Distinct gastrointestinal microbial signatures predict parasite levels in controlled Plasmodium infections in both rhesus macaques and humans

Domain: biology
Published: 2025-12-19
Editorial outcome: minor_revision

Abstract
Functionsofthegastrointestinal(GI)microbiomeincludemaintenanceof
immunehomeostasisandprotectionagainstinfectiousdisease.Current
assessmentsoftheroleoftheGImicrobiomeinPlasmodiuminfectionhave
beenprimarilyconductedusingmousemodelsandobservationalhuman
cohorts.Here,weexperimentallyassessedassociationsbetweenpre-infection
GImicrobiomecompositionandacutePlasmodiumparasitemiausing16S
rRNAsequencingandsamplesfromrhesusmacaques(RMs)andadulthumans
enrolledinapreviouslyconductedcontrolledhumanmalariainfection(CHMI)
trial(NCT04072302)originallydesignedtotesttheefficacyofKAF156,anovel
imidazolopiperazineclassofantimalarialdrugs.Weidentifieddistinctpre-
infection16Smicrobialsignaturesthatwereassociatedwithincreasedriskfor
abovemedianparasitemiainRMsinfectedwithP.fragileandCHMIpartici-
pantsinfectedwithP.falciparum.Further,weidentifiedaBifidobacterium
featuresetthataccuratelystratifiedparasitemiariskandcouldthereforeserve
asafoundationforapotentialbiomarkerpaneltoaidpreventioneffortsin
malariaendemicregions.Together,ourfindingsdemonstratethatpre-
infectionGImicrobiomecompositionisindicativeofriskforPlasmodium
parasitemia,andourobservationthatthepre-infectionmicrobiome-P.fragile
dynamicinRMsmirrorsthepre-infectionmicrobiome-P.falciparuminterac-
tioninCHMIparticipantssupportsthefutureuseofthismodelinpre-clinical
investigationsofnovelmicrobiome-targetingapproachestoreducemalaria
burden.

Introduction
In2022therewere249millioncasesofmalariaworldwide,resultingin
over608,000deaths1,demonstratingthatPlasmodiuminfection,the
causativeagentofmalaria,remainsasignificantglobalpublichealth
burden.Inadditiontotheclassicclinicalfeaturesoffeverandanemia,
P. falciparum malaria causes gastrointestinal (GI) manifestations,
includingabdominalpain,diarrhea,andvomiting2.Moreover,elevated
GIP.falciparumsequestration3–6causesdamagetoGIbloodvessels
andcontributestodisruptionsinvilliformation7,increasedepithelial
permeability7,8,andnutrientmalabsorption9–11.Thesealterationsmay
contribute to Plasmodium-induced microbial translocation8,12–15 and
dissemination of nontyphoidal salmonella (NTS)16,17. However, the
precise biological mechanisms underlying Plasmodium-induced GI
disruptionsremainpoorlydefined.
GImicrobesareessentialinpreservingepithelialbarrierintegrity
and maintaining appropriate immune surveillance; suboptimal GI
microbial community structure influences acquisition of enteric
pathogens18. Interestingly, GI microbial community structure may be
linked with protection against severe Plasmodium infection. Indeed,
fecalmicrobialcommunitycompositionvariedbetweenUgandanchil-
drenthatdevelopedseveremalariaanemiaversusthosewithasymp-
tomaticP.falciparuminfection19.Additionally,anobservationalstudyof
childreninMalidemonstratedthattimetoincidentP.falciparumblood-
stage infection was significantly delayed in those with higher stool
abundancesofBifidobacteriumandStreptococcusspecies,suggestingan
association between GI microbiome composition and decreased pro-
spective risk for Plasmodium acquisition20. Further, recent work in a
pediatric Malian cohort demonstrated that GI microbiome composi-
tions enriched for inflammatory bacteria in the order Eubacteriales,
which are associated with impaired gut barrier function and mucin
degradation, were associated with prospective risk of febrile malaria
following P. falciparum infection21. In mice, higher GI abundances of
LactobacillusandBifidobacteriumwereassociatedwithreducedparasite
burden following P. yoelii challenge22, while decreased abundance of
LactobacillaceaeandincreasedabundancesofEnterobacteriaceaeand
Verrucomicrobiaceae were inversely and positively correlated with P.
bergheiANKAburden,respectively23.Finally,differencesinGImicrobial
communitiesinmiceresistantorsusceptibletoP.yoeliiinfectionwere
associatedwithvariationsinthequalityandspecificityofanti-Plasmo-
diumantibodiesdetectedinserum,providingapotentialmechanismby
whichtheGImicrobiomecouldinfluencetheseverityofPlasmodium
spp.infection24.Insum,thesefindingssupportthetheorythatthepre-
existingGImicrobiotacompositionmayinfluenceprospectiveriskfor
andseverityofPlasmodiuminfection.
Whilethesepriorstudieshavelinkedmicrobiotacompositionto
the prospective risk for and severity of Plasmodium infection, the
developmentofananimalmodelthatreplicatesthecomplexitiesof
intestinal immunity and mucosal barrier function in humans could
accelerate assessments of the mechanistic relationships between
PlasmodiuminfectionandGImicrobiota.Inthisregard,whilemouse
models have provided useful insights, a non-human primate (NHP)
modelthatmimicshumanimmuneresponsesandPlasmodiuminfec-
tionoutcomeswouldhelptoadvancedevelopmentofmicrobiome-
targetinginterventions,aswellasrevealnoveltargetsthatcouldbe
leveragedtomitigatePlasmodiuminfection.Additionally,theuseof
controlledhumanmalariainfection(CHMI)studiescouldaideffortsto
determinetheroleofthemicrobiomeinshapingPlasmodiuminfection
outcome.Forexample,suchstudiescouldallowforhighlycontrolled
experimentstodeterminewhetherpreviouslyidentifiedrelationships
betweenmicrobiomecompositionandriskforPlasmodiuminfection
are broadly applicable across age ranges or whether microbiome-
targetingtherapiescouldbeusedtoreducePlasmodiumburden.Here,
we present evidence from studies conducted with adult NHPs and
fromaCHMIstudyconductedinhealthyadultsfromtheUnitedStates
that(1)demonstratesthatNHPsareasuitableanimalmodelforbasic
and translational investigations of microbiome-Plasmodium interac-
tions;and2)identifiesasignificantrelationshipbetweendistinctpre-
infection GI microbiota signatures and the potential for above or
belowmedianparasitelevels.

Results
P.fragileinfectioninRMscausesperipheralparasitemia
andanemia
PlasmodiumfragileisanNHPPlasmodiumspecieswithanasexualcycle
of48handtertianperiodicitythatinducesparasitemiaandanemiain
rhesusmacaques(RMs)25.Moreover,similartoP.falciparum,P.fragile
is capable of endothelial adherence, tissue sequestration, and anti-
genicvariation,andhasbeenpreviouslyusedasamodelforcerebral
malariaandfortestingofanti-malarialdrugsandvaccines26–34.Here,
weintravenously(i.v.)inoculatedtwocohortsofmaleRMs(n=8each;
total n=16) at the Washington National Primate Research Center
(WaNPRC) with P. fragile and assessed levels of clinical signs of
Plasmodiuminfectionfollowinginoculation,includingparasitemiaand
changes in complete blood counts. We detected parasite-infected
erythrocytesinperipheralbloodsmearsofallRMswithintwoweeks
postinoculation(p.i.)withP.fragile(Fig.S1A).RMsweretreatedwith
antimalarialdrugsbasedonperipheralparasitemiathresholds.Para-
sitemia above 0.5%was treated with a dose of quinine sulfate once
perday.Parasitemiaabove1%wastreatedwithquininesulfatetwice
perday.Parasitemiaabove0.5%fortwoconsecutiveweeksresultedin
a switch in treatment to chloroquine. Antimalarial treatments were
haltedwhenparasitemiawasobservedtobebelow0.5%.Anemiawas
characterized through complete blood counts and was defined as
having a percent hematocrit (HCT) below 34.7%. Mild anemia was
defined as%HCT between 27.1–34.7%, moderate anemiaas20–27%,
and severe anemia as below 20%. Following P. fragile inoculation,
anemiawassustainedinRMsthroughWeek3p.i.andbegantoresolve
byWeek4p.i.,with14of16RMsreturningtonormallevelsbyWeek6
p.i. (Fig. S1B). Anemia was linked with detection of parasitemia; at
Week 2 p.i., n=5 RMs were mildly anemic, n=5 were moderately
anemic,andn=5wereseverelyanemic(Fig.S1BandS2).Finally,we
observed that P. fragile infection of RMs caused changes in other
hematological parameters, including significant decreases in white
blood cell count, red blood cell count, hemoglobin, platelet count,
meanplateletvolume,andabsoluteneutrophilcount,withconcurrent
increasesinmeancorpuscularvolume,meancorpuscularhemoglobin,
mean corpuscular hemoglobin concentration, red blood cell dis-
tribution width, and lymphocyte and monocyte absolute count fol-
lowing P. fragile inoculation as compared to baseline (Fig. S1C-P).
These findings are in line with prior work demonstrating that Plas-
modium infection in humans leads to alterations in hematological
parameters35–38.Insum,thesedataconfirmthati.v.inoculationofP.
fragile in RMs results in parasitemia, anemia, and other changes in
hematologicalparametersindicativeofmalaria.

Housinginseparatefacilitieswithdifferentdietspromotesdis-
tinctGImicrobialprofilesinRMsthatassociatewithpotential
forelevatedparasitemia
Todetermineifthepre-existingGImicrobiomeplayedaroleinpara-
sitemia kineticsduring P. fragile infection in RMs, we characterized
stoolmicrobialcommunitiestwoweekspriortoP.fragileinoculation
(Week−2),thedayofinoculation(Week0),andthroughoutthesix
weeksp.i.(Week1throughWeek6).Givenourhypothesisthatexisting
differences in GI microbial composition would impact subsequent
infection outcomes, we first sought to identify any pre-existing
microbial distinctions within RMs prior to P. fragile inoculation.
Usingaggregateddatafromthetwotimepointsthatoccurredpriorto
inoculation(Weeks −2 and 0) for each animal, weperformed unsu-
pervised hierarchical clustering and identified two major microbial
communitybranches(Fig.1A).Thisanalysisidentifiedacleardistinc-
tionin16SrRNAprofilesthatalignedwiththehousingfacilitiesused
during the study (WaNPRC: Building A, teal; WaNPRC: Building B,
purple;Fig.1A).Crucially,RMsinthisstudywereraised,purchased,
andtransportedtogethertotheWaNPRCbeforebeingdividedinto
cohorts and housed in the separate buildings. While housing and
feedingconditionsforRMsintheseseparatefacilitiesweredesignedto
identical, investigation of facility records identified that RMs in
BuildingAwereprovidedwithahighfiberprimatechowwhileRMsin
BuildingBwereprovidedwithahighproteinprimatechow,adiffer-
ence capable of inducing the divisions in pre-infection microbiome
observedbetweenthetwogroups39,40.
Notably,theresponse toP. fragile inoculation aligned with RM
housing and diet, with animalspossessing the Building A/high fiber
diet microbial signature experiencing significantly higher peak per-
ipheralparasitemia,despitebeingchallengedfromthesameP.fragile
stock and at the same dose as RMs possessing the Building B/high
proteindietmicrobialsignature(Fig.1B).Specifically,BuildingA/high
fiber diet RMs reached peak parasite loads 3.75 times greater than
BuildingB/highproteindietRMs(p=0.00031;Fig.1B)andwere49
times as likely to develop above median parasitemia as Building B-
housed/high protein diet RMs (odds ratio=49.00; p=0.010; 95%
CI=3.83–1987).RMswiththeBuildingA/highfiberdietmicrobialsig-
naturealsoexperiencedpeakparasitemiasoonerthanRMswiththe
Building B/high protein diet microbial signature (Fig. 1C). The sig-
nificantly different reactions to P. fragile infection between RM
cohorts,inthecontextoftheiruniqueGImicrobialprofiles,suggests
that the pre-infection microbiome composition of RMs influences
subsequent parasitemia and that this risk factor may be modifiable
throughdietaryintervention.

GImicrobiotaprofilesremaindistinctbetweenRMcohorts
throughoutP.fragileinfectiondespitevariableprofiles
overtime
Given our observation that differences in pre-infection microbiome
were associated with variations in P. fragile parasitemia, we next
endeavored to determine what distinguishing microbiota features
couldsubsequentlypromoteenhancedP.fragileparasitemia.Wefirst
assessed all samples by principal coordinate analysis (PCoA) using
Aitchisondistancetoconfirmthatthemicrobialdistinctionsobserved
prior to challenge represented stable differences (Fig. 2A). Impor-
tantly,theAitchisonmetricallowsforassessmentofdistancebetween
sampleswhileaccountingforthecompositionalnatureofsequencing
data. This analysis demonstrated a durable distinction between
microbial profiles, as samples from Building A/high fiber diet and
Building B/high protein diet RMs rarely overlapped in the first 2
dimensionsofprincipalcoordinatespace(Fig.S3A,B).Thisdistinction
was notable given that microbial profiles were relatively dynamic
withineachofthecohorts(Fig.2AandFig.S3C),withBuildingB/high
proteindietRMs,whichexperiencedlowerparasitemia,demonstrat-
ingmorevariabilityoverthecourseofthestudy(Fig.2B).Finally,we
conductedadditionalhierarchicalclusteringof16SrRNAdatabytime
pointandfoundthatwhilethemicrobiotaprofilesconvergedtosome
degreeatWeek3p.i.,RMcohortsremaineddistinctatalltimepoints
prior to and following P. fragile infection, thus confirming the
separationbetweenhousing/dietcohortsobservedbyPCoA(Fig.S4).

Microbialdistinctionsbetweencohortsarenotduetodiffer-
encesofwithin-samplediversity
TodetermineiffeaturesoftheGIcommunitystructurecouldexplain
thedistinctionsbetweenRMshousedindifferentbuildingsoneithera
high fiber or high protein diet, we next evaluated alpha diversity
metricswithinandacrosscohorts.Microbialcommunitystructurecan
be assessed via 16S rRNA analysis using metrics such as Shannon
diversity, which is a widely used measure of within-sample (alpha)
diversity that accounts for both sample richness (the number of
amplicon sequence variants (ASVs) detected) and sample evenness
(therelativebalanceofampliconsequencevariantabundances).Our
analysisofaggregatedcountsfromthetwopre-inoculationtimepoints
(baseline) showed no significant differences in Shannon diversity
between Building A/high fiber dietand Building B/high protein diet
RMspriortoinoculation(Fig.3A),althoughBuildingB/highprotein
dietRMs,whoexperiencedlowerparasitemia,trendedtowardhigher
Shannon diversity levels over time as compared to Building A/high
fiberdietRMs,whoexperiencedhigherparasitemia(Fig.S5A).Notably,
ShannondiversitylevelswerevirtuallyequalatWeek3p.i.,whenPCoA
andclusteringanalysissuggestedaconvergenceofmicrobiotacom-
munities. We also observed no differences in total observed ASVs
(richness, Fig. S5B) or sequencing depth (Fig. S5C), which supports
Shannondiversityestimates.Finally,weexaminedtherelativeabun-
danceofthecoremicrobiota(Fig.S5D).Thethresholdforinclusionin
the core microbiome as defined by the R package “microbiome”
includesallASVsdetectedabove0.2%inatleast50%ofsamples,and
bythismetric,wedeterminedthatcoreASVsconstitutedroughlythe
samepercentageofreadsacrossbothcohorts(Fig.3B).Insum,these
dataonalphadiversitylevelsatpre-challengetimepointsindicatethat
differencesinoverallcommunitydiversitywerenotadistinguishing
featurebetweenBuildingA/highfiberdietandBuildingB/highprotein
dietcohorts,andlikelydidnotcontributetoincreasedriskofpara-
sitemiaforRMshousedinBuildingAonahighfiberdiet.

RMsthatexperiencedelevatedparasitemiaweresignificantly
enrichedforLactobacillusandpossessedsignificantlylower
levelsofPrevotella
Toidentifyorganismsthatwereeitheruniquetooroverrepresentedin
each RM cohort, we utilized orthogonal approaches covering both
univariate analyses (differential abundance analysis via DESeq2 and
differential prevalence via Fisher’s exact test) and the multivariate
approach known as partial least squares discriminant analysis (PLS-
DA). DESeq2 is a canonical tool in 16S rRNA studies that identifies
individual ASVs with statistically significant differences in detection
levels(abundances)betweengroups.GivenourobservationbyPCoA
thatthemicrobiotainBuildingA/highfiberdietversusBuildingB/high
protein diet RMs remained highly distinct throughout the study
despitevariabilitywithinanimalsovertime,weaimedtoidentifysig-
nificant differences in the abundances of individual taxa that were
robustovertime.Todoso,countswereaveragedwithinanimalsover
timeandsubsequentlynormalizedusinggeometricmeaninDESeq2.
WecomparedabundancesatthelevelofindividualASVsandallhigher
levelsofphylogeneticclassificationacrosscohortsusingDESeq2.
We
detected 41 ASVs that significantly differed between RM cohorts
(Fig.4A;SourceDataFile1).AmongBuildingA/highfiberdietRMs,4of
23ASVsthatweresignificantlyelevatedcouldbeconfidentlyresolved
atthespecieslevel,3ofwhichwereLactobacilliincludingL.coleoho-
minis,L.mucosae,andL.salivarius(Fig.4A).AmongBuildingB/high
protein diet RMs, all 5 of the 18 ASVs that were significantly more
abundantthatcouldbeconfidentlyresolvedatthespecieslevelwere
identified as Prevotella, including Prevotella copri or Prevotella ster-
corea (Fig. 4A). Importantly, these differences in Lactobacillus and
Prevotella also remained significant at the genus and family levels
(LactobacillaceaeandPrevotellaceae,respectively;Fig.4A).Addition-
ally,Lactobacillusremainedsignificantlyelevatedin BuildingA/high
fiberdietRMsatthelevelsoforder(Lactobacillales)andclass(Bacilli)
(Fig. 4A). Other notable differences included elevated detection of
Succinivibrio, Moryella, and Parabacteroides in Building A/high fiber
diet RMs experiencing higher peak peripheral parasitemia, while
Building B/high protein diet RMs with lower peak peripheral para-
sitemia possessed higher levels of Aggregatibacter and Pasteur-
ellaceae(Fig.4A).
WhiletheDESeq2approachtodifferentialabundanceisrigorous,
it was originally designed to measure gene transcript, a data type
wheresparsity,orfrequentzeros,isnotcommon.Inmicrobiomedata,
however,bacteriathataredetectableinonecohortmaybefrequently
missing in another. While DESeq2 can provide information about
whetheranorganismismoreabundantinonecohortoveranother,it
does not identify these differences in prevalence. Therefore, we per-
formedFisher’sexactteststoevaluateASVprevalence,allowingusto
seeifdifferencesinabundancewereduetotheabsenceofspecific
microbesineitherhousingcohort.Indeed,whenweassessedalldif-
ferentiallyabundantASVs,wedetectedsignificantdifferencesinthe
prevalenceofcoremicrobiotamembersaswellasseverallesscom-
monASVs,whichwereoftendetectedinonlyoneRMcohort(Fig.4B;
SourceDataFile2).BuildingA/highfiberdietRMsthatexperienced
higherperipheralparasitemiapossessedL.coleohominis,L.mucosae,
andL.salivariussignificantlymoreoftenandpossessedsomeP.copri
strainslessfrequentlyornotatall,ascomparedtoBuildingB/high
protein diet RMs that experienced lower peripheral parasitemia
(Fig.4B).Inadditiontoconsideringthesestrainsindependently,we
alsoevaluatedtheLactobacillustoPrevotellaratio(Fig.5A),ametric
that has been associated with altered immune cell recruitment and
survivalintheGItract41.BuildingA/highfiberdietRMsthatexperi-
enced higher peripheral parasitemia consistently possessed a far
greaterLactobacillustoPrevotellaratiothanBuildingB/highprotein
diet RMs that experienced relatively low peripheral parasitemia
(Fig.5A).Together,thesefindingssuggestapotentialprotectiverole
for Prevotella species or strains during P. fragile infection. Interest-
ingly,lowerlevelsofPrevotellamayfosterhigherlevelsofLactoba-
cillus, a genus associated with animals experiencing high peak
peripheralparasitemia.
Tomovebeyondcase-by-caseevaluationofindividualASVs,we
nextperformedmultivariatepartialleastsquaresdiscriminantanalysis
(PLS-DA),whichconsidersallfeaturesinthedatasetsimultaneouslyto
identifyASVsthatbestdistinguishbetweengroups(Fig.5B;Fig.S6A,
B). This analysis supported several key findings from earlier
assessments,includingthedefiningnatureoftheimbalancebetween
LactobacillusandPrevotellaASVsbetweenBuildingA/highfiberdiet
and Building B/high proteindiet RMs.PLS-DA alsohighlighted bac-
terialfamiliesfrequentlyassociatedwithbothcohortsbywayofASVs,
including Coriobacteriaceae, Lachnospiraceae, and Ruminococcaceae
(Fig. S6B). Taken together, these results identify Lactobacillus and
PrevotellaascentralfeaturesofRMmicrobiomesassociatedwithP.
fragile parasitemia and highlight the need for more comprehensive
sequencingtoresolvestrain-leveldifferencesanddiscernmorefinely
theimportanceofdifferentialabundanceandprevalence.

Pre-infectionhumanmicrobiotacompositionassociateswith
parasitemiaduringCHMI
Ourinvestigationintoassociationsbetweenthemicrobiomeandper-
ipheralparasitemiainRMssuggestedapotentialrolefortheGImicro-
biotaindeterminingtheresponsetoPlasmodiumchallenge.Toincrease
the relevance to humans, we also assessed the stool microbiota in
healthy U.S. adults that participated in a controlled human malaria
infection (CHMI) study42. As previously reported, study participants
wereinoculatedviainfectedmosquitobite(n=35).Ofnote,onlypar-
ticipantsthatprogressedtodetectableparasitemiabyqRT-PCR(n=32)
wereincludedinthesequencinganalysisperformedhere42.Allpartici-
pantsweretreatedwithmalarone(atovaquone/proguanil),whichwas
initiated according to predetermined criteria of (1) two consecutive
positive qRT-PCR test results,with atleastone measurement greater
than250parasites/mlinanafebrileparticipant,(2)onepositiveqRT-
PCRresultinafebrileparticipant,or(3)onepositivethickbloodsmear
inafebrileorsymptomaticparticipant42.Stoolsampleswerecollected
(1) prior to challenge, (2) at peak parasitemia (prior to antimalarial
treatment),(3)post-malaronetreatment,and(4)duringfollow-up.
AsinourRMstudy,wehypothesizedthatthemagnitudeofacute
Plasmodiumfalciparumperipheralparasitemiawouldvaryaccording
toparticipants’pre-challengeGImicrobiotacomposition.Totestthis
hypothesis,weclusteredstudyparticipantsaccordingtotheirbaseline
microbiotaprofilesbyperformingunsupervised,divisivehierarchical
clusteringofCHMIparticipant16SrRNAgenesequencingdata.Base-
linemicrobiotacompositionscouldbesegregatedintothreedistinct
clusters(Fig. 6A).When peakperipheral parasitemia was compared
acrossclusters,wefoundthatparticipantswhosebaselinemicrobiota
wascategorizedashumanmicrobiomecluster1(HC1;gold;median
parasitemia=9491 parasites/ml) developed significantly higher levels
ofparasitemiaascomparedtoHC3participants(darkcyan;p=0.043;
medianparasitemia=1873parasites/ml;Fig.6B).HC1parasitemiawas
also elevated compared to HC2 participants (gray; median para-
sitemia=5,293 parasites/ml), although this difference did not reach
statistical significance (p=0.17; Fig. 6B). Differences in parasitemia
betweenHC2andHC3werealsonon-significant(p=0.23;Fig.6B).To
quantify the risk of elevated peripheral parasitemia associated with
distinct microbiota profiles, we stratified participants according to
their peak peripheral parasitemia, with “high” or “low” parasitemia
defined as above or below the median parasite level, respectively.
Participants in HC1 wereeight times morelikely to be identified as
having above median parasitemia than HC3 participants (OR=8.17;
p=0.047;95%CI=1.17–83.38; Fig.6C). HC2participantswere more
than four times as likely to be identified as having above median
parasitemia than HC3 participants, but these results did not reach
statistical significance (OR=4.08; p=0.149; 95% CI=0.67–35.22;
Fig.6C).Importantly,neitherparticipantageorBMIweresignificantly
differentacrosscluster(Fig.S7A,B).Moreover,whilepost-challenge
treatmentwithKAF156wouldnotimpactpre-challengemicrobiome42,
wealsoevaluatedthedistributionoftreatmentmodalitiesacrossthese
cohortsandfoundthedifferencestobeinsignificantbyFisher’sexact
test(p=0.195;Fig.S7C).Takentogether,GImicrobiotacomposition
priortoP.falciparumchallengewassignificantlyassociatedwithpeak
peripheralparasitemiainCHMIparticipants.

HC1CHMIparticipantsexperiencinghighperipheralpara-
sitemiaexhibitedsignificantlygreaterlevelsofmicrobial
diversityandintra-clusterheterogeneity
Given the statistical differences in outcome that associated with
baselinemicrobiota,wenextsoughttoidentifydistinctionsbetween
microbiotaclustersobservedamongCHMIparticipants.Aswithour
RMstudy,wevisualizedtheAitchisondistancebetweenallcollected
samplesusingPCoA(Fig.7A).Whilewecouldinferfromhierarchical
clusteringofbaselinesamplesthatHC1wouldbeperipheraltoHC2
and HC3 (Fig. 7A), PCoA indicated that two additional traits dis-
tinguishedparticipantsinHC1.First,samplescollectedatsubsequent
timepoints from HC1 participants remained outside of the space of
specimenscollectedfromHC2andHC3(Fig.7A;Fig.S8A,B).Second,
participantsinHC1experiencedmoreshiftsinGImicrobiotabetween
timepoints(Fig.7B),withthedistancebetweenbaselineandfollow-up
beingsignificantlygreaterforparticipantsinHC1ascomparedtoHC2
andHC3(p=0.0015and0.0076,respectively;Fig.7C).Together,these
datademonstratethatthemicrobiotacompositionfromCHMIparti-
cipants who developed elevated acute peripheral parasitemia was
statistically different from CHMI participants who developed below
medianparasitemia.
We next sought to identify community characteristics that could
distinguishtheHC1microbiotafromHC2andHC3.PriortoP.falciparum
challenge, baseline samples for participants in HC1 possessed
significantlymorediversitythansamplesclassifiedasHC2(p=0.0011)
and HC3 (p=0.0021) and remained more diverse when comparing
aggregatedpost-challengetimepoints(p=0.03and0.022,respectively;
Fig. 7D). In light of these findings, we also assessed the cumulative
number of unique ASVs detected at any time within each microbiota
cluster.ThenumberofuniqueASVsatbaselineinHC1wassignificantly
greaterthaninHC2(p=0.01)andHC3(p=0.0055;Fig.S8C),indicatinga
greaterrangeoftaxaandexplainingthewidedispersionofHC1samples
in PCoA space; importantly these results were robust to sequencing
depth(Fig.S8D).Thesignificantdifferencesindiversitypromptedusto
evaluate the relative abundance of the “core” microbiome across
microbiotaclusters,whichdemonstrated25coreASVs.Importantly,this
corecommunityofmicrobeswasasignificantlysmallercomponentof
sampleswithinHC1comparedtoHC2andHC3atbaselineandwhen
comparing aggregated post-challenge timepoints (Fig. 7E). Further,
decreasingcoreabundancewassignificantlyassociatedwithincreased
peakperipheralparasitemiawhencoremicrobiomerelativeabundance
wasregressedagainstpeakperipheralparasitemia(p=0.026;Fig.7F).
Together, these findings suggest that a relative reduction in the core
microbiomeisassociatedwithsignificantincreasesinbothmicrobiota
diversityandriskforelevatedperipheralparasitemia.

Genus-leveldistinctionsinintestinalmicrobialcompositionare
linkedwithparasitemiainCHMIparticipants
ToidentifyASVswiththepotentialtodistinguishHC1fromHC2and
HC3, we again conducted univariate differential analyses using the
DESeq2 pipeline to make comparisons at all taxonomic levels,
including the ASV rank (Fig. 8A). Significant differences existed
betweenHC1andparticipantsfromHC2/HC3evenatthetaxonomic
rank of Phylum, with participants in HC1 possessing significantly
lowerlevelsofActinobacteriaandFirmicutesandsignificantlyhigher
levelsofProteobacteria(Fig.8A;SourceDataFile3).Thespecifictaxa
driving these differences can be observed by descending the taxo-
nomicranks.Specifically,participantsinHC1hadlowerlevelsofsev-
eral genera from the core microbiome, including Lactococcus,
Aldercreutzia, Streptococcus, Bifidobacterium, and Blautia (Fig. 8A).
Lower levels of these organisms were balanced by higher levels of
Escherichia,Finegoldia,Peptoniphilus,andtheS24-7group(Fig.8A).
Several ASVs not included in these genera were also differentially
abundantbetweenparticipantsinHC1andthoseinHC2/HC3.Speci-
fically, HC1 microbiotas were enriched for unresolved Catenibacter-
ium, Coriobacteriaceae, and Phascolarctobacterium, while HC2 and
HC3 participants possessed higher levels of several Roseburia and
BacteroidesASVs(Fig.8A).
Toprovideadditionalcontextfortheseresults,weassessedthe
prevalenceofdifferentiallyabundantASVsandfoundsignificantdif-
ferences in prevalence across multiple genera (Source Data File 4).
Specifically, the differentially abundant ASV Bifidobacterium ado-
lescentis was frequently missing from HC1 participants who experi-
encedhigherperipheralparasitemia(Fig.8B;Fig.S9A).Interestingly,
viewingtheseresultsinthecontextofdifferentiallyabundantgenera
revealedthatseveralBifidobacteriumASVswerealsofrequentlymiss-
ingfromtheseparticipants,includingB.longum,B.pseudolongum,and
B.bifidum,afindingthatagreeswithsurveillance-basedstudiesthat
linked increased Bifidobacterium abundance with increased time to
first malaria episode in humans20 and reduced malaria severity in
mice22 (Fig. 8B). We also detected similar trends within the Adler-
creutzia and Streptococcus genera (Fig. 8B). For participants with
higher peripheral parasitemia, the frequent absences of these taxa
werefilledbythepresenceofASVsfromEscherichia,Finegoldia,and
Peptoniphilus (Fig. 8B). These significant differences in ASV pre-
valence,togetherwithseveralgenera-widetrends,providesexpanded
evidence for the association between participants’ GI microbiota
compositionandoddsofelevatedparasitemiafollowingP.falciparum
challenge.
Weconductedasimilardifferentialabundanceanalysiswithpar-
ticipantssegregatedintoeitheraboveorbelowmedianpeakperiph-
eral parasitemia, but this approach only yielded one ASV, an
unresolvedCatenibacterium,thatassociatedwithincreasedperipheral
parasitemia(Fig.S9B).Toovercomestatisticallimitationsassociated
with univariate approaches, we again utilized the multivariate
approachPLS-DAtoidentifytheASVsthatmostdistinguishedparti-
cipantswithelevatedparasitemiafromthosewithlowpeakparasite
levels(Fig.S9C).ThissupervisedapproachranksASVsaccordingto
theirassociationwithaparticulargroupforeachcomponentofthe
plotthroughavaluecalledtheloadingscore.Inouranalysis,samples
from participants with above median peak peripheral parasitemia
could be clearly distinguished along the first component by their
positive loading scores. To maximize our ability to identify dis-
criminatoryASVsthatwereassociatedwithelevatedperipheralpara-
sitemia, we performed an identical analysis that leveraged the
clustering assignments based on baseline microbiota profiles, with
participants from HC1 again clustering along the x axis (Fig. S9D).
We then combined the loadings from both data sets to identify
(1)taxathatwereassociatedwithbothelevatedperipheralparasitemia
and HC1; and (2) taxa associated with both lower peripheral
parasitemia and HC2/HC3 (Fig. 9A; Fig. S10). This analysis demon-
strated a pattern wherein ASVs that associated with HC1 and
elevatedperipheralparasitemiaconsistentlymappedtotaxathatwere
identifiedasthesamespeciesorresolvedwithasimilarlevelofcer-
tainty.Specifically,theseASVsrepeatedlymappedtoPrevotellacopri
and several unresolved Coriobacteriaceae, Catenibacterium, and
Phascolarctobacterium (Fig. 9A; Fig. S10). A large number of these
ASVswereonlydetectedinHC1,afindingthatpointsbacktoearlier
measurementsofincreasedalphadiversityinthisgroup.Whencon-
sidering ASVs that most associated with both HC2/HC3 and lower
peripheral parasitemia, we observed similar taxonomic grouping,
including Bifidobacterium adolescentis and Bifidobacterium longum
grouping with an unresolved Bifidobacteriaceae, and several ASVs
that were unresolved at the species level, including multiple
Bacteroides and Erysipelotrichaceae (Fig. 9A; Fig. S10). Interestingly,
manyoftheASVsthatdistinguishedparticipantsfromHC2andHC3
who experienced low peak peripheral parasitemia were previously
identifiedaspartofthecoremicrobiomeinthisstudy.Takentogether,
thesedatafurtherindicatethatthereduceddetectionofcoremicro-
biota in HC1 participants was associated with many low prevalence
ASVs,andthatthisphenotypeiseitheradirectriskfactorforelevated
malaria burden or a biomarker for increased risk of elevated acute
parasitemia.
Because 16S rRNA sequencing analysis is not yet scalable for
population-wideriskassessment,wenextsoughttodefineareduced
featuresetthatcouldserveasapotentialbiomarkerpaneltopredict
risk of elevated parasite burdens in individuals living in malaria
endemicregions.OurfirstattemptsoughttoutilizeanyASVwithin
genera that were differentially abundant between HC1 and HC2/
HC3 (Fig. 8A), creating a panel of 194 ASVs. Given the low number
ofparticipantsinourdataset(n=32),weestablishedamodeltraining
setup using Leave-One-Out Cross Validation (LOOCV) to ensure
robust evaluation, with a focus on optimizing the Receiver
Operating Characteristic (ROC) area. This model achieved an Area
UndertheROC(AUROC)of0.957,whichinthesettingofhighfeature
numbers and low sample numbers indicates overfitting. Looking
to avoid overfitting and to produce a panel that could be more
easilydeployedinaclinicalsetting,weevaluatedstrategiestoreduce
the feature set. Recent papers exploring machine learning in the
microbiome context have identified limitations associated with
large, granular feature sets, finding that Family and Genus-level
feature sets outperform ASVs by AUROC metrics and are more
generalizable,dueinlargeparttosparsityof16SrRNAdatasets43,44.We
thuschosetofocusonthefamilyBifidobacteriaceaefor3reasons:(1)
Bifidobacteriaceae was detected as differentially abundant between
microbiomeclusters(Fig.8A)andcontainedseveralASVsprioritized
byPLS-DAanalysis;(2)severalASVsfromBifidobacteriaceaewerepart
of the “core” microbiome and likely more generalizable to a wider
population;and(3)previousobservationalstudieshavedemonstrated
arelationshipbetweenBifidobacteriumandriskofcontractingmalaria
inendemicregions.Ournewfeaturesetincluded9ASVs:2ASVseach
for Bifidobacterium adolescentis, Bifidobacterium longum, and unre-
solved Bifidobacterium; and 1 ASV each for Bifidobacterium pseudo-
longum, Bifidobacterium bifidum, and an unresolved
Bifidobacteriaceae. When the model was trained and evaluated as
describedabove,ourpanelof9ASVsachievedanAUROCof0.801,
suggestingthatBifidobacteriaceaelevelscouldhaveapredictivecap-
abilityinclinicalsettings(Fig.9B).

MicrobialpathwaysinHC1communitiesareenrichedforpro-
inflammatoryproducts
Toconcludeouranalyses,weutilizedour16SrRNAcountdatatoper-
formaPicrust2analysistoidentifyfunctionsthatarepotentiallyenri-
ched in a given sample based on its compositional makeup.
Unsupervised analysis of the pathway enrichments via PCA
demonstrated that the majority of HC1 participants who developed
elevatedparasitemialevelsclusteredawayfromparticipantsgroupedin
HC2 and HC3 (Fig. S11A). Interestingly, when Picrust2 results were
assessedthroughPLS-DAtoidentifypathwaysthatbestdistinguished
HC1(Fig.S11B),HC1participantswhodevelopedelevatedparasitemia
were enriched for key steps in lipopolysaccharide and polyamine
synthesis, both of which are known risk factors for epithelial inflam-
mationandexfoliation(Fig.S11C).Theseanalysesalsosuggestedthat
ASVfoundinHC1wereenrichedforbacteriawithpotentialforpoly-
myxinresistance.

Methods
Nonhumanprimatestudyanimals,approval,andsample
collection
Male Indian-origin rhesus macaques (Macaca mulatta; RMs; n=16)
betweentheagesof3.09–3.41years(median=3.2years;TableS1)were
housed andcaredforattheWashington NationalPrimateResearch
Center (WaNPRC) under a protocol reviewed and approved by the
UniversityofWashingtonOfficeofAnimalWelfare(OWA)Institutional
Animal Care and Use Committee (IACUC; Protocol 4266-13; Animal
WelfareAssuranceNumberD16-00292;seeSourceDataFile5forRM
metadata).Thehousing,care,andproceduresperformedinthisstudy
were done in AAALAC-accredited facilities, in accordance with the
regulationsputforthbytheUSDepartmentofAgriculture,including
theAnimalWelfareAct(9CRF)andtheAnimalCarePolicyManual,as
wellaswiththeguidelinesoutlinedbytheNationalResearchCouncilin
the Guide for the Care and Use of Laboratory Animals and the
Weatherall Report. RMs in this study were raised, purchased, and
transportedtogethertotheWaNPRCandhousedinWaNRCBuilding
A. RMs were then divided into two cohorts of n=8 RMs each. One
cohortofn=8RMsremainedinBuildingAwhilethesecondcohortof
n=8 RMs was relocated to WaNPRC Building B and housed in this
locationfor6weekspriortothefirststoolcollection,whichoccurred
twoweekspriortoP.fragileinoculation.AllRMswereprovidedwitha
commercialprimatechow(BuildingA:LaboratoryFiber-PlusMonkey
Diet5049,Catalog#0005326;BuildingB:HighProteinMonkeyDiet
5045,Catalog#0001335,bothfromLabDiet,PMINutritionInterna-
tional,St.Louis,MO)twiceperday,supplementedwithdailyfruitsand
vegetablesandwateradlibitum.Compositionofeachdietisgivenin
TableS2.Novelfooditems,foragingopportunities,anddestructible
and indestructible manipulanda were provided as environmental
enrichment.AllRMswerehousedinstainlesssteelcageswitha12/12
light cycle. All cage pans and animalrooms were cleaned daily and
sanitizedatleasteverytwoweeks.StoolwascollectedfromeachRM
twoweekspriortoP.fragileinoculation(Week−2),thedayofP.fragile
inoculation (Week 0), and during P. fragile infection and treatment
(Weeks1,2,3,4,and6).Theeveningpriortostoolcollection,RMsin
fullsocialcontactwereshiftedintoprotected(grooming)contactonly.
Thefollowingmorning,stoolwascollectedfromeachanimal’scage
panandimmediatelystoredat-80°C.Followingcagepanstoolcol-
lection,RMswerereturnedtofullsocialcontact.Uponthawing,DNA
was extracted from NHP stool samples and used for 16S rRNA
sequencinganalysis.

P.fragileinoculation,post-infectionmonitoring,andtreatment
Inthisstudy,weusedtheSriLankastrainofP.fragile26,27toexperi-
mentallyinoculateRMs.ToprepareP.fragile-infectedRMerythrocytes
forinocculation67,frozenerythrocytesfromaP.fragile-infected
RMwerethawedrapidlyat37°Candtransferredintoasteriletube.Ery-
throcyteswereresuspendedin12%NaClwhilegentlyshaking,followed
bya5minincubationatroomtemperaturewithoutshaking.Next,1.6%
NaCl was added dropwise while gently shaking, followed by cen-
trifugationat228 xg,roomtemperature,10min.Aftercentrifugation,
the supernatant was aspirated and discarded, and the pellet resus-
pendedinasolutionof0.9%NaCland2%dextrose,followedbycen-
trifugation at 228xg, room temperature, 10min. Finally, the
supernatantwasaspiratedanddiscarded,andthepelletresuspended
in 0.9% NaCl solution. RMs were intravenously inoculated with
20×10^6P.fragile-infectederythrocytesin1ml0.9%NaClsolution.
GiemsastainingofthinbloodsmearswasusedtoassessP.fragile
parasitemiaineachRM.Bloodforthesmearswascollectedfromnon-
sedatedRMsusingpositivereinforcementconditioning.Parasitemia
checksoccurredthreetimesperweekfollowingP.fragileinoculation,
withanadditionalcheckoccurringatweek6post-P.fragileinfection.
Parasitemia was quantified using a light microscope and 1:10 ratio
Miller Disk Reticle (Microscope World, Carlsbad, CA)68. Briefly, the
total number of all red blood cells (RBCs), including both infected
RBCs (iRBCs) and non-infected RBCs, inside the minor square are
enumerated,followedbycountingofalliRBCsinthemajorsquare.A
totaloffifteenfieldswerecountedinthreeseparateareasoftheslide,
foratotalof45fields.The%parasitemiawascalculatedineachofthe
threeareasasfollows:(#ofiRBCinmajorsquare/#ofallRBCinminor
square)x10.The%parasitemiaineachofthethreeareaswasthen
averagedtogethertodeterminethefinal%parasitemiaforeachRMat
eachtimepoint.
The % parasitemia at each time point was used to determine
the need for anti-malarial treatment. RMs were treated using the
following guidelines: if parasitemia was between 0.5–1%, RMs
weretreatedorallywithasuboptimaldoseofquininesulfate(150mg;
NDC: 53489-0700-07; Sun Pharmaceuticals, Princeton, NJ)69,70 with
thegoalofestablishingachronicmalariainfectionthatwouldmodel
the prolonged exposure to Plasmodium infection that occurs in
malaria-endemicareas71,72. If parasitemia was above 1%, RMs were
treatedorallywith150mgquininesulfatetwiceperday.Ifparasitemia
was above 0.5% for two consecutive weeks despite quinine sulfate
treatment, RMs were switched to oral chloroquine treatment
(10−20mg/kg; NDC: 64980-0178-02; Rising Pharmaceuticals, East
Brunswick,NJ)onceperday.Ofnote,twoRMs(A18028andA18021)
had three consecutive weeks of parasitemia above 0.5% before
switchingtochloroquinetreatment.Quininesulfateandchloroquine
treatments were halted as soon asRMs were observed to be below
the 0.5% parasitemia threshold. Treatment information for all RMs
included in this study, including timings and dose, are shown in
TableS1.

Controlledhumanmalariainfection(CHMI)studyandapproval
Healthy male and female adult participants (n=35) in the US were
enrolledinapreviouslyconducted,2-part,randomized,double-blin-
ded,placebo-controlled,single-centerstudycompletedattheSeattle
MalariaClinicalTrialsCentertotesttheprophylacticefficacy,safety,
andpharmacokineticsofKAF156,anovelimidazolopiperazineclassof
antimalarial drug42 (see Source Data File 6 for CHMI participant
metadata). The study was approved by the Western Institutional
Review Board with reliance agreements for the University of
Washington and Fred Hutchinson Cancer Research Center (clinical
trialsregistrationnumber:NCT04072302)42.Allparticipantsprovided
written informed consent for the present study. As reported pre-
viously,KAF156wasfoundtobesafe,well-tolerated,andresultedina
high degree of protective efficacy from P. falciparum infection at
higherdoses,whichwastestedthroughacontrolledhumanmalaria
infection(CHMI)model42.Asubsetofparticipantsthatreceivedeither
placebo(n=8),20mg(n=13),or50mg(n=14)ofKAF156post-CHMI
exposure to P. falciparum-infected mosquitoes also provided stool
samplesatfourdistincttimepoints:(1)within1−2daysofCHMI;(2)
during a three-day course of treatment with the antimalarial drug
Malarone (atovaquone/proguanil); (3) at the end of Malarone treat-
ment;and(4)duringCHMIandtreatmentfollow-up(withinD28-D42).
Alln=35participantsreceivedtheMalaronetreatmentpriortocol-
lectionofthefinalfollow-upsample.P.falciparuminfectioninstudy
participants was monitored in whole peripheral blood using a Plas-
modium18SribosomalRNA(rRNA)qRT-PCRthatquantifiedPfA-type
18S rRNA42,73,74. All participants in the placebo and 20mg KAF156
treatmentgroups,andallbutthreeofthe 50mgKAF156treatment
groups developed detectable P. falciparum infection. Of note, the
threeparticipantsthatdidnotdevelopdetectibleP.falciparumpara-
sitemia by qPCR were not included in the sequencing analysis per-
formedhere.Stoolsampleswerefrozenat -80°C within 24h of
collection.Uponthawing,DNAwasextractedfromhumanstoolsam-
plesandusedfor16SrRNAsequencinganalysis,asdetailedbelow.

16SrRNAsequencing
DNAwasextractedfrombothNHPandCHMIparticipantstoolsamples
using the PowerFecal DNA Isolation Kit (Qiagen, Valencia, CA).
Sequencingofthe16Ssmallsubunitribosomalribonucleicacid(SSU
rRNA)genewasperformedattheUniversityofMinnesotaGenomics
Center.Briefly,aqualitycontrolqPCRwasperformedintriplicateto
verifythatsufficientquantitiesofthe16SSSUrRNAgenewerepresent
inthesampleforsequencing.Afterconfirmationthatadequatelevels
ofthe16SSSUrRNAgenewereextracted,thefollowingprimerswere
usedtosequencetheV3/V4regionofthe16SSSUrRNAgene:357F(5′-
CCTACGGGNGGCAGCAG-3′) – 806R (5′-GGACTACNVGGGTWTCT
AAT-3′)75–78.Next,adual-indexingqPCRwasusedtobarcodeandadd
indexesforgeneampliconsequencing79.Theresultingampliconswere
normalized, pooled, cleaned, and quantified (KAPA quantification,
KAPABiosystems,Wilmington,MA).AnIlluminaMiSeq(Illumina,San
Diego,CA)wasusedtosequencethelibrarywitha2×300bpcycle
with15%PhiX(Illumina).

Bioinformatics,statistics,andreproducability
16SrRNAampliconsequencingreadsweredemultiplexedinIllumina
Basespace.Fastqfileswereassessedforreadquality,trimmed(trim-
Left=12, truncLen=150), dereplicated, and filtered using the dada2
pipeline in R studio (Version 4.2). Following removal of chimeric
sequences, reads were assigned a taxonomy using greengenes
(gg_13_8_train_set_97.fa.gz) and a phylogenetic tree was built using
MSA and Phangorn. The reads, metadata, and tree were then com-
bined into a phyloseq object for additional quality controls and fil-
tering,whichincludedremovingampliconsequencevariants(ASVs)
mappingtomitochondria,chloroplastandcyanobacteriareads,aswell
asanyASVdetectedinonlyonesample.Theresultingphyloseqobject
wasusedfordownstreamanalyses.
Unsupervisedhierarchicalclusteringwasconductedoncenterlog
ratio(CLR)-transformedreadcounts,usingadivisiveapproach(Clus-
ter::diana). CLR transformation is a widely recognized method for
analyzingcompositionaldata80–82.TheCLRtransformationmitigates
compositionalconstraintbyconvertingproportionstolog-ratios,thus
enablingthecomparisonofrelativeabundancesacrosssamplesina
mathematically coherent and interpretable manner. The same CLR-
transformedreadcountswereusedtocalculateAitchisondistanceand
visualizethesedistancesinprincipalcoordinateanalysis(PCoA)space.
Alpha diversity metrics, including Shannon diversity, Pielou’s even-
ness,andcoreabundance,werecalculatedusingmicrobiome::alphato
assessrawreads.PrevalencefiguresandcorrespondingFisher’sexact
testswereassessedusingcustomscripts.Differentialabundancewas
performedusingDESeq2toassessrawreadsonASVs,andhigherlevel
taxonomic classifications were assessed using objects generated via
phyloseq::tax_glom. Partial Least Squares Discriminant Analysis
(PLSDA) was performed using the sparse option (mixomics::plsda).
Area under the receiver operating curve (AUROC) was utilized to
assessperformanceoffeaturesetsforstratifyingparasitemiariskusing
a leave-one-out cross validation (LOOCV) approach and a model
trainedthroughtheCaretpackageusingtheROCmetric.
Allstatisticalcomparisonsacrossgroups(peripheralparasitemia,
Aitchisondistancetravelled,PCoAaxiscoordinates,Shannondiversity,
coreabundance,observedASVs(richness),sequencingdepth,Pielou’s
evenness,ASVprevalence,BMI,andage)werecomparedusingaWil-
coxon rank-sum test (Mann-Whitney U test) unless otherwise noted.
Regression analysis of peripheral parasitemia and core microbiota
abundancewasperformedusingstats::lm.Oddsratioswerecalculated
using the finalfit package. Significant differences in prevalence were
assessedbyFisher’sexacttest.Significantdifferentialabundancewas
performedusingDESeq2withbetaPriors=FALSEtoassessrawreads.
ForboththeRMandCHMIstudiespresentedhere,nostatistical
method was used to predetermine sample size. As previously
reported42,maleand female participants inthe CHMI study (n=35)
were randomly assigned to experimental groups receiving various
doses of KAF156 following P. falciparum infection (50 or 20mg
KAF156)orcontrolgroups.Inthecurrentstudy,allparticipantsthat
progressed to detectable P. falciparum infection (n=32) were first
considered together as a single group, then assigned to human
microbiomeclusters(HC1,HC2,andHC3)basedontheresultsfrom
unsupervised hierarchical clustering of 16S rRNA gene sequencing
data.FortheRMstudy,allRMs(n=16)wereinoculatedwithP.fragile
andwerefirstconsideredtogetherasasinglegroup,thenweresub-
sequentlyanalyzedastwobuilding-dependentgroupsbasedonthe
results from unsupervised hierarchical clustering of 16S rRNA gene
sequencing data and supported by housing records. Although the
parentCHMIstudyincludedbothmaleandfemaleparticipants,dueto
an insufficient number of female participants, post hoc sex based
analyseswerenotconductedinthepresentanalyses.AllRMsincluded
inthisstudyweremale,thusposthocsexbasedanalyseswerenot
possible.IntheCHMIstudy,sequencingdatafromthreeparticipants
were excluded because they did not progress to detectable
parasitemia.No datawereexcluded from theRMstudy. Finally,for
boththeRMandCHMIstudy,thedataanalystwasblindedtoexperi-
mentalinfectiongroupsandoutcomesduringtheanalysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Prior publications have demonstrated associations between gut bacteria and malaria outcomes in Malian and Ugandan children and murine models of malaria have demonstrated differential gut microbiota can cause differential severity of malaria. Murine models also demonstrate gut bacteria compositions change following Plasmodium infection. The present study extends these observations by providing correlations between stool bacteria composition within days of Plasmodium fragile infection in Rhesus macaques, and malaria-naïve US adults infected with Plasmodium falciparum who were participating in a controlled human malaria infection study. In addition to assessment of the pre-infection bacterial composition the present study also assessed post-Plasmodium infection stool bacteria composition at various time points through treatment-induced convalescence. Exciting aspects of this study include the investigation of gut bacteria-induced modulation of malaria in Rhesus macaques as this non-human primate model has the potential to serve as a model to investigate novel gut microbiome-based therapeutics, and the human study which provides the closest assessment of stool bacteria composition prior to Plasmodium infection and its association with malaria outcomes. Stool in the Malian study was collected from children weeks to months before incident Plasmodium infection and in the Ugandan study stool samples were collected from children after hospital admission with severe malaria. Unfortunately, there are a number of weaknesses with the analytical approaches, assumptions, statistical analyses, etc. that raise concerns with the conclusions and substantially dampened enthusiasm for publication of these results.\n1. The present analyses do not provide strong evidence in support of the conclusions that have been made.\na. Inclusion of the phrase “High-risk” in the title requires experimental validation to demonstrate causality. At present the analysis only provides an intriguing association. Inclusion of the phrase “high-risk” should be removed throughout the text.\nb. Throughout the text the authors refer to “severe parasitemia” or “severe malaria”. I don’t find the data support the use of these desrcriptors. The definition of severe parasitemia in humans is variable in the literature, in lower transmission areas it can be between 2% (100,000 parasites/uL) and 0.5% (25,000 parasites/uL), which might be more relevant for malaria-naive US residents. Based on the authors’ results, peak parasitemia for the severe and mild groups was ~log2 13.5 and ~log11, which is approximately 729 parasites/uL (0.015%) and 60 parasites/uL (0.0012%). Also, it is the highest detected after first passing the threshold (0.250 parasites/uL). Thus, it is hard to say if they would go on to be severe or not, or if there were simply more hours of replication time between infection and PCR detection between the individuals. Within the paper, parasitemia status of humans and macaques were categorically defined based on above and below the average parasitemia at time treatment was initiated. The text should more accurately reflect this by classifying groups as above average parasitemia and below average parasitemia. Additionally, none of the humans or macaques progressed to “severe malaria”, as such this phrase should not be used in the text.\nc. In various places in the text (e.g., Line 42, Line 89, etc.) the authors make claims the results “strongly” associate a microbial outcome with malaria outcome. Yet, in many cases the statistical analyses are barely significant. The inclusion of “strong” should be removed in these instances.\n2. The authors should provide information on the human subjects (ages, sex, nationality, HbA/S status – if known, etc.). These covariates should be accounted for in the bacterial community analyses. Additionally, the authors should provide a clear description of which subjects received placebo, KAF156 – including dosage, or Malarone. Again, these differential treatments should also be accounted for in stool samples collected post treatment to assess for any treatment induced effects. Finally, the “baseline” stool sample was indicated as being collected -2 to +2 days after infection. Analysis of whether there were differences in when the stool was collected between the severe and mild groups would be helpful to rule out whether that had any impact on microbiome differences.\na. With differential treatments and differential timing of treatments, the study may be better off focusing on association between the pre-infection stool samples and malaria outcomes.\n3. Stool samples were reported to be frozen up to 24 hours post collection. As this is a considerable length of time in which bacterial communities can change quite considerably, how much variation in time to -80C is there between subjects and does this explain HC (HC1, HC2, or HC3) status?\n4. Bioinformatic analyses\na. The analyses and conclusions would be strengthened by inclusion of multiple alpha diversity metrics.\nb. Likewise, the authors should conduct and report on multiple beta diversity metrics.\nc. It is not clear that center log ratio (CLR) transformation is the most appropriate. The authors should also report bacteria community analysis on non-transformed read counts.\nd. L194: Was differential abundance done on CLR-transformed read counts? It is also not clear if raw read counts were normalized before DESeq2 analysis.\ne. Have the authors used appropriate statistical tests for longitudinal analysis?\n5. Figure 1\na. Bottom of Panel A indicates Study “Week”, this should be “Day”\nb. L212: How many days p.i. for each participant did it take to reach 250 parasites/ml? Are there differences in time to antimalarial treatment between HC1-HC3? Time to treatment (time to peak parasite level) might be related to malaria severity. Did gut microbiota composition affect time to peak parasitemia? When was peak parasitemia reached?\nc. Panel C should include CI in the graph.\nd. Panel D – would prefer to see this data presented as actual number of parasites/ml rather than non-Log2 transformed. How often was parasitemia measured in the subjects and can the data be presented as area under the parasitemia curve to provide a more cumulative assessment of the parasite burden within each subject over the course of the study? Differences in parasitemia are not that large, and possibly attributable to differences in time between infection and detection. Do the authors have precise time (hours) between infection and parasitemia burden surpassing the treatment threshold? Was parasitemia measured in all individuals at defined time points post infection?\ne. Samples were clustered based on gut microbiota composition on CLR transformed data and then investigated for differences in parasitemia, mild (below median) vs severe (above median), and gut bacteria. It would be interesting to analyze based on parasitemia, mild (below median) vs severe (above median) and look for differentially abundant gut bacteria between below average vs above average parasitemia?\nf. How do samples cluster based on normalized (non-CLR transformed) read counts data?\ng. L218: What is the basis of 3 clusters? Why not two clusters?\nh. L230-31: Can it be implied that HC2 enterotype did not impact parasitemia but HC1 and HC3 enterotype modulates parasite burden?\n6. Figure S1\na. The data and analyses do not provide strong support that KAF156 or placebo treatment had no effect on bacterial composition. First there is no statistics provided to justify this claim. Second there appears to be differential abundance of the 20 mg KAF156 and 50 mg KAF156 treatment between the HC groups:\ni. 20 mg = 60% of HC1 subjects, 15% of HC2 subjects, 56% of HC3 subjects\nii. 50 mg = 20% of HC1 subjects, 54% of HC2 subjects, 22% of HC3 subjects\n7. Figure 2\na. A-B ¬– Dimensions 1 and 2 explain only 11% of all variability and there are no statistical analyses of bacterial compositions based on this CLR-transformed PCA plot.\nb. L234-37 (A-B): Display this data in a longitudinal fashion using linear mixed effect model for appropriate statistical analysis.\nc. C-D – by far the more informative analysis as it focuses on baseline stool samples, but again there are not statistical analyses to go along with these data.\nd. Panel E – would be better displayed as the average number of ASVs among subjects within the three HC groups, showing ranges, and statistical analysis.\ne. Panel F shows significant difference in Shannon diversity at baseline between HC1 and HC2, yet there are no differences in parasitemia between these groups in Figure 1D.\n8. Figure S2\na. Lines 237-239 indicate that Dim3 differentiates bacterial communities between HC1 compared to HC2 and HC3.\ni. HC1 symbols are difficult to see.\nii. There are only 6 stool samples that appear to differentiate on Dim3 from the rest of the samples. This doesn’t lend strong support the claims made in Lines 234-243.\niii. These data would be more informative if they were the baseline samples.\n9. Line 233 refers to malaria “progression”. It is not clear what is meant, and since all subjects developed patent parasitemia followed by treatment, this statement should probably be removed.\n10. L260: There are no data regarding transient changes induced by P. falciparum infection.\n11. Figure 3\na. Figure 3 and Figure 6 – It would be nice to see compositional data. What percentage of the microbiome is represented by the taxa. It is just the prevalence (presence/absence) within a cluster and CLR counts, which are standardized by each taxa, so you can’t tell how prevalent the bacteria are. The differences between the taxa in the severe and mild group could be very minimal, but statistically significant.\nb. Panel B is missing label on Y-axis and would be more appropriate to only include those taxonomy that are significant following Bonferroni correction.\nc. Panel B: Do linear mixed effect modeling unless data are solely baseline data.\nd. L280: What is meant by this sentence? There are no apparent data provided to support this claim.\ne. Panel D-H – what criteria were used to selection of “select differentially abundant genera”. What statistical test was used?\nf. Panel I – Are any of these taxa showing significant differences between time points (statistics)? Do they behave differently between the groups?\n12. Figure 4\na. Again “mild” and “severe” parasitemia are not defined. Based on the human study, this is presumably categorically defined as parasitemia below or above average, respectively.\nb. The Y axis in 4C is labelled as % parasitemia, but the legend lists it as being log2-transformed. I think it is probably untransformed parasitemia, as otherwise it would be much lower than the human parasitemia (which doesn’t make sense given the different treatment thresholds), also Fig S4 uses untransformed parasitemia.\nc. Panel B: Is divisive hierarchical clustering based on CLR transformed data?\n13. Figure 5\na. Analysis would be much stronger if focused on the Week 0 stool sample. Indeed, data in panel C suggest there are little to no differences between the groups, which questions interpretation of data presented in Figure 4.\nb. The authors need to provide statistical analyses of these comparisons\nc. What is “microbiome bins”?\nd. L322: Data is not interpretable unless shown in longitudinal fashion. Distance of each time point can be calculated from the baseline and plotted to see the impact of infection on gut microbiota.\n14. Figure S3\na. Analysis of the Week 0 sample reveals no difference. This outcome, combined with the lack of statistical analysis and near overlapping samples in the Week 0 UniFrac distance PCoA plot (Figure 5C) argues against the conclusion provided in Lines 322-324.\n15. Figure S4\na. Provision of parasitemia curves with all time points would be optimal. Additionally, those data should then be analyzed as area under the parasitemia curve to assess cumulative parasite burden over time within each macaque. This would provide additional information to that provided in Figure 4.\nb. Line 315 – What is community organization?\nc. Line 323 – “variable levels of microbiota” makes it sound like different quantities of microbiota\n16. Figure 6\na. Panel B is missing label on Y-axis and would be more appropriate to only include those taxonomy that are significant following Bonferroni correction.\n17. The authors need to acknowledge of some of the possible limitations of the study. The gut microbiota is known to be different between Western and non-industrialized societies, so the microbiota composition associated with protection here might not be the same as those associated with protection in Africa. Also, malaria interventions, like vaccination, have a history of working very well in healthy US adults but then not being transferrable to African children in malaria endemic areas, possibly microbiota related, but maybe also due to previous exposure to malaria (and other pathogens), overall health and nutrition, etc.\nAdditional comments:\n1. For accuracy, it would be optimal for the authors to use the descriptor “healthy US adults” throughout the study in place of “humans” to avoid inference to African children, or other tropical regions, that suffer from the vast majority of P. falciparum infections.\n2. L184: Taxonomic assignment was performed using old gg database. The authors should confirm the fundamental findings using updated SILVA database.\n3. L198: The authors should deposit custom scripts and codes to GitHub page.\n4. Line 207: age range of the US adults should be included.\n5. L266: ASVs instead of ASCs.\n6. Multiple occasions within the Results the authors make note of non-significant differences. These comments should be excluded or limited as there is no strong justification to discuss those things that are not different.\n7. The authors may want to consider going from the macaque study to the human study. It felt awkward going from the gold-standard human malaria study to the macaque “model organism” study.\n8. Regarding the author’s claim that the RM model is a good translational model, Bifidobacterium is seen in their human data set as a taxon of interest and noted that previous studies have also seen this, but this was not observed in the RM.", "reviewer_2": "This paper examines the microbiome of (1) humans who are challenged with P. falciapurum malaria by infected mosquito bite and (2) NHP who are challenged with P.fragile malaria by injection of infected red blood cells. Both the humans and the NHP were subdivided into two groups, “high parasitemia” and “low parasitemia”. There were microbiome differences between the two groups that were identified with unsupervised clustering. My main concerns with this article are with the clinical definitions and descriptions of the CHMI.\nHuman CHMI:\n- The human CHMI was conducted as part of an antimalarial drug study (KAF156). The current paper evaluates microbiomes from 35 participants in that study, from the placebo group, the 20 mg group, and the 50 mg group (lines 103-104. Of note, in looking at the original publication of KAF157, 3 of the volunteers in the 50 mg group did not get malaria. Therefore, their inclusion in this study is questionable.\n- The participants were divided into two groups of “mild” or “severe” parasitemia (lines 218-219) based on whether their parasitemia was above or below a median parasitemia value. This is an extremely flawed way to differentiate the participants clinically. All of the participants were diagnosed with a highly sensitive molecular test. Based on the parent study, few had malaria symptoms, and none had severe malaria (if the clinical study site had allowed severe malaria to occur in the context of a drug efficacy study, this would be unethical). All mention of “severe malaria” and “severe parasitemia” needs to be removed from the manuscript if it is considered for publication. In addition, although the parasitemia levels are quantitative, the timing of sample collection in relation to the exact time of the CHMI and the exact time within the synchronized cycles of parasite emergence from RBC all affect the parasitemia levels. High parasitemia levels in the setting of ongoing malaria infection correlate with malaria severity. Differences between very low levels that are detected soon after blood stage infection has started also reflect some amount of luck (what time was the CHMI 8 days ago? What time was the volunteers appointment on the day of the PCR blood draw). This makes dividing a grouping of 35 very low parasitemias, 3 of which had a value of “ZERO” into two groups questionable.\n- Nonetheless, the authors have found differences between the two groups (figure 1B) that may be worth considering. However, the statistics that differentiate the two groups of parasitemia within the three clusters (lines 220-228) are not described and need to be specified. Also, it would be helpful to know the actual parasitemia levels that were being discussed (which presumably were very low).\n- Consider a discussion of the fact that most of the malaria infection was a liver infection. The blood stage of infection was brief before treatment was initiated – how does this relate to your postulations about the authors’ observations of the microbiome?\nNHP challenge:\n- The authors state that this is a new challenge model and provide a rationale for why this challenge model is suitable for microbiome/malaria studies. As a new model, many important details were lacking:\no What clinical manifestations were observed in the animals, and what parasitemia levels were these clinical manifestations associated with? Line 300 states that P.fragile induces similar clinical signs in RM as in Pf in humans – does this include cerebral malaria and other clinical manifestations of severe malaria? Also, which/how many of the animals needed the escalated treatment regimens, and which did not?\no Treatment regimen questions: Why did the treatment begin only after parasitema was >0.5% and not as soon as parasitemia was detectable? The treatment regimens appeared to be titrated to be lower than abortive – there was an expectation that one regimen could be given for 2 weeks consecutively without clearing parasitemia. If sub-abortive treatment was being used, why?\no How was “high parasite load” defined (line 304)? (defined for the humans but I did not see it for the RM.\no Line 349 “more severe P. fragile infection” – without information about clinical symptoms in this new animal model, I don’t know how to assess this (whereas in humans there were NO severe P. falciparum infections)\nRelating the human challenge to the NHP challenge: Human challenge was by infected mosquito bite, therefore, there was 7 days of clinically silent liver infection prior to blood stage infection, which was treated at a very early stage (because it was detected molecularly, which is a very sensitive method of detection. NHP challenge was by injection of infected RBC, so there is no liver stage infection. NHP challenge seemed to be permissive of ongoing parasitemia, and it is unknown what symptoms the NHP had. The many differences between the human challenge and the NHP challenge, and whether the data are comparable to each other in consideration of all these differences need to be discussed.", "reviewer_3": "This is an interesting study but there are a few areas that require further clarification, and the overall conclusion is not clearly demonstrated by the results and figures as they currently are presented.\nAbstract\n- How similar is P. fragile to P. falciparum? Since these are being directly compared, the authors should explicitly compare how similar or different these two infections are\n- Some statements seem over-stated, like “the combined weight of this evidence demonstrates that pre-infection GI microbiome composition is highly indicative of P. falciparum disease risk” (but yet the combined data is not on P. falciparum, half of it is on P. fragile)\nMaterials and Methods\n- This is a nice sized human study (35 people) paired with a nice sized macaque study (16)\n- Stool samples are collected in such a way that it is unclear whether there was enough time for any microbes to replicate during the time period between collection and storage (“within 24hours of collection”). A major suggestion here is that the authors need to do an analysis of time interval between collection and freezing and the results (if there is indeed an impact of time interval between collection and freezing, this would undermine the results and suggest that some of the collections were not frozen down quick enough to get a good sense of the microbial community)\n- The stool collection for the macaques is well done and thought-out with respect to collecting stool from individual animals and immediately storing it at -80C\n- Why do the authors use the 357F-806R primers (those published and used originally by the Earth Microbiome Project)? These are not necessarily the best primers for studies related to human gut microbiome health. I think other options should have been considered, or at least the authors should consider whether they are missing any information that they could have gained by using a different set of primers.\nResults\n- The authors should clarify whether the HC1 group could have another issue that confounds the data (e.g. low yield). The authors should show an analysis in which they see whether any metadata affect the groupings (e.g., total DNA yield, time between collection and freezing, etc)\n- Similarly, in the rhesus macaque study, the authors should also test and consider whether there is a cage/room effect. Even though the authors do a great job of making sure they are collecting the stool from a specific animal, this doesn’t rule out the very likely possibility that socially housed animals may have more similar microbiomes than those in which they are not housed. The authors should do a analysis with metadata to see whether any of their clustering patterns are associated with these logistical factors (e.g., cage, room)\n- In Line 323, I am unclear on how the authors conclude that “parasite burden was primarily driven by major compositional differences, rather than variable levels of microbiota found in both groups”. This point should be further explained. Particularly the idea that parasite burden itself is “driven” by microbiome differences. I did not find evidence in this paper that parasite burden is definitively “driven” by microbiome differences.\nDiscussion\n- The authors seem to interchange the concepts of parasitemia, “disease progression”, etc and it is unclear what the main message is that they are trying to say – are they arguing that gut microbiota composition CAUSES parasitemia to be higher or lower? Or is this a mere association without a known causation. The wording here should be clarified.\n- The overall idea seems to be that pre-infection microbiome composition can predict the severity of the malaria infection. Could the authors create a model to test this theory (by using part of their data and doing cross-validation to see if this holds up?)\n- Going back to the main conclusion of the paper, the authors state that they “identified a high-risk microbial signature that is strongly associated with increased risk of developing severe parasitemia” and they say they find this in both humans and rhesus macaques, but after reading the paper and looking at the figures, I still am not clear on what exact microbes are associated with this increased risk of severe disease (that are in common between humans and rhesus macaques)."}, "author_response": "Point-by-point response to the reviewers\nWe thank the editors and the reviewers for their helpful and thoughtful feedback on our manuscript that has the updated title of “Distinct gastrointestinal microbial signatures predict acute parasite levels in controlled Plasmodium infections in both rhesus macaques and humans”. We have addressed the issues brought up by the reviewers (detailed below) and believe our manuscript is greatly improved. Thank you for your time and consideration of our manuscript.\nReviewer #1 (Remarks to the Author):\nPrior publications have demonstrated associations between gut bacteria and malaria outcomes in Malian and Ugandan children and murine models of malaria have demonstrated differential gut microbiota can cause differential severity of malaria. Murine models also demonstrate gut bacteria compositions change following Plasmodium infection. The present study extends these observations by providing correlations between stool bacteria composition within days of Plasmodium fragile infection in Rhesus macaques, and malaria-naïve US adults infected with Plasmodium falciparum who were participating in a controlled human malaria infection study. In addition to assessment of the pre-infection bacterial composition the present study also assessed post-Plasmodium infection stool bacteria composition at various time points through treatment-induced convalescence. Exciting aspects of this study include the investigation of gut bacteria-induced modulation of malaria in Rhesus macaques as this non-human primate model has the potential to serve as a model to investigate novel gut microbiome-based therapeutics, and the human study which provides the closest assessment of stool bacteria composition prior to Plasmodium infection and its association with malaria outcomes. Stool in the Malian study was collected from children weeks to months before incident Plasmodium infection and in the Ugandan study stool samples were collected from children after hospital admission with severe malaria. Unfortunately, there are a number of weaknesses with the analytical approaches, assumptions, statistical analyses, etc. that raise concerns with the conclusions and substantially dampened enthusiasm for publication of these results.\nWe thank the reviewer for their positive comments regarding our investigation of gut bacteria-induced modulation of malaria in Rhesus macaques and assessment of stool bacteria composition in humans during malaria infection. We also thank the reviewer for their detailed and thoughtful questions and comments on each component of our manuscript. We have detailed our specific efforts to address these points and improve the manuscript in our responses below.\n1. The present analyses do not provide strong evidence in support of the conclusions that have been made.\na. Inclusion of the phrase “High-risk” in the title requires experimental validation to demonstrate causality. At present the analysis only provides an intriguing association. Inclusion of the phrase “high-risk” should be removed throughout the text.\nWe thank the reviewer for this important comment. We have removed the phrase “high-risk” from the title and have changed it to be “Distinct gastrointestinal microbial signatures predict acute parasite levels in controlled Plasmodium infections in both rhesus macaques and humans” (Page 1, Line 1). We have also removed the phrase “high-risk” from the abstract and throughout the manuscript (Page 2, Line 51).\nb. Throughout the text the authors refer to “severe parasitemia” or “severe malaria”. I don’t find the data support the use of these desrcriptors. The definition of severe parasitemia in humans is variable in the literature, in lower transmission areas it can be between 2% (100,000 parasites/uL) and 0.5% (25,000 parasites/uL), which might be more relevant for malaria-naive US residents. Based on the authors’ results, peak parasitemia for the severe and mild groups was ~log2 13.5 and ~log11, which is approximately 729 parasites/uL (0.015%) and 60 parasites/uL (0.0012%). Also, it is the highest detected after first passing the threshold (0.250 parasites/uL). Thus, it is hard to say if they would go on to be severe or not, or if there were simply more hours of replication time between infection and PCR detection between the individuals. Within the paper, parasitemia status of humans and macaques were categorically defined based on above and below the average parasitemia at time treatment was initiated. The text should more accurately reflect this by classifying groups as above average parasitemia and below average parasitemia. Additionally, none of the humans or macaques progressed to “severe malaria”, as such this phrase should not be used in the text.\nWe agree with the reviewer that our use of the phrases “severe malaria” and “severe parasitemia” did not accurately reflect the parasitemia categories that we used in our analyses. To avoid confusion, we have therefore edited the manuscript to refer only to “parasitemia” and to describe groups as “above median parasitemia” and “below median parasitemia” where appropriate, as recommended by the reviewer.\nc. In various places in the text (e.g., Line 42, Line 89, etc.) the authors make claims the results “strongly” associate a microbial outcome with malaria outcome. Yet, in many cases the statistical analyses are barely significant. The inclusion of “strong” should be removed in these instances.\nWe have removed the word “strong” from the manuscript text (Page 2, Line 52; Page 3, Line 60; Page 5, Line 113; Page 21, Line 573; Page 24, Line 664; Page 28, Line 757; Page 31, Line 854).\n2. The authors should provide information on the human subjects (ages, sex, nationality, HbA/S status – if known, etc.). These covariates should be accounted for in the bacterial community analyses. Additionally, the authors should provide a clear description of which subjects received placebo, KAF156 – including dosage, or Malarone. Again, these differential treatments should also be accounted for in stool samples collected post treatment to assess for any treatment induced effects. Finally, the “baseline” stool sample was indicated as being collected -2 to +2 days after infection. Analysis of whether there were differences in when the stool was collected between the severe and mild groups would be helpful to rule out whether that had any impact on microbiome differences.\nWe thank the reviewer for raising this point. Data on age and sex for the CHMI participants were published in the original KAF156 study (PMID: 32644127). Data on nationality and HbA/S status are unfortunately not available. For sex, with the exception of 1 female in the 50mg post-exposure KAF156 group, all study participants were male, thus, we do not account for sex in our analyses. We have, however, now included Supplementary Figures 6A and B that depict analyses we conducted to assess whether participant age or BMI were different across clusters. We found that there were no significant differences in either age or BMI between CHMI participants stratified into either HC1, HC2, or HC3. We have incorporated these findings into the manuscript text (Page 17, Lines 472-473).\nFor treatments, all CHMI participants were treated with Malarone prior to collection of the final follow-up sample. We have now clarified this in the methods section (Page 7, Lines 191-192). We have now also included Supplementary Figure 6C, which depicts the number of CHMI participants in each cluster that received either placebo or 20mg or 50mg KAF156 post-CHMI. We evaluated the distribution of treatment modalities across clusters via Fisher’s exact test and found that the differences between groups were not significant (p=0.195). We have incorporated these findings in the manuscript (Pages 17-18, Lines 473-477).\nGiven that no significant differences were found between the distribution of treatment groups across the clusters, we are confident that the post-inoculation KAF156 treatment did not impact our results, particularly as we have focused our associations of parasitemia with pre-infection microbiome data, as recommended by Reviewer 1 in Point 2a (see below).\nIn terms of analysis of whether there were differences in when stool was collected between the above and below median parasitemia groups, we reviewed what day stool was collected from each participant relative to P. falciparum inoculation. We found that of the n=32 participants included in our analysis, n=2 participants had stool collected 2 days prior to CHMI (day -2), n=15 participants had stool collected 1 day prior to CHMI (day -1), n=12 participants had stool collected the day of CHMI (day 0), and n=3 participants had stool collected 1 day after CHMI (day 1). None of the 3 participants that had stool collected after CHMI were in HC1, which was the group that had greater risk of above median parasitemia. Of the two participants that had stool collected two days prior to CHMI, one was in HC1 and the other was in HC2. From these data, we can conclude that the collection date did not have a consistent relationship with microbiome profile.\na. With differential treatments and differential timing of treatments, the study may be better off focusing on association between the pre-infection stool samples and malaria outcomes.\nWe agree with the reviewer that focusing on the associations between the pre-infection stool samples and malaria outcomes is preferred. Indeed, while we included some analyses in the paper which examine microbial community changes following challenge (alpha diversity, core microbiome), the microbiome-based clustering we use to assess risk is based on aggregated pre-inoculation data for the RMs or pre-challenge data from the CHMI participants. We have made efforts throughout the manuscript to clarify when the aggregated pre-treatment data is being assessed.\n3. Stool samples were reported to be frozen up to 24 hours post collection. As this is a considerable length of time in which bacterial communities can change quite considerably, how much variation in time to -80C is there between subjects and does this explain HC (HC1, HC2, or HC3) status?\nWe agree with the reviewer that immediate freezing of the human CHMI samples would have been ideal. Unfortunately, although we ensured that all samples were frozen within 24hours of collection, data on the exact amount of time that each sample was kept at room temperature prior to freezing was not collected, thus we are unable to conduct any analyses to see whether length of time prior to freezing was associated with differences between the three CHMI groups. We are, however, reassured that room temperature storage of stool samples of less than 24hours would likely not impact our conclusions based on prior studies of lengthy room temperature storage of fecal specimens. Indeed, prior work has shown that storage of stool samples for 72hours at room temperature has been reported to cause less shifts in detected communities than chemical storage methods (PMID: 26572876). An even lengthier preservation of 1 week at room temperature as compared to immediate freezing demonstrated that variation within donors was less than variation between samples frozen immediate or kept at room temperature for 1 week (PMID: 27822526). We have incorporated these references and this limitation into the discussion of our manuscript (Page 30, Lines 814-824).\n4. Bioinformatic analyses.\na. The analyses and conclusions would be strengthened by inclusion of multiple alpha diversity metrics.\nWe appreciate the reviewer’s comment and now include in the manuscript additional measures of alpha diversity that address the compositional makeup of individual samples in three ways: 1) we address Shannon diversity, which is a commonly used approach; 2) we provide richness data that is evaluated in the context of sequencing depth; and 3) we provide information on the core microbiome, specifically what percent of the total sample is comprised of core microbes.\nb. Likewise, the authors should conduct and report on multiple beta diversity metrics.\nBecause 16s rRNA studies are compositional in nature, we utilized a compositionally aware method of normalizing count data (see response to point 4C below for greater detail/references). Using the CLR to account for differences in sequencing depth means that assessing differences between samples (beta diversity) should be done using Aitchison distance, which is the Euclidean distance between samples that have been normalized using CLR.\nc. It is not clear that center log ratio (CLR) transformation is the most appropriate. The authors should also report bacteria community analysis on non-transformed read counts.\nNormalizing 16s rRNA and other sequencing data types remains an active area of investigation. The relative nature of the data (compositionality) means that a reduced detection of a given taxa in relative terms could be due to a true decrease in the sample, or that levels remain the same but appear lower due to an increase in another taxa. CLR and other compositionally aware methods that address this “sum of 1” constraint by creating ratios instead of proportions. While we provide raw read counts within the paper, it would be inappropriate to assess non-transformed raw read counts due to differences in sequencing depth.\nd. L194: Was differential abundance done on CLR-transformed read counts? It is also not clear if raw read counts were normalized before DESeq2 analysis.\nAs stated in the methods section (Page 9, Lines 235-237), raw read counts were used for DESeq2 analysis, which is essential to satisfy assumptions of this analysis.\ne. Have the authors used appropriate statistical tests for longitudinal analysis?\nThe reviewer raises an important question for microbiome analyses using sequential sampling from the same individuals. Mixed effects models that account for the nested correlations within individuals are useful tools for addressing these constrains. In this study, however, we do not make comparisons over time where individuals have multiple datapoints. Because we found evidence that baseline microbiome composition predicted risk for parasitemia following Plasmodium exposure, we did not perform differential abundance assessments of changes in microbiome composition induced by infection. Therefore, these models were not employed here.\n5. Figure 1\na. Bottom of Panel A indicates Study “Week”, this should be “Day”\nWe have removed the study timeline from this figure. Please note, as per Reviewer 1’s suggestion to discuss the RM results first followed by the CHMI results, Figure 1 is now Figure 5.\nb. L212: How many days p.i. for each participant did it take to reach 250 parasites/ml? Are there differences in time to antimalarial treatment between HC1-HC3? Time to treatment (time to peak parasite level) might be related to malaria severity. Did gut microbiota composition affect time to peak parasitemia? When was peak parasitemia reached?\nThe reviewer raises a good point. In fact, the days p.i. to reach 250 parasites/ml was variable and related to the post-infection KAF treatment, which was outlined in the original manuscript (PMID: 32644127). However, the microbiome clusters we identified (HC1, HC2, and HC3) were determined by pre-infection, pre-treatment microbiome composition, and assessment of treatment distribution showed that there were no significant differences in treatment assignment between clusters HC1, HC2, and HC3. This finding is now demonstrated in Supplemental Figure S6. Beyond statistics, HC1 and HC3, which evidenced the greatest difference in risk for elevated parasitemia, are virtually identical in their treatment assignments (Fig. S6C) and were equally influenced by post-infection treatment.\nc. Panel C should include CI in the graph.\nThe updated Figure 5C (Formerly Figure 1) now includes confidence intervals.\nd. Panel D – would prefer to see this data presented as actual number of parasites/ml rather than non-Log2 transformed. How often was parasitemia measured in the subjects and can the data be presented as area under the parasitemia curve to provide a more cumulative assessment of the parasite burden within each subject over the course of the study? Differences in parasitemia are not that large, and possibly attributable to differences in time between infection and detection. Do the authors have precise time (hours) between infection and parasitemia burden surpassing the treatment threshold? Was parasitemia measured in all individuals at defined time points post infection?\nTo enhance our ability to compare changes in parasitemia between CHMI participants and RM models, parasite burden for both studies now include a z-score of parasitemia. We also include the direct parasitemia results collected by the distinct measurement methods. Therefore, for RMs (Figure 1B), we depict z-score for % infected RBC (black lines, left axis), as well as the actual % infected RBC (red lines, right axis). For humans (Figure 5B), we depict z-score for parasites/ml (black lines), as well as the actual parasites/ml (red lines), no longer in log2 format, which was originally used to aid visual interpretation but may have impeded easy interpretation of parasite levels.\nAs outlined in the original KAF156 study (PMID: 32644127), parasitemia in CHMI participants was measured daily until subjects reached treatment criteria, at which time treatment began. CHMI participants were then assessed until parasite burden dropped and remained below treatment levels. Unfortunately, records from the original study do not include the precise time in hours and minutes that elapsed between infection and parasitemia.\nImportantly, we utilize the pairwise Wilcoxon tests here, which evaluate by rank and not exact parasite levels. The use of a non-parametric test is important in clinical data when the assumption of normality required by T tests is not assumed. Additionally, T tests are sensitive to outliers, where the Wilcoxon tests is not.\ne. Samples were clustered based on gut microbiota composition on CLR transformed data and then investigated for differences in parasitemia, mild (below median) vs severe (above median), and gut bacteria. It would be interesting to analyze based on parasitemia, mild (below median) vs severe (above median) and look for differentially abundant gut bacteria between below average vs above average parasitemia?\nWe appreciate the reviewer’s comment. We conducted differential abundance analysis with participants segregated into either above or below median peak peripheral parasitemia, but this approach only yielded one ASV, an unresolved Catenibacterium, that associated with increased peripheral parasitemia (Pages 21-22, Lines 585-588, Supplemental Figure S8B). To overcome this limitation, we employed a multivariate method known as partial least squares discriminant analysis (PLS-DA) which has greater power to resolve differences between groups and combines information from microbiome clustering and parasite levels. This technique allowed us to confirm the important distinctions across hierarchically determined clusters made by differential abundance analysis, while also prioritizing taxa that most discriminate between groups. Our use of this approach includes a combined score that was generated from independent assessments of microbiome cluster and parasite burden. This allows us to investigate the most defining taxa between above median and below median parasitemia in the context of original clusters (Page 22-23, Lines 600-625).\nf. How do samples cluster based on normalized (non-CLR transformed) read counts data?\nIt would not be appropriate to cluster without transformation, as the major discriminator would become sequencing depth, not composition.\ng. L218: What is the basis of 3 clusters? Why not two clusters?\nWe appreciate the reviewer’s comment. Cluster assignments can be subjective and require some interpretation from the analyst. Considering the new Figure 5C (originally Figure 1B), the divisive tree can be interpreted from the top down. Using this approach, we see that the first three splits (horizontal lines) separate what we call HC1 from the rest of the baseline samples, indicating they are the most “different”. The fourth major horizontal split separates 2 sample groups, which we call HC2 and HC3. This approach creates interpretable splits and, by change, 3 balanced groups in terms of size. While one could argue that HC2 and HC3 could be combined, this would ignore the fact that they are cleanly split, and that HC2 has greater vertical line heights, indicating more variability within this cluster than HC1. This interpretation is reinforced by examining the PCoA plot shown in Figure 6A.\nh. L230-31: Can it be implied that HC2 enterotype did not impact parasitemia but HC1 and HC3 enterotype modulates parasite burden?\nUsing Figure 5C, we see that CHMI participants in HC2 had an estimated 4-fold increase in the likelihood of being in the above median parasitemia group as compared to participants grouped in HC3. While these results are not statistically significant (p=0.149), when combined with the data shown in Figure 5B, where HC2 has elevated parasitemia compared to HC3, it suggests some underlying biology. It is possible that future cohorts with a larger sample size would move these results from trends to significance.\n6. Figure S1\na. The data and analyses do not provide strong support that KAF156 or placebo treatment had no effect on bacterial composition. First there is no statistics provided to justify this claim. Second there appears to be differential abundance of the 20 mg KAF156 and 50 mg KAF156 treatment between the HC groups:\ni. 20 mg = 60% of HC1 subjects, 15% of HC2 subjects, 56% of HC3 subjects\nii. 50 mg = 20% of HC1 subjects, 54% of HC2 subjects, 22% of HC3 subjects\nWe agree with the reviewer that a statistical assessment is useful here. To assess if the treatments were significantly associated with the microbiome clusters, we first created a contingency table that summarized the number of samples from each treatment with each cluster. Because several cells in the contingency table had small counts (less than 5), we used Fisher’s Exact Test instead of the chi-squared test. Supplemental Figure S6 provides the proportions of treatments within each cluster and reveals an insignificant difference in treatment assignment. More simply, the contingency table shows that the number of individuals assigned to each treatment from HC1 and HC3, where the greatest difference in peak parasitemia occurred, are virtually identical:\nTo address the math provided by the reviewer, this works out to 60% of HC1 subjects and 56% of HC3 subjects receiving the KAF156 treatment at the 20mg dose, 20% of HC1 subjects and 22% of HC3 subjects receiving the KAV156 treatment at the 50mg dose, and 20% of HC1 subjects and 22% of HC3 subjects receiving placebo.\n7. Figure 2\na. A-B – Dimensions 1 and 2 explain only 11% of all variability and there are no statistical analyses of bacterial compositions based on this CLR-transformed PCA plot.\nIn our new Figure 6A, the amount of variability explained by the first two principal components amounts to 14%. While this is an increase over 11%, we would not argue that this is “better”. The amount of variability explained by two components of PCoA (or dimensions of PCA) is always related to the sample population. For example, if we examined a population that included strict vegans and enthusiastic meat eaters, we would likely see far more variability. Here, the patient population was selected for its similarity in terms of age and overall health, and due to volunteer rates, were predominantly male. Thus the 14% suggests that while we find significant differences between groups in each of the first two components (see new Supplemental Figure S7A), their microbiomes contain a high degree of overlap, which would be expected in this case. Thus, our downstream analysis focuses on the differences that do exist within this population. We also now include statistical evaluation of the distances traveled by each subject over time (Supplemental Figure 6A).\nb. L234-37 (A-B): Display this data in a longitudinal fashion using linear mixed effect model for appropriate statistical analysis.\nWe are unclear on what the Reviewer means by showing the PCA in longitudinal fashion. However, our new PCoA plots highlight baseline and final samples, and connect samples within an individual over time to track variability over time within each group (Figure 2A and Figure 6A).\nc. C-D – by far the more informative analysis as it focuses on baseline stool samples, but again there are not statistical analyses to go along with these data.\nWe thank the Reviewer for this comment and believe that this is addressed by our response to Points 7A and B above.\nd. Panel E – would be better displayed as the average number of ASVs among subjects within the three HC groups, showing ranges, and statistical analysis.\nWe appreciate this comment from the Reviewer. Our analysis of sequencing depth now shows a range of sequencing depths in each microbiome cluster (Supplemental Figure S7C), and also shows that baseline differences in observed ASVs (richness) was not impacted by sequencing depth (Supplemental Figure S7D).\ne. Panel F shows significant difference in Shannon diversity at baseline between HC1 and HC2, yet there are no differences in parasitemia between these groups in Figure 1D.\nWe agree with the reviewer that significant differences do exist in Shannon diversity and although parasitemia is elevated in HC1 compared to HC2, this difference does not reach statistical significance (p=0.17, Figure 5B), possibly due to our small sample size. To clarify, we do not propose in our analysis that alpha diversity itself is the sole driver of differences in parasitemia. Rather, our data suggests that the increased diversity in HC1 is at the expense of a decreased core microbiome, and that the taxa missing or reduced from the core microbiome are the drivers of increased parasitemia. This relationship between loss of core microbiome and increase in parasitemia is reinforced by our regression analysis shown in Figure 6F.\n8. Figure S2\na. Lines 237-239 indicate that Dim3 differentiates bacterial communities between HC1 compared to HC2 and HC3.\nThis figure has been removed from the revised manuscript, but to address the Reviewer’s concern, we note that the 3-dimensional PCA plot in the original manuscript can be difficult to interpret due to constrains in the figure’s development in the R environment. Spinning the image to show all three dimensions can over- and/or under-emphasize certain dimensions. In this case, the longest dimension viewed is Dim2, while the shortest is Dim1, despite these dimensions contributing equally to variation observed (5.5% each).\nWe believe that he updated PCoAs utilizing the first two components (Figures 2A, Figure 6A, and Supplemental Figure S2A and C) are more interpretable than the original PCA plots.\ni. HC1 symbols are difficult to see.\nWe have including black outlines for the symbols in Figure 6A to make HC1 points more visible.\nii. There are only 6 stool samples that appear to differentiate on Dim3 from the rest of the samples. This doesn’t lend strong support the claims made in Lines 234-243.\nPlease see response to 8A.\niii. These data would be more informative if they were the baseline samples.\nAs noted above, this figure has been removed from the revised manuscript. However, we agree with the Reviewer and our new PCoA plots in Figures 2 and 6 include indicates of which timepoints were from baseline samples.\n9. Line 233 refers to malaria “progression”. It is not clear what is meant, and since all subjects developed patent parasitemia followed by treatment, this statement should probably be removed.\nThis heading has been adjusted in the manuscript to state “HC1 CHMI participants experiencing high peripheral parasitemia exhibited significantly greater levels of microbial diversity and intra-cluster heterogeneity” (Page 18, Lines 481-483).\n10. L260: There are no data regarding transient changes induced by P. falciparum infection.\nAs shown in Figure 6A-C, HC1 (which had a higher likelihood of experiencing above median parasitemia), had greater Aitchison distance than HC2 or HC3, indicating changes in the microbiome over time.\n11. Figure 3\na. Figure 3 and Figure 6 – It would be nice to see compositional data. What percentage of the microbiome is represented by the taxa. It is just the prevalence (presence/absence) within a cluster and CLR counts, which are standardized by each taxa, so you can’t tell how prevalent the bacteria are. The differences between the taxa in the severe and mild group could be very minimal, but statistically significant.\nWhile presenting taxa as a percentage of the sample might intuitively seem more straightforward, microbiome data inherently comes with compositional constraints that can lead to misinterpretation if not properly addressed.\nThe primary challenge with microbiome data lies in its compositional nature; the data are proportions and sum to a constant (e.g., 100%), which introduces a dependency between taxa. This dependency complicates the direct comparison of raw or percentage abundance data across samples because differences in the abundance of one taxon necessarily affect the apparent abundances of others, due to the constant sum constraint. This issue is known as the “compositionality problem.”\nTo address this, we employed the Centered Log Ratio (CLR) transformation, which is a widely recognized method for analyzing compositional data. The CLR transformation mitigates the compositional constraint by converting proportions to log-ratios, thus enabling the comparison of relative abundances across samples in a mathematically coherent and interpretable manner. By using CLR-transformed values, we can directly compare the relative dominance and variation of taxa across different conditions (e.g., above and below median parasitemia groups) without the misleading effects that arise from the compositional nature of the data. We have added this information on CLR transformation to the methods (Page 9, Lines 225-231).\nb. Panel B is missing label on Y-axis and would be more appropriate to only include those taxonomy that are significant following Bonferroni correction.\nThis figure panel has been removed from the revised manuscript and has been replaced by the data shown in Figure 7 and Supplemental Figures S8 and S9.\nc. Panel B: Do linear mixed effect modeling unless data are solely baseline data.\nThis figure panel has been removed from the revised manuscript and has been replaced by the data shown in Figure 7 and Supplemental Figures S8 and S9.\nd. L280: What is meant by this sentence? There are no apparent data provided to support this claim.\nThis figure panel has been removed from the revised manuscript and has been replaced by the data shown in Figure 7 and Supplemental Figures S8 and S9.\ne. Panel D-H – what criteria were used to selection of “select differentially abundant genera”. What statistical test was used?\nThis figure panel has been removed from the revised manuscript and has been replaced by the data shown in Figure 7 and Supplemental Figures S8 and S9.\nf. Panel I – Are any of these taxa showing significant differences between time points (statistics)? Do they behave differently between the groups?\nThis figure panel has been removed from the updated manuscript. Our revised manuscript now emphasizes baseline differences and examines them more closely.\n12. Figure 4\na. Again “mild” and “severe” parasitemia are not defined. Based on the human study, this is presumably categorically defined as parasitemia below or above average, respectively.\nWe agree with the Reviewer, and as recommended, we have now adjusted our manuscript to refer to parasitemia that is above or below median parasitemia.\nb. The Y axis in 4C is labelled as % parasitemia, but the legend lists it as being log2-transformed. I think it is probably untransformed parasitemia, as otherwise it would be much lower than the human parasitemia (which doesn’t make sense given the different treatment thresholds), also Fig S4 uses untransformed parasitemia.\nWe apologize for any confusion. We have updated the parasitemia panels for both RMs and CHMI participants to avoid the confusion we created by using a log2 scale for visualization. Figures 1B and 5B now depict the z-score for parasitemia as well as the direct parasitemia results collected by the distinct measurement methods. Specifically, for RMs (Figure 1B), we depict z-score for % infected RBC (black lines, left axis), as well as the actual % infected RBC (red lines, right axis). For CHMI participants (Figure 5B), we depict z-score for parasites/ml (black lines), as well as the actual parasites/ml (red lines), no longer in log2 format, which was originally used to aid visual interpretation but may have impeded easy interpretation of parasite levels. Figure S4 in the original manuscript has been removed.\nc. Panel B: Is divisive hierarchical clustering based on CLR transformed data?\nYes, divisive hierarchical clustering was based on CLR transformed data. Please see our response to point 11A above for our justification for using CLR transformed data.\n13. Figure 5\na. Analysis would be much stronger if focused on the Week 0 stool sample. Indeed, data in panel C suggest there are little to no differences between the groups, which questions interpretation of data presented in Figure 4.\nThe original Figure 5 has been replaced with Supplemental Figure S2, which has been updated in the revised manuscript to include statistical evaluation of the separation at each timepoint. Additionally, we have now included a new Supplemental Figure S3, which provides hierarchical clustering for each timepoint, which we feel depicts the strength of the separations we observed.\nb. The authors need to provide statistical analyses of these comparisons.\nWe agree with the reviewer. Supplemental Figure S2B and C now provide statistics demonstrating separation.\nc. What is “microbiome bins”?\nWe have updated the manuscript to replace “bins” with “clusters”. This was an inadvertent holdover from initial figure generation.\nd. L322: Data is not interpretable unless shown in longitudinal fashion. Distance of each time point can be calculated from the baseline and plotted to see the impact of infection on gut microbiota.\nWe have updated our figures to reflect movement over time within individual CHMI subjects and RMs (Figures 2 and 6), while retaining facet timepoint PCoAs in the supplemental figures (Supplemental Figures S2 and S7).\n14. Figure S3\na. Analysis of the Week 0 sample reveals no difference. This outcome, combined with the lack of statistical analysis and near overlapping samples in the Week 0 UniFrac distance PCoA plot (Figure 5C) argues against the conclusion provided in Lines 322-324.\nWe agree with the reviewer that there are no statistically significant differences in alpha diversity at Week 0 in the RM data. However, as we now show in the new Supplemental Figure S3C, hierarchical clustering, which uses all dimensions, shows that there are distinctions between the two groups of RMs, therefore strengthen the argument that there are distinctions between RM cohorts.\n15. Figure S4\na. Provision of parasitemia curves with all time points would be optimal. Additionally, those data should then be analyzed as area under the parasitemia curve to assess cumulative parasite burden over time within each macaque. This would provide additional information to that provided in Figure 4.\nWe appreciate this suggestion from the Reviewer, however, all CHMI participants were treated with Malarone once the participant tested PCR positive for P. falciparum infection. This means that the duration of P. falciparum infection was relatively short and prevents us from being able to draw associations with the microbiome through area under the parasitemia curve analysis. Therefore, both our original manuscript and our revised manuscript focus on associations with peak parasitemia.\nb. Line 315 – What is community organization?\nWe have edited “organization” to be “structure” to be consistent with terminology used elsewhere in the manuscript.\nc. Line 323 – “variable levels of microbiota” makes it sound like different quantities of microbiota\nWe have removed this sentence from the manuscript.\n16. Figure 6\na. Panel B is missing label on Y-axis and would be more appropriate to only include those taxonomy that are significant following Bonferroni correction.\nWe apologize for the missing label and agree with the reviewer that this figure was too busy. We have now constrained the taxa discussed in the revised manuscript to be only those with significant differences by DESeq2 or PLS-DA.\n17. The authors need to acknowledge of some of the possible limitations of the study. The gut microbiota is known to be different between Western and non-industrialized societies, so the microbiota composition associated with protection here might not be the same as those associated with protection in Africa. Also, malaria interventions, like vaccination, have a history of working very well in healthy US adults but then not being transferrable to African children in malaria endemic areas, possibly microbiota related, but maybe also due to previous exposure to malaria (and other pathogens), overall health and nutrition, etc.\nWe appreciate this comment from the reviewer and agree that there are limitations in our study that should be discussed. In particular, we agree that differences in diet may cause our identification of specific microbes associated with risk or protection from P. falciparum parasitemia in healthy US adults to not fully mirror the dominant microbial communities that could be linked with P. falciparum parasitemia in individuals located in areas of moderate-to-high malaria transmission. We also agree that a lack of regular exposure to malaria antigens, as would be expected in malaria endemic areas, could lead to differences between the outcomes we observed among CHMI participants and what would be seen in individuals that reside in malaria endemic areas. We have incorporated and expanded upon these limitations into the discussion (Pages 29-30, Lines 802-814).\nAdditional comments:\n1. For accuracy, it would be optimal for the authors to use the descriptor “healthy US adults” throughout the study in place of “humans” to avoid inference to African children, or other tropical regions, that suffer from the vast majority of P. falciparum infections.\nWe thank the reviewer for this suggestion. We have made this change throughout the text, where possible. For accuracy, we also use the descriptor “CHMI participants” in place of “humans”.\n2. L184: Taxonomic assignment was performed using old gg database. The authors should confirm the fundamental findings using updated SILVA database.\nGreengenes 97% OTU is appropriate for studies where a conservative approach to taxonomy is desired. Our macaque and human data identify major differences between clusters that are robust at the genus level, and frequently contain species-specific information as well (see Bifidobacterium, Lactobacillus, and Prevotella amongst others). SILVA provides increased species-specific information but accepts lower confidence in those calls. Rather than trying to overclassify these samples, our study aimed to find robust patterns within known bacterial genera, families, and onwards. Future studies will bypass these 16s rRNA databases entirely by employing shotgun sequencing and alignment to derive strain-level resolution.\n3. L198: The authors should deposit custom scripts and codes to GitHub page.\nWe appreciate the desire to have codes and scripts on GitHub. Our code is available immediately, and we plan to upload on GitHub upon publication.\n4. Line 207: age range of the US adults should be included.\nAge ranges of CHMI participants are depicted in Supplemental Figure S6B.\n5. L266: ASVs instead of ASCs.\nThis typo has been corrected.\n6. Multiple occasions within the Results the authors make note of non-significant differences. These comments should be excluded or limited as there is no strong justification to discuss those things that are not different.\nWe have edited the results section to reduce reference to non-significant differences.\n7. The authors may want to consider going from the macaque study to the human study. It felt awkward going from the gold-standard human malaria study to the macaque “model organism” study.\nWe appreciate this suggestion from the reviewer and have adjusted our manuscript to show the RM study results first, followed by the results from the CHMI study.\n8. Regarding the author’s claim that the RM model is a good translational model, Bifidobacterium is seen in their human data set as a taxon of interest and noted that previous studies have also seen this, but this was not observed in the RM.\nWe thank the reviewer for highlighting this important point. The differences in core taxa in CHMI participants as compared to RMs that are associated with above or below median parasitemia may be due in part to differences in the fiber-rich, plant-based diet consumed by RMs as compared to the more varied diet that the CHMI participants may have partaken of. However, despite these taxonomic differences in the GI microbiome between species, it is possible that the specific pre-infection microbial species identified in CHMI participants and RMs that associate with above or below median parasitemia risk may have similar functionality that influences risk or protection from Plasmodium parasite burden. We have incorporated these points into the discussion (Page 27, Lines 744-753).\nReviewer #2 (Remarks to the Author):\nThis paper examines the microbiome of (1) humans who are challenged with P. falciapurum malaria by infected mosquito bite and (2) NHP who are challenged with P.fragile malaria by injection of infected red blood cells. Both the humans and the NHP were subdivided into two groups, “high parasitemia” and “low parasitemia”. There were microbiome differences between the two groups that were identified with unsupervised clustering. My main concerns with this article are with the clinical definitions and descriptions of the CHMI.\nWe thank the reviewer for their careful review of our manuscript. We have detailed our specific efforts to address their comments below and believe that we have improved the manuscript.\nHuman CHMI:\n The human CHMI was conducted as part of an antimalarial drug study (KAF156). The current paper evaluates microbiomes from 35 participants in that study, from the placebo group, the 20 mg group, and the 50 mg group (lines 103-104. Of note, in looking at the original publication of KAF157, 3 of the volunteers in the 50 mg group did not get malaria. Therefore, their inclusion in this study is questionable.\nWe agree that the inclusion of the three participants in the 50mg KAF156 group that did not progress to detectible P. falciparum parasitemia is not appropriate. Indeed, we did not include these three participants in the sequencing analysis described in the current study. We apologize for the confusion and have made efforts to clarify that these participants were not included in the analyses described in our manuscript in both the Materials and Methods (Pages 7-8, Lines 195-197) and Results (Page 16, Lines 439-441).\n The participants were divided into two groups of “mild” or “severe” parasitemia (lines 218-219) based on whether their parasitemia was above or below a median parasitemia value. This is an extremely flawed way to differentiate the participants clinically. All of the participants were diagnosed with a highly sensitive molecular test. Based on the parent study, few had malaria symptoms, and none had severe malaria (if the clinical study site had allowed severe malaria to occur in the context of a drug efficacy study, this would be unethical). All mention of “severe malaria” and “severe parasitemia” needs to be removed from the manuscript if it is considered for publication.\nWe agree that our original way of referring to participants that were above or below the median parasitemia value as having “mild” or “severe” parasitemia was flawed. As suggested by Reviewer 1, we have edited our manuscript such that both CHMI participants and NHPs are described simply as having above or below median parasitemia. We have also carefully edited the manuscript to remove references to severe malaria and severe parasitemia, unless when describing the results from prior publications that utilize these descriptors.\n In addition, although the parasitemia levels are quantitative, the timing of sample collection in relation to the exact time of the CHMI and the exact time within the synchronized cycles of parasite emergence from RBC all affect the parasitemia levels. High parasitemia levels in the setting of ongoing malaria infection correlate with malaria severity. Differences between very low levels that are detected soon after blood stage infection has started also reflect some amount of luck (what time was the CHMI 8 days ago? What time was the volunteers appointment on the day of the PCR blood draw). This makes dividing a grouping of 35 very low parasitemias, 3 of which had a value of “ZERO” into two groups questionable.\nWe agree that inclusion of the three participants that did not have parasitemia as detected by qPCR would have been questionable. As stated above, these participants were not included in the analyses described in the current manuscript, and we have made efforts to clarify this in the text. We have also included a column which indicates the parasitemia levels experienced by each of the n=32 participants included in our analysis in Supplementary Table 2.\n Nonetheless, the authors have found differences between the two groups (figure 1B) that may be worth considering. However, the statistics that differentiate the two groups of parasitemia within the three clusters (lines 220-228) are not described and need to be specified. Also, it would be helpful to know the actual parasitemia levels that were being discussed (which presumably were very low).\nWe thank the reviewer for this comment. We have clarified this section of the results to explicitly state that the differentiation between the two groups of “high” and “low” parasitemia was defined as having peak peripheral parasitemia either above or below the median parasite level, respectively. This section of the results now states: “To quantify the risk of elevated peripheral parasitemia associated with distinct microbiota profiles, we stratified participants according to their peak peripheral parasitemia, with “high” or “low” parasitemia defined as above or below the median parasite level respectively. Participants in HC1 were eight times more likely to be identified as having above median parasitemia than HC3 participants (OR=8.17; p=0.47; 95% CI=1.03-64.94; Fig 5C). HC2 participants were more than four times as likely to be identified having above median parasitemia than HC3 participants, but these results did not reach statistical significance (OR=4.08; p=0.149; 95% CI=0.60-27.65; Fig. 5C) (Page 17, Lines 462-471). We have included parasitemia levels in the CHMI participant metadata table (Supplementary Table 2). Finally, we have stated in the methods that the statistical test used to identify significant differences in parasitemia between the three CHMI groups was a pairwise Wilcoxon test (Page 9, Lines 246-249).\n Consider a discussion of the fact that most of the malaria infection was a liver infection. The blood stage of infection was brief before treatment was initiated – how does this relate to your postulations about the authors’ observations of the microbiome?\nThe reviewer is correct that because the participants in the CHMI study were infected via mosquito bite, the infection went through the liver stage before progressing to a brief blood stage prior to treatment. This fact, in combination with our observation of an association between pre-infection GI microbiome structure and very early blood stage parasitemia, further supports a link between pre-infection GI microbiome composition and parasitemia that is detectable even in the beginning phases of the blood stage of infection. We have incorporated this point into the discussion (Page 30, Lines 826-837).\nNHP challenge:\n The authors state that this is a new challenge model and provide a rationale for why this challenge model is suitable for microbiome/malaria studies. As a new model, many important details were lacking:\no What clinical manifestations were observed in the animals, and what parasitemia levels were these clinical manifestations associated with? Line 300 states that P. fragile induces similar clinical signs in RM as in Pf in humans – does this include cerebral malaria and other clinical manifestations of severe malaria? Also, which/how many of the animals needed the escalated treatment regimens, and which did not?\nWe thank the reviewer for this question. P. fragile has previously been shown to be similar to P. falciparum in that it is capable of endothelial adherence, tissue sequestration, and antigenic variation, and has been previously used as a model for cerebral malaria and for testing of anti-malarial drugs and vaccines (PMIDs: 17152944, 116557, 1111348, 3354764, 3553414, 7515825, 9371096, 12711101). In the animals included in this study, we assessed anemia (% hematocrit) as a clinical feature of P. fragile infection. We detected parasite-infected erythrocytes in peripheral blood smears of all RMs within two weeks following i.v. inoculation with P. fragile, with peak parasitemia being reached by Week 3 post-infection. Corresponding with parasitemia, mild (25-35% hematocrit; n=2), moderate (20-25% hematocrit; n=6), or severe (<20% hematocrit; n=2) anemia developed by Week 2 post-P. fragile infection. Anemia was sustained in RMs through Week 3 post-infection, began to resolve by Week 4 post-infection, with 14 of 16 RMs returning to normal by Week 6 post infection. Taken together, these data confirm that i.v. inoculation of P. fragile in the RMs in our study induces clinical features of indicative of malaria. We have incorporated reference to these prior works and our parasitemia and anemia data into the manuscript text (Page 10, Lines 257-269) and included the parasitemia and anemia data into the supplemental materials (Fig. S1A, B, Supplemental Table 1). For treatment information, n=12 of the n=16 RMs in our study required the escalated treatment regimens (n=5 in Building A animals and n=7 in Building B animals). We have included treatment information and timing of treatments in Supplemental Table 2.\no Treatment regimen questions: Why did the treatment begin only after parasitemia was >0.5% and not as soon as parasitemia was detectable? The treatment regimens appeared to be titrated to be lower than abortive – there was an expectation that one regimen could be given for 2 weeks consecutively without clearing parasitemia. If sub-abortive treatment was being used, why?\nThe reviewer is correct that we used a sub-optimal treatment regimen that began when RMs reach a parasitemia threshold of >0.5% parasitemia. The reason for using this approach, rather than abortive treatment, is that we wanted to establish a chronic malaria infection. This dose and method of sub-optimal quinine sulfate treatment after the parasitemia threshold of >0.5% was reached has been used previously in P. fragile infected RMs (PMIDs: 21917966, 23785209). Our main reason for establishing a chronic malaria infection in these animals, rather than curing the infection, was because we wanted to model the prolonged exposure to Plasmodium infection that occurs in malaria-endemic areas (PMIDs: 23029082, 23750733). Although this approach does not precisely mimic the approach that was taken in the CHMI study, our intention was to provide additional real-world relevance to our RM study. Our second reason for using a sub-curative dose for malaria treatment was that after establishment of the chronic malaria and collection of samples for the current study, these animals went on to be used in a study focused on assessing the impact of concurrent malaria infection on SIV/HIV vaccine efficacy. Our use of this strategy was to collect samples for comparison to the CHMI study and followed by use in a co-infection vaccine study was intended to reduce the number of animals needed for research purposes, as outlined in the Three Rs principle (Replacement, Reduction, and Refinement). We have incorporated the goal of establishing a chronic malaria infection in RMs into the Methods (Pages 6-7, Lines 165-169).\no How was “high parasite load” defined (line 304)? (defined for the humans but I did not see it for the RM.\nWe apologize for this lack of clarity. As with the CHMI study, we assessed parasitemia as above or below the median parasitemia in the context of pre-infection microbial clusters. We have edited our reference to high and low parasite loads to be “above median parasitemia” and “below median parasitemia”, as suggested by Reviewer 1. We have clarified this in the Results section (Page 17, Lines 462-464).\no Line 349 “more severe P. fragile infection” – without information about clinical symptoms in this new animal model, I don’t know how to assess this (whereas in humans there were NO severe P. falciparum infections)\nWe have removed this sentence from the manuscript.\nRelating the human challenge to the NHP challenge: Human challenge was by infected mosquito bite, therefore, there was 7 days of clinically silent liver infection prior to blood stage infection, which was treated at a very early stage (because it was detected molecularly, which is a very sensitive method of detection. NHP challenge was by injection of infected RBC, so there is no liver stage infection. NHP challenge seemed to be permissive of ongoing parasitemia, and it is unknown what symptoms the NHP had. The many differences between the human challenge and the NHP challenge, and whether the data are comparable to each other in consideration of all these differences need to be discussed.\nWe agree with the reviewer that differences in the route of infection between CHMI participants and RMs is a caveat of our study that requires further discussion. Notably, several prior studies have also demonstrated that the overarching links between GI microbiome structure and malaria severity that have been observed in children in Mali and Uganda infected with P. falciparum via mosquito bite also exist in mice inoculated with Plasmodium via the i.v. route (PMIDs: 33979614, 26296559, 26858424, 33326773, 30837607, 27729904). Therefore, although it is possible that differences in the route of infection may contribute to variations in the observed outcomes between CHMI participants and RMs, further work will be necessary to definitively identify the precise ways in which the GI microbiome may impact, and in turn be impacted by, Plasmodium when infection occurs via different pathways. Notably, the strain of P. fragile used in the current studies has previously been shown to retain the ability to produce infective gametocytes and P. fragile sporozoite transmission via mosquito bite has been successfully conducted (PMID: 17152944), thus future pre-clinical assessments of microbiome-targeting interventions to reduce parasitemia in RMs in the context of vector transmission of P. fragile parasites are feasible and warranted. We are currently developing the capability to inoculate RMs with P. fragile transmitted via infected mosquito bite and plan to incorporate this methodology into our current and future experiments. We have incorporated this caveat into the discussion (Pages 30-31, Lines 826-841).\nAdditional comments (paraphrased from additional marked-up manuscript comments)\n L51: Update malaria case reports to 2022/2021 mortality.\nThis update has been made (Page 3, Lines 64-66)\n L62: GI microbial consortiums – are these microbiomes? Is this word acceptable to the microbiome researchers?\nWe used “microbial consortium” as an alternative phrase for “microbial community structure”. Although we feel this phrase would be appropriate, we have edited the word “consortiums” to be “community structure” to be consistent with terminology used throughout the manuscript (Page 3, Line 75).\n L82: I don’t follow how CHMI studies will help with determining how the microbiome shapes Plasmodium infection outcome.\nWe have updated this section to read “Additionally, the use of controlled human malaria infection (CHMI) studies could aid efforts to determine the role of the microbiome in shaping Plasmodium infection outcome. For example, such studies could allow for highly controlled experiments to determine whether previously identified relationships between microbiome composition and risk for Plasmodium infection are broadly applicable across age ranges or whether microbiome-targeting therapies could be used to reduce Plasmodium burden.” (Page 4, Lines 106-111).\n L96: Nonconfirmatory – I am not familiar with this term. Is it nonconfirmatory because it was first? Or because the results did not confirm the previous study? If the 2nd, nonconfirmatory should not be used to describe the trial.\nWe have removed the word nonconfirmatory to reduce confusion.\n L164: Not familiar with RM challenge, but why not use a sure-bet treatment?\nAs stated above, the reason for using a sub-optimal treatment regimen was to establish a chronic malaria infection. We have incorporated this into the methods (Pages 6-7, Lines 165-169).\n L178: Accession number pending – needs update when available.\nWe have updated the manuscript with the accession number for the SRA deposit of our raw sequencing data (PRJNA918700) (Page 8, Line 213).\n L183: R studio – do we care about the GUI? For completeness, can include R version #.\nWe have incorporated the version of R studio used in these analyses into the manuscript (Page 8, Line 218).\n L184: gg_13_8_train_set_97.fa.gz – Where is this file?\nThis is a standard training dataset that is publicly available. It can be found at:\nhttps://zenodo.org/records/158955.\n L213: Use dash instead of colon for ranges.\nWe have removed this panel in Figure 1 (now Figure 5).\n L219: Severe parasitemia – this has a clinical meaning, should not be used here.\nWe have removed all wording of “severe parasitemia” or “mild parasitemia” throughout the manuscript and instead use “above median parasitemia” or “below median parasitemia”, as recommended by Reviewer 1.\n L224: What statistical methods were used here? Include in methods.\nPeripheral parasitemia between the three CHMI groups was assessed using a pairwise Wilcoxon test. We have updated this in the methods (Page 9, Lines 246-249).\n L235: “Principal” missing from PCA definition.\nWe have updated the methods to define principal coordinate analysis (PCoA) at first mention in the methods section (Page 9, Lines 229-231).\n L261: Need to define what statistical tests are used. If only baseline samples for Fig 1, why are all samples needed in Fig 2?\nWe used baseline samples for risk associated measurements. We used all samples to determine changes over time. As described in the methods, all statistical comparisons across groups (peripheral parasitemia, Aitchison distance travelled, PCoA axis coordinates, Shannon diversity, core abundance, observed ASVs (richness), sequencing depth, Pielou’s evenness, ASV prevalence, BMI, and age) were compared using a pairwise Wilcoxon test unless otherwise noted (Page 9, Lines 246-249). We have also updated all figure legends with relevant statistical test information.\n L304: High parasite loads – misused.\nWe have removed this phrase and now refer to either above or below median parasitemia throughout the manuscript.\n L307: Severe malaria – definition?\nWe have removed all wording of “severe parasitemia” or “mild parasitemia” throughout the manuscript and instead use “above median parasitemia” or “below median parasitemia”, as recommended by Reviewer 1.\n L349 and L353: Severe P. fragile and severe P. falciparum infection – Overstated.\nWe have removed all wording of “severe parasitemia” or “mild parasitemia” throughout the manuscript and instead use “above median parasitemia” or “below median parasitemia”, as recommended by Reviewer 1.\n L379: Need to explain RM model better – what clinical manifestations were observed? Which RMs needed more than the first time Rx? What was progression of parasitemia with treatment?\nAs described above, we assessed anemia (% hematocrit) as a clinical feature of P. fragile infection. We have included the clinical feature data (anemia and parasitemia) in Supplemental Figure 1.\n L485: What are the p value lines? Specify.\nOf note, Figure 1 is now Figure 5. We have updated the Figure 5B legend to state that differences in parasitemia between the three groups were assessed using a pairwise Wilcoxon test (Page 37, Lines 1023-1024). We have also updated this in the methods (Page 9, Lines 246-249).\n L488: What are the p value lines? Specify.\nOf note, Figure 1 is now Figure 5. We have updated the Figure 5C legend to state that the statistics above the horizontal bars represent the odds ratio (95% confidence interval, p value) (Page 37, Lines 1025-1026).\n L491: CLR-transformed count data – for which samples? It’s in paper but should be here.\nFigure 2A is now Figure 6A and has been updated to show the principal coordinate analysis (PCoA) of Aitchison distance for CHMI participants. As shown in the figure legend to the right of Figure 6A, this analysis includes all samples from all participants at all timepoints. This has also been updated in the figure legend (Page 38, Lines 1047-1051).\n L497: ASVs – define.\nWe have updated all figure legends to define ASV at first reference.\n L502: What are the p value lines? Specify.\nOf note, Figure 2 is now Figure 6. We have updated the Figure 6 legend to state that the statistics above the horizontal bars in Figures 6B, C, D, and E were assessed using a pairwise Wilcoxon test (Page 38, Lines 1058-1060).\nReviewer #3 (Remarks to the Author):\nThis is an interesting study but there are a few areas that require further clarification, and the overall conclusion is not clearly demonstrated by the results and figures as they currently are presented.\nWe thank the reviewer for their thoughtful consideration of our manuscript. We have detailed our specific efforts to clarify the areas that the reviewer has pointed out as needing work and hope that our efforts have helped to better illustrate our overall conclusions.\nAbstract\n How similar is P. fragile to P. falciparum? Since these are being directly compared, the authors should explicitly compare how similar or different these two infections are\nAs described above in response to a similar question from Reviewer 2, P. fragile has been shown to have similarities to P. falciparum in that it is capable of endothelial adherence, tissue sequestration, and antigenic variation, and has been previously used as a model for cerebral malaria and for testing of anti-malarial drugs and vaccines (PMIDs: 17152944, 116557, 1111348, 3354764, 3553414, 7515825, 9371096, 12711101). In the animals included in this study, we assessed anemia (% hematocrit) as a clinical feature of P. fragile infection. We detected parasite-infected erythrocytes in peripheral blood smears of all RMs within two weeks following i.v. inoculation with P. fragile, with peak parasitemia being reached by Week 3 post-infection. Corresponding with parasitemia, mild (25-35% hematocrit; n=2), moderate (20-25% hematocrit; n=6), or severe (<20% hematocrit; n=2) anemia developed by Week 2 post-P. fragile infection. Anemia was sustained in RMs through Week 3 post-infection, began to resolve by Week 4 post-infection, with 14 of 16 RMs returning to normal by Week 6 post infection. Taken together, these data confirm that i.v. inoculation of P. fragile in RMs induces clinical features of indicative of malaria. We have incorporated reference to these prior works and our clinical feature data into the manuscript text (Page 10, Lines 257-269) and included the clinical feature data in a new supplemental figure (Fig. S1).\n Some statements seem over-stated, like “the combined weight of this evidence demonstrates that pre-infection GI microbiome composition is highly indicative of P. falciparum disease risk” (but yet the combined data is not on P. falciparum, half of it is on P. fragile)\nWe have made efforts to remove phrases throughout the manuscript that unintentionally overstate our findings.\nMaterials and Methods\n This is a nice sized human study (35 people) paired with a nice sized macaque study (16)\nWe thank the reviewer for this comment. Of note, in response to a comment from Reviewer 2, we have clarified in the text that our human study only assessed the 32 participants that went on to develop P. falciparum parasitemia as detectable by qPCR. Nonetheless, we agree that the inclusion of 32 human participants and 16 NHPs makes for nicely sized studies.\n Stool samples are collected in such a way that it is unclear whether there was enough time for any microbes to replicate during the time period between collection and storage (“within 24hours of collection”). A major suggestion here is that the authors need to do an analysis of time interval between collection and freezing and the results (if there is indeed an impact of time interval between collection and freezing, this would undermine the results and suggest that some of the collections were not frozen down quick enough to get a good sense of the microbial community)\nAs stated in our response to Reviewer 1, Point 3, we agree that immediate freezing of the human CHMI samples would have been ideal. Unfortunately, although we ensured that all samples were frozen within 24hours of collection, data on the exact amount of time that each sample was kept at room temperature prior to freezing was not collected, thus we are unable to conduct any analyses to see whether length of time prior to freezing was associated with differences between the three CHMI groups. We are, however, reassured that room temperature storage of stool samples of less than 24hours would likely not impact our conclusions based on prior studies of lengthy room temperature storage of fecal specimens. Indeed, prior work has shown that storage of stool samples for 72hours at room temperature has been reported to cause less shifts in detected communities than chemical storage methods (PMID: 26572876). An even lengthier preservation of 1 week at room temperature as compared to immediate freezing demonstrated that variation within donors was less than variation between samples frozen immediate or kept at room temperature for 1 week (PMID: 27822526). We have incorporated these references and this limitation into the discussion of our manuscript (Page 30, Lines 814-824).\n The stool collection for the macaques is well done and thought-out with respect to collecting stool from individual animals and immediately storing it at -80C\nWe thank the reviewer for their positive comment regarding our approach to collecting and preserving NHP stool samples for this study.\n Why do the authors use the 357F-806R primers (those published and used originally by the Earth Microbiome Project)? These are not necessarily the best primers for studies related to human gut microbiome health. I think other options should have been considered, or at least the authors should consider whether they are missing any information that they could have gained by using a different set of primers.\nThe original intent of the Earth Microbiome Project (EMP) was to facilitate comparisons across studies. By using the same primer set, we believe that our study can be placed into context with prior literature. Future studies will focus on shotgun metagenomic sequencing to get at strain level differences that could have an impact.\nResults\n The authors should clarify whether the HC1 group could have another issue that confounds the data (e.g. low yield). The authors should show an analysis in which they see whether any metadata affect the groupings (e.g., total DNA yield, time between collection and freezing, etc)\nWe thank the reviewer for this question. As stated in our response to Reviewer 1, Point 2, Data on age and sex for the CHMI participants were published in the original KAF156 study (PMID: 32644127). Data on nationality and HbA/S status are unfortunately not available. For sex, with the exception of 1 female in the 50mg post-exposure KAF156 group, all study participants were male, thus, we do not account for sex in our analyses. We have, however, now included Supplementary Figures 6A and B that depict analyses we conducted to assess whether participant age or BMI were different across clusters. We found that there were no significant differences in either age or BMI between CHMI participants stratified into either HC1, HC2, or HC3. We have incorporated these findings into the manuscript text (Page 17, Lines 472-473). Finally, DNA input for all specimens was the sample between samples and as shown in Supplemental Figure S7D, sequencing depth was not significantly different between CHMI participants grouped in HC1, HC2, or HC3.\n Similarly, in the rhesus macaque study, the authors should also test and consider whether there is a cage/room effect. Even though the authors do a great job of making sure they are collecting the stool from a specific animal, this doesn’t rule out the very likely possibility that socially housed animals may have more similar microbiomes than those in which they are not housed. The authors should do a analysis with metadata to see whether any of their clustering patterns are associated with these logistical factors (e.g., cage, room)\nWe thank the reviewer for this important suggestion. In order to address this comment, we re-assessed our RM data with a focus on RM group and housing information. All RMs in this study were raised, purchased, and transported together to the WaNPRC before being divided into cohorts and housed in separate buildings (Buildings A and B). Despite being housed separately, diets for RM groups housed in both buildings remained identical. Animals housed in Buildings A and B both underwent baseline sampling, were inoculated with the same stock and with the same dose of P. fragile and underwent post-infection sampling according to the same study timeline, with the only difference being housing in different buildings. Unexpectedly, when assessing 16s rRNA profiles in all animals with an eye to building/housing, we identified a clear distinction in microbial profiles that aligned with these housing facilities. Notably, the response to malaria challenge aligned similarly, with animals possessing the Building A microbial signature experiencing significantly higher peak peripheral parasitemia, despite being challenged from the same P. fragile stock and at the same dose as RMs possessing the Building B signature. Given this finding, we restructured and reanalyzed all RM data in reference to the two groups that were housed in different buildings, which resulted in different microbial profiles (Pages 10-16, Lines 271-431; Figures 1-4, Supplemental Figures S1-S5). We have also commented on the finding that housing factors (different buildings) affected our RM study in the discussion (Page 27, Lines 727-732).\n In Line 323, I am unclear on how the authors conclude that “parasite burden was primarily driven by major compositional differences, rather than variable levels of microbiota found in both groups”. This point should be further explained. Particularly the idea that parasite burden itself is “driven” by microbiome differences. I did not find evidence in this paper that parasite burden is definitively “driven” by microbiome differences.\nWe thank the reviewer for this suggestion. Due to the restructuring of our manuscript that pertains to the RM samples, as described above, this sentence has been removed from the manuscript.\nDiscussion\n The authors seem to interchange the concepts of parasitemia, “disease progression”, etc and it is unclear what the main message is that they are trying to say – are they arguing that gut microbiota composition CAUSES parasitemia to be higher or lower? Or is this a mere association without a known causation. The wording here should be clarified.\nWe apologize for the confusion. To clarify, our main message is that we identified distinct pre-infection microbial profiles that were associated with risk for subsequent development of above or below median P. falciparum parasitemia in CHMI participants. We also identified a distinct pre-infection microbial profile that was associated with risk for above or below median P. fragile parasitemia in RMs. We have carefully edited throughout the manuscript to clarify this message.\n The overall idea seems to be that pre-infection microbiome composition can predict the severity of the malaria infection. Could the authors create a model to test this theory (by using part of their data and doing cross-validation to see if this holds up?)\nBased on the Reviewer’s suggestion, we have used our CHMI data to define a reduced feature set that could serve as a potential biomarker panel to predict risk of elevated parasite burdens in individuals living in malaria endemic regions. Our first attempt sought to utilize any ASV within genera that were differentially abundant between HC1 and HC2/HC3, creating a panel of 194 ASVs. Given the low number of participants in our dataset (n=32), we established a model training setup using Leave-One-Out Cross Validation (LOOCV) to ensure robust evaluation, with a focus on optimizing the Receiver Operating Characteristic (ROC) area. This model achieved an Area Under the ROC (AUROC) of 0.957, which in the setting of high feature numbers and low sample numbers indicates overfitting. Looking to avoid overfitting and to produce a panel that could be more easily deployed in a clinical setting, we evaluated strategies to reduce the feature set. Recent papers exploring machine learning in the microbiome context have identified limitations associated with large, granular feature sets, finding that family and Genus-level feature sets outperform ASVs by AUROC metrics and are more generalizable, due in large part to sparsity of 16s rRNA data sets (PMIDS: 32518182 and 35012354). We thus chose to focus on the Family Bifidobacteriaceae for 3 reasons (1) Bifidobacteriaceae was detected as differentially abundant between microbiome clustesr and contained several ASVs prioritized by PLS-DA analysis; (2) several ASVs from Bifidobacteriaceae were part of the “core” microbiome and likely more generalizable to a wider population; and (3) previous observational studies have demonstrated a relationship between Bifidobacterium and risk of contracting malaria in endemic regions. Our new feature set included 9 ASVs: 2 ASVs each for Bifidobacterium adolescentis, Bifidobacterium longum, and unresolved Bifidobacterium; and 1 ASV each for Bifidobacterium pseudolongum, Bifidobacterium bifidum, and an unresolved Bifidobacteriaceae. When the model was trained and evaluated as deacribed above, our panel of 9 ASBs achieved an AUROC of 0.801, suggesting that Bifidobacteriaceae levels could have a strong predictive capability in the preclinical setting. We have incorporated these findings into the manuscript (Pages 23-24, Lines 627-649, Figure 7A).\n Going back to the main conclusion of the paper, the authors state that they “identified a high-risk microbial signature that is strongly associated with increased risk of developing severe parasitemia” and they say they find this in both humans and rhesus macaques, but after reading the paper and looking at the figures, I still am not clear on what exact microbes are associated with this increased risk of severe disease (that are in common between humans and rhesus macaques).\nWe apologize for the lack of clarity in our message. In CHMI participants, we observed that significantly lower levels of the core GI commensal Bifidobacterium adolescentis in participants that experienced higher peripheral parasitemia. Not only were levels of B. adolescentis lower, but their prevalence was significantly reduced, with many participants who experienced the highest peripheral parasitemia lacking this taxa during the study. These findings extended to trends for several other Bifidobacterium, including B. longum, B. pseudolongum, and B. bifidum, and importantly we show that participant parasitemia levels could be accurately predicted prior to challenge using a biomarker panel comprised of just 9 ASVs within Bifidobacteriaceae. In RMs, pre-challenge differences in the Lactobacillus:Prevotella ratio between cohorts remained stable over time, with RMs that developed the highest peripheral parasitemia enriched for Lactobacillus taxa and lacking Prevotella.\nThe Reviewer is correct that in contrast to our data from CHMI participants, RMs did not exhibit differences in pre-infection abundances of Bifidobacterium that were subsequently associated with above or below median P. fragile parasitemia. Rather, the presence of Prevotella in RMs was noted as being associated with lower risk for P. fragile parasitemia. These findings do not negate our results, as the overall differences in core taxa between humans and RMs are well documented (PMID: 30349024). Importantly, despite these taxonomic differences in the GI microbiome between species, it is possible that the specific pre-infection microbial species identified in CHMI participants and RMs that associate with above or below median parasitemia risk may have similar functionality that influences risk or protection from Plasmodium parasitemia. More work will be needed to test this theory and elucidate the specific mechanism by which pre-infection GI microbiome may influence subsequent parasitemia.\nWe have made efforts to clarify these points in the discussion (Pages 24-25, Lines 665-672; Page 26, Lines 720-724; Pages 27-28, Lines 744-755).\nPoint-by-point response to the reviewers\nWe thank the editors and the reviewers for their helpful and thoughtful feedback on our manuscript titled “Distinct gastrointestinal microbial signatures predict parasite levels in controlled Plasmodium infections in both rhesus macaques and humans”. We have carefully revised our manuscript as suggested and believe that it is much improved. We have provided a point-by-point response to all the reviewer’s comments below. We have also completed and made updates to the manuscript according to the Author Checklist. Thank you again for your time and consideration of our manuscript.\nPlease note that all Page and Line details below refer to the Clean manuscript unless otherwise noted.\nReviewer #1 (Remarks to the Author):\nThe authors have provided a thorough and thoughtfully revised manuscript that is an important contribution towards our growing understanding of gut microbiota-malaria interactions. I have no major concerns. There are a few remaining items that I think should be clarified.\nWe thank Reviewer 1 for these positive comments and assessment of the importance of our work to the field of gut microbiota-malaria interactions. We also thank Reviewer 1 for taking the time to provide these final suggestions and have detailed our specific efforts to address these remaining points and improve the manuscript in our responses below.\nLine 70-74, Additional support for pre-infection gut bacteria composition contributing to P. falciparum infection outcome (i.e., risk of febrile malaria) has been reported (PMID: 39500866) and would be appropriate to note.\nWe thank Reviewer 1 for highlighting these additional findings in support of a link between GI microbiome composition and P. falciparum infection outcome. We have added this reference and the following statement to the Introduction: “Further, recent work has shown that GI microbiome compositions that are enriched for bacteria associated with inflammation, impaired gut barrier function, and mucin degradation, particularly those in the order Eubacterialies, in Malian children were associated with prospective risk of febrile malaria following P. falciparum infection (PMID: 39500866)”. (Page 3, Lines 78-81)\nLine 78-81, Regarding the description of Plasmodium-specific antibodies in mice differentially susceptible to P. yoelii parasitemia. The referenced manuscript detected those antibodies in the serum, not the spleen. For clarity, the text should be revised accordingly.\nWe have updated this sentence as suggested; it now reads: “Finally, differences in GI microbial communities in mice resistant or susceptible to P. yoelii infection were associated with variations in the quality and specificity of anti-Plasmodium antibodies detected in serum, providing a potential mechanism by which the GI microbiome could influence the severity of Plasmodium spp. infection.” (Page 4, Lines 85-88)\nLines 483-487, The beginning of this paragraph and sentence is human-centric. Reference 21 is from the murine model. As such, it would be optimal to make clear reference 21 refers to murine malaria and unlike reference 20 is not human data.\nWe have updated this sentence as suggested; it now reads: “Interestingly, viewing these results in the context of differentially abundant genera revealed that several Bifidobacterium ASVs were also frequently missing from these participants, including B. longum, B. pseudolongum, and B. bifidum, a finding that agrees with surveillance-based studies that linked increased Bifidobacterium abundance with increased time to first malaria episode in humans and reduced malaria severity in mice.” (Pages 13-14, Lines 361-366)\nReviewer #2 (Remarks to the Author):\nThe authors have a unique contribution to the literature (microbiome associated with malaria challenge in monkeys and humans) and they have worked hard to address critiques. My main concern has been that the human malaria infections described are very low parasitemia (as is ethical). The HIGHEST parasitemia group had a MEDIAN 10,000 parasites per ml or 10 parasites per microliter. By contrast, the LOD of a thick blood smear is ~4 parasites per microliter. In most cases, the authors have described this correctly (i.e. instances of \"severe malaria) are removed. I have the following small critiques for this article but otherwise the authors have addressed my concerns:\nWe thank Reviewer 2 for these positive comments on the efforts we have made to address the reviewer’s critiques. We also thank Reviewer 2 for taking the time to provide these final suggestions and have detailed our specific efforts to address these remaining points and improve the manuscript in our responses below.\nLine numbers on the redlined version.\nFigure 5 B – The parasitemias in figure 5 are very low compared to what is seen in clinical malaria. The actual numbers should be stated in the text.\nhighest parasite burden group had a median of 10,000 parasites/mL or 10 parasites per microliter. Limit of detection thick blood smear: 4 parasites per microliter.\nWe have added the median parasite burdens for each group to the text. The sentences now read: “When peak peripheral parasitemia was compared across clusters, we found that participants whose baseline microbiota was categorized as human microbiome cluster 1 (HC1; gold; median parasitemia=9,491 parasites/ml) developed significantly higher levels of parasitemia as compared to HC3 participants (dark cyan; p=0.043; median parasitemia=1,873 parasites/ml; Fig. 5B). HC1 parasitemia was also elevated compared to HC2 participants (gray; median parasitemia=5,293 parasites/ml), although this difference did not reach statistical significance (p=0.17; Fig. 5B). (Page 11, Lines 288-294)\nLine 433 – I still disagree with the statement “given the significant differences in outcome…” based on the non-difference of parasitemia levels as all were very low. I don’t mind the whole premise of the paper, which is that there are different microbiomes found. Just want to make sure it clinically makes sense.\nWe have removed the word significant and replaced it with “statistical”. This sentence now reads: “Given the statistical differences in outcome that associated with baseline microbiota, we next sought to identify distinctions between microbiota clusters observed among CHMI participants.” (Page 12, Lines 311-312)\nLine 702 – “relatively low levels” I disagree. The are not relatively low, they are just “very low”.\nWe have removed “relatively” from this sentence. This sentence now reads: “A further limitation of our CHMI study is participants were treated immediately after detection of blood-stage infection, as required by the parent protocol, which likely contributed to the low levels of parasitemia observed.” (Page 22, Lines 600-603)\nLine 624 – consider also adding to methods the amount of time of monkey separation.\nWe have added information on the amount of time of NHP separation to the methods. This section now reads: “RMs in this study were raised, purchased, and transported together to the WaNPRC and housed in WaNPRC Building A. RMs were then divided into two cohorts of n=8 RMs each. One cohort of n=8 RMs remained in Building A while the second cohort of n=8 RMs was relocated to WaNPRC Building B and housed in this location for 6 weeks prior to the first stool collection, which occurred two weeks prior to P. fragile inoculation.” (Pages 24-25, Lines 670-674)\nLine 712 – add stool (participant stool samples)\nWe have added “stool” to this sentence. This sentence now reads: “Finally, CHMI participant stool samples in our study were frozen within 24hours of collection.” (Page 22, Lines 612-613)\nOptional to list ALL of the hemotologic parameters. Could include only anemia, thrombocytopenia, and neutropenia, and then you will be covered. (the red cell indices suggest reticulocytosis and the lymphocyte/monocyte proportions would go up if the neutrophil % goes down)\nWe thank Reviewer 2 for this feedback and the option not to list all of the hematological parameters. We have elected to leave all of the parameters in the manuscript and supplemental materials for completeness and in the hopes that these data may serve as a reference for other investigators that may use the NHP P. fragile model in the future.\nFigure 5 – remind reader how the parasitemia was measured (by qtPCR).\nWe have specified both in the text (Pages 10-11, Lines 275-280; Page 27, Lines 741-743) and the Figure 6 legend that parasitemia was measured by qRT-PCR (Pages 28-29, Lines 1174-1179).\nMaterials and methods, state the quinine dose. (line 155)\nWe have included the quinine dose. This sentence now reads: “If parasitemia was above 1%, RMs were treated orally with 150mg quinine sulfate twice per day.” (Page 26, Lines 717-718)\nLine 248 – “based” not abased\nWe have corrected this typo (Page 5, Line 120).\nFigure S7 legend: add “or treatment group” to the end of the title\nWe have added “or treatment group” to the end of the Figure S7 legend title."}]

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