| Title: |
Machine learning-based phenotypic imaging to characterise the targetable biology of Plasmodium falciparum male gametocytes for the development of transmission-blocking antimalarials. |
| Authors: |
Tsebriy, Oleksiy1 (AUTHOR), Khomiak, Andrii1 (AUTHOR), Miguel-Blanco, Celia2 (AUTHOR), Sparkes, Penny C.3 (AUTHOR), Gioli, Maurizio4 (AUTHOR), Santelli, Marco1 (AUTHOR), Whitley, Edgar5 (AUTHOR), Gamo, Francisco-Javier2 (AUTHOR), Delves, Michael J.3 (AUTHOR) michael.delves@lshtm.ac.uk |
| Source: |
PLoS Pathogens. 10/6/2023, Vol. 19 Issue 10, p1-21. 21p. |
| Subject Terms: |
*MACHINE learning, *PLASMODIUM falciparum, *GERM cells, *PARASITE life cycles, *CYTOLOGY, *BIOLOGY |
| Abstract: |
Preventing parasite transmission from humans to mosquitoes is recognised to be critical for achieving elimination and eradication of malaria. Consequently developing new antimalarial drugs with transmission-blocking properties is a priority. Large screening campaigns have identified many new transmission-blocking molecules, however little is known about how they target the mosquito-transmissible Plasmodium falciparum stage V gametocytes, or how they affect their underlying cell biology. To respond to this knowledge gap, we have developed a machine learning image analysis pipeline to characterise and compare the cellular phenotypes generated by transmission-blocking molecules during male gametogenesis. Using this approach, we studied 40 molecules, categorising their activity based upon timing of action and visual effects on the organisation of tubulin and DNA within the cell. Our data both proposes new modes of action and corroborates existing modes of action of identified transmission-blocking molecules. Furthermore, the characterised molecules provide a new armoury of tool compounds to probe gametocyte cell biology and the generated imaging dataset provides a new reference for researchers to correlate molecular target or gene deletion to specific cellular phenotype. Our analysis pipeline is not optimised for a specific organism and could be applied to any fluorescence microscopy dataset containing cells delineated by bounding boxes, and so is potentially extendible to any disease model. Author summary: Interventions that prevent malaria parasite Plasmodium falciparum from transmitting from humans to mosquitoes are highly desirable to prevent both the spread of malaria and crucially the spread of drug resistance. Transmission is caused by the non-pathogenic gametocyte stage of the parasite life cycle which are insensitive to most current antimalarials. Consequently new drugs and new drug targets need to be identified to meet this need. Here we present PhIDDLI–a machine learning image analysis pipeline which we use to characterise the phenotype of male gametocytes treated with 40 novel transmission-blocking molecules. We found that the molecules formed 5 distinct phenotypic clusters according to their putative timing/mode of action within the cell. Molecules with similar chemical structures gave similar phenotypes. Interestingly, by varying the timing of drug exposure, different phenotypes manifest between molecules with seemingly similar initial phenotypes. Our study provides the first insights into the breadth of drug-targetable cell biology in male gametocytes and provides reference images and tool compounds for the identification and validation of new transmission-blocking drug targets. [ABSTRACT FROM AUTHOR] |
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