Featured Publications
Differential richness inference for 16S rRNA marker gene surveys
Kumar M, Slud E, Hehnly C, Zhang L, Broach J, Irizarry R, Schiff S, Paulson J. Differential richness inference for 16S rRNA marker gene surveys. Genome Biology 2022, 23: 166. PMID: 35915508, PMCID: PMC9344657, DOI: 10.1186/s13059-022-02722-x.Peer-Reviewed Original ResearchConceptsMarker gene surveysRRNA marker gene surveysGene surveysMicrobial assemblagesSpecies discoveryMicrobial taxaMicrobial communitiesMicrobiome surveysRichness estimationSequencing readsDiversity measuresTaxaGenus abundanceMicrobiome dataR packageAbundanceRichnessDiscoveryDiversityReadsAssemblagesObserved numberExperimental evidenceAccumulationInference
2024
Lubricating gel influence on vaginal microbiome sampling
Amitai Komem D, Hadar R, Paulson J, Mordechai Y, Eskandarian H, Efroni G, Amir A, Haberman Y, Tsur A. Lubricating gel influence on vaginal microbiome sampling. Scientific Reports 2024, 14: 18223. PMID: 39107405, PMCID: PMC11303677, DOI: 10.1038/s41598-024-68948-w.Peer-Reviewed Original ResearchConceptsMicrobial compositionVaginal samplesVaginal microbiome samplesBeta diversityVaginal microbiome studiesMicrobiome studiesMicrobiome samplesTaxa abundanceGynecological examinationPregnant womenLubricant gelReduce painMicrobial dataGel exposureEmergency roomSterile swabsEffect of gelMicrobial analysis
2021
Vaginal microbiome topic modeling of laboring Ugandan women with and without fever
Movassagh M, Bebell L, Burgoine K, Hehnly C, Zhang L, Moran K, Sheldon K, Sinnar S, Mbabazi-Kabachelor E, Kumbakumba E, Bazira J, Ochora M, Mulondo R, Nsubuga B, Weeks A, Gladstone M, Olupot-Olupot P, Ngonzi J, Roberts D, Meier F, Irizarry R, Broach J, Schiff S, Paulson J. Vaginal microbiome topic modeling of laboring Ugandan women with and without fever. Npj Biofilms And Microbiomes 2021, 7: 75. PMID: 34508087, PMCID: PMC8433417, DOI: 10.1038/s41522-021-00244-1.Peer-Reviewed Original ResearchConceptsIntrapartum feverClinical variablesHigh prevalenceVaginal microbiomeUgandan womenLonger labour durationMaternal clinical featuresYoung maternal ageDuration of pregnancyOnset of laborMicrobial communitiesVaginal microbial communitiesAfebrile mothersFebrile mothersPeripartum courseMaternal feverNeonatal outcomesLabor durationClinical featuresMaternal ageVaginal microbesFeverOutcome riskVeillonella genusMicrobiome influences
2019
Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome
Carrión V, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, Mendes L, van Ijcken W, Gomez-Exposito R, Elsayed S, Mohanraju P, Arifah A, van der Oost J, Paulson J, Mendes R, van Wezel G, Medema M, Raaijmakers J. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 2019, 366: 606-612. PMID: 31672892, DOI: 10.1126/science.aaw9285.Peer-Reviewed Original ResearchMeSH KeywordsBacteriaBacterial Physiological PhenomenaBacteroidetesBeta vulgarisBiodiversityChitinasesDisease ResistanceEndophytesFlavobacteriumGenes, BacterialGenome, BacterialMetagenomeMicrobiotaMutagenesis, Site-DirectedPeptide SynthasesPlant DiseasesPlant RootsPolyketide SynthasesRhizoctoniaSoil MicrobiologyConceptsEndophytic root microbiomeNonribosomal peptide synthetasesGene clusterPolyketide synthaseRoot microbiomeBiosynthetic gene clusterInfection of plant rootsFungal root diseasesSite-directed mutagenesisPromote plant growthGenome reconstructionPeptide synthetasesRoot endosphereFunctional traitsNRPS-PKSFunctional diversityChitinase geneEndophytic consortiaFungal infectionsDisease suppressionRoot diseasePlant growthNetwork inferenceDisease-suppressive functionsPlant roots
2018
Metaviz: interactive statistical and visual analysis of metagenomic data
Wagner J, Chelaru F, Kancherla J, Paulson J, Zhang A, Felix V, Mahurkar A, Elmqvist N, Bravo H. Metaviz: interactive statistical and visual analysis of metagenomic data. Nucleic Acids Research 2018, 46: gky136-. PMID: 29529268, PMCID: PMC5887897, DOI: 10.1093/nar/gky136.Peer-Reviewed Original ResearchConceptsWeb servicesInteractive exploratory data analysisMetagenomic shotgun sequencingState-of-the-artState-of-the-art analysis toolsMetagenomic samplesShotgun sequencingUser navigationMicrobial communitiesCommunity profilesData featuresData valuesDisease phenotypeMarker genesMetavizUsersData resourcesProcess dataVisual analysisAnalysis toolsHierarchical structureSignificant effortBioconductorMetagenomicsMicrobiomeMeta-analysis of the lung microbiota in pulmonary tuberculosis
Hong B, Paulson J, Stine O, Weinstock G, Cervantes J. Meta-analysis of the lung microbiota in pulmonary tuberculosis. Tuberculosis 2018, 109: 102-108. PMID: 29559113, DOI: 10.1016/j.tube.2018.02.006.Peer-Reviewed Original ResearchConceptsSpecies signatureNext generation sequencing dataLung microbiotaGeneration sequencing dataClustering of microbiotaMycobacterium tuberculosisCaulobacter henriciiLung microbiota compositionSequence dataBody nichesR. mucilaginosaMicrobiota compositionSputum microbiotaActinomyces graevenitziiBioinformatics analysisMicrobiotaRothia mucilaginosaTB patientsHealthy controlsTB casesAssociated with pulmonary TBHaemophilus parahaemolyticusMeta-analysisLower respiratory tractBody sites
2017
Simplified and representative bacterial community of maize roots
Niu B, Paulson J, Zheng X, Kolter R. Simplified and representative bacterial community of maize roots. Proceedings Of The National Academy Of Sciences Of The United States Of America 2017, 114: e2450-e2459. PMID: 28275097, PMCID: PMC5373366, DOI: 10.1073/pnas.1616148114.Peer-Reviewed Original ResearchConceptsCommunity assemblyBacterial communitiesDynamics of community assemblyComposition of plant microbiomesBacterial interspecies interactionsSynthetic bacterial communityPlant-associated microbesCulture-dependent methodsRoot microbiome assemblyHost-mediated selectionFusarium verticillioides</i>Maize rootsRoot microbiotaMicrobiome assemblyPlant microbiomeRoot microbiomePlant hostsHost genotypeKeystone speciesDominant phylaAbiotic factorsInterspecies interactionsModel ecosystemsMicrobiomeMaize seedlingsMentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens
Chopyk J, Chattopadhyay S, Kulkarni P, Claye E, Babik K, Reid M, Smyth E, Hittle L, Paulson J, Cruz-Cano R, Pop M, Buehler S, Clark P, Sapkota A, Mongodin E. Mentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens. Microbiome 2017, 5: 22. PMID: 28202080, PMCID: PMC5312438, DOI: 10.1186/s40168-017-0235-0.Peer-Reviewed Original ResearchConceptsQuantitative Insights Into Microbial EcologyResistant to harsh environmental conditionsBacterial pathogensHarsh environmental conditionsV3-V4 hypervariable regionIllumina MiSeq platformHuman bacterial pathogensBacterial community profilesBacterial community richnessPotential bacterial pathogensEnvironmental conditionsRRNA geneMiSeq platformAbundant generaBacterial microbiotaBacterial communitiesMicrobial ecologyGenomic DNACommunity richnessBacterial compositionHypervariable regionPCR amplificationCommunity profilesPseudomonas putidaLysis protocol
2013
Survey of Culture, GoldenGate Assay, Universal Biosensor Assay, and 16S rRNA Gene Sequencing as Alternative Methods of Bacterial Pathogen Detection
Lindsay B, Pop M, Antonio M, Walker A, Mai V, Ahmed D, Oundo J, Tamboura B, Panchalingam S, Levine M, Kotloff K, Li S, Magder L, Paulson J, Liu B, Ikumapayi U, Ebruke C, Dione M, Adeyemi M, Rance R, Stares M, Ukhanova M, Barnes B, Lewis I, Ahmed F, Alam M, Amin R, Siddiqui S, Ochieng J, Ouma E, Juma J, Mailu E, Omore R, O'Reilly C, Hannis J, Manalili S, DeLeon J, Yasuda I, Blyn L, Ranken R, Li F, Housley R, Ecker D, Hossain M, Breiman R, Morris J, McDaniel T, Parkhill J, Saha D, Sampath R, Stine O, Nataro J. Survey of Culture, GoldenGate Assay, Universal Biosensor Assay, and 16S rRNA Gene Sequencing as Alternative Methods of Bacterial Pathogen Detection. Journal Of Clinical Microbiology 2013, 51: 3263-3269. PMID: 23884998, PMCID: PMC3811648, DOI: 10.1128/jcm.01342-13.Peer-Reviewed Original ResearchConceptsEnteroaggregative Escherichia coliEnteropathogenic E. coliEnterotoxigenic E. coliDNA samplesShigella sppGoldenGate assaySequencing of 16S rRNA genesRRNA gene sequencesBiosensor assayCultivation-based assaysMolecular technologiesStool samplesIdentification of pathogensBacterial pathogen detectionDetect bacterial pathogensEnzyme-linked immunosorbent assay (ELISA)-based methodVirulence genesRRNA geneVirulence factorsGene sequencesBacterial pathogensDiarrheal pathogensEnteric pathogensPathogen detectionDNA