Exploring <i>Leishmania</i> -Host Interaction with Reactome, a Database of Biological Pathways and Processes
Bibliographic record
Abstract
Abstract Leishmaniasis is a parasitic disease with a wide range of clinical manifestations. Multiple aspects of the Leishmania -host interaction, such as genetic factors and modulation of microbicidal functions in host cells, influence pathogenesis, disease severity and treatment outcome. How do scientists contend with this complexity? Here, we work towards representing detailed, contextual knowledge on Leishmania -host interactions in the Reactome pathway database to facilitate the extraction of novel mechanistic insights from existing datasets. The Reactome database uses a hierarchy of abstractions that allows for the incorporation of detailed contextual knowledge on biological processes matched to differentially expressed genes. It also includes tools for enhanced over-representation analysis that exploits this extra information. We conducted a systematic curation of published studies documenting different aspects of the Leishmania -host interaction. The “ Leishmania infection pathway” included four sub-pathways: phagocytosis, killing mechanisms, cell recruitment, and Leishmania parasite growth and survival. As proof-of-principle of the usefulness of the released pathway, we used it to analyze two previously released transcriptomic datasets of human and murine macrophages infected with Leishmania . Our results provide insights on the participation of ADORA2B signaling pathway in the modulation of IL10 and IL6 in infected macrophages. This work opens the way for other researchers to contribute to, and make use of, the Reactome database. Importance Leishmaniasis is a neglected disease infectious disease which affects more than 1.5 million people annually. Many researchers in the field apply -omic technologies to dissect the basis of clinical and therapeutic outcomes and access drug targetable features in the host-parasite interaction, among others. However, getting mechanistic insights from -omics data to such end is not an easy task. The most common approach is to use the -omics data to inquire pathways databases. The retrieved list of pathways often contains vague names that lack the biological context. In this study, we worked to create the Leishmania infection pathway in the Reactome database. With two practical examples from transcriptomics and microarray data, we demonstrated how this pathway facilitates the analysis of such data. In both datasets, we found a common mechanism of IL10 and IL6 production that the authors did not advert in their previous analysis, providing proof-of-principle of the tool’s enhanced potential for knowledge extraction. Leishmania infection pathway is in its first version, and must be expanded to cover the current knowledge base of the Leishmania -host interaction. We strongly encourage contributions from domain experts for the completion of Leishmania infection pathways.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".