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Metabolome modulation of the host adaptive immunity in human malaria

Nature Metabolism volume 3, pages 1001–1016 (2021)Cite this article

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Abstract

Host responses to infection with the malaria parasite Plasmodium falciparum vary among individuals for reasons that are poorly understood. Here we reveal metabolic perturbations as a consequence of malaria infection in children and identify an immunosuppressive role of endogenous steroid production in the context of P. falciparum infection. We perform metabolomics on matched samples from children from two ethnic groups in West Africa, before and after infection with seasonal malaria. Analysing 306 global metabolomes, we identify 92 parasitaemia-associated metabolites with impact on the host adaptive immune response. Integrative metabolomic and transcriptomic analyses, and causal mediation and moderation analyses, reveal an infection-driven immunosuppressive role of parasitaemia-associated pregnenolone steroids on lymphocyte function and the expression of key immunoregulatory lymphocyte genes in the Gouin ethnic group. In children from the less malaria-susceptible Fulani ethnic group, we observe opposing responses following infection, consistent with the immunosuppressive role of endogenous steroids in malaria. These findings advance our understanding of P. falciparum pathogenesis in humans and identify potential new targets for antimalarial therapeutic interventions.

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Fig. 1: Study design and global impact of P. falciparum malaria on the metabolome.
Fig. 2: Metabolite set enrichment analysis of infection and parasitaemia-associated metabolites.
Fig. 3: Major changes in lipid metabolism and their association with blood cell counts.
Fig. 4: Integrative steroid–transcript analysis.
Fig. 5: Signalling and metabolic pathway analysis of steroid-associated transcripts.
Fig. 6: Causal mediation and moderation analysis of the effect of pregnenolone sulfate on gene expression in lymphocytes.
Fig. 7: In vitro inhibition of T cell proliferation.
Fig. 8: Comparative analysis of infection-associated steroids between Fulani and Gouin children.

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Data availability

Metabolomic data for the Gouin and Fulani cohorts are available at the National Institutes of Health Common Fund National Metabolomics Data Repository website (Metabolomics Workbench81; https://www.metabolomicsworkbench.org/), under project ID PR000960 and accession nos. ST001516 and ST001517. Gene expression data is deposited in the Gene Expression Omnibus database under accession no. GSE156791. The HMDB and KEGG databases are publicly available. Source data are provided with this paper.

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Acknowledgements

We express our appreciation to all the children who participated in the study and their families. We thank the staff of the Centre National de Recherche et de Formation sur le Paludisme (T. B. Alfred, C. S. Aboubacar, A. Barry, A. Ouédraogo and N. Ouédraogo), and of the Groupe de Recherche Action en Santé (S. B. Sodiomon, A. Diarra and S. Benjamin) for facilitating sample collection and clinical work in Burkina Faso. We thank members of the laboratory of Y.I. for assistance with various aspects of the project. We thank the Center for Genomics and Systems Biology, NYUAD Core Bioinformatics and Technology Platforms (K. Gunsalus, N. Drou, M. Arnoux and M. Sultana), J. Teo and J. Sapudom for assistance with technical work. We thank Metabolon for conducting the metabolomics measurements. We also thank the PhD committee of W.A. (J. Carlton, S. Boissinot, N. Sanjana and J. Ayroles) and G. Gibson and D. Scicchitano for helpful discussions. W.A. is supported by an NYUAD global PhD fellowship. This work is funded by NYUAD grants ADHPG AD105 and ADHPG-RE105 to Y.I.

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Authors and Affiliations

  1. Program in Biology, Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

    Wael Abdrabou, Mame Massar Dieng, Aïssatou Diawara, Vinu Manikandan & Youssef Idaghdour

  2. Department of Biology, New York University, New York, NY, USA

    Wael Abdrabou

  3. Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou, Burkina Faso

    Samuel Sindié Sermé, Dareen Almojil, Salif Sombié, Noelie Bere Henry, Désiré Kargougou & Issiaka Soulama

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Contributions

Y.I. conceptualized and supervised the project. I.S. supervised fieldwork. W.A., I.S., M.M.D. and A.D. coordinated the study. W.A., M.M.D., A.D., D.A., S.S.S., S.S., H.N. and D.K. performed fieldwork, data collection and sample processing. W.A and M.M.D. performed laboratory experiments. V.M. performed bioinformatic analysis of RNA-seq data. W.A. performed statistical analysis. W.A. and Y.I. interpreted the results and wrote the manuscript with input from M.M.D. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Youssef Idaghdour.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Christoph Schmitt.

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Extended data

Extended Data Fig. 1 Distribution analysis of normalized metabolomic data.

Distribution analysis of interquartile-normalized metabolomics data for the Gouin dataset analyzed alone (a, n = 199) and the Gouin/Fulani merged datasets analyzed combined (b, n = 305).

Source data

Extended Data Fig. 2 Effect of hemoglobin genotype and 2-way hierarchical clustering of the full Gouin metabolomic dataset.

a–c, Impact of hemoglobin genotype on the serum metabolomes. PCA plots show the clustering patterns of 15 children with non-AA hemoglobin genotype (AC, triangles, n = 10; AS, diamonds, n = 4; and SC stars, n = 1) in principal component analysis of the Gouin children metabolomic dataset before infection (a), during infection (b) and in the combined dataset (c). d, Two-way hierarchical clustering of the full 667 metabolites dataset (rows) in Gouin children (columns) before (n = 99 samples) and during (n = 100) infection. The bar to the left shows children’s infection status (before infection; blue, during infection; red). Clustering was done using Ward’s method. e, Principal variance component analysis of the full Gouin metabolomic dataset showing the proportion of variance of the first three principal components explained by infection status, age, metabolomic experiment run day, sex, hemoglobin level, ratio of lymphocytes to neutrophils and individual effect (left). The bar graph (right) shows the weighted average proportion of variance of the first three principal components explained by each indicated variable.

Source data

Extended Data Fig. 3 Association between pregnenolone sulfate levels and expression levels of Th cell master regulators.

Pearson correlation between the levels of pregnenolone sulfate and the expression levels of GATA3, TBX21 and BCL6 before (blue) and during (red) infection measured using RNASeq (top panel, n = 36 children) or RT-qPCR (bottom panel, n = 23 children).

Source data

Extended Data Fig. 4 RT-qPCR validation of RNA sequencing data.

a, Box plots show matched relative expression of levels of 19 T cell transcription factors, ligands and receptors before (blue) and during (red) infection quantified using RNASeq (n = 36 children) and RT-qPCR (n = 23 children). Bar and whiskers represent mean ± SD. Differences in RNASeq and qPCR data were assessed using paired two-tailed Student’s t-test and Wilcoxon test, respectively (****P <0.0001). b, Bar plot shows the log2 fold change of expression levels for each of the 19 genes between before and during infection stages using RNASeq (blue) and RT-qPCR (orange) data. c, Correlation plots showing the moderation effect of infection on the association between pregnenolone sulfate and lymphocytes percentage (left panel) through the expression of CD274 (right panel) quantified in 23 individuals before infection (blue) and during infection (red) using RT-qPCR.

Source data

Extended Data Fig. 5 Effect of pregnenolone sulfate on T cell proliferation.

a, Experimental design of the T cell proliferation assay (created with biorender.com). CFSE-labelled PBMCs (1.5 × 105) from 10 healthy donors were cultured with or without anti-CD3/anti-CD28 stimulation in the presence or absence of 400 µM pregnenolone sulfate. T cell proliferation was assessed using flow cytometry after five days of culturing. b, Bar plots showing the division (right) and expansion (left) indices in the four experimental conditions for PBMCs from the 10 individuals. Proliferation and replication indices are shown in Fig. 6. c, Flow cytometry plots of CFSE-stained PBMCs derived from gated T cells for each of the 10 individuals under the indicated unstimulated untreated (blue), unstimulated pregnenolone sulfate-treated (purple), stimulated untreated (green), and stimulated pregnenolone sulfate-treated (red) conditions. d, Bar plots showing log2 mean fluorescence intensities (log2 MFI) of IL-2 and IL-4 cytokines quantified in culture supernatants of unstimulated untreated (n = 4, blue), unstimulated pregnenolone sulfate-treated (n = 4, purple), stimulated untreated (n = 10, green), and stimulated pregnenolone sulfate-treated (n = 10, red) PBMCs using flow cytometry. Cytokines that show statistically significant differences between stimulated pregnenolone sulfate-treated/untreated conditions are shown in Fig. 6. Differences between the groups presented in b. and d. were assessed using a paired two-tailed Student’s t-test. Bar and whiskers represent mean ± SD. The grey lines in bar plots connect matched samples. (e) Flow cytometric plots showing the gating strategy used in the T cell proliferation assay shown in Fig. 7. Lymphocytes were gated according to FS-A and SS-A. CD3+ live cells were gated according to 7-AAD and PE-A, and CFSE stained CD3+ cells were detected using FITC-A.

Source data

Extended Data Fig. 6 One-way hierarchical clustering heatmap of metabolite levels of 12 infection-associated steroids in the Gouin-Fulani merged dataset.

Metabolite data for the Gouin (a, 99 samples before infection and 100 samples during infection) and Fulani (b, 53 samples before infection and 53 samples during infection) is shown. The children (columns) are labeled based on their infection state (before infection; blue, and during infection; red).

Source data

Supplementary information

Reporting Summary

Supplementary Tables 1–12

Supplementary Table 1 Demographic and haematological information of the Gouin study participants. Supplementary Table 2 Global metabolomics data of the Gouin cohort and list of differentially abundant metabolites (log transformed and IQR normalized). Supplementary Table 3 Results of metabolomic quantitative pathway enrichment analysis. Supplementary Table 4 Results of linear regression analysis between steroid levels and lymphocyte fraction values adjusting for age and sex. Supplementary Table 5 Cross-correlation of steroids and transcripts in the Gouin cohort. Supplementary Table 6 IPA and CluoGO enrichment analysis of genes whose expression levels were significantly correlated with infection-associated steroids. Supplementary Table 7 Transcriptomic data of the Gouin participants. Supplementary Table 8 Results of causal mediation and moderated mediation analysis input data and analyses. Supplementary Table 9 RT–qPCR validation data of the RNA-seq data for 19 T cell transcription factors, ligands and receptors. Supplementary Table 10 Flow cytometry and cytokine data of the T cell proliferation assay. Supplementary Table 11 Demographic and haematological information of the Fulani study participants and merged global metabolomic data of the Gouin and Fulani cohorts (log transformed and IQR normalized). Supplementary Table 12 Blood serum cytokine (IL-2, IL-4, IL-10, IL-13, TNF and INF-γ) data from Gouin and Fulani children before and during infection.

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Abdrabou, W., Dieng, M.M., Diawara, A. et al. Metabolome modulation of the host adaptive immunity in human malaria. Nat Metab 3, 1001–1016 (2021). https://doi.org/10.1038/s42255-021-00404-9

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