Abstract

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.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01AI124316-03
Application #
9440969
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Brown, Liliana L
Project Start
2016-03-15
Project End
2021-02-28
Budget Start
2018-03-01
Budget End
2019-02-28
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost

  Institution

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

  Publications

Quinn, Robert A; Comstock, William; Zhang, Tianyu et al. (2018) Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci Adv 4:eaau1908
Kavvas, Erol S; Seif, Yara; Yurkovich, James T et al. (2018) Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions. BMC Syst Biol 12:25
Fang, Xin; Monk, Jonathan M; Nurk, Sergey et al. (2018) Metagenomics-Based, Strain-Level Analysis of Escherichia coli From a Time-Series of Microbiome Samples From a Crohn's Disease Patient. Front Microbiol 9:2559
Kavvas, Erol S; Catoiu, Edward; Mih, Nathan et al. (2018) Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun 9:4306
Choudhary, Kumari S; Mih, Nathan; Monk, Jonathan et al. (2018) The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures. Front Microbiol 9:1082
Fang, Xin; Monk, Jonathan M; Mih, Nathan et al. (2018) Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa. BMC Syst Biol 12:66
Reynolds, Kirk A; Luhavaya, Hanna; Li, Jie et al. (2018) Isolation and structure elucidation of lipopeptide antibiotic taromycin B from the activated taromycin biosynthetic gene cluster. J Antibiot (Tokyo) 71:333-338
Seif, Yara; Kavvas, Erol; Lachance, Jean-Christophe et al. (2018) Genome-scale metabolic reconstructions of multiple Salmonella strains reveal serovar-specific metabolic traits. Nat Commun 9:3771
Pekar, Jonathan E; Phaneuf, Patrick; Szubin, Richard et al. (2018) Gapless, Unambiguous Genome Sequence for Escherichia coli C, a Workhorse of Industrial Biology. Microbiol Resour Announc 7:
Kumaraswamy, Monika; Collignon, Sean; Do, Carter et al. (2018) Decontaminating surfaces with atomized disinfectants generated by a novel thickness-mode lithium niobate device. Appl Microbiol Biotechnol 102:6459-6467

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