Forsomethingascomplexandmultifacetedasbacterialantibioticresistance(AR),ourdrugevaluation paradigmisstrikinglynarrowandhomogenous:MIC/MBCtestinginstandardizedbacteriologicmedia.We haveshownthatthisdrugevaluationparadigmisinadequate,evenmisleading,aschangesinthemedia conditionsoftheprocedureleadtodramaticallydifferentresults.Amoreholisticdefinitionofantibiotictherapy thatcentersonunderstandingantibioticactivityinsynergywithhostinnateimmunefactorssuchascationic antimicrobialpeptides(AMPs),serumandphagocyticcells(e.g.neutrophils)revealstherapeuticoptions unrecognizedinstandardtesting.TheproposedU01programrepresentsagroundbreakingapproachtouse systemsbiologyapproachesandinformmoreeffectiveantibioticutilizationinthecontextofhostinnate immunity.Weproposeto:1)buildaniterativesystemsbiologyworkflowthatintegratesmultipleexperimental andcomputationalapproachestogiveacomprehensiveassessmentofAR?and2)applythisworkflowtohigh prioritypathogenstosystematicallyelucidateARmechanismsandtheirconditiondependency.Theiterative workflowincludes:(i)omicsandphysiologicaldatageneration.Clinicallyisolatedstrainsoftheselected pathogenswillbegrownunderconventionaltesting(bacteriologicmedia)andmorephysiologicconditions (tissueculturemedia,serum,andinpresenceofAMPsandneutrophils)toprobeforadvantageousgainof activity.Theomicsdatatypescollectedare:DNAresequencing,RNAseq,andmetabolomics.(ii) Bioinformaticsanddatamodelinganalysisinvolvesthreeapproaches:bigdataanalysisfordataset dimensionalityandcoarsegrainedvariabledependenciesassessment,genomescalemodelingfor mechanisticelucidationandanalysis,andmachinelearningthatusesARrelatedmetadatatoclassifythe overallbiologicalfunctions.ThisanalysiswillleadtounderstandingofARmechanisms.(iii)Multiscale validationfromanimalmodels,tolaboratoryevolution,tocytology,togeneexpressionalteration,tostructural proteinanalysisofputativetargets.Thevalidationthusrangesfromhostbehaviortoatomisticdetailof ligandtargetinteractions.Theiterativeloopthencloses,comparingcomputationalpredictiontoexperimental outcomes.Falsenegativeandfalsepositivepredictionsarethenalgorithmicallyanalyzedbyahypothesis generatingfamilyofalgorithmsthatthenmakessuggestionsaboutwhatconditionstouseinthenextiteration oftheloop.Thepathogensthatwewillfocusonaremethicillinresistant?Staphylococcusaureus?(MRSA),the carbapenemresistantEnterobacteriaceae(CRE)Klebsiella?pneumoniae?and?Acinetobacterbaumannii,?and Pseudomonasaeruginosa?.Theteamofinvestigatorshasmadethefoundationalobservationsandledthe developmentofthetechnologiesonwhichtheiterativeworkflowisbased.Amultiandgenomescalemethods ofsystemsbiologyfulfillsrequirementsofRFAAI14064towhichitresponds.

Public Health Relevance

Thecurrentevaluationofantibioticdrugcandidatesindrugdiscoveryandinclinicalmedicineisconductedin laboratorymediathatignorestheactualphysiologicconditionsinthehostandthehostimmunesystem.We havediscoveredpotentantimicrobialactivitiesofexistingantibioticsagainsthighly?drugresistantsuperbugs? thatarecurrentlyignoredbutrevealedinsynergywiththehumanimmunesystem.Thisprogramproposesa holisticandcomprehensivesystemsbiologyapproachtosystematicallydiscovernoveltreatmentopportunities andunderlyingmechanismsusinganoveliterativedatageneration,analysis,andmodelingworkflow.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01AI124316-01
Application #
9108693
Study Section
Special Emphasis Panel (ZAI1-EC-M (J1))
Program Officer
Dugan, Vivien G
Project Start
2016-03-15
Project End
2021-02-28
Budget Start
2016-03-15
Budget End
2017-02-28
Support Year
1
Fiscal Year
2016
Total Cost
$1,989,374
Indirect Cost
$615,465
Name
University of California San Diego
Department
Pediatrics
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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Munguia, Jason; Nizet, Victor (2017) Pharmacological Targeting of the Host-Pathogen Interaction: Alternatives to Classical Antibiotics to Combat Drug-Resistant Superbugs. Trends Pharmacol Sci 38:473-488
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Bosi, Emanuele; Monk, Jonathan M; Aziz, Ramy K et al. (2016) Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A 113:E3801-9

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