Origins of symbiosis: shared mechanisms underlying microbial pathogenesis, commensalism and mutualism of plants and animals
Bibliographic record
Abstract
Regardless of the outcome of symbiosis, whether it is pathogenic, mutualistic or commensal, bacteria must first colonize their hosts. Intriguingly, closely related bacteria that colonize diverse hosts with diverse outcomes of symbiosis have conserved host-association and virulence factors. This review describes commonalities in the process of becoming host associated amongst bacteria with diverse lifestyles. Whether a pathogen, commensal or mutualist, bacteria must sense the presence of and migrate towards a host, compete for space and nutrients with other microbes, evade the host immune system, and change their physiology to enable long-term host association. We primarily focus on well-studied taxa, such as Pseudomonas, that associate with diverse model plant and animal hosts, with far-ranging symbiotic outcomes. Given the importance of opportunistic pathogens and chronic infections in both human health and agriculture, understanding the mechanisms that facilitate symbiotic relationships between bacteria and their hosts will help inform the development of disease treatments for both humans, and the plants we eat.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".