Systems
approaches
to
coronavirus
pathogenesis
Alexandra
Scha¨fer
1,
Ralph
S
Baric
1and
Martin
T
Ferris
2Coronavirusescomprisealargegroupofemergenthumanand
animalpathogens,includingthehighlypathogenicSARS-CoV
andMERS-CoVstrainsthatcausesignificantmorbidityand
mortalityininfectedindividuals,especiallytheelderly.As
emergentvirusesmaycauseepisodicoutbreaksofdisease
overtime,humansamplesarelimited.Systemsbiologyand
genetictechnologiesmaximizeopportunitiesforidentifying
criticalhostandviralgeneticfactorsthatregulatesusceptibility
andvirus-induceddiseaseseverity.Theseapproachesprovide
discoveryplatformsthathighlightandallowtargeted
confirmationofcriticaltargetsforprophylacticsand
therapeutics,especiallycriticalinanoutbreaksetting.Although
poorlyunderstood,ithaslongbeenrecognizedthathost
regulationofvirus-associateddiseaseseverityismultigenic.
Theadventofsystemsgeneticandbiologyresourcesprovides
newopportunitiesfordeconvolutingthecomplexgenetic
interactionsandexpressionnetworksthatregulatepathogenic
orprotectivehostresponsepatternsfollowingvirusinfection.
UsingSARS-CoVasamodel,dynamictranscriptionalnetwork
changesanddisease-associatedphenotypeshavebeen
identifiedindifferentgeneticbackgrounds,leadingtothe
promiseofpopulation-widediscoveryoftheunderpinningsof
Coronaviruspathogenesis.
Addresses
1DepartmentofEpidemiology,SchoolofPublicHealth,Universityof North Carolina at Chapel Hill, United States
2Department of Genetics, School of Medicine, University of North CarolinaatChapelHill,UnitedStates
Correspondingauthor:Ferris,MartinT([email protected])
CurrentOpinioninStructuralBiology 2014, 6:61–69
This review comes from a themed issue on Viralpathogenesis
EditedbyMarkHeise
ForacompleteoverviewseetheIssueandtheEditorial
Available online 17th May 2014
http://dx.doi.org/10.1016/j.coviro.2014.04.007
Introduction
SevereAcuteRespiratorySyndromeCoronavirus
(SARS-CoV) emergedin Guangdong province,China,in 2002,
causing a global epidemic that resulted in about 8000
reportedcasesandanoverallmortalityrateof10%[1].
The virus was initially present in horseshoe bat
popu-lations, and eitherevolved mutationsthatallowed
tran-sitiontoPalmCivetsandRaccoonDogsbeforeemerging
in human populations,or was directly transmitted from
bats to humans and subsequently amplified through
intermediatehosts[2–4].Fromthere,SARS-CoVrapidly
spread across the globe,with focal outbreaks in China,
Singapore, Vietnam, Taiwan and Canada [1]. More
recently, the antigenically distinct MiddleEast
Respir-atorySyndrome(MERS-CoV)emergedin2012andisstill
currentlycirculatinginanimalandhumanpopulationsin
theMiddleEast,resultingin184casesand80deathsto
date (http://www.promed.org). MERS-CoV most likely
emergedfromcirculatingbatstrainsandappearstoalso
replicateefficientlyincamels[5,6].Bothpathogenscause
arespiratorydisease,withmanyseverelyimpacted
indi-viduals transitioning into an acute respiratory distress
syndrome(ARDS)[7–10].AlthoughtheSARS-CoV
out-break was controlled by epidemiological measures, the
recent identification of SARS-like bat-CoVs that can
recognize human angiotensin 1 converting enzyme 2
receptorsandreplicateefficientlyin primatecells
docu-ments the inevitability of a SARS-CoV-like virus
re-emergenceeventinthenearfuture[11].Together,these datahighlightprototypicaloutbreakconcernsforthe21st
century,whereincreasedtravelandcommunitypressures
on wildlife areas present numerous opportunities for
novel viraldiseaseemergencefollowedbyrapid spread
worldwide,sometimeswithinamatterofmonths[12–14].
Rapidresponseplatformsareclearlyneededtomaximize
publichealthpreparednessagainstemergingviruses.
Afundamentalproblemindealingwithemerging
infec-tious diseasecontrol isboth thelimited accessibilityto
andthelimitednumberof biologicalsamplesassociated
with an expanding epidemic, confoundinginsights into
susceptibility and mechanistic disease processes which
arecriticalfor rationalantiviralandvaccinedesign
strat-egies. In order to advance our understanding of those
disease processesat work, novel approacheshave been
evolved that utilize newly developed state-of-the-art
techniques and technologies. Systems biology [15]
uti-lizes an integration of traditional pathogenesis
approaches,as wellas high-throughputmolecular
profil-ing, and computational modeling to identify key host
genesandpathwaysinvolvedinpathogenesis.Inarelated
way[16],systemsgeneticsintegratesmolecularprofiling
and pathogenesis readouts within genetically complex
populationstoidentifygenesandpathwaysthat
contrib-uteto diseasevariationacross geneticallydiverse
popu-lations. Integration of both platforms provides
unparalleled power in identifying and studying host
susceptibility networks that contribute to disease
out-comes.Thecommonfeatureofbothdiscoveryplatforms
regulatingseverediseaseoutcomesfollowinglethalvs
sub-lethalinfections.SubsequentstudyofSerpine1knockouts
aswell as knockoutsfrom otherpathwaymembers
con-firmed a protective role for these Urokinase pathway
members in regulating severe SARS-CoV disease
out-comes. Illustrating the power of these de novo
compu-tational algorithms, it seems unlikely that this pathway
wouldhavebeenotherwiseimplicatedinSARS-CoV
in-fection.Theseapproachescanbecomeevenmore
power-ful by integrating analyses across multiple large-scale
datasets.Gibbsetal.[19]wereabletofurtherrefinethese
approachesby independently assemblingtranscriptional
andproteomicnetworksandthencross-contrastingthese
twonetworktypes.Thismethodwasable toclarify
net-workmembershipandconnections,aswellasenhancethe
relationshipbetweenthesejointnetworksandaspectsof
SARS-induced lung pathology. In addition, such
approachesalsoresultedin highly prioritizedlist of
reg-ulatorswithconservedbehaviorforSARS-CoVand
influ-enza A viruses (IAV) via a combined analyses, which
providevaluablecandidatesfordownstreamexperimental
validationsandtherapeuticintervention[21].
Iterative rounds of perturbation are another key
com-ponentofthesystemsbiologyparadigm.Theseiterative
perturbationsareutilizedinordertorefineand
re-evalu-atenetworkswhen keymembersof these networksare
modified.Whileperturbationsaretypicallythoughtofas
host perturbations, in somecases they canalso beviral
perturbations. In this way, SARS-CoV ORF6 [23] was
identified as a key inhibitor of multiple antiviral cell
intrinsic host genetic responses byblocking the import
of targeted clusters of transcription factors into the
nucleusduringinfectionandtherebyreprogramminghost
response networks following infection. Chromosome
immunoprecipitation studies further validated the role
of ORF6 expression in the nuclear import and DNA
binding of select transcription factors, and loss of
ORF6 attenuated virus pathogenesis. In a parallel
example,theSARS-CoVEproteinisaknownvirulence
determinant[24].Usingsystemsbiology, Eproteinwas
found to suppress the expression of 25 stress related
proteins and specifically down-regulated the
inositol-requiring enzyme 1 (IRE-1) signaling pathway of
unfoldedproteinresponses.IntheabsenceofEprotein,
an increase in stress responses and the reduction of
inflammation likely contributed to the attenuation of
rSARS-CoV-DE,validatingthesystemswidepredictions.
In other cases, contrasting SARS-CoV with immune
stimulatory molecules (e.g. interferon stimulation) or
different pathogens can be used for cross-comparison.
In this way, Danesh et al. [25] were ableto show that
incontrasttoastrictinterferonresponseinaferretmodel
ofSARS-CoVinfection,awidervarietyofcellmigratory
andinflammatorygeneswereinduced.
standard reductionist strategies, these approaches still
relyonstandardgenetic,molecularbiology,biochemical
and immunologic strategies to validate the role of
tar-geted genes and networks in disease processes. Using
theseapproaches,thereishopethatmodelsystemsand
platform approaches can be utilized to identify critical
regulators of disease across genetically diverse human
populations,andtotransitionthesefindingsinto
prophy-lacticand therapeuticdrugs.
Systems
biology
approaches
Overthepastdecade,aseriesofimportanttechnological
advances, genome wide molecular screening platforms
andcomputationalstrategieshaveemergedthatprovide
newopportunitiesforrapidresponseagainstnewly
emer-gingviraldiseasethreats,globally.Theparadigmofthese systems biologyapproaches [15,17] is that (Figure 1) a
model system or systems (e.g. tissue culture model,in
vivoanimal model,orevenhumanchallengemodeland
vaccinestudies)areperturbed,inourcasebyviral
chal-lenge,preferably resultingin a spectraof disease
seve-rities(e.g.,lethalvssub-lethal)tomaximizecontrastfor
downstream data mining and modeling. Over a time
course,multipleglobalmeasuresofthesystem’s
perform-anceare takeninresponseto infection, including
high-throughput molecular measures (transcriptome,
pro-teome,metabolome,etc.),aswellasavarietyofvirologic,
immunologicand pathologicmeasures(e.g. weightloss,
respiratory function, inflammatory response, mortality
andhistopathologicaldamage).Avarietyofcomputation
methodologies([18,19,20,21]andreviewedmorefully
in[22])andnetworkapproachesarethenusedtodenovo
identify regulatory networks, with these networks and
theirkineticresponsesthenbeingcorrelatedtodifferent
diseaseoutcomesin thesystem.Following theseinitial
descriptions, there are a series of continuing cycles of
testing and perturbations (host gene knockout, virus
mutantor therapeutic intervention)designed to further
validateandthenrefine themodelandto elucidatethe
mechanisticunderpinningsofthesystems’performance
asafunctionof infectionanddiseaseseverity.
Modelingalgorithmsarerapidlyevolvinginresponseto
the emergence of these complex and comprehensive
systemswidedatasets andare beyond thefocus of this
review (but see [22] for more information); however,
manyoftheseapproachesdenovoassemblethenetworks,
independent of annotated pathways or interactions. By
allowing this de novo assembly within the context of
infection,newrelationshipsbetweengenes(orthe
break-ing of previously annotated relationships) emerge that
allowfortheidentificationofcriticalsubnetworks.Sucha methodwasrecentlysuccessfullyusedtoidentifycritical
componentsofSARS-CoVinducedpathogenesis
Population-wide
variation
in
coronavirus
responses
Population-widevariationindiseaseresponsesisknown
to occur for many pathogens, and there was notable
variability within the disease severity and clinical
out-comesafterSARS-CoVandMERS-CoVinfections,most
notably in theelderly population. For SARS-CoV,
sys-tems approaches were used to differentiate resolution
fromfatalityinapatientcohort[26].Thisstudyshowed
that although initial immune responses were fairly
uniform, fatal cases of SARS-CoV infection exhibited
aberrant interferon stimulation, persistent chemokine
responses and disregulated adaptive immune networks.
Similarly,MERS-CoVinfectionshavemostlyclusteredin
men, and those with underlying medical conditions,
althoughthismayrepresentagenderdifferencein
acces-sibilitytohealthcareintheMiddleEast[9].However,as
isoftenthecasewithheterogeneoushumanpopulations,
whilecleartrendscanbeobservedindiseaseresponses,it
isunclearwhetherthoseobserveddifferentiating
patho-logic/responseclassesareduetounderlyinggenetic vari-ationwithinthepopulation,orduetootherfactors,such
as environmental factors, demography or exposure
histories. For example, SARS-CoV exhibited a 10%
mortalitythroughouttheoutbreak,butthismortalityrate
rose to 50% in the aged population [1,12]. A mouse
model of thisphenomenonsuggested ageneticlink,in
that increased disease severity correlates with aberrant
PGD(2)expressionthatimpairsrespiratoryDCmigration
andassociated reducedTcellresponses [27].
Figure1
(a) (b)
(c)
Current Opinion in Virology
The Systems Biology Paradigm. Systems Biology focuses on an iterative cycle of experiments. In model system (a)mouse is infected. (b)
[40,41]. These lines (e.g. C57Bl/6J or Balb/cJ) have
playedcriticalrolesinthedevelopmentofanimalmodels
and reagents that are useful for the study of host
responses;however,theydonotrecapitulatethegenetic
variationpresentwithintheoutbredhumanpopulation,
which is critical to disease responses. Recently, newly
developedmouseresourceswereexplicitlydesigned for
systemsgeneticsanalysisas wellas bettercapturingthe
geneticvariationseenwithinhumanpopulations.
Specifi-cally the Collaborative Cross (CC) [42] recombinant
inbredpanelandDiversityOutbred(DO)[43]population
arenovelmouse resourceswhichcombinetheutility of
experimentalmousemodelswiththegeneticvariability
criticalto contrastingexperimental modelswith human
responses.TheCCandDOarecomplimentaryresources
(Figure3)withlevelsofnaturalgeneticvariationroughly
consistentwith common variantssegregating across the
human population (107 single nucleotide
polymorph-ismsand106smallinsertion/deletions),and
character-izedbyrelativelyuniformdistributionsofvariationacross
the genome. The large number of CC lines, and the
continualgenerationofnovelgenomesofDOmicegive
rise to an incredibly large number of combinations of
geneticvariantsacross thosegenomes.Theseattributes
arecriticalforfirst,mappingofgeneticvariantsassociated
withinfectiousoutcomes,second,creatingnovelgenetic
backgroundwithwhichtostudytranscriptionaland
regu-latory networks, third, describing new models of virus
diseasesand pathologies,andfourth,accurate modeling
of the human population’s genetic composition while
maintaining experimentally tractable systems [44].
Importantly for systems genetics approaches, the CC
and theDO notonly facilitate initial discovery,but by
allowing for the generation of new crosses and animals
withsimilarallelefrequenciesbutinnewcombinations,
they alsoallow for thevalidationof the roleof specific
polymorphic genes and further mechanistic study
(Figure3).
Systemsgeneticsapproacheshavebeenusedextensively
instudyingtheresponsestoinfluenza[44–46,47].
Over-all, these studies have found that multiple host
poly-morphisms contribute to differential disease outcomes
following influenza infection, that some of these
poly-morphismsact in virusstrain-specific manners,andthat
different subsets of transcripts associate with specific
disease responses following these infections.
Further-more,byintegrating thesesystems geneticsapproaches
throughout multiple timepoints, Nedelko et al. [47]
wereabletoshowthatpolymorphismsworkedatspecific
points throughout the infection process, pointing to
furthercomplexityintheroleofgeneticregulation
under-lyingdifferentialdiseaseoutcomes.Together,these
stu-dies highlight the incredible power and precision that
systemsgeneticsapproachescanprovide,especiallywhen
genetic causesremainsunclear. Itisclearfrom studies
following the SARS-CoV outbreak that host genetic
variants do have significant associations with variant
immune phenotypes following SARS-CoV infection,
althoughtheclinicalrelevanceofthesepolymorphisms
and their connections to pathologic outcomes are less
understood[28–31].Moregenerally, itiswellaccepted
that host genetic variants play key roles in onset,
severity and resolution of viral infection (reviewed in
[32]). Despitethepresence ofseveral well-known and
highly penetrant susceptibility genes of large effect
(e.g. CCR5 and HIV [33], FUT2 in norovirus and
perhapsrotavirusinfections[34,35]),thereisan
increas-ing awareness that responses to viral pathogens are
likely regulated by complex interactions involving
multiplevariantgenes andtheircorresponding
expres-sion networks that are activated following infection
[36]. However, identification of these polymorphic
genes and their associated pathways and outcomes is
confounded by the large controlled cohorts typically
needed to detect moderate to small effect alleles in
association studies [37]. Therefore, novel approaches
areneededtoaidinthediscoveryofthosepolymorphic
networkswhichcontribute toviralpathogenesisinthe
cases of emerging pathogens with limited human
samples
Systems
genetics
approaches
While genome wide association studies within human
populations can provide powerful insight into disease
responses, both the absence of large human cohorts to
conduct such association studies, and the difficulty in
transitioningsuchassociationsintomechanismsof
patho-logicorprotectiveoutcomesprovideroadblocksfordirect
humanstudies.Inanswertosuchneeds,systemsgenetics
approaches utilize genetically diverse experimental
modelstorecapitulatethepopulation-widevariationseen
acrossthehumanpopulationandattempttodisentangle
complextraits,suchasimmuneresponses[38,39].
Specifi-cally, by integrating not only pathologic and
high-throughputmoleculardata,but alsoexplicitinformation
on the genetic composition of the experimental
popu-lation,systemsgeneticsseekstoidentifygenesand
path-ways of polymorphic genes that directly contribute to
variation in responses to infection across genetically
diversepopulations,aswellas fortofurtherdisentangle
theunderlyingmolecularsignaturesandpathways
associ-atedwith variousdiseaseoutcomes(Figure 2).
Further-more, by explicitly contrasting the high-throughput
molecular and phenotypic data across unique genetic
backgrounds, robust virus-response signatures can be
identified across host genetic backgrounds, attaining a
better resolution of the dynamic and host regulatory
responses that act in host-genetic background specific
blended with systems biology and computational modeling.
Systemsapproacheshaveclassicallyusedtraditional
tran-scriptome profiling, suchas microarray andmRNAseq.
However, there is increasing evidence that non-coding
RNAsplayrolesinregulatingimmuneresponses[48,49],
and can have direct impact on viral infection [50].
Relevant to Coronavirus pathogenesis, two studies of
contrasting IAV and SARS-CoV induced long [51] and
small [52] non-coding RNAs were recently conducted
withinasubsetofthefounderanimalsoftheCC,focusing
onfounderlinesfromthethree geneticallydistant
sub-speciesofMusmusculus,whichhavedistinctresponsesto
bothSARS-CoVandIAVinfection.Bothofthesestudies
foundthattherewerepervasivechangesintheexpression
levels of these noncoding transcripts during infections.
Importantly for systems genetics approaches, they
showed that these two pathogens led to differential
regulationof thesenoncoding RNAsand thatthelevels
Figure2
(a) (b) (c)
(d)
Strength of
phenotype-genotype
association
Genome Position 1
0 2 4 6 8 10
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19x
Current Opinion in Virology
ofdifferentialexpressionforthesenoncodingRNAsvary
dependingonhostgeneticbackground.Thiswork
high-lights that unique interactions between specific viral
infections and host genetic variation drive differential
disease outcomes, and through the use of systems
geneticsapproaches,hostresponsesandthecritical
path-wayscausingvariouspathologicoutcomescanbedefined.
With a growing appreciation for the overall roles of
noncoding RNAs in regulating immune responses and
pathogenesis [53],as wellas evidence that
polymorph-ismswithinnoncodingRNAscandirectlyimpact
patho-logic outcomes during infection, such as clearance of
HepatitisBinfection[54],theinvestigationanddetection
of noncoding RNAs in future systems genetics
(a)
(d)
(e)
(g)
(f)
(b) (c)
Current Opinion in Virology
approacheswillprovidearichinvestigativeenvironment
for investigating how host genetic variation shapes
immune responsesandpathologicoutcomes.
Future
prospects
As illustrated throughout this study, the integration of
systemsapproachesin traditionalstudiesonviral
patho-genesisprovidesimmenselypowerfultoolswithwhichto
identifythehostfactorscriticalforpathologicor
protec-tive outcomesfollowingviralinfectionsin experimental
systems. A key challenge for the field is to transition
targets generated by systems approaches into
thera-peutics and prophylactics. Recently this hasbeen seen
for both MERS-CoV [55], and H7N9 avian influenza
[56],usingcellculturemodels.Inbothcases,application
ofsystemsapproachesandcontrastinginfections
(MERS-CoV and SARS-CoV;H7N9 andH3N2influenza) were
used to identify pathways differentially regulated
be-tween relatedpathogens,and thenthisinformation was
applied to selectand testpotentialantiviralcompounds
which were able to inhibit both the target and related
virus in the case of Coronaviruses [55], or just the
specific H7N9 target virus but not the related H3N2
virus[56].Futureapproachesin theseveins,and
transi-tioningsuchresultsto invivosystemsgeneticplatforms
such as the CC will further improve our capacity to
combatconventionalandnewviraldiseasesofthefuture.
A longstanding divide in the scientific community has
beenbridgingthegapbetweenexperimentalsystemsand
human populations. Indeed, some commonalities exist
betweenmurineandhuman immuneresponses[57,58],
such as the roleof IFITM3 in both humanand mouse
responses to influenza [58]. However, there are other
studies highlighting discordance between humans and
mice[59].Whilesystemsapproachesidentifykeygenes,
boththeirfocusonpathwaysandsystemicresponses,and
theexplicitintegrationofgeneticvariationwillallowfor
morerobustdescriptionsofhowpathogenscausevariant
diseaseresponseswithinandacrossspecies.Theseresults
will increase thelikelihoodthat,whileindividualgenes
might not be key regulators of disease across species,
there will be commonly identified pathways regulating
diseasethatcanbeidentifiedinexperimentalmodelsand
transitionedintohumansystems.Insupportofthishope,
Mitchell [21]was ableto showcommontranscriptional
signatures between human cells and mice following
highly pathogenic flu and SARS infections. Similarly,
Sims[23]foundconservedsignalsbetweenimmortalized
Calu3 cells and primary airway epithelial cultures.
Furthermore, systems based approachesstudying
influ-enza vaccine responses within humans were able to
identifytheCaMKIVkinasepathwayascriticalforthese
responses, and this molecule was validated in murine
knockout systems [57]. The further advancement and
refinementofsuchapproachesinexperimentalsystems,
combinedwith state-of-the-artexperimentalapproaches
such asgeneediting[60],as wellas molecularprofiling
anddiseasedatagatheredfromhumancohorts[61],hold
keys for transitioning bench-top findings to clinical
results.Giventheexpandingnatureofviralemergences,
due to increased connectivity and ease of travel, the
continuing refinement and further developmentof
sys-temsapproachescombinedwiththeadvanced
methodo-logicalapproachesbeingdevelopedshouldprovidenovel
avenues with which to quickly addressthe added
com-plexityofhostgeneticvariationincombattingemerging
pathogens.
Acknowledgements
WeacknowledgeSPandRGforassistancewithfigures.Theauthorswere
supported by the Division of Intramural Research, National Institute of
AllergyandInfectiousDiseasesunderawardnumberU19AI100625.The
contentissolelytheresponsibilityoftheauthorsanddoesnotnecessarily
represent the official views of the National Institutes of Health.
References
and
recommended
reading
Papersofparticularinterest,publishedwithintheperiodofreview, havebeenhighlightedas:
ofspecialinterest
ofoutstandinginterest
1. PeirisJS,GuanY,YuenKY:Severeacuterespiratorysyndrome.
NatMed2004,10:S88-S97.
2. LiW,WongSK,LiF,KuhnJH,HuangIC,ChoeH,FarzanM: Animaloriginsofthesevereacuterespiratorysyndrome coronavirus:insightfromACE2-S-proteininteractions.JVirol
2006,80:4211-4219.
3. LauSK,WooPC,LiKS,HuangY,TsoiHW,WongBH,WongSS, LeungSY,ChanKH,YuenKY:Severeacuterespiratory syndromecoronavirus-likevirusinChinesehorseshoebats.
ProcNatlAcadSciUSA2005,102:14040-14045.
4. GuanY,ZhengBJ,HeYQ,LiuXL,ZhuangZX,CheungCL, LuoSW,LiPH,ZhangLJ,GuanYJetal.:Isolationand characterizationofvirusesrelatedtotheSARScoronavirus fromanimalsinsouthernChina.Science2003,302:276-278.
5. HaagmansBL,AlDhahirySH,ReuskenCB,RajVS,GalianoM, MyersR,GodekeGJ,JongesM,FaragE,DiabAetal.:Middle Eastrespiratorysyndromecoronavirusindromedarycamels: anoutbreakinvestigation.LancetInfectDis2014,14:140-145.
6. MemishZA,MishraN,OlivalKJ,FagboSF,KapoorV,EpsteinJH, AlhakeemR,DurosinlounA,AlAsmariM,IslamAetal.:Middle Eastrespiratorysyndromecoronavirusinbats,SaudiArabia.
EmergInfectDis2013,19:1819-1823.
7. PeirisJS:SevereAcuteRespiratorySyndrome(SARS).JClin Virol2003,28:245-247.
8. MemishZA,ZumlaAI,Al-HakeemRF,Al-RabeeahAA, StephensGM:FamilyclusterofMiddleEastrespiratory syndromecoronavirusinfections.NEnglJMed2013, 368:2487-2494.
9. AssiriA,Al-TawfiqJA,Al-RabeeahAA,Al-RabiahFA,Al-HajjarS, Al-BarrakA,FlembanH,Al-NassirWN,BalkhyHH,Al-HakeemRF
etal.:Epidemiological,demographic,andclinical characteristicsof47casesofMiddleEastrespiratory syndromecoronavirusdiseasefromSaudiArabia:a descriptivestudy.LancetInfectDis2013,13:752-761.
10. AssiriA,McGeerA,PerlTM,PriceCS,AlRabeeahAA, CummingsDA,AlabdullatifZN,AssadM,AlmulhimA, MakhdoomHetal.:HospitaloutbreakofMiddleEast respiratorysyndromecoronavirus.NEnglJMed2013, 369:407-416.
11.
viruses (a) use thesame ACE2 receptor asSARS-CoV and(b) can specificallyutilizethehumanACE2,thispaperhighlightstheneedfor vigilanceandthedevelopmentofmethodologiestoquicklyrespondto novel disease outbreaks.
12. CherryJD:Thechronologyofthe2002–2003SARSmini pandemic.PaediatrRespirRev2004,5:262-269.
13. ZhongNS,WongGW:Epidemiologyofsevereacuterespiratory syndrome(SARS):adultsandchildren.PaediatrRespirRev
2004,5:270-274.
14. LipkinWI:Thechangingfaceofpathogendiscoveryand surveillance.NatRevMicrobiol2013,11:133-141.
15. AderemA,AdkinsJN,AnsongC,GalaganJ,KaiserS,KorthMJ, LawGL,McDermottJG,ProllSC,RosenbergerCetal.:Asystems biologyapproachtoinfectiousdiseaseresearch:innovating thepathogen–hostresearchparadigm.MBio2011, 2:e00325-e00410.
16. ThreadgillDW,MillerDR,ChurchillGA,deVillenaFP:The collaborativecross:arecombinantinbredmousepopulation forthesystemsgeneticera.ILARJ2011,52:24-31.
17. LawGL,KorthMJ,BeneckeAG,KatzeMG:Systemsvirology: host-directedapproachestoviralpathogenesisanddrug targeting.NatRevMicrobiol2013,11:455-466.
18. McDermottJE,ShankaranH,EisfeldAJ,BelisleSE,NeumanG, LiC,McWeeneyS,SabourinC,KawaokaY,KatzeMG, WatersKM:Conservedhostresponsetohighlypathogenic avianinfluenzavirusinfectioninhumancellculture,mouse andmacaquemodelsystems.BMCSystBiol2011,5 190-0509-5-190.
19. GibbsDL,GralinskiL,BaricRS,McWeeneySK:Multi-omic networksignaturesofdisease.FrontGenet2014,4:309.
20.
Gralinski LE, Bankhead A 3rd, Jeng S, Menachery VD, Proll S, Belisle SE, Matzke M, Webb-Robertson BJ, Luna ML, Shukla AK
etal.: Mechanismsofsevereacuterespiratorysyndrome
coronavirus-inducedacutelunginjury. MBio2013, 4http://
dx.doi.org/10.1128/mBio.00271-13.
By utilizing de novo network assemblyapproaches, anda seriesof escalatingdosesofSARS-CoV,theauthorswereabletoidentifyand validatetheroleofserpine1andtheurokinasepathwayasprotectivein SARS-CoVinfection.Thisstudyhighlightedtheabilityofsystemsbiology approachesthatcanbeusednotonlyinvitro,butalsoindissectinginvivo Coronaviruspathogenesisresponses.
21.
MitchellHD,EisfeldAJ,SimsAC,McDermottJE,MatzkeMM, Webb-RobertsonBJ,TiltonSC,TchitchekN,JossetL,LiCetal.:A networkintegrationapproachtopredictconservedregulators relatedtopathogenicityofinfluenzaandSARS-CoV respiratoryviruses.PLoSONE2013,8:e69374.
By explicitly integrating multiple pathogens and multiple pathogen strains in this analysis, the authors were able to identify sets of transcripts and key regulators that acted in virus-specific and pan-virus ways. Further-more, they were able to show that these approaches could be used to predict responses derived from invitro systems into exvivoprimary human airway cultures.
22. DiercksA,AderemA:Systemsapproachestodissecting immunity.CurrTopMicrobiolImmunol2013,363:1-19.
23. SimsAC,TiltonSC,MenacheryVD,GralinskiLE,SchaferA, MatzkeMM,Webb-RobertsonBJ,ChangJ,LunaML,LongCE
etal.:Releaseofsevereacuterespiratorysyndrome coronavirusnuclearimportblockenhanceshosttranscription inhumanlungcells.JVirol2013,87:3885-3902.
24. DeDiegoML,Nieto-TorresJL,Jimenez-GuardenoJM, Regla-NavaJA,AlvarezE,OliverosJC,ZhaoJ,FettC,PerlmanS, EnjuanesL:Severeacuterespiratorysyndromecoronavirus envelopeproteinregulatescellstressresponseand apoptosis.PLoSPathog2011,7:1002315.
25. DaneshA,CameronCM,LeonAJ,RanL,XuL,FangY,KelvinAA, RoweT,ChenH,GuanYetal.:Earlygeneexpressioneventsin ferretsinresponsetoSARScoronavirusinfectionversusdirect interferon-alpha2bstimulation.Virology2011,409:102-112.
associatedwithatypicalinnateandadaptiveimmune responsesinpatientswithsevereacuterespiratorysyndrome.
JVirol2007,81:8692-8706.
27. ZhaoJ,ZhaoJ,LeggeK,PerlmanS:Age-relatedincreasesin PGD(2)expressionimpairrespiratoryDCmigration,resulting indiminishedTcellresponsesuponrespiratoryvirusinfection inmice.JClinInvest2011,121:4921-4930.
28. ZhaoK,WangH,WuC:TheimmuneresponsesofHLA-A*0201 restrictedSARS-CoVSpeptide-specificCD8(+)Tcellsare augmentedinvaryingdegreesbyCpGODN,PolyI:CandR848.
Vaccine2011,29:6670-6678.
29. WangSF,ChenKH,ChenM,LiWY,ChenYJ,TsaoCH,YenMY, HuangJC,ChenYM:Human-leukocyteantigenclassICw1502 andclassIIDR0301genotypesareassociatedwithresistance tosevereacuterespiratorysyndrome(SARS)infection.Viral Immunol2011,24:421-426.
30. ChanKY,ChingJC,XuMS,CheungAN,YipSP,YamLY,LaiST, ChuCM,WongAT,SongYQetal.:AssociationofICAM3genetic variantwithsevereacuterespiratorysyndrome.JInfectDis
2007,196:271-280.
31. ChanKY,XuMS,ChingJC,ChanVS,IpYC,YamL,ChuCM, LaiST,SoKM,WongTYetal.:Associationofasinglenucleotide polymorphismintheCD209(DC-SIGN)promoterwithSARS severity.HongKongMedJ2010,16:37-42.
32. FerrisMT,HeiseMT:Quantitativegeneticsinthestudyof virus-induceddisease.AdvVirusRes2014,88:193-225.
33. HuangY,PaxtonWA,WolinskySM,NeumannAU,ZhangL,HeT, KangS,CeradiniD,JinZ,YazdanbakhshKetal.:Theroleofa mutantCCR5alleleinHIV-1transmissionanddisease progression.NatMed1996,2:1240-1243.
34. Imbert-MarcilleBM,BarbeL,DupeM,LeMoullac-VaidyeB, BesseB,PeltierC,Ruvoen-ClouetN,LePenduJ:AFUT2gene commonpolymorphismdeterminesresistancetorotavirusA oftheP[8]genotype.JInfectDis2014,209:1227-1230.
35. LindesmithL,MoeC,MarionneauS,RuvoenN,JiangX, LindbladL,StewartP,LePenduJ,BaricR:Humansusceptibility andresistancetoNorwalkvirusinfection.NatMed2003, 9:548-553.
36. NozawaY,UmemuraT,JoshitaS,KatsuyamaY,ShibataS, KimuraT,MoritaS,KomatsuM,MatsumotoA,TanakaE,OtaM: KIR,HLA,andIL28Bvariantpredictresponsetoantiviral therapyingenotype1chronichepatitisCpatientsinJapan.
PLoSONE2013,8:e83381.
37. HouL,ZhaoH:Areviewofpost-GWASprioritization approaches.FrontGenet2013,4:280.
38. ThreadgillDW,ChurchillGA:Tenyearsofthecollaborativecross. G3(Bethesda).2012,2:153-156.
39. BlairRH,KliebensteinDJ,ChurchillGA:Whatcancausal networkstellusaboutmetabolicpathways?PLoSComputBiol
2012,8:e1002458.
40. RobertsA,DemingD,PaddockCD,ChengA,YountB,VogelL, HermanBD,SheahanT,HeiseM,GenrichGLetal.:A mouse-adaptedSARS-coronaviruscausesdiseaseandmortalityin BALB/cmice.PLoSPathog2007,3:e5.
41. RobertsA,LamirandeEW,VogelL,JacksonJP,PaddockCD, GuarnerJ,ZakiSR,SheahanT,BaricR,SubbaraoK:Animal modelsandvaccinesforSARS-CoVinfection.VirusRes2008, 133:20-32.
42. CollaborativeCrossConsortium:Thegenomearchitectureof theCollaborativeCrossmousegeneticreference,population.
Genetics2012,190:389-401.
43. SvensonKL,GattiDM,ValdarW,WelshCE,ChengR,CheslerEJ, PalmerAA,McMillanL,ChurchillGA:High-resolutiongenetic mappingusingthemousediversityoutbredpopulation.
44. FerrisMT,AylorDL,BottomlyD,WhitmoreAC,AicherLD,BellTA, Bradel-TrethewayB,BryanJT,BuusRJ,GralinskiLEetal.: Modelinghostgeneticregulationofinfluenzapathogenesisin thecollaborativecross.PLoSPathog2013,9:e196-e1003.
45. BoonAC,deBeauchampJ,HollmannA,LukeJ,KotbM,RoweS, FinkelsteinD,NealeG,LuL,WilliamsRW,WebbyRJ:Host geneticvariationaffectsresistancetoinfectionwithahighly pathogenicH5N1influenzaAvirusinmice.JVirol2009, 83:10417-10426.
46. BottomlyD,FerrisMT,AicherLD,RosenzweigE,WhitmoreA, AylorDL,HaagmansBL,GralinskiLE,Bradel-TrethewayBG, BryanJTetal.:Expressionquantitativetraitlociforextreme hostresponsetoInfluenzaAinpre-collaborativecrossmice.
GenesGenomesGenet2012,2:213-221.
47.
NedelkoT,KollmusH,KlawonnF,SpijkerS,LuL,HessmannM, AlbertsR,WilliamsRW,SchughartK:Distinctgenelocicontrol thehostresponsetoinfluenzaH1N1virusinfectionina time-dependentmanner.BMCGenomics2012,13:411.
Utilizing the genetically diverse, recombinant inbred BxD panel of mice, the authors were able to show that host responses to influenza A virus were under the control of multiple polymorphisms. Importantly for both systems genetics approaches, and the study of host polymorphisms in humanpopulations,manyofthesepolymorphismsactedatspecifictimes post-infection.
48. PodshivalovaK,SalomonDR:MicroRNAregulationof T-lymphocyteimmunity:modulationofmolecularnetworks responsibleforT-cellactivation,differentiation,and development.CritRevImmunol2013,33:435-476.
49. LiZ,ChaoTC,ChangKY,LinN,PatilVS,ShimizuC,HeadSR, BurnsJC,RanaTM:ThelongnoncodingRNATHRILregulates TNFalphaexpressionthroughitsinteractionwithhnRNPL.
ProcNatlAcadSciUSA2014,111:1002-1007.
50. SwaminathanG,Navas-MartinS,Martin-GarciaJ:MicroRNAs andHIV-1Infection:antiviralactivitiesandbeyond.JMolBiol
2014,426:1178-1197.
51. PengX,GralinskiL,ArmourCD,FerrisMT,ThomasMJ,ProllS, Bradel-TrethewayBG,KorthMJ,CastleJC,BieryMCetal.:
UniquesignaturesoflongnoncodingRNAexpressionin
responsetovirusinfectionandalteredinnateimmune
signaling.MBio2010,1:6-10http://dx.doi.org/10.1128/
mBio.00206-10.
52. PengX,GralinskiL,FerrisMT,FriemanMB,ThomasMJ,ProllS, KorthMJ,TisoncikJR,HeiseM,LuoSetal.:Integrativedeep
sequencingofthemouselungtranscriptomereveals
differentialexpressionofdiverseclassesofsmallRNAsin
responsetorespiratoryvirusinfection.MBio2011,2:10http://
dx.doi.org/10.1128/mBio.00198-11.
53. ZhouA,LiS,WuJ,KhanFA,ZhangS:Interplaybetween microRNAsandhostpathogenrecognitionreceptors(PRRs) signalingpathwaysinresponsetoviralinfection.VirusRes
2014,184C:1-6.
54. CheongJY,ShinHD,KimYJ,ChoSW:Associationof polymorphisminMicroRNA219-1withclearanceofhepatitis Bvirusinfection.JMedVirol2013,85:808-814.
55.
JossetL,MenacheryVD,GralinskiLE,AgnihothramS,SovaP, CarterVS,YountBL,GrahamRL,BaricRS,KatzeMG:Cellhost responsetoinfectionwithnovelhumancoronavirusEMC predictspotentialantiviralsandimportantdifferenceswith SARScoronavirus.MBio2013,4e00165-13.
The authorsutilize a systemsbiology approach and contrasting cell cultureinfectionsofSARS-CoVandMERS-CoVtoidentifycritical net-workscontrollingtheseinfections.Mostimportantly,theywereableto identifykinaseinhibitorswhichattenuatedgrowthofbothviruses, high-lightingtheabilitytotransitionsystemsapproachestotherapeutics.
56. JossetL,ZengH,KellySM,TumpeyTM,KatzeMG:
Transcriptomiccharacterizationofthenovelavian-origin
InfluenzaA(H7N9)virus:specifichostresponseand
responsesintermediatebetweenAvian(H5N1andH7N7)and
human(H3N2)virusesandimplicationsfortreatmentoptions.
MBio2014,5http://dx.doi.org/10.1128/mBio.01102-13.
57. NakayaHI,WrammertJ,LeeEK,RacioppiL,Marie-KunzeS, HainingWN,MeansAR,KasturiSP,KhanN,LiGMetal.:Systems biologyofvaccinationforseasonalinfluenzainhumans.Nat Immunol2011,12:786-795.
58. EverittAR,ClareS,PertelT,JohnSP,WashRS,SmithSE, ChinCR,FeeleyEM,SimsJS,AdamsDJetal.:IFITM3restricts themorbidityandmortalityassociatedwithinfluenza.Nature
2012,484:519-523.
59. SeokJ,WarrenHS,CuencaAG,MindrinosMN,BakerHV,XuW, RichardsDR,McDonald-SmithGP,GaoH,HennessyLetal.: Genomicresponsesinmousemodelspoorlymimichuman inflammatorydiseases.ProcNatlAcadSciUSA2013, 110:3507-3512.
60. SiggsOM:Dissectingmammalianimmunitythroughmutation.
ImmunolCellBiol2014.Feb11.
61. ZaasAK,ChenM,VarkeyJ,VeldmanT,Hero AO3rd,LucasJ, HuangY,TurnerR,GilbertA,Lambkin-WilliamsRetal.:Gene
expressionsignaturesdiagnoseinfluenzaandother
symptomaticrespiratoryviralinfectionsinhumans.CellHost
