The protein kinases of Dictyostelia and their incorporation into a signalome
Bibliographic record
Abstract
Protein kinases are major regulators of cellular processes, but the roles of most kinases remain unresolved. Dictyostelid social amoebas have been useful in identifying functions for 30% of its kinases in cell migration, cytokinesis, vesicle trafficking, gene regulation and other processes but their upstream regulators and downstream effectors are mostly unknown. Comparative genomics can assist to distinguish between genes involved in deeply conserved core processes and those involved in species-specific innovations, while co-expression of genes as evident from comparative transcriptomics can provide cues to the protein complement of regulatory networks. Genomes and developmental and cell-type specific transcriptomes are available for species that span the 0.5 billion years of evolution of Dictyostelia from their unicellular ancestors. In this work we analysed conservation and change in the abundance, functional domain architecture and developmental regulation of protein kinases across the 4 major taxon groups of Dictyostelia. All data are summarized in annotated phylogenetic trees of the kinase subtypes and accompanied by functional information of all kinases that were experimentally studied. We detected 393 different protein kinase domains across the five studied genomes, of which 212 were fully conserved. Conservation was highest (71%) in the previously defined AGC, CAMK, CK1, CMCG, STE and TKL groups and lowest (26%) in the "other" group of typical protein kinases. This was mostly due to species-specific single gene amplification of "other" kinases. Apart from the AFK and α-kinases, the atypical protein kinases, such as the PIKK and histidine kinases were also almost fully conserved. The phylogeny-wide developmental and cell-type specific expression profiles of the protein kinase genes were combined with profiles from the same transcriptomic experiments for the families of G-protein coupled receptors, small GTPases and their GEFs and GAPs, the transcription factors and for all genes that upon lesion generate a developmental defect. This dataset was subjected to hierarchical clustering to identify clusters of co-expressed genes that potentially act together in a signalling network. The work provides a valuable resource that allows researchers to identify protein kinases and other regulatory proteins that are likely to act as intermediates in a network of interest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".