Mapping immune variation and var gene switching in naive hosts infected with Plasmodium falciparum

  1. Kathryn Milne
  2. Alasdair Ivens
  3. Adam J Reid
  4. Magda E Lotkowska
  5. Aine O'Toole
  6. Geetha Sankaranarayanan
  7. Diana Munoz Sandoval
  8. Wiebke Nahrendorf
  9. Clement Regnault
  10. Nick J Edwards
  11. Sarah E Silk
  12. Ruth O Payne
  13. Angela M Minassian
  14. Navin Venkatraman
  15. Mandy J Sanders
  16. Adrian VS Hill
  17. Michael Barrett
  18. Matthew Berriman
  19. Simon J Draper
  20. J Alexandra Rowe  Is a corresponding author
  21. Philip J Spence  Is a corresponding author
  1. Institute of Immunology and Infection Research, University of Edinburgh, United Kingdom
  2. Centre for Immunity, Infection and Evolution, University of Edinburgh, United Kingdom
  3. Wellcome Sanger Institute, United Kingdom
  4. Institute of Evolutionary Biology, University of Edinburgh, United Kingdom
  5. Instituto de Microbiologia, Universidad San Francisco de Quito, Ecuador
  6. Wellcome Centre for Integrative Parasitology, University of Glasgow, United Kingdom
  7. Glasgow Polyomics, University of Glasgow, United Kingdom
  8. The Jenner Institute, University of Oxford, United Kingdom
4 figures, 1 table and 9 additional files

Figures

Figure 1 with 1 supplement
Immune variation in falciparum malaria.

Log2 expression values of 517 protein-coding genes in whole blood during infection. Genes (rows) are ordered by hierarchical clustering whereas whole blood samples (columns) are ordered by volunteer …

Figure 1—source data 1

An identical immune challenge leads to diverse outcomes in falciparum malaria.

Log2 fold-change of the 100 protein-coding genes with highest variance in whole blood during infection. Data are presented as deviation from median (within each volunteer). Genes (rows) are ordered by hierarchical clustering, whereas whole blood samples (columns) are ordered by time-point (pre-infection to diagnosis, left to right). Each volunteer was analysed independently and therefore every top 100 gene list is unique.

https://cdn.elifesciences.org/articles/62800/elife-62800-fig1-data1-v1.pdf
Figure 1—source data 2

Log2 transformed expression values of the 517-gene superset in whole blood during infection.

https://cdn.elifesciences.org/articles/62800/elife-62800-fig1-data2-v1.xlsx
Figure 1—figure supplement 1
Immune quiescence is a common early outcome of infection.

Principal component analysis of the 517-gene superset in whole blood during infection. Each dot represents one time-point (pink is pre-infection) and every volunteer is centred around their own …

Figure 2 with 1 supplement
Interferon-stimulated inflammation is the dominant response to blood-stage infection.

(A) Gene ontology network of 2028 genes differentially expressed at diagnosis in the inflammatory group. Each node represents a significantly enriched GO term (adj p<0.05) and node size is …

Figure 2—figure supplement 1
Sporozoites do not trigger a systemic transcriptional response in human malaria.

(A) Rlog expression values of the 117-gene superset in whole blood after mosquito challenge. Genes (rows) are ordered by hierarchical clustering, whereas whole blood samples (columns) are ordered by …

Figure 3 with 3 supplements
Systemic inflammation coincides with the onset of clinical symptoms.

(A) Parasite growth curves colour-coded by host response; each line represents one volunteer. Blood samples were collected every 12 hr for qPCR analysis of circulating parasite density and the …

Figure 3—figure supplement 1
Adverse events after blood challenge.

Data on symptoms were collected every 12 hr from day 2 post-infection either on diary cards (self-reporting) or during clinic visits. Adverse events were graded as mild (transient or mild discomfort …

Figure 3—figure supplement 2
Inflammation is linked to hallmark symptoms of malaria.

(A) Proportion of mild, moderate and severe adverse events at diagnosis. For each volunteer, adverse events were recorded for 13 clinical categories and these were summed to give a clinical score at …

Figure 3—figure supplement 3
A conserved early metabolic signature of falciparum malaria.

Volcano plot showing the 10 plasma metabolites that were differentially abundant during infection. We grouped unresponsive (v018 and v208), inflammatory (v016, v017 and v013), and suppressor (v022 …

Figure 4 with 1 supplement
Parasite variants associated with severe disease do not rapidly expand in naive hosts.

(A and B) Rlog expression values of var (A) and rifin (B) genes in the inoculum and diagnosis parasite samples after blood challenge (blue study); parasites obtained from volunteers infected by …

Figure 4—source data 1

Rlog expression values of var and rifin genes in parasites isolated from whole blood after controlled human malaria infection.

https://cdn.elifesciences.org/articles/62800/elife-62800-fig4-data1-v1.xlsx
Figure 4—figure supplement 1
Group B var genes dominate the PfEMP1 landscape in naive hosts.

(A) Parasite growth curves in five volunteers infected by mosquito bite; each line represents one volunteer and the horizontal green line represents the lower limit of detection (five parasites ml−1)…

Tables

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Strain, strain background (Plasmodium falciparum)clone 3D7Cheng et al., 1997PMID:9347970
Sequence-based reagentTaqMan probeApplied Biosystems5′ FAM
AACAATTGGAGGGCAAG
NFQ-MGB 3′
Sequence-based reagentISPCR primerbiomers.net5’- AAG
CAGTGGTATCAACGCAG
AGT −3’
Sequence-based reagentLNA-modified TSOexiqon.com5’- AAGCAGTGGTATCAACGCAGAGTACATrGrG+G −3’
Sequence-based reagentOligo-dT30VNbiomers.net5’- AAGC
AGTGGTATCAACGCAGAGT
ACT30VN −3’
Commercial assay or kitTempus spin RNA isolationApplied Biosystemscat. no.
4378926
Commercial assay or kitRNA clean and concentratorZymo Researchcat. no.
R1016
Commercial assay or kitGlobin-Zero GoldIlluminacat. no. GZG1224
Commercial assay or kitRNA 6000 pico chipAgilentcat. no.
5067–1513
Commercial assay or kitHigh sensitivity DNA kit bioanalyzerAgilentcat. no.
5067–4626
Commercial assay or kitTruSeq stranded mRNA libraryIlluminacat. no.
RS-122–2101
Commercial assay or kitNEBNext Ultra DNA library prep kitNew England Biolabscat. no.
E7370
Software, algorithmKallisto v0.42.3Bray et al., 2016RRID:SCR_016582PMID:27043002
Software, algorithmEdgeRRobinson et al., 2010RRID:SCR_012802PMID:19910308
Software, algorithmClueGO v2.5.4Bindea et al., 2009RRID:SCR_005748PMID:19237447
Software, algorithmIDEOMCreek et al., 2012PMID:22308147
Software, algorithmmzCloudRRID:SCR_014669
Software, algorithmDESeq2Love et al., 2014RRID:SCR_015687PMID:25516281
OtherSaponin from quillaja barkSigma Aldrichcat. no.
S7900
test each batch
OtherLeucoflex
LXT filters
Macopharmamacopharma.com/transfusion
OtherKAPA HiFi HotStart ReadyMixBiosystemscat. no.
KK2601
OtherAMPure XP beadsBeckman Coultercat. no.
A63881

Additional files

Supplementary file 1

Demographics of volunteers infected by blood or mosquito challenge; includes genetic and non-genetic variables known to influence human immune variation in vitro.

Classification of volunteers by host response after blood challenge is described in the results and methods.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp1-v1.xlsx
Supplementary file 2

Three metrics that measure the magnitude of the human immune response to blood challenge.

[1] The mean variance of the top 50 most variable protein-coding genes in each volunteer. [2] The Euclidean distance travelled during principal component analysis of the 517-gene superset. [3] The number of differentially expressed genes at diagnosis in each group. Note that uninfected control volunteers set a threshold for baseline variation in gene expression through time; a Mann Whitney test was then used to support the observation that unresponsive volunteers were comparable to uninfected controls. Classification of volunteers by host response after blood challenge is described in the results and methods.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp2-v1.xlsx
Supplementary file 3

List of the 2028 differentially expressed genes identified in inflammatory volunteers at diagnosis (compared to pre-infection samples).

These data underpin the gene ontology network in Figure 2a.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp3-v1.xlsx
Supplementary file 4

List of the 217 significantly enriched GO terms identified in inflammatory volunteers at diagnosis.

GO terms are arranged into their functional groups and groups are ordered by size. The leading GO term in the top 12 functional groups is highlighted in red. On sheet 2, we highlight the top 20 GO terms as ordered by p value.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp4-v1.xlsx
Supplementary file 5

List of the 77 differentially expressed genes identified in suppressor volunteers at diagnosis (compared to pre-infection samples).

These data underpin the heatmap in Figure 2d.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp5-v1.xlsx
Supplementary file 6

List of the 893 genes that were differentially expressed between inflammatory and suppressor hosts at diagnosis.

There were no differentially expressed genes between these volunteers prior to infection (adj p<0.05).

https://cdn.elifesciences.org/articles/62800/elife-62800-supp6-v1.xlsx
Supplementary file 7

Rlog expression values of the 117-gene superset in whole blood after mosquito challenge.

These data underpin the heatmap in Figure 2—figure supplement 1a. On sheet two we highlight the volunteers in which EdgeR identified these genes as differentially expressed. And on sheet three we look at the 21 genes that were most upregulated in inflammatory volunteers after blood challenge.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp7-v1.xlsx
Supplementary file 8

List of the 44 significantly enriched GO terms identified in the 117-gene superset.

GO terms are arranged into their functional groups and groups are ordered by size. On sheet two we highlight the top 20 GO terms as ordered by p value.

https://cdn.elifesciences.org/articles/62800/elife-62800-supp8-v1.xlsx
Transparent reporting form
https://cdn.elifesciences.org/articles/62800/elife-62800-transrepform-v1.docx

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