Host Manipulation, Gene Editing, and Non-Traditional Model Organisms: A New Frontier for Behavioral Research?
Bibliographic record
Abstract
Insects and parasites dominate the biosphere, in terms of known biodiversity and mode of life, respectively. Consequently, insects play a part in many host-parasite systems, either as parasite, host, or both. Moreover, a lot of these systems involve adaptive parasite-induced changes of host phenotype (typically behavior or morphology), which is commonly known as host manipulation. While many host manipulation systems have been described within the last few decades, the proximate mechanisms that underpin host phenotypic change are still largely unknown. Given the intimate co-evolutionary history of host-parasite systems, teasing apart the intricate network of biochemical reactions involved in host manipulation requires the integration of various complementary technologies. In this perspective, we stress the importance of multidisciplinary research on host manipulation, such as high-throughput sequencing methods (genomics and transcriptomics) to search for candidate mechanisms that are activated during a manipulation event. Then, we argue that gene editing technologies, specifically the CRISPR-Cas9 system, are a powerful way to test for the functional roles of candidate mechanisms, in both the parasite and the host. Finally, given the sheer diversity of unique host-parasite systems discovered to date, there is indeed a tremendous potential to create novel non-traditional model systems that could greatly expand our capacity to test the fundamental aspects of behavior and behavioral regulation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".