Editorial: Dictyostelium: A Tractable Cell and Developmental Model in Biomedical Research
Bibliographic record
Abstract
Dictyostelium: A Tractable Cell and Developmental Model in Biomedical ResearchFor almost a century, the social amoeba Dictyostelium discoideum has been used as an inexpensive and high-throughput model system for studying a variety of fundamental cellular and developmental processes including cell movement, chemotaxis, differentiation, and autophagy (Müller-Taubenberger et al., 2013;Mathavarajah et al., 2017).The life cycle of Dictyostelium is comprised of a unicellular growth phase and a 24-h multicellular developmental phase with distinct stages (Figure 1A).Dictyostelium development shares many common features with metazoan development but occurs in a much shorter time frame, which allows for the rapid detection of developmental phenotypes.The fully sequenced, low redundancy genome of Dictyostelium provides a less complex system to work with, whilst still maintaining many genes and related signalling pathways found in more complex eukaryotes (Eichinger et al., 2005).The Dictyostelium genome is haploid, which allows researchers to introduce one or multiple gene disruptions with relative ease, and gene function can be studied in a true multicellular organism with measurable phenotypic outcomes (Kuspa, 2006;Faix et al., 2013;Friedrich et al., 2015;Sekine et al., 2018).In addition, insertional mutant libraries facilitate pharmacogenetics screens that have enhanced our understanding of the function of bioactive compounds at a cellular level (Damstra Oddy et al., 2021;Warren et al., 2020).Finally, a variety of expression constructs are available that enable studies on protein localization and function in Dictyostelium (Levi et al., 2000;Veltman et al., 2009;Müller-Taubenberger and Ishikawa-Ankerhold, 2013).More recently, Dictyostelium has emerged as a valuable biomedical model system for studying several human diseases.The genome encodes orthologs of genes associated with human disease and the signalling pathways that regulate the behaviour of Dictyostelium cells are remarkably similar to those observed in mammalian cells, which has allowed findings from Dictyostelium to be successfully translated to mammalian systems (Alexander and Alexander, 2011;Chang et al., 2012).As a result, Dictyostelium has, and will continue to offer, excellent opportunities to advance biomedical research.This Research Topic contains 23 articles that showcase the use of Dictyostelium as a tractable cell, molecular, and developmental model system in biomedical research, and includes two methods articles that enhance the biomedical applications of this valuable model organism (Figure 1B).Yamashita et al. describe the application of CRISPR-based gene disruption in Dictyostelium, while Williams et al. report the development of a new positive selection high throughput genetic screen.The use of Dictyostelium as a model system for studying fundamental cellular and developmental processes is well established and this Research Topic contains several articles describing new findings on conserved processes in Dictyostelium with biomedical relevance (Figure 1B).
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".