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MAP-X reveals distinct protein complex dynamics across Plasmodium falciparum blood stages

Nature Microbiology volume 10, pages 3229–3244 (2025)Cite this article

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Abstract

The malaria parasite Plasmodium falciparum undergoes a complex intraerythrocytic developmental cycle (IDC) that relies on a dynamic network of protein–protein interactions. These are usually mapped ex vivo, limiting our understanding of their dynamics and composition in natural environments. Here we introduce the meltome-assisted profiling of protein complexes (MAP-X) that maps the complexome through thermal proteome profiling in intact cells. We applied MAP-X across seven timepoints in the P. falciparum IDC. MAP-X predicted more than 20,000 interactions, resolving conserved protein complexes, reproducing previously identified interactions and finding previously unreported associations. We found that malaria protein complexes undergo distinct dynamic alterations, and we predicted their moonlighting subunits that dissociate from their native complex to assume different biological functions. Altogether, our findings provide a resource for uncovering Plasmodium biology and show that MAP-X can characterize protein complexes in intact cells to reveal cellular physiology at a proteome-wide level.

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Fig. 1: MAP-X predicts protein–protein interactions.
Fig. 2: MAP-X complexomes map protein complexes.
Fig. 3: Local subnetworks replicate experimental evidence of protein–protein interactions.
Fig. 4: Experimental validation of MAP-X-predicted interactions.
Fig. 5: Dynamics of the P. falciparum protein complexes.
Fig. 6: Prediction of moonlighting proteins.

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Data availability

The raw mass spectrometry data were deposited in the PRIDE database under the identifier PXD056075. The model of P. falciparum RNA exosome ring is available in ModelArchive at https://www.modelarchive.org/doi/10.5452/ma-a867m (ref. 106). The gold-standard dataset and all prediction score values have been deposited in Zenodo36. Source data are provided with this paper.

Code availability

The R codes used to process the data and create the figures are available in the R package MAP-X available in GitHub89. The code used to generate all tables and figures is available in Zenodo107. The trained random forest models have been deposited in Zenodo36,108.

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Acknowledgements

This work was supported by Singapore Ministry of Education grant #MOE2019-T3-1-007 and Singapore National Science Foundation grant #NRF-CRP24-2020-0005 (ZB); EMBO LTF #ALTF 1115-2009 (NP); Deutsche Forschungs-gemeinschaft GRK2771–No 453548970 (G.B.F., T.-W.G.); Leibniz ScienceCampus InterACt W75/2022 InterACt and ‘Hamburg-X Infektionsforschung’ (G.B.F., T.-W.G.); and Wellcome Trust Principal Research Fellowship Ref. 083811/Z/07/Z (A.P.W.). We thank T. Spielmann (BNITM, Germany) for the MON1-GFP-expressing parasite line and for providing the mCherry-Rab7 plasmid.

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Authors and Affiliations

  1. School of Biological Sciences, Nanyang Technology University, Singapore, Singapore

    Samuel Pazicky, Seth Tjia & Zbynek Bozdech

  2. Centre for Structural Systems Biology, Hamburg, Germany

    Guilherme B. Farias, Nick Piwon & Tim-Wolf Gilberger

  3. Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany

    Guilherme B. Farias, Nick Piwon & Tim-Wolf Gilberger

  4. University of Hamburg, Hamburg, Germany

    Guilherme B. Farias, Nick Piwon & Tim-Wolf Gilberger

  5. Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, UK

    Nisha Philip

  6. Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Radoslaw M. Sobota

  7. Wellcome Centre for Integrative Parasitology, University of Glasgow, Glasgow, Scotland, UK

    Andrew P. Waters

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S.P. and Z.B. conceptualized the project and curated data. S.P., G.B.F. and N. Philip conducted formal analysis. Z.B., N. Philip and T.-W.G. acquired funding. S.P., S.T., G.B.F., N. Piwon and N. Philip performed investigation. S.P., S.T., G.B.F., N. Piwon, N. Philip, A.P.W., T.-W.G., R.M.S. and Z.B. designed the methodology. S.P. developed software. A.P.W., T.-W.G. and Z.B. supervised the project. S.P., G.B.F. and N. Philip performed visualization. S.P. and Z.B. wrote the original paper draft. S.P., G.B.F., N. Philip, A.P.W., T.-W.G., R.M.S. and Z.B. reviewed and edited the paper.

Corresponding author

Correspondence to Zbynek Bozdech.

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The authors declare no competing interests.

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Peer review information

Nature Microbiology thanks Mercedes Pardo Calvo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Synchronized parasites.

Representative images of Giemsa-stained tightly synchronized parasites after enrichment by magnetic sorting (28-40 hpi) or SLO (4-22 hpi).

Extended Data Fig. 2 Predicting PPIs by MAP-X.

a, Scaled soluble protein abundances measured across the temperature gradient (gray points) with particular protein complex subunits marked (red points). These charts show that the principle of TPCA is preserved in the data. b, Counts of overlapping protein identifications across seven timepoints. Inset: total number of detected proteins for each timepoint and replicate. c, Computational approach for machine learning. The positive interactions are randomly split to four parts and only 3/4 are used to predict the left-out 1/4 and other interactions not included in the gold standard. The procedure is repeated 25x to get 100 models that are averaged into a composite model. The composite model of each replicate is than averaged to get final model scores for each timepoint. d, Number of clusters of specific sizes (node counts) is similar across all timepoints. e, Overlap between three global PPI mapping studies in malaria parasites. Interactions from 34 hpi were used from this study.

Extended Data Fig. 3 Complexome maps.

a, MAP-X global mapping split the ribosome into two clusters at 40 hpi. b, Prefoldin subunits do not follow the principles of TPCA. c, Example of a cluster in which subunits of two protein complexes (ribosomal subunits yellow, and exported proteins orange) coexist as the clustering algorithm cannot separate them further. d,e, Sequence alignment of yeast RRP43 (d) and RRP46 (e) to P. falciparum 3D7 counterparts PF3D7_0626200 and PF3D7_0412200, respectively.

Source data

Extended Data Fig. 4 Complexome maps vs local subnetworks.

a, Number of known co-clustering protein complex subunits in global maps (GM) and local subnetworks (LS) across different timepoints shows that local subnetworks retrieve additional unmapped interactions. b, Comparison of proportion of co-clustering subunits for different known protein complexes between GM and LS. c-g, Integration check PCR on genomic DNA from 3D7 and the indicated cell lines to validate correct integration of the SLI plasmid at the 3’- and 5’-end and disruption of the original locus (WT locus); PF3D7_1149100 (c), PTP7 (d), PF3D7_1149100 with PTP7 background (e), CCZ1 (f) and RMC1 (g). The PCR results are representative of results from three different clones. One clone was always chosen for the subsequent experiments. h Western blot of GST and GST-ALBA1/3 bound to Glutathione Sepharose resin confirming the presence of bait for interaction assays.

Extended Data Fig. 5 Conservation of Alba proteins.

Sequence alignment of Alba proteins 1-4 between Plasmodium falciparum and berghei shows high sequence identity.

Extended Data Fig. 6 Dynamics of protein complexes.

a, Transcriptomic stage profiling shows a high degree of agreement of stage used for proteomic experiments between the two replicates used for dynamic assembly studies and assembly index calculation. b, Centered assembly index across blood stage timepoints plotted for known protein complexes categorized by their function. c, Assembly profiles of investigated protein complexes, supplementing Fig. 5d-g. d, Transcription profile of RhopH complex subunits shows that their RNA levels peak at 34 hpi. e, Circular probability density between transcriptomic and proteomic profiles of detected proteins shows that the peak protein abundance is delayed by 4-20 hpi (median 14 hpi) after the peak RNA abundance. f, Phase shifts of cross-corelation coefficients across a series of hpi lags between the protein complex assembly profiles and the corresponding transcriptome (cyan) or proteome (red) profiles.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables 1–3, 5 and 6

Supplementary Tables 1–3, 5 and 6.

Supplementary Table 4

List of known protein complexes and their prediction by MAP-X.

Supplementary Data 1

Cytoscape files of globe complexome maps.

Supplementary Data 2

PCA plots and clustering for prediction of moonlighting proteins across all complexes, timepoints and replicates.

Source data

Source Data Fig. 4

Unprocessed western blot of Alba pulldown experiment.

Source Data Extended Data Fig. 3

Uncropped gel and western blot images.

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Pazicky, S., Tjia, S., Farias, G.B. et al. MAP-X reveals distinct protein complex dynamics across Plasmodium falciparum blood stages. Nat Microbiol 10, 3229–3244 (2025). https://doi.org/10.1038/s41564-025-02173-7

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