Insights into the Evolution of Hydroxyproline-Rich Glycoproteins from 1000 Plant Transcriptomes
Bibliographic record
Abstract
The carbohydrate-rich cell walls of land plants and algae have been the focus of much interest given the value of cell wall-based products to our current and future economies. Hydroxyproline-rich glycoproteins (HRGPs), a major group of wall glycoproteins, play important roles in plant growth and development, yet little is known about how they have evolved in parallel with the polysaccharide components of walls. We investigate the origins and evolution of the HRGP superfamily, which is commonly divided into three major multigene families: the arabinogalactan proteins (AGPs), extensins (EXTs), and proline-rich proteins. Using motif and amino acid bias, a newly developed bioinformatics pipeline, we identified HRGPs in sequences from the 1000 Plants transcriptome project (www.onekp.com). Our analyses provide new insights into the evolution of HRGPs across major evolutionary milestones, including the transition to land and the early radiation of angiosperms. Significantly, data mining reveals the origin of glycosylphosphatidylinositol (GPI)-anchored AGPs in green algae and a 3- to 4-fold increase in GPI-AGPs in liverworts and mosses. The first detection of cross-linking (CL)-EXTs is observed in bryophytes, which suggests that CL-EXTs arose though the juxtaposition of preexisting SPn EXT glycomotifs with refined Y-based motifs. We also detected the loss of CL-EXT in a few lineages, including the grass family (Poaceae), that have a cell wall composition distinct from other monocots and eudicots. A key challenge in HRGP research is tracking individual HRGPs throughout evolution. Using the 1000 Plants output, we were able to find putative orthologs of Arabidopsis pollen-specific GPI-AGPs in basal eudicots.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".