Use of gene expression studies to investigate the human immunological response to malaria infection

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REVIEW

Use of gene expression studies to investigate

the human immunological response to malaria

infection

Susanne H. Hodgson

1,2*

_{, Julius Muller}

1

_{, Helen E. Lockstone}

3

_{, Adrian V. S. Hill}

1,3

_{, Kevin Marsh}

4

_{, Simon J. Draper}

1

and Julian C. Knight

3

Abstract

Background: Transcriptional profiling of the human immune response to malaria has been used to identify diagnos-tic markers, understand the pathogenicity of severe disease and dissect the mechanisms of naturally acquired immu-nity (NAI). However, interpreting this body of work is difficult given considerable variation in study design, definition of disease, patient selection and methodology employed. This work details a comprehensive review of gene expres-sion profiling (GEP) of the human immune response to malaria to determine how this technology has been applied to date, instances where this has advanced understanding of NAI and the extent of variability in methodology between studies to allow informed comparison of data and interpretation of results.

Methods: Datasets from the gene expression omnibus (GEO) including the search terms; ‘plasmodium’ or ‘malaria’ or ‘sporozoite’ or ‘merozoite’ or ‘gametocyte’ and ‘Homo sapiens’ were identified and publications analysed. Datasets of gene expression changes in relation to malaria vaccines were excluded.

Results: Twenty-three GEO datasets and 25 related publications were included in the final review. All datasets related to Plasmodium falciparum infection, except two that related to Plasmodium vivax infection. The majority of datasets included samples from individuals infected with malaria ‘naturally’ in the field (n ₌ 13, 57%), however some related to controlled human malaria infection (CHMI) studies (n ₌ 6, 26%), or cells stimulated with Plasmodium in vitro (n ₌ 6, 26%). The majority of studies examined gene expression changes relating to the blood stage of the parasite. Significant heterogeneity between datasets was identified in terms of study design, sample type, platform used and method of analysis. Seven datasets specifically investigated transcriptional changes associated with NAI to malaria, with evidence supporting suppression of the innate pro-inflammatory response as an important mechanism for this in the majority of these studies. However, further interpretation of this body of work was limited by heterogeneity between studies and small sample sizes.

Conclusions: GEP in malaria is a potentially powerful tool, but to date studies have been hypothesis generating with small sample sizes and widely varying methodology. As CHMI studies are increasingly performed in endemic settings, there will be growing opportunity to use GEP to understand detailed time-course changes in host response and understand in greater detail the mechanisms of NAI.

Keywords: Plasmodium falciparum, Gene expression, Malaria, Immunity

© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Background

Malaria, caused by infection with parasites of the genus Plasmodium, remains a significant public health concern [1]. Despite a vaccine in pilot implementation trials [2] and widespread application of control measures [3], the disease is still responsible for a huge burden of mortality

Open Access

*Correspondence: [email protected]

1_{The Jenner Institute, University of Oxford, Old Road Campus Road}

Building, Off Roosevelt Drive, Oxford OX3 7DQ, UK

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and morbidity worldwide and a concerning increase in incidence has been seen in previously well-controlled areas [3].

With repeated exposure to infection, individuals in malaria-endemic regions develop naturally acquired immunity (NAI), first to the most severe clinical forms, such as cerebral malaria and then more slowly to infec-tion itself [1]. Although the role of antibodies in con-trolling parasite density, symptomatology and severity of disease is well established [4, 5], less is known about mechanism in terms of the role of the innate and cellu-lar immune responses [6]. Increased understanding of the immune response to malaria, in particular those that mediate NAI, could aid identification of diagnostic and prognostic markers, inform vaccine development and assist with the identification of treatment strategies to modify the immunological mechanisms mediating severe pathology [1].

Transcriptomics, which allows the expression of thou-sands of genes to be assessed in parallel for a single RNA sample, is an exciting, expanding area of research with vast potential application in the field of infection [7]. Facilitating a systems biology approach, gene expres-sion data from high-throughput technologies (such as microarrays [8] and next generation sequencing enabling RNA sequencing for bulk cell populations and at single-cell resolution [9, 10]) can allow greater understanding of individuals’ response to infection. To date, expression data have been used to dissect mechanisms of vaccine immunogenicity [11], inform the design of new vaccines [12, 13], predict response to infection and outcome [14, 15], characterize and improve understanding of sepsis [16], and offer a novel approach to the diagnosis of infec-tious pathogens [17–19] together with RNA expression in the pathogen [20].

Given the limited understanding of the mechanisms of NAI to malaria from traditional immunological studies, a systems approach characterizing the gene expression patterns associated with infection could provide novel and valuable insights [21, 22]. Transcriptional profiling of the immune response to malaria in humans to date has sought to identify markers to aid diagnosis [23], to understand the pathogenicity of severe disease [24] and dissect the mechanisms of NAI [25, 26]. However, inter-preting this body of work is difficult given considerable variation in study design, definition of disease, patient selection and methodology employed.

This review outlines a comprehensive analysis of all GEP studies of the human immune response to malaria with two aims: (i) to understand the application of this technology to date, in particular how these studies have informed understanding of NAI; and (ii) to determine the extent of variability in methodology between studies to

allow informed comparison of data and interpretation of results.

Methods

A search of Gene Expression Omnibus (GEO) [27] for datasets including the search terms; ‘plasmodium’ or ‘malaria’ or ‘sporozoite’ or ‘merozoite’ or ‘gametocyte’ and ‘Homo sapiens’ was performed on 10th September 2019. Each of these datasets were examined and those not relating to the human immune response to malaria infection or using the Homo sapiens platform excluded. Of note, datasets of gene expression changes in relation to malaria vaccines were excluded.

Results

Studies identified

The search identified 30 GEO datasets. Seven of these datasets were excluded, as published analyses were una-vailable. Twenty-three datasets and 25 related publica-tions were therefore included in the final review (Table 1 and Additional file 1: Figure S1). All datasets related to Plasmodium falciparum infection except two that related to Plasmodium vivax infection (Table 1). The majority of datasets included samples from individuals infected with malaria ‘naturally’ in the field (n = 13, 57%),

how-ever some related to controlled human malaria infec-tion (CHMI) studies (n = 6, 26%), or cells stimulated

with Plasmodium in vitro (n = 6, 26%). Studies included

samples from individuals with a wide range of ages (from 2 months—varying ages of adulthood) with differing degrees of prior exposure and, therefore, NAI to malaria. Samples were often collected as part of wider immuno-epidemiological studies or vaccine trials, leading to varia-tion in study design and sampling intervals.

Review of methodological approaches

Significant heterogeneity in the datasets was found in terms of study design, sample type, platform used and method of analysis (Tables 1, 2 and Fig. 1), making direct comparison of results between studies difficult. Most datasets were generated from whole blood samples (n = 11, 48%), however some used PBMCs (n = 3, 13%)

or individual tissue or cells types (n = 8, 35%) (Table 1).

For the majority of studies, expression profiling was per-formed by array (n = 16, 70%), with others using high

throughput sequencing (n = 6, 26%) or RT-qPCR [28]

(n = 1, 4%) (Table 1). There was heterogeneity in data

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Table

1

Summar

y of gene e

xpr

ession da

tasets in

vestiga

ting the human immunolo

gic al r esp onse t o malaria inf ec tion GEO series Title of da taset Publica tion Desig n Inf ec tion/ an tigenic Stimula tion Species Tissue Ag e Par ticipan t orig in Expr ession pr ofiling Subjec ts (samples) a Con tr ols Pla tf orm name Pla tf orm technology GSE2900 Host r esponse malar ia Gr iffiths et al . (2005) Compar

ison of GEP

in f ebr ile childr en with con valescent samples 2 w eeks post dischar ge Field P. f alciparum

Whole blood: PA

X gene Childr en 2–126 months Ken ya Ar ra y 22 (28) Subjec t pair ed

samples: diag

nosis

and post treatment

LC-36 Spott ed DNA/ cDNA GSE5418 G ene expr es

-sion analysis in malar

ia inf ec tion O ck enhouse et al . (2006) Compar

ison of GEP in

ear

ly

, pr

e-sympt

o-matic blood-stage infec

tion post CHMI

with sympt omatic malar ia-exper ienced

adults with naturally acquir

ed malar

ia

CHMI and Field

P. f

alciparum

PBMC

A

dults; 19–49

y

ears

USA and Camer

oon

Ar

ra

y

37 (74)

22 uninf

ec ted malar ia-naïv e Amer ican adults A ffym -etr ix human

genome U133A ar

ra

y

In situ oligo

-nucleotide GSE15221 M alar ia pr imes the innat e immune

response due to IFNγ induced enhancement of Toll-lik

e r ecept or expr ession and func tion Frank lin et al .

(2009) and Shar

ma et

al

.

(2011) and Hirak

o et

al

.

(2018)

Compar

ison GEP at

malar ia diag nosis and 28 da ys post tr eatment Field P. f alciparum PBMC A

dults 30

± 10 y ears Brazil; P or to Velho Ar ra y 21 (42) Subjec t pair ed

samples: diag

nosis

and post treatment Illumina human-6 v2.0

Oligonucleo -tide beads GSE26876 Time k inetics of gene expr ession

in NK92 cells af

ter P. f alciparum -iRBC encount er D e C ar valho et al . (2011) Compar

ison of GEP

var

iation of NK92

cells af

ter 6, 12, and

24

h of co

-cultur

e

with either inf

ec ted or uninf ec ted RBC compar ed t o time -point 0 In vitr o—iRBC P. f alciparum

NK92 cell line

N/A N/A Ar ra y N/A (12) Pair ed sam -ples: pr e

and post exposur

e A ffym -etr ix human

gene 1.0 ST arra

y

In situ oligo

-nucleotide

GSE33811

Pair

ed whole blood _{human transcr}

ip -tion pr ofiles fr om childr en with se ver e malar ia

and mild malar

ia Krupk a et al . (2012) Compar

ison of GEP in

se

ver

e malar

ia and

subsequent mild malar

ia in same

subjec ts 1 month lat er Field P. f alciparum

Whole blood: tr i- _reagent _BD

Childr en: 8–45 months M ala wi Ar ra y 5 (10) Subjec t pair ed sam -ples: se ver e

and mild malar

ia A ffym -etr ix Human G

ene 1.0 ST

Ar

ra

y

In situ oligo

-nucleotide

GSE34404

The genomic ar

chit

ec

tur

e of

host whole blood transcr

iptional response t o malar ia inf ec tion

Idaghdour et

al

. (2012)

Compar

ison of GEP in

mild malar ia with age mat ched un-inf ec ted contr ols Field P. f alciparum

Whole blood: Tempus

Childr en; median age 3.7 y ears Benin Ar ra y 94 subjec ts

(94) and 64 contr

ols

(64)

Uninf

ec

ted

age mat

ched

Illumina HumanHT

-

12 V4.0 expr

ession

bead chip

Oligonucleo

[image:3.595.85.498.84.731.2]

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Table 1 (c on tinued) GEO series Title of da taset Publica tion Desig n Inf ec tion/ an tigenic Stimula tion Species Tissue Ag e Par ticipan t orig in Expr ession pr ofiling Subjec ts (samples) a Con tr ols Pla tf orm name Pla tf orm technology GSE55843

Loss and dysfunc

-tion of V delta2 + gamma delta-lo w

T cells is associ

-at

ed with clinical

tolerance t

o

malar

ia

Jagannathan et

al

. (2014)

Compar

ison of GEP of

Vδ2

+

T cells fr

om childr en with ‘ high ’ and ‘ lo w ’ episodes of malar

ia in the

pr eceding y ear In vitr o—iRBC P. f alciparum Vδ2 + T cells Childr en: 4–5 y ears Uganda Ar ra y 78 (156) N/A Ag i- _lent -039494 Sur eP rint G3

Human GE v2 8

× 60K M icr oar ra y 03938

In situ oligo

-nucleotide GSE53292 Transcr ipt omic

analysis of Plasmodium PBANK

A, PBSL TR iP -K O , PB268-K O parasit e inf ec ted and uninf ec ted host cell Jaijyan et al . (2015) Compar

ison of GEP of

uninf

ec

ted HepG2

with those inf

ec

ted

with wild-t

ype and

knock out spor

o-zoit es In vitr o— spor oz oit es P. f alciparum

HepG2 cells

N/A

H

igh through

-put sequenc

-ing NK NK Illumina G enome Analyz er

IIx (Homo sapiens)

H

igh- throughput sequencing

GSE50957 M olecular hallmar ks of exper imentally acquir ed immu -nit y t o malar ia [P ilot Study] Tran et al .

(2016) and Vallejo et

al

.

(2018)

Compar

ison of GEP

pr

e and post inf

ec -tion CHMI P. f alciparum

Whole blood: PA

X

gene

A

dults: 19–22

y

ears

USA

H

igh through

-put sequenc

-ing 5 (10) Subjec t pair ed sam -ples: P re

and post infec

tion Illumina H iS eq 2000 (Homo sapiens) H

GSE52166

M

olecular hallmar

ks

of naturally acquir

ed immu -nit y t o malar ia Tran et al . (2016) Compar

ison of GEP

pr

e and post inf

ec -tion Field P. f alciparum

Whole blood: Tempus

A

dults and Childr

en 13.5–23.3 y ears M ala wi H

igh through

-put sequenc

-ing

8 (16)

Pair

ed same _subjec

t pr e inf ec tion Illumina H iS eq 2000 (Homo sapiens) H

GSE64338

Expr

ession data

from whole blood samples of Rwandan adults with mild malar

ia with mat ched sample 30 da ys lat er ( con vales -cence)

Subramaniam et

al

. (2015)

Compar

ison of GEP

in mild malar

ia and 30 da ys lat er Field P. f alciparum

Whole blood: Tr

i-Reagent BD

A dults Rwandan Ar ra y 19 (38) Subjec t pair ed

samples: diag

nosis

and post treatment

[HuG

ene

-1_0-st] Affym

-etr

ix Human

G

ene 1.0 ST

Ar

ra

y

In situ oligo

-nucleotide GSE64493 FCRL5 deline -at es func tion -ally impair ed memor

y B cells

associat ed with malar ia exposur e Sullivan (2015) Compar

ison of GEP

bet

w

een classi

-cal and at

ypical

memor

y B cells in

Uganda childr en Field P. f alciparum PBMC Childr en 8–10 y ears Uganda Ar ra y 12 NK Ag i- _lent -039494 Sur eP rint G3

Human GE v2 8

× 60K M icr oar ra y 039381

In situ oligo

[image:4.595.87.498.86.732.2]

(5)

Table

1

(c

on

tinued)

GEO series

Title of da

taset

Publica

tion

Desig

n

Inf

ec

tion/

an

tigenic

Stimula

tion

Species

Tissue

Ag

e

Par

ticipan

t

orig

in

Expr

ession

pr

ofiling

Subjec

ts

(samples)

a

Con

tr

ols

Pla

tf

orm

name

Pla

tf

orm

technology

GSE67184

Transcr

iption

pr

ofiling of

malar

ia-naïv

e and

semi-immune colombian volunt

eers in

a

Plasmodium

viv

ax

spor

oz

oit

e

challenge

Rojas-P

enas

(2015), Vallejo (2018) and Gar

dinassi

(2018)

Compar

ison of GEP

changes bet

w

een

malar

ia naïv

e and

semi-immune adults pre-inf

ec

tion and at

diag

nosis

CHMI

P. viv

ax

Whole blood: Tempus

A

dults

Columbia

H

igh through

-put sequenc

-ing

12 (24)

Subjec

t

pair

ed

samples: pre-inf

ec

-tion and diag

nosis

Illumina H

iS

eq

2500

(Homo sapiens)

H

GSE67469

Transcr

iption

pr

ofiling of

malar

ia-naïv

e and

semi-immune colombian volunt

eers in

a

Plasmodium

viv

ax

spor

oz

oit

e

challenge

Rojas-P

enas

(2015)

Compar

ison of GEP

changes bet

w

een

malar

ia naïv

e and

semi-immune adults over the time

-course of malar

ia

inf

ec

tion: pr

e-inf

ec

tion, da

y 5, da

y

7, da

y 9, diag

nosis

and month 4

CHMI

P. viv

ax

Whole blood: Tempus

A

dults

Columbia

RT

-qPCR

16 (85)

Subjec

t

pair

ed

samples: Pre inf

ec

-tion and multiple time

-points

post inf

ec

-tion

Fluidig

m

96Ã—96 nanofluidic arra

ys f

or

96 genes: blood infor

mativ

e

transcr

ipts

RT

-PCR

GSE7586

G

enome wide analysis of pla

-cental malar

ia

M

uehlenbachs (2007)

Compar

ison of GEP

in w

omen with pla

-cental malar

ia and

those without

Field

P. f

alciparum

Placenta

A

dults

Tanzania

Ar

ra

y

20 (20)

NK

[HG-U133_ Plus_2] A

ffym

-etr

ix Human

G

enome

U133 P

lus

2.0 Ar

ra

y

In situ oligo

-nucleotide

GSE77122

In

volv

ement of

β-def

ensin 130

(DEFB130) in the macr

ophage

micr

obicidal

mechanisms f

or

killing

Plasmo

-dium f

alciparum

Ter

ka

wi (2017)

Human monoc

yt

e-der

iv

ed mac

-rophages w

er

e

co

-cultur

ed with

P.

falciparum

iRBCs

,

saponin-tr

eat

ed

iRBCs

, or

non-inf

ec

ted RBCs

In vitr

o—iRBC

P. f

alciparum

M

ac

-rophages

NK

Ar

ra

y

NK (8)

NK

Ag

i- _lent

-028004

Sur

eP

rint

G3 Human GE 8

×

60K

M

icr

oar

ra

y

In situ oligo

-nucleotide

GSE93664

Compar

ison of the

transcr

ipt

omic

pr

ofile of

P.

falciparum

reac

-tiv

e polyfunc

-tional and IFNγ monofunc

tional

human

CD4 T

cells

Bur

el (2017)

Compar

ison of GEP in

monofunc

tional and

polyfunc

tional IFN

pr

oducing T

cells

collec

ted 21

da

ys

post CHMI inf

ec

tion

CHMI

+

in

vitr

o—iRBC

P. f

alciparum

IFN pr

o-ducing T cells

18–42

y

ears

A

ustralia

Ar

ra

y

8 (2)

NK

[HuG

ene

-2_0-st] Affym

-etr

ix Human

G

ene 2.0 ST

Ar

ra

y

In situ oligo

[image:5.595.85.501.83.730.2]

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Table 1 (c on tinued) GEO series Title of da taset Publica tion Desig n Inf ec tion/ an tigenic Stimula tion Species Tissue Ag e Par ticipan t orig in Expr ession pr ofiling Subjec ts (samples) a Con tr ols Pla tf orm name Pla tf orm technology GSE100562

RNA-sequencing analysis of response t

o

P. f

alciparum

inf

ec

tion in F

ulani and M ossi ethnic gr oups , Bur kina Faso Quin (2017) Compar

ison of GEP

in onoc yt es and CD14 − cells in P. f al -ciparum inf ec ted and uninf ec ted malar ia-exposed F ulani and M ossi sympatr ic ethnic g roups Field P. f alciparum M onoc yt es (CD14 + ) and lym -phoc yt es (CD14 − ) 15–24 y ears Bur kino F aso H

igh through

-put sequenc

-ing 23 (23) NK Illumina H iS eq 2500 (Homo sapiens) H

GSE1124

Whole blood transcr

ipt

ome

of childhood malar

ia

Boldt (2019)

Compar

ison of GEP

of childr en with asympt omatic parasit emia, uncomplicat ed malar ia, malar ia with se ver

e anaemia and

cer ebral malar ia Field P. f alciparum

Whole blood: PA

[image:6.595.86.532.88.732.2]

X gene 0.5–6 y ears G abon Ar ra y NK Health y contr ol childr en [HG-U133A] A ffym -etr ix Human G enome U133A Ar ra y

In situ oligo

-nucleotide GSE114076 Diff er ential gene expr ession pr ofile

of human neutr

o-phils cultur ed with Plasmodium f alci -parum -parasitiz ed er ythr oc yt es Ter ka wi (2018) Compar

ison of GEP

in neutr

ophils incu

-bat

ed with iRBC or

non-inf ec ted RBC In vitr o—iRBC P. f alciparum Neutr o-phils NK NK Ar ra y 1 (8) Cultur e with

non- infec

ted RBC Ag i- _lent -072363 Sur eP rint G3

Human GE v3 8

× 60K M icr oar ra y

In situ oligo

-nucleotide

GSE97158

Transcr

iptional

responses induced b

y con -tr olled human malar ia inf ec tion (CHMI) Rothan (2018) Compar

ison of GEP

in whole blood pr

e

and post spor

oz

oit

e

CHMI in malar

ia

exposed adults

CHMI

P. f

alciparum

Whole blood: PA

X gene A dults Tanzania H

igh through

-put sequenc

-ing 10 (40) Subjec t pair ed

samples: pre and post CHMI Illumina H iS eq 2000 (Homo sapiens) H

GSE65928 M alar ia-associat ed at ypical memor y

B cells exhibit mar

kedly

reduced B cell recept

or sig

nal

-ing and eff

ec

tor

func

tion

Por

tugal ₍₂₀₁₅₎

Comapr

ison of GEP of

naïv

e B cells

, clas

-sical and at

ypical

memor

y B cells in

immune adults Field P. f alciparum B cells A

dults: 18–37

y ears M ali Ar ra y 20 (20) US health y adults [HuG ene -2_0-st] Affymetr ix Human G ene

2.0 ST Ar

ra y [transcr ipt (gene) v er -sion]

In situ oligo

-nucleotide GSE72058 A ctivat ed neutr ophils ar e associat ed with pediatr ic cer ebral malar ia vasculop -ath

y in M

ala

wian

childr

en

Feintuch (2016)

Compar

ison of GEP

in cer ebral malar ia bet w een childr en with malar ia r etin -opath

y and those

without

Field

P. f

alciparum

Whole blood: Tr

i-Reagent BD

Childr en 6 month– 12 y ears M ali Ar ra y 98 (98) NK [HuG ene -1_0-st] Affymetr ix Human G ene

1.0 ST Ar

ra y [transcr ipt (gene) v er -sion]

In situ oligo

-nucleotide PBM C per ipher

al blood mononuclear c

ells , GEP gene e xpr ession pr ofile , CHMI c on tr

olled human malar

ia inf ec tion, iRBCs inf ec ted r

ed blood c

ells , N/A not applicable , NK not k no wn

a S

amples analy

sed f

or publica

(7)

Table

2

C

omparison of metho

dolo

gic

al appr

oaches f

or analy

sis of gene e

xpr

ession da

ta

Da

taset

Da

ta gener

ation

G

ene on

tology analy

sis

GEO series

Publica

tion

RNA Quan

tifica

tion

Pla

tf

orm

Normaliza

tion

A

djustmen

t

for c

ov

aria

tes

Definition expr

ession

Expr

essed

genes

Thr

eshold

FC

Thr

eshold

P

Test

Multiple testing

GO analy

sis

Thr

eshold

GO enrichmen

t

p

Test

GSE2900

Gr

iffiths (2005)

Stanf

or

d Uni

-versit

y cDNA

lymphochip two color micr

oar

ra

y

Scaled t

o geo

-metr

ic mean of

sample:r

ef

er

ence

sig

nal ratio fr

om

all ar

ra

y f

eatur

es

NS

Sig

nal _threshold

9869

2.5 (fr

om

median in >

4

samples)

0.1

Per

muta

-tion

FDR

NA

GSE5418

O

ck

enhouse

(2006)

A

ffymetr

ix

U133A G

ene

-Chips

RMA

NS

No

0.01

SA

M, t

-t

est

FDR

Ont

o Expr

ess

and Path

wa

y

Ar

chit

ec

t

0.05

NS

FDR

GSE15221

Frank

lin

(2009) and Shar

ma

(2011)

Illumina Human W

G-6 v2.0

Cubic spline

NS

Sig

nal _threshold

NS

1.7

0.01

Pair

ed t

-t

est

FDR

Ont

o Expr

ess

Var

ying

NS

GSE15221

H

irak

o (2018)

Illumina Human W

G-6 v2.0

Cubic spline

NS

Sig

nal _threshold

NS

1.5

0.01

Per

muta

-tion and t-test

FDR

D

AVID

, GSEA

0.05

M

ultiple

FDR

GSE26876

de C

ar

valho

(2011)

A

ffymetr

ix

Human G

ene

1.0 ST Ar

ra

y

RMA

NS

1.5

0.05

Student t-t

est

No

Ingenuit

y

path

wa

y

analysis

NS

GSE33811

Krupk

a

(2012)

A

ffymetr

ix

Human G

ene

1.0 ST Ar

ra

y

RM

A and Quantile

NS

Sig

nal and _variation _threshold

3110

2

0.05

Pair

ed t

-t

est

No

G

ene set enr

ichment

analysis on selec

ted

GO t

er

ms

0.01

Pair

ed _t-test

FDR

GSE34404

Idaghdour (2012) Illumina Human HT

-12 Bead

-Chips

Quantile

Location, S

ex,

H

b, t

otal cell

counts (RBCs and WBCs) and ancestr

y

Sig

nal and _normalit

y

thr

eshold

NS

2 (f

or com

-par

ison)

0.01

ANO

VA,

ANC

O

VA

FDR

G

ene set enr

ichment

analysis on cust

omiz

ed

MsigDB database

0.05

NS

Bonf

er

roni

GSE55843

Jagannathan (2014)

A

gilent Sur

e

Pr

int G3

Human G

ene

Expr

es

-sion

8

×

60K

v2 gene expr

ession

micr

oar

ra

ys

Quantile

NS

Sig

nal _threshold

NS

2

0.05

SAM

FDR

NA

GSE53292

Jaijyan (2015) Illumina G

enome Ana

-lyz

er I

ix 72SE

NS

0.05

t-t

est

No

G

eneC

odis3,

Bingo 2.3 plug

in

(C

yt

oscape

2.8.3)

0.05

NS

[image:7.595.80.531.87.728.2]

(8)

Table

2

(c

on

tinued)

Da

taset

Da

ta gener

ation

G

ene on

tology analy

sis

GEO series

Publica

tion

RNA Quan

tifica

tion

Pla

tf

orm

Normaliza

tion

A

djustmen

t

for c

ov

aria

tes

Definition expr

ession

Expr

essed

genes

Thr

eshold

FC

Thr

eshold

P

Test

GO analy

sis

Thr

eshold

GO enrichmen

t

p

Test

GSE50957 GSE52166

Tran (2016)

Illumina H

iS

eq

2000

2

×

100

PE

TA

M

Bat

ch, S

ex,

Ag

e,

P

re

-inf

ec

tion

baseline

Sig

nal and _variation _threshold

,

remo

val Y

chr

omo

-somes

NS

1.5

0.05

Limma

FDR

Ingenuit

y

path

wa

y

analysis

0.05

Fisher exac

t

test

FDR

GSE50957 GSE67184

Vallejo (2018)

Illumina H

iS

eq

2000

2

×

100

PE

CP

M, TP

M

NS

Sig

nal _threshold

NS

0.05

EdgeR

FDR

W

GSEA, ToppG

ene

,

STRING

0.05

M

ultiple

FDR

GSE64338

Subrama

-niam (2015)

A

ffymetr

ix

Human G

ene

1.0 ST Ar

ra

y

Nonlinear nor

-malization based on

Li-W

ong

methods

NS

1.2

0.001

Pair

ed t

-t

est

FDR

Ingenuit

y

Path

wa

y

Analysis

0.05

NS

FDR

GSE64493

Sullivan (2015)

A

gilent Sur

e

Pr

int G3

Human G

ene

Expr

es

-sion

8

×

60K

v2 gene expr

ession

micr

oar

ra

ys

Quantile

NS

Sig

nal _threshold

NS

1.5

0.03

Limma

FDR

D

AVID

0.05

NS

FDR

GSE67184

Rojas-P

enas

(2015)

Illumina H

iS

eq

2500

2

×

100

PE

SNM

Location/time

-point, sub

-jec

t (random

eff

ec

t)

Sig

nal _threshold

6154

No

0.05

NS

FDR

NA

GSE67184

G

ar

dinassi ₍₂₀₁₈₎ Illumina H

iS

eq

2500

2

×

100

PE

NS

No

0.05

Limma, repeat

ed

measur

es

ANO

VA

FDR

GSEA on blood tran

-scr

ipt

ome

modules (BTM, Li et al

.)

0.05

per

mu

-tation

FDR

GSE7586

M

uehlen

-bachs (2007)

A

ffymetr

ix

U133 P

lus 2.0

G

eneChip

GC RM

A

NS

2.5

0.01

t-t

est

No

NA

GSE77122

Tara

wa

(2017)

A

gilent Sur

e

Pr

int G3

Human G

ene

Expr

es

-sion

8

×

60K

gene expr

es

-sion micr

oar

-ra

ys

Each gene expr

es

-sion ar

ra

y dataset

was nor

maliz

ed

to the in silicon pool f

or the

macr

ophages

cultur

ed with

RBCs

NS

No

0.05

Pair

ed t

-t

est

No

D

AVID

0.05

Fisher exac

t

test

[image:8.595.84.526.93.731.2]

(9)

Table

2

(c

on

tinued)

Da

taset

Da

ta gener

ation

G

ene on

tology analy

sis

GEO series

Publica

tion

RNA Quan

tifica

tion

Pla

tf

orm

Normaliza

tion

A

djustmen

t

for c

ov

aria

tes

Definition expr

ession

Expr

essed

genes

Thr

eshold

FC

Thr

eshold

P

Test

GO analy

sis

Thr

eshold

GO enrichmen

t

p

Test

GSE93664

Bur

l (2017)

A

ffymetr

ix

Human G

ene

ST 2.0 gene arra

y

RMA

NS

2

0.05

NS

No

STRING

0.01

NS

Cor

rec

ted

unspeci fied

GSE100562

Quin (2017)

Illumina H

iS

eq

2500

2

×

50

PE

NS

No

0.05

Limma

FDR

NA

GSE1124

Boldt (2019)

A

ffymetr

ix

U133A

+

B

G

eneChips

RMA

NS

Sig

nal _threshold

NS

1.9

0.004

SAM

FDR

D

AVID and Ingenuit

y

Path

wa

y

Analysis

0.05

NS

GSE114076

Ter

ka

wi

(2018)

A

gilent Sur

e

Pr

int G3

Human G

ene

Expr

es

-sion

8

×

60K

gene expr

es

-sion micr

oar

-ra

ys

Each gene expr

es

-sion ar

ra

y dataset

was nor

maliz

ed

to the in silicon pool f

or the neu

-tr

ophils cultur

ed

with RBCs

NS

2

0.01

Limma

No

G

enomatix GeneR

-ank

er

,

D

AVID

,

NE

T-GE and

Enr

icher

0.05

NS

Cor

rec

ted

unspeci fied

GSE97158

Rothan (2018) Illumina H

iS

eq

2500

2

×

51

PE

TMM

Block

ing b

y

subjec

t, in

tw

o separat

e

models int

er

-ac

tion with

cell count and time of parasit

emia

was added

Sig

nal _threshold

16,473

1.5

0.05

Limma

FDR

GSEA (camera) on blood tran

-scr

ipt

ome

modules (BTM, Li et al

.)

0.05

Fisher exac

t

test

FDR

GSE65928

Por

tugal ₍₂₀₁₅₎

A

ffymetr

ix

Human G

ene

ST 2.0 gene arra

y

RMA

NS

0.05

ANO

VA

FDR

Ingenuit

y

path

wa

y

analysis

NS

GSE72058

Feintuch (2016)

A

ffymetr

ix

Human G

ene

1.0 ST ar

ra

y

RM

A and Quantile

Per

ipheral _parasit

emia

NS

No

0.05

t-t

est

No

GSEA, C

at

e-GOr

iz

er and

ingenuit

y

path

wa

y

analysis

0.2 and 0.06

NS

FDR

FDR

false disc

ov

er

y r

at

e,

Hb

haemoglobin,

NA

not a

vailable

,

NS

not specified in publica

tion,

RBCs

red blood c

ells

,

RMA

R

obust M

ultichip a

ver

age

,

SNM

super

vised nor

maliza

tion of micr

oar

ra

y,

TMM

tr

immed mean of

M

-v

alues

,

GEO

G

ene Expr

ession Omnibus

,

GE

gene on

[image:9.595.85.487.88.731.2]

(10)

also varied and there was variable, often incomplete reporting of analysis methods used (Table 2).

Transcriptional insights into the immune response to malaria infection

Seven datasets provided insight into the transcriptional changes associated with NAI to malaria (Table 3) [24– 26, 28–31]. However, given the difficulty in defining or quantifying NAI for an individual, studies varied in their approach, choosing to examine GEPs in settings of vary-ing history of prior exposure to malaria [25, 26, 28, 29], symptomatology during infection [25] or severity of dis-ease [24, 32]. All studies examining NAI included small numbers of subjects and all deployed different experi-mental designs (Table 3).

The findings from a number of studies supported a dampening of the innate pro-inflammatory immune response as a mechanism underpinning NAI [24–26, 33] although this finding was not observed in all studies [28,

29, 31].

One study by Franklin et al. provided evidence of ‘pro-inflammatory priming’ of the innate immune system in acute malaria infection [34]. Comparison of GEP in Brazilian adults presenting with uncomplicated malaria with paired convalescent samples showed an increase in expression in genes involved in TLR signalling pathways supporting a role for TLR hyper responsiveness in the pathology of malaria infection [34, 35].

Quin et al. sought to use RNA sequencing to eluci-date the mechanism driving lower infection rates, lower

Field CHMI _In-vitro

0 20 40 60 80

% of Datasets

Whole Bloo d

PBMC

Single Cell type 0

20 40 60 80

% of Datasets

Arra y

HTS

RT-qPC R

0 20 40 60 80

% of Datasets

Definition of Expression Adjustment for Multiple Testin

g

Adjustment for Co-variants

Threshold for GO enrichment p value

0 50 100

% Publications

Yes Not Stated

a _b

c

d

Fig. 1 Comparison of key methodological variables between datasets or publications. a Antigenic stimulation; CHMI controlled human malaria infection, ‘field’ infection naturally by mosquito bite, ‘in-vitro’ in vitro stimulation by sporozoites or infected red blood cells. Some datasets employed more than one method of antigenic stimulation. b Tissue type analysed; PBMC peripheral blood mononuclear cells. c Expression profiling method:

[image:10.595.56.538.306.675.2]

(11)

Table

3

G

ene e

xpr

ession studies inf

orming understanding of na

tur ally ac quir ed immunit y t o malaria inf ec tion M easur e of NAI Publica tion Desig n Sample Species Subjec ts f or c omparison Ke y finding Commen t Pr ior exposur e t o malar ia Tran et al . (2016) Compar

ison of GEP

changes fr

om pair

ed

inf

ec

ted and unin

-fec ted samples Whole blood P. f alciparum M alar ia-naïv e, sympt omatic Dut ch CHMI v olunt eers at diag nosis (n = 5) M alar ia exper i-enced M alian childr en (> 13

years) and adults infec

ted in the

field (n = 8) Graded ac tivation of path wa

ys of do

wn -str eam pr oinflammat or y cyt ok

ines with highest

ac

tivation in malar

ia-naiv e subjec ts and sig nificantly r educed ac

tivation in malar

ia exper ienced M alians O ck enhouse et al . (2006) Compar ison of

GEP changes in infec

tion-contr

ols

samples US malar

ia naïv e subjec ts PBMC P. f alciparum US malar ia-naïv e CHMI v olunt eers with ear ly , blood-stage inf ec tion (n = 22) M alar ia-exper i-enced C am -er oonian adults pr esenting

with naturally acquir

ed f ebr ile malar ia (n = 15) Similar induc tion of pr o-inflammat or y cyt ok

ines seen bet

w een pr e-sympt omatic and sympt omatic individu -als r egar

dless of pr

ior malar ia exposur e Rojas-P ena et al . (2015) and V allejo et al . (2018) Compar

ison of GEP

changes fr

om pair

ed

inf

ec

ted and unin

-fec ted samples Whole blood P. viv ax Columbian malar ia-naïv

e (MN) CHMI

volunt

eers at diag

-nosis (n

=

7)

Columbian malar

ia-exposed

(ME) CHMI volunt

eers at diag nosis (n = 9) Little diff er entiation seen bet w

een MN and ME

populations b y R ojas-Penas et al . Ho w ev er net w or k co -expr ession analysis b y Vallejo et al . sho w

ed the inflam

-mat or y r esponse was att enuat

ed in ME v

ol

-unt

eers with decr

eased

class II antigen pr

esen

-tation in dendr

itic cells No sig nificant diff er -ence bet w een g roups for pr e-pat ent per iod

or parasitaemia at diag

nosis suggest -ing ther e ma y ha ve

been no diff

er ence in func tional immunit y bet w een g roups Jagannathan et al . (2014) Compar

ison of GEP

bet

w

een g

roups

Vδ2

+ T

cells P. f alciparum Ugandan childr en with lo w pr ior malar ia incidence (n = 4) Ugandan childr en with lo w pr ior malar ia inci -dence (n = 4) Compar

ison of basal gene

expr ession patt er ns of sor ted , un-stimulat ed Vδ2

+ T cells identi

-fied 48 diff

er entially expr essed genes , man y with k no wn r oles in

immunomodulation. For each of these genes

,

expr

ession was higher

among childr en with high pr ior exposur e t o malar ia

Data suggest r

ecur rent malar ia inf ec tion causes up -r egulation of immunor egula -tor y path wa ys

that dampen the pro-inflammat

or y immune r esponse t o P. f alciparum inf ec tion

and help explain immunolog

ical t

oler

ance t

o the parasit

[image:11.595.67.473.87.710.2]

(12)

Table

3

(c

on

tinued)

M

easur

e of NAI

Publica

tion

Desig

n

Sample

Species

Subjec

ts f

or c

omparison

Ke

y finding

Commen

t

Sympt

oms at diag

-nosis

Tran et

al

. (2016)

Compar

ison of GEP

changes fr

om pair

ed

inf

ec

ted and unin

-fec

ted samples

Whole blood

P. f

alciparum

M

alar

ia exper

ienced

M

alian childr

en

(>

13 y

ears) and

adults inf

ec

ted in

the field and asymp

-tomatic at diag

nosis

(EA, n

=

5)

M

alar

ia exper

i-enced M

alian

childr

en

(>

13

years) and adults infec

ted in the

field and symp

-tomatic with fever at the time of diag

nosis (EF

,

n

=

3)

Only 70 diff

er

entially

expr

essed genes

(DEGs) w

er

e identified

bet

w

een these g

roups

despit

e the appar

ent

clinical diff

er

ences

2 of the 5 individu

-als in the EA g

roup

pr

og

ressed t

o f

ebr

ile

malar

ia within 5

da

ys

of initial diag

nosis

by PCR

Disease se

ver

ity

Krupk

a et

al

. (2012)

Compar

ison of GEP

in same subjec

ts

at diag

nosis with

se

ver

e and subse

-quent mild malar

ia

Whole blood

P. f

alciparum

M

ala

wian childr

en who

, af

ter pr

esenting

with se

ver

e malar

ia (all had cer

ebral

malar

ia), w

er

e f

ound t

o ha

ve mild malar

ia

one month lat

er on scr

eening b

y blood

smear (n

=

5)

Path

wa

y analysis sho

w

ed

relativ

e up r

egulation

of

Type I IFN sig

nal

-ing path

wa

y, r

egula

-tion of inflamma-tion, regula-tion of leuk

oc

yt

e

pr

olif

eration and

T cell

ac

tivation in episodes of

mild malar

ia

Boldt et

al

. (2019)

Compar

ison of GEP

bet

w

een g

roups

Whole blood

P. f

alciparum

Health

y uninf

ec

ted

G

abonese childr

en

G

abonese childr

en with

asympt

omatic

parasitaemia, mild malar

ia,

malar

ia with

se

ver

e anaemia

and cer

-ebral anaemia (0.5–6

y

ears)

GEP of 22 genes sig

nifi

-cantly diff

er

ed among

gr

oups

. I

mmunoglobu

-lin pr

oduc

tion, comple

-ment r

egulation and

IFN beta sig

naling w

er

e

most conspicuous

PBM

C

per

ipher

al blood mononuclear c

ells

,

GEP

gene e

xpr

ession pr

ofile

,

CHMI

c

on

tr

olled human malar

ia inf

ec

[image:12.595.79.373.112.726.2]

(13)

parasite densities and fewer symptomatic cases of P. fal-ciparum in the population of Fulani compared to other sympatric ethnic groups [33]. Comparison of the GEP of monocytes from infected and uninfected Fulani and Mossi adults showed a marked difference, with a signifi-cantly greater number of differentially expressed (DE) genes in infected Fulani compared to infected Mossi par-ticipants (1239 versus 3 DE genes respectively). Pathway analysis showed that infected Fulani, but not infected Mossi, individuals demonstrated a marked reduction in expression of inflammasome pathway components, suggesting a blunting of the innate pro-inflammatory immune response post-infection could explain the differ-ences in susceptibility.

Another study sought to examine the genetic basis of gene expression variation in malaria [36]. Idaghdour et al. compared GEP in children diagnosed with uncom-plicated malaria (n = 94) in Benin with age matched controls (n = 64) [36] and performed a genome wide association test of transcript abundance. Testing for gen-otype-by-infection interactions demonstrated the exist-ence of genome wide significant interactions and other genes subject to interaction effects beneath genome-wide significance but still likely to have important roles in modulating the course of infection. These interactions affected the complement system, antigen processing and presentation and T cell activation [36].

In work to identify a transcriptional signature to dis-tinguish acute malaria from other febrile illnesses, Grif-fiths et al. compared the GEP of twenty-two Kenyan children admitted with febrile illnesses (fifteen of which had malaria infection alone) with six convalescent sam-ples collected 2 weeks post discharge [23]. Two main GEPs relating to neutrophil and erythroid activity were shown to differentiate acutely ill and convalescent chil-dren, with significantly higher expression of genes in the neutrophil-related gene region in subjects with bacterial infections and significantly higher expression of genes related to lymphocyte and T cell activation in subjects with malaria. The authors also identified two gene pro-files whose expression intensity correlated with host parasitaemia.

Only two datasets included gene expression changes following P. vivax infection [28, 30, 37]. Rojas-Penas et al. interrogated GEP changes in malaria naïve (MN) and malaria-exposed (ME) Columbian volunteers following infection with P. vivax in a CHMI setting [28]. Significant GEP changes were consistent with time-point rather than prior malaria exposure, with a decline in innate immune signalling and neutrophil number (in contrast to strong up regulation of the same genes reported by Igadour et al. [36]) and an increase in interferon induction seen at diag-nosis. No significant GEP changes were noted at other

time points, including those relating to the liver stage of infection. Further analysis of this dataset by Vallejo et al. using network co-expression analysis showed that while P. vivax infection induced strong inflammatory responses in all participants, the inflammatory response was atten-uated with pathways associated with antigen processing and presentation less enriched in those with prior expo-sure to P. vivax, suggesting a more ‘tolerogenic’ immune response in these individuals [30].

In contrast to this work, Rothen et al. found that tran-scriptional changes post-CHMI via intradermal injection of cryopreserved P. falciparum sporozoites were most pronounced on day 5 after inoculation, during the clini-cally silent liver stage rather than during the blood-stage of infection [38].

Transcriptomic studies in specific cell types

Whilst the majority of studies examined the immune response from whole blood or PBMCs, some examined transcriptomic changes in other cell types or tissues [26, 33, 39–45]. For example, the work of Muehlenbachs et al. with placental tissue highlighted a previously unappre-ciated role for B cells in chronic placental malaria [39]; whilst Sullivan et al. compared GEPs of classical and ‘atypical’ memory B cells obtained from Ugandan chil-dren showing the latter demonstrated down-regulation of B cell receptor signalling and apoptosis [43].

Discussion

GEP is a powerful tool to analyse the immune response to infection. As this review demonstrates, the applica-tion of these studies for malaria are wide-ranging, from attempts to dissect the mechanisms of NAI to improving understanding of the interaction between host genotype and infection outcome. However, as a field in its rela-tive infancy, studies are often hypothesis generating with extremely small sample sizes. There is a lack of stand-ardization ranging from methodological (such as sample type, RNA extraction, platform and analysis) to pheno-type (including precision in disease context and immune status). This variation means interpreting published data and comparison between studies is challenging. Some of this is unavoidable, however, much could be addressed, for example by implementing standardization in blood sampling, methodological protocols for data genera-tion and analysis with robust significance testing and approaches to confounders, use of ontologies (for exam-ple human phenotype and gene ontologies) and expert curation and annotation of data on deposition [46–49].

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correlate or universally accepted definition of NAI, meaning identifying the immune status of individuals or quantification of immunity is problematic [6, 50]. In field studies where the timing of infection and parasite burden and dynamics are unknown, and potentially hugely vari-able between individuals, only limited information can be reliably extrapolated from any GEP changes seen. Most studies assess gene expression from peripheral blood or its components, which does not provide reliable informa-tion regarding the transcripinforma-tional changes in key organs such as spleen, liver, and bone marrow. In addition, when subjects are recruited at presentation with disease, no baseline comparator data are available to use as a con-trol. Even if a clear difference in GEP were to be reported between individuals with and without NAI, it would be near impossible to distinguish GEP changes associated with parasitaemia from those mediating immunity.

However, there is much potential for the future use of GEP studies, particularly in CHMI studies [51, 52] where the parasite burden can be pre-defined and dynamics of infection closely monitored using highly sensitive qPCR. As these studies are increasingly performed in endemic settings [53–55], there will be growing opportunity to use GEP to understand detailed time-course changes in immune response, particularly at the skin, liver and pre-symptomatic blood-stage, which to date have been diffi-cult to study in human subjects infected in the field.

Conclusion

GEP in malaria is a potentially powerful tool, but to date studies have been hypothesis generating with small sam-ple sizes and widely varying methodology. As CHMI studies are increasingly performed in endemic settings, there will be growing opportunity to use GEP to under-stand detailed time-course changes in host response and understand in greater detail the mechanisms of NAI.

Supplementary information

Supplementary information accompanies this paper at https ://doi.

org/10.1186/s1293 6-019-3035-0.

Additional file 1: Figure S1. Flowchart summarizing identification of GEO datasets and publications.

Abbreviations

CHMI: controlled human malaria infection; GEO: gene expression omnibus; GEP: gene expression profile; NAI: naturally acquired immunity; PBMC: periph-eral blood mononuclear cells.

Acknowledgements

Not applicable.

Authors’ contributions

SH conceived the work, analysed the datasets and wrote the manuscript. JM conducted the methodological review of the datasets. HEL, SJD, AVSH, JCK and KM made significant contributions to the conception of the work. JCK

substantially revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by The Wellcome Trust [Grant Number 097940/Z/11/Z to SHH and Wellcome Trust Core Award Grant Number 090532/Z/09/Z]. SHH is a NIHR Academic Clinical Lecturer in Infectious Diseases & Microbiology at the University of Oxford and Research Fellow at St. Peter’s College, University of Oxford. SJD and AVSH are Jenner Investiga-tors, and SJD is also a Lister Institute Research Prize Fellow and a Wellcome Trust Senior Fellow [106917/Z/15/Z]. JCK is a Wellcome Trust Investigator. The funders had no role in the design, collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

1_{The Jenner Institute, University of Oxford, Old Road Campus Road Building,}

Off Roosevelt Drive, Oxford OX3 7DQ, UK. 2_{Department of Infectious Diseases}

& Microbiology, Oxford University Hospitals Trust, Oxford, UK. 3_Wellcome

Centre for Human Genetics, University of Oxford, Oxford, UK. 4_Department

of Tropical Medicine, University of Oxford, Oxford, UK.

Received: 15 April 2019 Accepted: 26 November 2019

References

1. White NJ, Pukrittayakamee S, Hien TT, Faiz MA, Mokuolu OA, Dondorp AM. Malaria. Lancet. 2014;383:723–35.

2. Cockburn IA, Seder RA. Malaria prevention: from immunological concepts to effective vaccines and protective antibodies. Nat Immunol. 2018;19:1199–211.

3. WHO. World malaria report. Geneva: World Health Organization; 2018. 4. Cohen S, Mc GI, Carrington S. Gamma-globulin and acquired immunity

to human malaria. Nature. 1961;192:733–7.

5. Sabchareon A, Burnouf T, Ouattara D, et al. Parasitologic and clinical human response to immunoglobulin administration in falciparum malaria. Am J Trop Med Hyg. 1991;45:297–308.

6. Doolan DL, Dobano C, Baird JK. Acquired immunity to malaria. Clin Micro-biol Rev. 2009;22:13–36.

7. Conesa A, Mortazavi A. The common ground of genomics and systems biology. BMC Syst Biol. 2014;8(Suppl 2):S1.

8. Schulze A, Downward J. Navigating gene expression using microarrays— a technology review. Nat Cell Biol. 2001;3:E190–5.

9. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for tran-scriptomics. Nat Rev Genet. 2009;10:57–63.

10. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18:35–45.

11. Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10:116–25.

12. Six A, Bellier B, Thomas-Vaslin V, Klatzmann D. Systems biology in vaccine design. Microb Biotechnol. 2012;5:295–304.