Using machine learning to predict protein–protein interactions between a zombie ant fungus and its carpenter ant host

Scientific Reports · 3 authors, 2 centres

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TL;DR

This computational study used the machine learning tool D-SCRIPT to predict protein–protein interactions between the parasitic zombie ant fungus and its carpenter ant host. The analysis identified thousands of predicted interactions, with functional enrichments suggesting potential mechanisms for host behavioral manipulation.

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This preclinical, computational study used the machine learning tool D-SCRIPT to predict interspecific protein–protein interactions (PPIs) between the parasitic fungus Ophiocordyceps camponoti-floridani and its host, the carpenter ant Camponotus floridanus. The authors filtered predictions to focus on parasite proteins upregulated during host manipulation and secreted. The analysis yielded approximately 6000 predicted PPIs. Functional enrichment analyses of the predicted interacting proteins highlighted overrepresentations in host proteins related to G-protein coupled receptors and oxidation-reduction processes, and in parasite proteins related to proteases. The study's key limitations are that the predictions are computational hypotheses based on a model with low recall, and experimental validation is required.

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other

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preclinical research

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AI pipeline

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100% · A

ORIGINAL PAPER

PMC10449854

Using machine learning to predict protein–protein interactions between a zombie ant fungus and its carpenter ant host

Scientific Reports

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ABSTRACT

Parasitic fungi produce proteins that modulate virulence, alter host physiology, and trigger host responses. These proteins, classified as a type of “effector,” often act via protein–protein interactions (PPIs). The fungal parasite Ophiocordyceps camponoti-floridani (zombie ant fungus) manipulates Camponotus floridanus (carpenter ant) behavior to promote transmission. The most striking aspect of this behavioral change is a summit disease phenotype where infected hosts ascend and attach to an elevated position. Plausibly, interspecific PPIs drive aspects of Ophiocordyceps infection and host manipulation. Machine learning PPI predictions offer high-throughput methods to produce mechanistic hypotheses on how this behavioral manipulation occurs. Using D-SCRIPT to predict host–parasite PPIs, we found ca. 6000 interactions involving 2083 host proteins and 129 parasite proteins, which are encoded by genes upregulated during manipulated behavior. We identified multiple overrepresentations of functional annotations among these proteins. The strongest signals in the host highlighted neuromodulatory G-protein coupled receptors and oxidation–reduction processes. We also detected Camponotus structural and gene-regulatory proteins. In the parasite, we found enrichment of Ophiocordyceps proteases and frequent involvement of novel small secreted proteins with unknown functions. From these results, we provide new hypotheses on potential parasite effectors and host targets underlying zombie ant behavioral manipulation.

CITATION EXPORT

Ian Will, William C. Beckerson, Charissa de Bekker. Using machine learning to predict protein–protein interactions between a zombie ant fungus and its carpenter ant host. Scientific Reports. (2023). https://doi.org/10.1038/s41598-023-40764-8

AUTHORS

3 · 2 CENTRES

Ian Will, William C. Beckerson, Charissa de Bekker

1https://ror.org/036nfer12grid.170430.10000 0001 2159 2859Department of Biology, University of Central Florida, 4110 Libra Drive, Orlando, FL 32816 USA
2https://ror.org/04pp8hn57grid.5477.10000 0001 2034 6234Department of Biology, Microbiology, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands