Changes in lipid composition of phosphonate treated Plasmodium falciparum
Data files
Aug 01, 2024 version files 378.85 MB
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01_Neg_DMSO_1.mzXML
27.94 MB
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01_Pos_DMSO_1.mzXML
19.96 MB
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02_Neg_DMSO_2.mzXML
33.54 MB
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02_Pos_DMSO_2.mzXML
24.81 MB
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03_Neg_DMSO_3.mzXML
32.84 MB
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03_Pos_DMSO_3.mzXML
24.70 MB
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07_Neg_22_1.mzXML
29.60 MB
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07_Pos_22_1.mzXML
21.34 MB
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08_Neg_22_2.mzXML
32.71 MB
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08_Pos_22_2.mzXML
24.38 MB
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09_Neg_22_3.mzXML
31.99 MB
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09_Pos_22_3.mzXML
23.12 MB
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10_Neg_22_4.mzXML
30.44 MB
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10_Pos_22_4.mzXML
21.49 MB
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README.md
1.56 KB
Abstract
Malaria, caused by Plasmodium falciparum, remains a significant health burden. The barrier to developing anti-malarial drugs is the ability of the parasite to rapidly generate resistance. We previously demonstrated that Salinipostin A (SalA), a natural product, potently kills parasites by inhibiting multiple lipid-metabolizing serine hydrolases, a mechanism with a low propensity for resistance. Given the difficulty of employing natural products as therapeutic agents, we synthesized a small library of lipidic mixed alkyl/aryl phosphonates as bioisosteres of SalA. Two constitutional isomers exhibited divergent anti-parasitic potencies which enabled the identification of therapeutically relevant targets. We also confirm that this compound kills parasites through a mechanism that is distinct from both SalA and the pan-lipase inhibitor, Orlistat. In addition, like SalA, our compound induces only weak resistance, attributable to mutations in a single protein involved in multidrug resistance. These data suggest that mixed alkyl/aryl phosphonates are promising, synthetically tractable antimalarials with a low propensity to induce resistance.
https://doi.org/10.5061/dryad.b5mkkwhmj
We have submitted each replicate of our raw data (Sample#_mode_treatment.mzxml). This data was generated by treating parasites with either a DMSO vehicle or a newly identified phosphonate inhibitor of parasite growth. The lipid composition of both treatment groups was analyzed after 20 hours of treatment with the different molecules to look at how the phosphonate inhibitor may disrupt lipid metabolism. Our published analysis compared negative and positive mode runs for lipid samples obtained from the WT W2 parasite (n=3-4 for each treatment). We found that neutral lipids (such as diacylglycerides and ceramides) were abundant in the lipids that had decreased concentrations in phosphonate-treated samples suggesting disruption of early lipid metabolism with our compound.
Description of the data and file structure
The files included are described as follows
Naming Scheme:
[Experiment number][ion mode][Treatment][replicate number].
[Experiment number] is the order in which the sample was run on the mass spectrometer.
[ion mode] denotes whether the sample was collected using either the positive or negative mode of collection
[Treatment] denotes whether the sample was treated with either DMSO or compound 22, an antimalarial phosphonate.
[replicate number] denotes the biological replicate performed.
Each file is formatted for downstream analysis using XCMS.
Preparation of Lipidomic Samples
For lipid analysis, synchronized W2 ring stage of 50 mL cultures of 10% parasitemia in 4 % hematocrit were treated with 920nM (5x IC50) of compound 22 or DMSO vehicle for 16 h. Red blood cells were saponin lysed and the parasite pellet was collected. Parasite pellets were subsequently collected and resuspended in 1 ml of 1xPBS buffer and transferred into glass vials pre-loaded with 2:1 chloroform/methanol to a final ratio of 2:1:1 chloroform/methanol/1xPBS. Samples were vigorously shaken and spun down at 1000g and the organic layer was collected. Three vials of lipids extracted from each treatment were dried down under a stream of argon.
Lipid analysis using high-performance liquid chromatography-mass spectroscopy
Lipidomics was performed on an Agilent 6545 Q-TOF LC/MS as previously described and data was analyzed using the online XCMS platform (Onguka et al., 2021). Metabolites shown to be significantly changed between the two treatment groups were then searched by their mass and classified using the online LipidMaps tool (Conroy et al., 2023).