Cell Reports

Supplemental Information

Genome-wide Analysis of Host-Plasmodium yoelii

Interactions Reveals Regulators

of the Type I Interferon Response

Jian Wu, Baowei Cai, Wenxiang Sun, Ruili Huang, Xueqiao Liu, Meng Lin, Sittiporn

Pattaradilokrat, Scott Martin, Yanwei Qi, Sethu C. Nair, Silvia Bolland, Jeffrey I. Cohen,

Christopher P. Austin, Carole A. Long, Timothy G. Myers, Rong-fu Wang, and Xin-zhuan

Su

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Supporting Online Materials

Supplemental Figures

Figure S1. Patterns of mRNA Expression Measured in Spleens of Mice Infected

With Plasmodium yoelii yoelii 17XNL, P. yoelii nigeriensis N67, and Their Progeny

(Related to Figure 1)

(A) Relative expression levels for 12,951 microarray probes (9,701 distinct genes) found

to be differentially expressed 4 days post infection relative to naïve control. All of the

selected probes were statistically significant for at least one of the progeny at the FDR <

0.05 cutoff. Color scale is the mean expression in units of standard deviation across all

the parasites. The colors on the top indicate major clusters.

(B) Expression responses (infected vs. naïve control) of 137 microarray probes (112

distinct genes) selected based their differential response to infection by the two parents

N67 vs 17XNL. For this focus list of genes, both fold-change (2-fold up or down) and P-

value filters (FDR < 0.05) were applied. Color scale is the expression ratio (log2 scale)

for infection by the indicated parasite relative to naïve control (see Table S2 for the gene

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list). The color bars on the left indicate clusters with gene names (zoom in to read the

names).

Figure S2. Genome-Wide Pattern of LOD Scores for Genes in Three Clusters With

Related Functions (Related to Figure 3)

(A) GPLSs for genes in cluster 23 containing SMAD5 and other 16 genes that likely play

a role in negatively regulating hematopoiesis.

(B) GPLSs for genes in cluster 640 containing genes encoding 12 histone proteins.

(C) GPLSs for genes in cluster 277 containing the gene encoding Fas cell surface death

receptor (CD95) and 10 other genes that may play a role in Fas-related apoptosis.

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Figure S3. Changes in Luciferase Signals after Co-Transfection With Plasmids

Containing the Human Genes as Indicated (Related to Figure 5)

Fold changes in luciferase signal after co-transfection of the 293T cell line with plasmids

containing indicated gene under the control of the ISRE (interferon-sensitive response

element) promoter plus gene encoding MDA5 (A), RIG-I (B), VISA/MAVS (C), TBK1

(D), IKK1 (E), and IRF3 (F). Vec cont is vector control; all the genes were cloned into

the same expression vector. *, P < 0.05; **, P < 0.01 (t-test, standard deviation from

three replicates).

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Supplemental Tables:

Table S1. Genes expressed at significantly different levels in mice infected with

Plasmodium yoelii yoelii 17XNL, Plasmodium yoelii nigeriensis N67, and their 24

progeny. Related to Figure 1.

Table S2. Genes significantly differentially expressed between N67 and 17XNL parasites

and log2 ratios of mRNA levels from infected/uninfected mice for the parents and their

progeny. Related to Figure 1.

Table S3. Host genes significantly linked to parasite microsatellite markers at LOD ≥3.0,

showing LOD scores for each gene-marker interaction. Related to Figure 1.

Table S4. Host genes significantly linked to parasite microsatellite markers at LOD ≥3.0,

showing the two markers with the highest LOD scores. Related to Figure 1.

Table S5. Host genes suggestively linked to parasite microsatellite markers at LOD ≥2.0,

showing LOD scores for each gene-marker interaction. Related to Figure 1.

Table S6. Host GO-terms enriched among the genes significantly linked to parasite

microsatellite markers. Related to Figure 1 and Figure 2.

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Table S7. Clusters of host genes significantly enriched for functional GO-terms at FDR

<0.01 based on genes linked to parasite loci at LOD scores ≥2.0. Related to Figure 1 and

Figure 2.

Table S8. Gene clusters with similar genome-wide pattern of LOD scores that likely play

a role in host type I interferon responses. Related to Figure 3.

Table S9. Gene clusters with similar genome-wide pattern of LOD scores that likely play

a role in hematopoiesis and blood cell development. Related to Figure 4.

Table S10. Confirmation of candidate genes in regulation of IFN-I responses using

plasmid overexpression, shRNA knockdown or viral infection. Related to Figure 5,

Figure 6, and Figure 7.

Table S11. Comparison of false discovery rate values for gene lists enriched for type I

interferon related GO terms from clusters obtained using genome-wide pattern LOD

score or gene expression level. Related to Figure 1.

Supplemental Experimental Procedures

Parasites and Mice

Parasites Plasmodium y. yoelii 17XNL, Plasmodium y. nigeriensis N67, and their 24

progeny have been described (Li et al., 2011). Procedures for infection of mice with the

parasites were as reported previously (Li et al., 2011; Pattaradilokrat et al., 2014).

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Briefly, parasites were thawed from frozen stocks and injected into naïve C57BL/6 mice

to initiate infection. An inoculum containing 1 × 106 infected red blood cells (iRBCs)

suspended in 100 µL phosphate buffered saline (PBS), pH 7.4, from the donor mice was

injected i.v. into experimental mice (n=3 mice per clone or strain). Naïve mice receiving

an equivalent number of uninfected RBCs served as a negative control group. Inbred

female C57BL/6 mice, aged 6–8 weeks old, were obtained from Charles River

Laboratory, Jackson Laboratory, or NIAID/Taconic repository, NIH. The Fcγr1 knockout

(KO) mice were obtained from Dr. J.S. Verbeek, Department of Human Genetics, Leiden

University Medical Center (Ioan-Facsinay et al., 2002). All the experiments were

performed in accordance with NIH–approved animal study protocol LMVR-11E.

Parasitemia was monitored daily by microscopic examination of Giemsa-stained thin tail

blood smears.

RNA and Microarray

Methods of RNA isolation, labeling, and microarray hybridization were as described (Wu

et al., 2014). Briefly, mRNA from spleens of uninfected mice or mice infected with the

parents or the progeny were isolated using an RNeasy Mini Kit following the

manufacturer's protocol (Qiagen, Valencia, CA). Approximately 500 ng mRNA from

each parasite was amplified and labeled using the Illumina TotalPrep RNA Amplification

Kit (Applied Biosystems, Foster City, CA). Biotinylated cDNA was hybridized to

Illumina MouseRef-8 v2.0 Expression BeadChip (GEO Accession GPL6885) having

25,697 unique probes using reagents provided. Illumina HiScan-SQ was used for chip

imaging, and Genome Studio software was used for data extraction. Signals from

triplicate hybridizations for each probe were averaged and expressed as mean expression

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in units of standard deviation. Quantile normalization, mixed-effect ANOVA, P-value

adjustments, and gene set enrichment analysis were performed using JMP/Genomics

software (SAS Institute, Cary, NC). Gene sets were derived from Gene Ontology term

assignments available from NCBI.

Transformation of Cell Lines and Gene Overexpression

Plasmids containing mouse target genes obtained from commercial companies (Thermo

Scientific, Waltham, MA and Origene, Rockville, MD) were amplified and purified using

QIAfilter Plasmid Midi Kit (QIAGEN). HEK293T cells (1.2 × 105) were plated in 24-

well plates and transfected with plasmid encoding the candidate genes (200 ng or as

indicated in the figures) using Lipofectamine 2000 (Invitrogen, Carlsbad, CA) 24 h later.

IFN-β luciferase reporter (firefly luciferase; 50 ng) and β-actin luciferase reporter (renilla

luciferase plasmid; 20 ng) together with plasmid (200 ng) encoding VISA, TBK1, TRIF,

or TBK1 were co-transfected in various experiments. Empty pCMV14 vector was used to

maintain equal amounts of DNA among wells. For stimulation of IFN-I response, cells

were transfected with Poly(dAdT) or Poly(I:C) 24 h after plasmid introduction. Cells

were lysed at 24 h after transfection or stimulation, and luciferase activity was measured

with a Dual-Luciferase Assay (Promega, Fitchburg, WI). Relative reporter gene activity

was determined by normalization of the firefly luciferase activity to renilla luciferase

activity.

Transfection of human genes in pcDNA3.1-HA tag plasmid into 293Tcells was done

similarly. Briefly, 1.0 × 105 293T cells were seeded in 24-well plates and co-transfected

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with plasmids containing indicated genes together with ISRE luciferase reporter (firefly

luciferase; 100 ng), pRL-TK (renilla luciferase plasmid; 5 ng), and plasmids containing

one of the Flag-tagged genes (RIG-I, MDA5, MAVS, TBK1, IKK1, or empty pcDNA3.1

vector to maintain equal amounts of DNA) using lipofectamine 2000 24 h later, or were

stimulated with poly(I:C) 48 h after introduction of plasmids with candidate genes.

shRNA Knockdown (KD)

The 293T cells (1.0 × 105) were seeded in 24-well plates and were transfected with

indicated shRNA plasmid (or ref-shRNA as control) using lipofectamine 2000, following

the manufacturer's protocol, 24 h later. The cells were stimulated with low molecular

weight Poly(I:C) (3 µg per well) 24 h after transfection. Luciferase activities were

measured 24 h later.

Vesicular Stomatitis Virus (VSV) Infection

The 293T cells (2×105 cells per well) were seeded in 12-well plates18 h before

transfection. Plasmids containing indicated genes or empty vector were transfected into

the cells, and after 24 h incubation, the cells were infected (MOI = 0.05) with VSV

containing green fluorescence protein (VSV-GFP). Cells with VSV-GFP were counted

using flow cytometry 18 h after infection. For VSV infection with treatment of IFN-α,

HEK293T cells were first transfected with 1 µg of indicated plasmid. Twenty-four h after

transfection, the cells were treated with 5 x103 U/ml IFN-α for 30 min and then infected

with VSV-GFPat (MOI = 0.05) and cultured in the presence of IFN-α. Cells were

collected 24 h after infection and subjected to flow cytometry analysis. The mean GFP

intensity was normalized with pCMV14 empty vector control and expressed as relative

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GFP intensity (fold change). Data from four independent experiments were averaged and

Student’s t-test was used to estimate the mean differences.

Supplemental Discussion

In this study, we mainly focused on the prediction and functional verification of host

genes involved in innate responses. We established a database containing clusters of host

genes predicted to function in related pathways. For example, the Fas and 10 other genes

in cluster 277 are implicated to function in apoptosis pathways. The almost identical

GPLSs suggested these genes function closely with Fas; a search of PubMed shows that

6 of 11 genes are known to play a role in apoptosis (Ccr5, Crat, Fas, E2f1, Gsto1, and

Stat4). The remaining five genes are likely to function in Fas associated apoptosis, and

this can be tested experimentally later. Similarly, functional investigation on the clusters

in Table S9 containing many known or unknown genes but predicted to play a role in

blood cell development will greatly improve our understanding of hematopoiesis.

Malaria parasites are eukaryotic organisms that are more complex than viruses and

bacteria, and host responses to the parasites likely involve unique pathways not present

during viral or bacterial infections. Some of the genes we identified may play a role in

pathways unique to infection by eukaryotic pathogens or malaria parasites; in fact, only 7

of the 14 genes we identified affected VSV replication in vitro. Comparison of these

results with those from infections with viruses and bacteria may reveal novel pathways in

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host innate immunity against eukaryotic organisms.

Another interesting observation was that parasite genes stimulating host innate responses

were mostly distributed in chromosomes 4, 8, 10, 13, and 14, and were also enriched at

subtelomeric regions. The subtelomeric distribution is not surprising because these

regions contain many antigenic gene families such as yir, fam-a/b, and exported protein

genes (Carlton et al., 2005; Otto et al., 2014). At present, we do not have any explanation

for the clustering of interactive parasite genes on the limited numbers of chromosomes. It

would be interesting to investigate whether these chromosomes contain more known

antigen genes or genes with higher diversity due to immune pressure.

Our analyses also link many parasite genetic loci to various host genes although the

parasite candidate genes were not the focus of the current study. Although the highest

LOD scores for the linked parasite loci were generally between 3-5, likely due to the

relatively small number of parasite progeny and multi-loci phenotypes, the linkage of

multiple host genes having functions in the same or related pathways provided additional

confidence for linked loci. Unfortunately, the limited numbers of progeny and genetic

crossovers among the progeny restrict our ability to pinpoint the candidate genes in the

majority of the linked loci, except for a few cases such as the one linked to glycophorin A

where a few candidate genes could be identified. Fine-mapping using additional progeny

and functional verification of the candidate genes will lead to parasite genes that can

modulate host innate responses.

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Supplemental References

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Ojik, H.H., Sedlik, C., da Silveira, S.A., Gerber, J., de Jong, Y.F., et al. (2002).

FcgammaRI (CD64) contributes substantially to severity of arthritis, hypersensitivity

responses, and protection from bacterial infection. Immunity 16, 391–402.

Li, J., Pattaradilokrat, S., Zhu, F., Jiang, H., Liu, S., Hong, L., Fu, Y., Koo, L., Xu, W.,

Pan, W., et al. (2011). Linkage maps from multiple genetic crosses and loci linked to

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Otto, T.D., Bohme, U., Jackson, A.P., Hunt, M., Franke-Fayard, B., Hoeijmakers, W.A.,

Religa, A.A., Robertson, L., Sanders, M., Ogun, S.A., et al. (2014). A comprehensive

evaluation of rodent malaria parasite genomes and gene expression. BMC Biol. 12, 86.

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