify quickly evolving TLR subfamilies that are likely to have novel immune recognition functions and other, more stable, subfamilies that may function more similarly to those of vertebrates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".