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 evidenceAccumulationInferenceDifferential abundance analysis for microbial marker-gene surveys
Paulson J, Stine O, Bravo H, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods 2013, 10: 1200-1202. PMID: 24076764, PMCID: PMC4010126, DOI: 10.1038/nmeth.2658.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsArea Under CurveCluster AnalysisComputer SimulationDatabases, GeneticGene Expression ProfilingGenetic MarkersGenetic VariationHumansIntestinesMetagenomicsMiceMicrobiotaModels, GeneticModels, StatisticalNormal DistributionPhenotypeRNA, Ribosomal, 16SSequence Analysis, DNASoftware
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 ResearchMeSH KeywordsAdultBacteriaFemaleGelsHumansLubricantsLubricationMicrobiotaPregnancyRNA, Ribosomal, 16SSpecimen HandlingVaginaVaginal Creams, Foams, and JelliesConceptsMicrobial compositionVaginal samplesVaginal microbiome samplesBeta diversityVaginal microbiome studiesMicrobiome studiesMicrobiome samplesTaxa abundanceGynecological examinationPregnant womenLubricant gelReduce painMicrobial dataGel exposureEmergency roomSterile swabsEffect of gelMicrobial analysis
2023
mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation
Movassagh M, Schiff S, Paulson J. mbQTL: an R/Bioconductor package for microbial quantitative trait loci (QTL) estimation. Bioinformatics 2023, 39: btad565. PMID: 37707523, PMCID: PMC10516520, DOI: 10.1093/bioinformatics/btad565.Peer-Reviewed Original ResearchConceptsSingle nucleotide variationsRNA sequencingMicrobial abundance dataQuantitative trait lociSingle nucleotide polymorphism dataRibosomal RNA sequencingField of genomicsWhole-genome sequencingEvidence of interplayMutational profileTrait lociMicrobial communitiesMicrobial abundancePolymorphism dataMicrobial populationsGenome sequencingAbundance dataFirst R packageHuman geneticsBioconductor packageGenetic variantsMicrobiome dataSequencingR packageAbundance
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 ResearchMeSH KeywordsAdultBacteriaBiodiversityCluster AnalysisFemaleHumansLabor, ObstetricLactobacillusMicrobiotaPregnancyRNA, Ribosomal, 16SUgandaVaginaConceptsIntrapartum 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 influencesNasal Microbiota and Infectious Complications After Elective Surgical Procedures
Hsiao C, Paulson J, Singh S, Mongodin E, Carroll K, Fraser C, Rock P, Faraday N. Nasal Microbiota and Infectious Complications After Elective Surgical Procedures. JAMA Network Open 2021, 4: e218386. PMID: 33914049, PMCID: PMC8085724, DOI: 10.1001/jamanetworkopen.2021.8386.Peer-Reviewed Original ResearchMeSH KeywordsAgedBacteremiaCardiac Surgical ProceduresCase-Control StudiesCraniotomyElective Surgical ProceduresFemaleHumansMaleMicrobiotaMiddle AgedNosePneumoniaPostoperative ComplicationsRisk AssessmentRisk FactorsRNA, Ribosomal, 16SSpinal FusionStaphylococcus aureusSurgical Wound InfectionVascular Surgical ProceduresConceptsBaseline clinical characteristicsSurgical proceduresElective surgical proceduresInfectious complicationsInfectious outcomesPostoperative infectionClinical characteristicsComposite of surgical site infectionsNasal carriage of Staphylococcus aureusNasal microbiotaNo history of autoimmune diseaseCarriage of Staphylococcus aureusHistory of autoimmune diseaseProspective cohort of patientsNasal microbiomeTertiary care university hospitalOccurrence of postoperative infectionsImmune-modulating medicationsPostoperative infectious complicationsHigher oddsSurgical site infectionCohort of patientsIntracranial surgical proceduresAssociated with higher oddsNasal carriage
2019
metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data
Olson N, Shah N, Kancherla J, Wagner J, Paulson J, Bravo H. metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data. Bioinformatics 2019, 35: 3870-3872. PMID: 30821316, PMCID: PMC6761971, DOI: 10.1093/bioinformatics/btz136.Peer-Reviewed Original Research
2018
Conventional wastewater treatment and reuse site practices modify bacterial community structure but do not eliminate some opportunistic pathogens in reclaimed water
Kulkarni P, Olson N, Paulson J, Pop M, Maddox C, Claye E, Goldstein R, Sharma M, Gibbs S, Mongodin E, Sapkota A. Conventional wastewater treatment and reuse site practices modify bacterial community structure but do not eliminate some opportunistic pathogens in reclaimed water. The Science Of The Total Environment 2018, 639: 1126-1137. PMID: 29929281, PMCID: PMC8290890, DOI: 10.1016/j.scitotenv.2018.05.178.Peer-Reviewed Original ResearchMeSH KeywordsRecyclingRNA, Ribosomal, 16SWaste Disposal, FluidWastewaterWaterWater MicrobiologyWater PurificationConceptsWastewater treatment plantsTreatment plantsWastewater treatmentSpray irrigation siteMunicipal wastewater treatment plantU.S. wastewater treatment plantsConventional wastewater treatmentReclaimed waterIrrigated sitesCommunity structureBacterial communitiesFuture treatment technologiesOpen-air storageOn-site treatmentTreatment technologiesAlpha diversity of bacterial communitiesUltraviolet treatmentOperational taxonomic unitsRRNA gene sequencesInfluentBacterial community structureSprayWastewaterPumphouseWater recycling
2017
Mentholation 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
2016
Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection
Morris A, Paulson J, Talukder H, Tipton L, Kling H, Cui L, Fitch A, Pop M, Norris K, Ghedin E. Longitudinal analysis of the lung microbiota of cynomolgous macaques during long-term SHIV infection. Microbiome 2016, 4: 38. PMID: 27391224, PMCID: PMC4939015, DOI: 10.1186/s40168-016-0183-0.Peer-Reviewed Original ResearchConceptsDevelopment of obstructive lung diseaseObstructive lung diseaseNon-human primate modelLung microbiomeSHIV infectionLung microbiotaRibosomal RNALung diseasePrimate modelNon-obstructive groupSHIV-infected animalsResponse to immunosuppressionBronchoalveolar lavage fluid samplesCynomolgous macaquesDevelopment of obstructive diseaseLavage fluid samplesMultiple other speciesSIV-HIVOral anaerobesOral bacteriaTropheryma whippleiObstructive diseaseCommunity compositionBacterial communitiesDisease onsetIndividual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment
Pop M, Paulson J, Chakraborty S, Astrovskaya I, Lindsay B, Li S, Bravo H, Harro C, Parkhill J, Walker A, Walker R, Sack D, Stine O. Individual-specific changes in the human gut microbiota after challenge with enterotoxigenic Escherichia coli and subsequent ciprofloxacin treatment. BMC Genomics 2016, 17: 440. PMID: 27277524, PMCID: PMC4898365, DOI: 10.1186/s12864-016-2777-0.Peer-Reviewed Original ResearchConceptsGene sequencesBackgroundEnterotoxigenic Escherichia coliHuman gut microbiotaRRNA gene sequencesEnterotoxigenic Escherichia coliHuman intestinal microbiotaFecal E. coliCiprofloxacin treatmentETEC infectionETEC diarrheaRibosomal RNAGut microbiotaFecal microbiotaIntestinal microbiotaE. coliMicrobiotaMonthly follow-up visitsCompared to variationsETECFollow-up visitQuantitative PCRHuman challenge studiesCiprofloxacinQuantitative culturesSequence
2014
Reply to: "A fair comparison"
Paulson J, Bravo H, Pop M. Reply to: "A fair comparison". Nature Methods 2014, 11: 359-360. PMID: 24681718, DOI: 10.1038/nmeth.2898.Peer-Reviewed Original ResearchDiarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition
Pop M, Walker A, Paulson J, Lindsay B, Antonio M, Hossain M, Oundo J, Tamboura B, Mai V, Astrovskaya I, Bravo H, Rance R, Stares M, Levine M, Panchalingam S, Kotloff K, Ikumapayi U, Ebruke C, Adeyemi M, Ahmed D, Ahmed F, Alam M, Amin R, Siddiqui S, Ochieng J, Ouma E, Juma J, Mailu E, Omore R, Morris J, Breiman R, Saha D, Parkhill J, Nataro J, Stine O. Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition. Genome Biology 2014, 15: r76. PMID: 24995464, PMCID: PMC4072981, DOI: 10.1186/gb-2014-15-6-r76.Peer-Reviewed Original ResearchConceptsMicrobiota compositionAnaerobic lineagesRRNA gene sequencesGut microbiota compositionLevels of PrevotellaDiarrhea-causing pathogensSevere diarrheal diseaseIntestinal microbiota compositionFecal microbiota compositionGene sequencesDiarrheal pathogensDiarrhea pathogensMolecular techniquesPathogensDiarrhea-free controlsGranulicatella speciesEscherichia/ShigellaDiarrheal diseaseLineagesMSD casesYears of ageBackgroundDiarrheal diseasesYoung childrenAssociated with MSDLow-income countries