2024
BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks
Pelletier S, Leclercq M, Roux-Dalvai F, de Geus M, Leslie S, Wang W, Lam T, Nairn A, Arnold S, Carlyle B, Precioso F, Droit A. BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks. Nature Communications 2024, 15: 3777. PMID: 38710683, PMCID: PMC11074280, DOI: 10.1038/s41467-024-48177-5.Peer-Reviewed Original ResearchConceptsLC-MS experimentsLC-MSLiquid chromatography mass spectrometry dataComplex biological samplesMass spectrometry dataLiquid chromatography mass spectrometryChromatography mass spectrometryMass spectrometrySpectrometry dataEffective removalBiological samplesExperimental conditionsBatch effect removalSample processing protocolBatch effectsSpectrometryBatch effect correction methodsCorrecting batch effectsRemoval of batch effects
2023
Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer’s disease in a large clinical sample
de Geus M, Leslie S, Lam T, Wang W, Roux-Dalvai F, Droit A, Kivisakk P, Nairn A, Arnold S, Carlyle B. Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer’s disease in a large clinical sample. Scientific Reports 2023, 13: 22406. PMID: 38104170, PMCID: PMC10725469, DOI: 10.1038/s41598-023-49440-3.Peer-Reviewed Original ResearchPeripheral signature of altered synaptic integrity in young onset cannabis use disorder: A proteomic study of circulating extracellular vesicles
Ganesh S, Lam T, Garcia-Milian R, D'Souza D, Nairn A, Elgert K, Eitan E, Ranganathan M. Peripheral signature of altered synaptic integrity in young onset cannabis use disorder: A proteomic study of circulating extracellular vesicles. The World Journal Of Biological Psychiatry 2023, 24: 603-613. PMID: 36994633, PMCID: PMC10471733, DOI: 10.1080/15622975.2023.2197039.Peer-Reviewed Original ResearchConceptsNeuron-derived extracellular vesiclesLabel-free quantification mass spectrometryProteomic studiesCannabis use disorderExtracellular vesiclesMass spectrometry proteomic analysisDifferential proteomic profilesAdapter proteinProteomic analysisPost-synaptic densityPeripheral signatureMolecular basisProteomic profilesProteinMarkers of neuropathologyBrain tissue samplesSynaptic pathologyVesiclesSynaptic integrityImmunoaffinity methodUse disordersFunctional integrityImportant insightsNeuropathologyPilot study
2022
Performance of a fully-automated Lumipulse plasma phospho-tau181 assay for Alzheimer’s disease
Wilson E, Young C, Ramos Benitez J, Swarovski M, Feinstein I, Vandijck M, Le Guen Y, Kasireddy N, Shahid M, Corso N, Wang Q, Kennedy G, Trelle A, Lind B, Channappa D, Belnap M, Ramirez V, Skylar-Scott I, Younes K, Yutsis M, Le Bastard N, Quinn J, van Dyck C, Nairn A, Fredericks C, Tian L, Kerchner G, Montine T, Sha S, Davidzon G, Henderson V, Longo F, Greicius M, Wagner A, Wyss-Coray T, Poston K, Mormino E, Andreasson K. Performance of a fully-automated Lumipulse plasma phospho-tau181 assay for Alzheimer’s disease. Alzheimer's Research & Therapy 2022, 14: 172. PMID: 36371232, PMCID: PMC9652927, DOI: 10.1186/s13195-022-01116-2.Peer-Reviewed Original ResearchConceptsPlasma p-tau181Alzheimer's Disease Research CenterP-tau181Disease Research CenterAlzheimer's diseasePositron emission tomographyAD dementiaBlood-based biomarker assaysAmyloid positron emission tomographyTreatment monitoringNovel blood-based biomarkersCSF p-tau181P-tau181 concentrationsDisease-modifying therapiesAβ42/Aβ40 ratioBlood-based biomarkersClinical AD diagnosisDetection of ADMild cognitive impairmentStudy cohortCSF biomarkersPlasma levelsAD groupPrognostic performanceCDR sum
2018
Transcriptome and epigenome landscape of human cortical development modeled in organoids
Amiri A, Coppola G, Scuderi S, Wu F, Roychowdhury T, Liu F, Pochareddy S, Shin Y, Safi A, Song L, Zhu Y, Sousa AMM, Gerstein M, Crawford G, Sestan N, Abyzov A, Vaccarino F, Akbarian S, An J, Armoskus C, Ashley-Koch A, Beach T, Belmont J, Bendl J, Borrman T, Brown L, Brown M, Brown M, Brunetti T, Bryois J, Burke E, Camarena A, Carlyle B, Chae Y, Charney A, Chen C, Cheng L, Cherskov A, Choi J, Clarke D, Collado-Torres L, Dai R, De La Torre Ubieta L, DelValle D, Devillers O, Dracheva S, Emani P, Evgrafov O, Farnham P, Fitzgerald D, Flatow E, Francoeur N, Fullard J, Gandal M, Gao T, Garrett M, Geschwind D, Giase G, Girdhar K, Giusti-Rodriguez P, Goes F, Goodman T, Grennan K, Gu M, Gürsoy G, Hadjimichael E, Hahn C, Haroutunian V, Hauberg M, Hoffman G, Huey J, Hyde T, Ivanov N, Jacobov R, Jaffe A, Jiang Y, Jiang Y, Johnson G, Kassim B, Kefi A, Kim Y, Kitchen R, Kleiman J, Knowles J, Kozlenkov A, Li M, Li Z, Lipska B, Liu C, Liu S, Mangravite L, Mariani J, Mattei E, Miller D, Moore J, Nairn A, Navarro F, Park R, Peters M, Pinto D, Pochareddy S, Polioudakis D, Pratt H, Price A, Purcaro M, Ray M, Reddy T, Rhie S, Roussos P, Sanders S, Santpere G, Schreiner S, Sheppard B, Shi X, Shieh A, Shin J, Skarica M, Song L, Sousa A, Spitsyna V, State M, Sullivan P, Swarup V, Szatkiewicz J, Szekely A, Tao R, van Bakel H, Wang Y, Wang D, Warrell J, Webster M, Weissman S, Weng Z, Werling D, White K, Willsey J, Wiseman J, Witt H, Won H, Wray G, Xia Y, Xu M, Yang Y, Yang M, Zandi P, Zhang J, Zharovsky E. Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science 2018, 362 PMID: 30545853, PMCID: PMC6426303, DOI: 10.1126/science.aat6720.Peer-Reviewed Original ResearchComprehensive functional genomic resource and integrative model for the human brain
Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, Clarke D, Gu M, Emani P, Yang YT, Xu M, Gandal MJ, Lou S, Zhang J, Park JJ, Yan C, Rhie SK, Manakongtreecheep K, Zhou H, Nathan A, Peters M, Mattei E, Fitzgerald D, Brunetti T, Moore J, Jiang Y, Girdhar K, Hoffman GE, Kalayci S, Gümüş ZH, Crawford GE, Roussos P, Akbarian S, Jaffe A, White K, Weng Z, Sestan N, Geschwind D, Knowles J, Gerstein M, Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Comprehensive functional genomic resource and integrative model for the human brain. Science 2018, 362 PMID: 30545857, PMCID: PMC6413328, DOI: 10.1126/science.aat8464.Peer-Reviewed Original ResearchConceptsQuantitative trait lociCell type proportionsComprehensive functional genomics resourceExpression quantitative trait lociFunctional genomics resourcesSingle-cell expression profilesGene regulatory networksFurther quantitative trait lociPsychENCODE ConsortiumGenomic resourcesComprehensive online resourceRegulatory networksKey genesCross-population variationExpression profilesMolecular mechanismsCell typesGenesAdult brainPolygenic risk scoresStudy variantsChromatinSplicingGenetic riskInterpretable deep learning modelIntegrative functional genomic analysis of human brain development and neuropsychiatric risks
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw C, Pardiñas AF, Hu M, Jin F, Li Y, Owen M, O’Donovan M, Walters J, Posthuma D, Reimers M, Levitt P, Weinberger D, Hyde T, Kleinman J, Geschwind D, Hawrylycz M, State M, Sanders S, Sullivan P, Gerstein M, Lein E, Knowles J, Sestan N, Willsey A, Oldre A, Szafer A, Camarena A, Cherskov A, Charney A, Abyzov A, Kozlenkov A, Safi A, Jones A, Ashley-Koch A, Ebbert A, Price A, Sekijima A, Kefi A, Bernard A, Amiri A, Sboner A, Clark A, Jaffe A, Tebbenkamp A, Sodt A, Guillozet-Bongaarts A, Nairn A, Carey A, Huttner A, Chervenak A, Szekely A, Shieh A, Harmanci A, Lipska B, Carlyle B, Gregor B, Kassim B, Sheppard B, Bichsel C, Hahn C, Lee C, Chen C, Kuan C, Dang C, Lau C, Cuhaciyan C, Armoskus C, Mason C, Liu C, Slaughterbeck C, Bennet C, Pinto D, Polioudakis D, Franjic D, Miller D, Bertagnolli D, Lewis D, Feng D, Sandman D, Clarke D, Williams D, DelValle D, Fitzgerald D, Shen E, Flatow E, Zharovsky E, Burke E, Olson E, Fulfs E, Mattei E, Hadjimichael E, Deelman E, Navarro F, Wu F, Lee F, Cheng F, Goes F, Vaccarino F, Liu F, Hoffman G, Gürsoy G, Gee G, Mehta G, Coppola G, Giase G, Sedmak G, Johnson G, Wray G, Crawford G, Gu G, van Bakel H, Witt H, Yoon H, Pratt H, Zhao H, Glass I, Huey J, Arnold J, Noonan J, Bendl J, Jochim J, Goldy J, Herstein J, Wiseman J, Miller J, Mariani J, Stoll J, Moore J, Szatkiewicz J, Leng J, Zhang J, Parente J, Rozowsky J, Fullard J, Hohmann J, Morris J, Phillips J, Warrell J, Shin J, An J, Belmont J, Nyhus J, Pendergraft J, Bryois J, Roll K, Grennan K, Aiona K, White K, Aldinger K, Smith K, Girdhar K, Brouner K, Mangravite L, Brown L, Collado-Torres L, Cheng L, Gourley L, Song L, Ubieta L, Habegger L, Ng L, Hauberg M, Onorati M, Webster M, Kundakovic M, Skarica M, Reimers M, Johnson M, Chen M, Garrett M, Sarreal M, Reding M, Gu M, Peters M, Fisher M, Gandal M, Purcaro M, Smith M, Brown M, Shibata M, Brown M, Xu M, Yang M, Ray M, Shapovalova N, Francoeur N, Sjoquist N, Mastan N, Kaur N, Parikshak N, Mosqueda N, Ngo N, Dee N, Ivanov N, Devillers O, Roussos P, Parker P, Manser P, Wohnoutka P, Farnham P, Zandi P, Emani P, Dalley R, Mayani R, Tao R, Gittin R, Straub R, Lifton R, Jacobov R, Howard R, Park R, Dai R, Abramowicz S, Akbarian S, Schreiner S, Ma S, Parry S, Shapouri S, Weissman S, Caldejon S, Mane S, Ding S, Scuderi S, Dracheva S, Butler S, Lisgo S, Rhie S, Lindsay S, Datta S, Souaiaia T, Roychowdhury T, Gomez T, Naluai-Cecchini T, Beach T, Goodman T, Gao T, Dolbeare T, Fliss T, Reddy T, Chen T, Hyde T, Brunetti T, Lemon T, Desta T, Borrman T, Haroutunian V, Spitsyna V, Swarup V, Shi X, Jiang Y, Xia Y, Chen Y, Jiang Y, Wang Y, Chae Y, Yang Y, Kim Y, Riley Z, Krsnik Z, Deng Z, Weng Z, Lin Z, Li Z. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362 PMID: 30545854, PMCID: PMC6413317, DOI: 10.1126/science.aat7615.Peer-Reviewed Original ResearchConceptsIntegrative functional genomic analysisFunctional genomic analysisCell typesGene coexpression modulesDistinct cell typesCell type-specific dynamicsGenomic basisEpigenomic reorganizationEpigenomic landscapeEpigenomic regulationGenomic analysisCoexpression modulesIntegrative analysisHuman brain developmentFetal transitionHuman neurodevelopmentGenetic associationCellular compositionNeuropsychiatric riskBrain developmentNeurodevelopmental processesGenesTraitsPostnatal developmentNeuropsychiatric disordersTranscriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
Gandal MJ, Zhang P, Hadjimichael E, Walker RL, Chen C, Liu S, Won H, van Bakel H, Varghese M, Wang Y, Shieh AW, Haney J, Parhami S, Belmont J, Kim M, Moran Losada P, Khan Z, Mleczko J, Xia Y, Dai R, Wang D, Yang YT, Xu M, Fish K, Hof PR, Warrell J, Fitzgerald D, White K, Jaffe AE, Peters M, Gerstein M, Liu C, Iakoucheva L, Pinto D, Geschwind D, Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 2018, 362 PMID: 30545856, PMCID: PMC6443102, DOI: 10.1126/science.aat8127.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyTranscriptome-wide characterizationPathogenic dysregulationTranscriptomic organizationDifferential splicingCoexpression networkGenetic enrichmentRegulatory regionsGenomic dataGene expressionAssociation studiesDisease locusComprehensive resourceMechanistic insightsCis effectSplicingBipolar disorderNeural-immune mechanismsMolecular pathologyTherapeutic developmentAutism spectrum disorderExpressionMajor psychiatric disordersBrain expressionDiseased brainRevealing the brain's molecular architecture
Ashley-Koch A, Crawford G, Garrett M, Song L, Safi A, Johnson G, Wray G, Reddy T, Goes F, Zandi P, Bryois J, Jaffe A, Price A, Ivanov N, Collado-Torres L, Hyde T, Burke E, Kleiman J, Tao R, Shin J, Akbarian S, Girdhar K, Jiang Y, Kundakovic M, Brown L, Kassim B, Park R, Wiseman J, Zharovsky E, Jacobov R, Devillers O, Flatow E, Hoffman G, Lipska B, Lewis D, Haroutunian V, Hahn C, Charney A, Dracheva S, Kozlenkov A, Belmont J, DelValle D, Francoeur N, Hadjimichael E, Pinto D, van Bakel H, Roussos P, Fullard J, Bendl J, Hauberg M, Mangravite L, Peters M, Chae Y, Peng J, Niu M, Wang X, Webster M, Beach T, Chen C, Jiang Y, Dai R, Shieh A, Liu C, Grennan K, Xia Y, Vadukapuram R, Wang Y, Fitzgerald D, Cheng L, Brown M, Brown M, Brunetti T, Goodman T, Alsayed M, Gandal M, Geschwind D, Won H, Polioudakis D, Wamsley B, Yin J, Hadzic T, De La Torre Ubieta L, Swarup V, Sanders S, State M, Werling D, An J, Sheppard B, Willsey A, White K, Ray M, Giase G, Kefi A, Mattei E, Purcaro M, Weng Z, Moore J, Pratt H, Huey J, Borrman T, Sullivan P, Giusti-Rodriguez P, Kim Y, Sullivan P, Szatkiewicz J, Rhie S, Armoskus C, Camarena A, Farnham P, Spitsyna V, Witt H, Schreiner S, Evgrafov O, Knowles J, Gerstein M, Liu S, Wang D, Navarro F, Warrell J, Clarke D, Emani P, Gu M, Shi X, Xu M, Yang Y, Kitchen R, Gürsoy G, Zhang J, Carlyle B, Nairn A, Li M, Pochareddy S, Sestan N, Skarica M, Li Z, Sousa A, Santpere G, Choi J, Zhu Y, Gao T, Miller D, Cherskov A, Yang M, Amiri A, Coppola G, Mariani J, Scuderi S, Szekely A, Vaccarino F, Wu F, Weissman S, Roychowdhury T, Abyzov A. Revealing the brain's molecular architecture. Science 2018, 362: 1262-1263. PMID: 30545881, DOI: 10.1126/science.362.6420.1262.Peer-Reviewed Original ResearchIsoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq
Carlyle B, Kitchen RR, Zhang J, Wilson R, Lam T, Rozowsky JS, Williams KR, Sestan N, Gerstein M, Nairn AC. Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq. Journal Of Proteome Research 2018, 17: 3431-3444. PMID: 30125121, PMCID: PMC6392456, DOI: 10.1021/acs.jproteome.8b00310.Peer-Reviewed Original ResearchConceptsRNA-seqProteomic dataGene expressionLiquid chromatography-tandem mass spectrometry proteomicsTandem mass spectrometry proteomicsHigh-throughput proteomic dataTranscriptomic profiling methodsDistinct amino acid sequencesTranscript-level expressionAmino acid sequenceMass spectrometry proteomicsHEK293 cell culturesTranslatome dataMost genesProfound functional implicationsProtein isoformsAlternate isoformsGene productsAcid sequenceCellular controlBiosynthetic stateGeneration of peptidesCell typesFunctional relevanceFunctional implications
2017
A multiregional proteomic survey of the postnatal human brain
Carlyle BC, Kitchen RR, Kanyo JE, Voss EZ, Pletikos M, Sousa AMM, Lam TT, Gerstein MB, Sestan N, Nairn AC. A multiregional proteomic survey of the postnatal human brain. Nature Neuroscience 2017, 20: 1787-1795. PMID: 29184206, PMCID: PMC5894337, DOI: 10.1038/s41593-017-0011-2.Peer-Reviewed Original ResearchConceptsProteomic surveyResident plasma membrane proteinsPostnatal human brainProtein dataPlasma membrane proteinsProtein abundance differencesQuantitative tandem mass spectrometryPost-translational eventsWhole transcriptome sequencingRNA expression dataMembrane proteinsFunctional variationExpression dataAbundance differencesBrain regionsTandem mass spectrometryHuman brainSimilar cortical regionsMass spectrometryEarly infancyRNACortical regionsSequencingProteinAbundanceReciprocal regulation of ARPP-16 by PKA and MAST3 kinases provides a cAMP-regulated switch in protein phosphatase 2A inhibition
Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Novère N, Nairn AC. Reciprocal regulation of ARPP-16 by PKA and MAST3 kinases provides a cAMP-regulated switch in protein phosphatase 2A inhibition. ELife 2017, 6: e24998. PMID: 28613156, PMCID: PMC5515580, DOI: 10.7554/elife.24998.Peer-Reviewed Original ResearchConceptsARPP-16ARPP-19Protein phosphatase 2A inhibitionProtein phosphatase PP2A.Inhibition of PP2ASwitch-like responseKinase inhibitsPhosphatase PP2A.Regulatory interactionsPKA phosphorylationAntagonistic interplayReciprocal regulationBasal phosphorylationPhosphorylationMAST3PP2APKAENSAKinaseStriatal signalingPP2A.Multiple sitesInhibitionMitosisSignaling
2015
STEP61 is a substrate of the E3 ligase parkin and is upregulated in Parkinson’s disease
Kurup PK, Xu J, Videira RA, Ononenyi C, Baltazar G, Lombroso PJ, Nairn AC. STEP61 is a substrate of the E3 ligase parkin and is upregulated in Parkinson’s disease. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112: 1202-1207. PMID: 25583483, PMCID: PMC4313846, DOI: 10.1073/pnas.1417423112.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCorpus StriatumCyclic AMP Response Element-Binding ProteinDown-RegulationGene Expression Regulation, EnzymologicHEK293 CellsHumansMAP Kinase Signaling SystemMiceMice, KnockoutMitogen-Activated Protein Kinase 3MPTP PoisoningProtein Tyrosine Phosphatases, Non-ReceptorRatsRats, Sprague-DawleyUbiquitinationUbiquitin-Protein LigasesUp-RegulationConceptsE3 ubiquitin ligase ParkinSubstantia nigra pars compactaPathophysiology of PDProtein tyrosine phosphataseUbiquitin ligase ParkinSporadic Parkinson's diseaseE3 ligase ParkinRegulation of ParkinParkinson's diseaseTyrosine phosphataseParkin mutantsE3 ligaseProteasome systemDopaminergic neuronsDownstream targetsAutosomal recessive juvenile parkinsonismNovel substrateSTEP61ParkinCellular modelSTEP61 levelsSNc dopaminergic neuronsProtein levelsFunction contributesERK1/2
2014
Decoding neuroproteomics: integrating the genome, translatome and functional anatomy
Kitchen RR, Rozowsky JS, Gerstein MB, Nairn AC. Decoding neuroproteomics: integrating the genome, translatome and functional anatomy. Nature Neuroscience 2014, 17: 1491-1499. PMID: 25349915, PMCID: PMC4737617, DOI: 10.1038/nn.3829.Peer-Reviewed Original Research
2013
Recent advances in quantitative neuroproteomics
Craft GE, Chen A, Nairn AC. Recent advances in quantitative neuroproteomics. Methods 2013, 61: 186-218. PMID: 23623823, PMCID: PMC3891841, DOI: 10.1016/j.ymeth.2013.04.008.Peer-Reviewed Original Research
2005
Control of the CFTR channel's gates.
Vergani P, Basso C, Mense M, Nairn A, Gadsby D. Control of the CFTR channel's gates. Biochemical Society Transactions 2005, 33: 1003-7. PMID: 16246032, PMCID: PMC2728124, DOI: 10.1042/bst20051003.Peer-Reviewed Original ResearchConceptsChannel gateIon channelsProtein family membersNBD dimer interfaceAnion-selective poreEvolutionary conservationABC proteinsCFTR moleculesForm homodimersTransmembrane domainATP bindingHeterodimer interfaceDimer interfaceMolecular mechanismsTight dimerizationNBDATPSingle-channel recordingsResiduesFamily membersNBD1NBD2Cystic fibrosis patientsMutagenesisHomodimer
2001
ARPP‐16/ARPP‐19: a highly conserved family of cAMP‐regulated phosphoproteins
Dulubova I, Horiuchi A, Snyder G, Girault J, Czernik A, Shao L, Ramabhadran R, Greengard P, Nairn A. ARPP‐16/ARPP‐19: a highly conserved family of cAMP‐regulated phosphoproteins. Journal Of Neurochemistry 2001, 77: 229-238. DOI: 10.1046/j.1471-4159.2001.00191.x.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsCHO CellsConserved SequenceCorpus StriatumCricetinaeCyclic AMPCyclic AMP-Dependent Protein KinasesHumansIn Vitro TechniquesMaleMiceMultigene FamilyOrgan SpecificityPhosphoproteinsPhosphorylationProtein IsoformsRatsRats, Sprague-DawleyReceptors, Dopamine D1Receptors, Dopamine D2Sequence Homology, Amino AcidConceptsProtein kinase AARPP-19ARPP-16Family of cAMPImportant cellular functionsActivation of PKAIsoform-specific antibodiesYeast genomeD. melanogasterC. elegansProtein familyCellular functionsNon-neuronal cellsSignal transductionConsensus sitesKinase ARelated proteinsΑ-endosulfinePhosphorylated formIntact cellsIntracellular messengerBi-directional regulationDopamine receptorsFamily membersPhosphorylationARPP-16/ARPP-19: a highly conserved family of cAMP-regulated phosphoproteins.
Dulubova I, Horiuchi A, Snyder G, Girault J, Czernik A, Shao L, Ramabhadran R, Greengard P, Nairn A. ARPP-16/ARPP-19: a highly conserved family of cAMP-regulated phosphoproteins. Journal Of Neurochemistry 2001, 77: 229-38. PMID: 11279279, DOI: 10.1046/j.1471-4159.2001.t01-1-00191.x.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsCHO CellsConserved SequenceCorpus StriatumCricetinaeCyclic AMPCyclic AMP-Dependent Protein KinasesHumansIn Vitro TechniquesMaleMiceMultigene FamilyOrgan SpecificityPhosphoproteinsPhosphorylationProtein IsoformsRatsRats, Sprague-DawleyReceptors, Dopamine D1Receptors, Dopamine D2Sequence Homology, Amino AcidConceptsProtein kinase AARPP-19ARPP-16Family of cAMPImportant cellular functionsActivation of PKAIsoform-specific antibodiesYeast genomeD. melanogasterC. elegansProtein familyCellular functionsNon-neuronal cellsSignal transductionConsensus sitesType dopamine receptorsKinase ARelated proteinsPhosphorylated formIntact cellsDopamine receptorsIntracellular messengerBi-directional regulationFamily membersPhosphorylationElongation Factor-2 Phosphorylation and the Regulation of Protein Synthesis by Calcium
Nairn A, Matsushita M, Nastiuk K, Horiuchi A, Mitsui K, Shimizu Y, Palfrey H. Elongation Factor-2 Phosphorylation and the Regulation of Protein Synthesis by Calcium. Progress In Molecular And Subcellular Biology 2001, 27: 91-129. PMID: 11575162, DOI: 10.1007/978-3-662-09889-9_4.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceAnimalsCalciumCalcium-Calmodulin-Dependent Protein KinasesCell CycleCell DivisionCyclic AMP-Dependent Protein KinasesCysteine EndopeptidasesElongation Factor 2 KinaseHumansMolecular Sequence DataMultienzyme ComplexesNeuronsPeptide Chain Elongation, TranslationalPeptide Elongation Factor 2PhosphorylationProteasome Endopeptidase ComplexProtein BiosynthesisSequence Homology, Amino AcidSignal TransductionUbiquitinConceptsProtein synthesisElongation factor 2 phosphorylationDephosphorylation of eEF2Eukaryotic protein synthesisAminoacyl-tRNA synthetasesFactor 2 phosphorylationElongation factor 2Ribosomal proteinsRegulated processInitiation factorsDependent kinasesKey proteinsRate of elongationPeptidyl-tRNAPhysiological roleKinasePhosphorylationFactor 2EEF2P siteThr56ProteinSynthetasesDephosphorylationRibosomesDecreased levels of ARPP-19 and PKA in brains of Down syndrome and Alzheimer’s disease
Kim S, Nairn A, Cairns N, Lubec G. Decreased levels of ARPP-19 and PKA in brains of Down syndrome and Alzheimer’s disease. Journal Of Neural Transmission. Supplementa 2001, 263-272. PMID: 11771749, DOI: 10.1007/978-3-7091-6262-0_21.Peer-Reviewed Original ResearchConceptsARPP-19Protein kinaseDifferential display polymerase chain reactionAlzheimer's diseaseDown syndromeCAMP-dependent protein kinaseTemporal cortexActivity of PKASignal transductionDownregulated sequenceBrain regionsNeurodegenerative disordersDiseaseImpaired mechanismsProtein levelsDecreased activityChain reactionFirst evidenceSignificant reductionSyndromeCortexDisordersTransductionHomologyKinase