Predicting LRR-RLK functions at scale in Arabidopsis thaliana using network biology and evolutionary tools
Bibliographic record
Abstract
Plants are sessile organisms and thus cannot move to avoid pathogenic attack or search for improved environmental conditions. Therefore, in order to survive plants must respond to numerous extracellular signals which guide both development and immunity. To aid in discriminating between these signals, plants have developed a large suite of cell surface receptors. My research focuses on a subset of cell surface receptors called leucine-rich repeat receptor-like kinases (LRR-RLKs), of which there are approximately 230 members present in the model organism Arabidopsis thaliana. Despite increased research, the functions of many of these LRR-RLKs remain unknown. My research uses network biology and evolutionary tools to make functional predictions about LRR-RLKs at scale and subsequently confirms the function of one LRR-RLK using classical genetic techniques. I used network analysis via various algorithms to create subnetworks of receptors which can then be used to predict functions for various unknown receptors. Through network analysis, I identified a robust 38-member immunity subnetwork which was predicted to contain immune related LRR-RLKs. Within this immunity subnetwork, I identified members of the LRR-V subfamily including SRF9, SRF7 and SRF6. In parallel, I also performed a large-scale evolutionary analysis to identify LRR-RLK subfamilies that are likely to contain novel immune related receptors. My findings demonstrated a lineage specific expansion of LRR-I within the Brassicales that had high rates of gene expansion. These characteristics are suggestive of families containing stress-related receptors and are likely to contain novel immune-related receptors. While LRR-V does not show these expected features, if they are damage associated molecular pattern (DAMP) receptors as predicted they would be expected to resemble LRR-RLKs involved in growth and development as they recognize self-derived molecules. Based on these results, I chose to focus on the SRF family and demonstrated a role for SRF6 in the DAMP pathway. My results demonstrated that SRF6 is necessary for proper response to trigalacturonic acid (TGA) elicitation including activation of defense genes and bacterial resistance. Overall, I demonstrated that both network and evolutionary analyses can be used to make accurate predictions about LRR-RLK function with more candidates waiting to be tested.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".