Ho-Joon Lee, PhD
Research Scientist in GeneticsCards
About
Research
Overview
Two primary projects are (1) AI/ML of ischemic stroke etiology classification using electronic health records and MRI images in collaboration with Dr. Richa Sharma (1 patent pending; https://www.nature.com/articles/s41746-024-01120-w) (2) AI/ML meta-modeling for ligand-protein binding affinity prediction with the Gerstein lab (1 patent pending; https://pubs.acs.org/doi/10.1021/acs.jcim.4c01116) and drug discovery applications for targeted protein degradation in collaboration with the Spiegel lab.
Previous projects include (1) single-cell systems immunology of West Nile virus infection with the Montgomery and Kleinstein labs (https://www.cell.com/iscience/fulltext/S2589-0042(23)02464-1) (2) single-cell multi-omics data analysis of zebrafish brains and embryos with the Giraldez lab (https://elifesciences.org/arti...).
In response to the COVID-19 pandemic, I worked on virus-host protein-protein interactions (PPIs) and computational drug discovery together with Dr. Prashant Emani (the Gerstein lab) as a COVID HASTE working group of the Yale School of Engineering and Applied Science (see Figure 1 below for an overview). The project on virus-host PPIs was partially supported by a seed grant from the Northeast Big Data Innovation Hub and a preprint has been published (https://nebigdatahub.org/a-lan...). As an additional effort, with help from two of my former colleagues, Dr. Vinayagam Arunachalam (Takeda Pharmaceuticals) and Dr. Yang-Yu Liu (Harvard Medical School, Brigham and Women's Hospital), I carried out controllability analysis of a directed human protein-protein interaction network for SARS-CoV-2 based on our previous paper (https://www.pnas.org/doi/10.10...). A preprint is available here, https://www.biorxiv.org/conten....
My previous research mostly concerned biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to high-throughput multi-dimensional data. It covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels. I previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (https://www.cell.com/cell-repo...; see Figure 2 below), for which I developed integrative analytical tools using diverse approaches from machine learning and network theory. My ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, cancer metabolism, and cell fate decision and (2) single cell-based and mass spectrometry-based omics data analysis. Two specific projects were (1) Dynamic metabolic network modeling of a mammalian cell cycle using multi-omics time-course data in collaboration with the Chandrasekaran lab at the University of Michigan (see Figure 3 below; https://www.biorxiv.org/conten...; https://www.cell.com/iscience/...) and (2) Tri-omics analysis of macrophage polarization in pancreatic cancer in collaboration with the Lyssiotis lab at the University of Michigan Medical School (https://elifesciences.org/arti...).
Medical Research Interests
Public Health Interests
Publications
Featured Publications
Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling
Lee H, Emani P, Gerstein M. Improved Prediction of Ligand–Protein Binding Affinities by Meta-modeling. Journal Of Chemical Information And Modeling 2024, 64: 8684-8704. PMID: 39576762, PMCID: PMC11632770, DOI: 10.1021/acs.jcim.4c01116.Peer-Reviewed Original ResearchBinding affinity predictionAffinity predictionMeta-modelMeta-modeling approachLigand-protein binding affinityState-of-the-art deep learning toolsState-of-the-artBinding affinityDeep learning modelsDeep learning toolsMolecular descriptorsInclusion of featuresVirtual screeningBase modelDatabase scalabilityGeneralization capabilityDiverse modeling approachesTraining databaseApplication benchmarksDrug ligandsLearning modelsLigandPhysicochemical propertiesLearning toolsDevelopment effortsStrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records
Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. Npj Digital Medicine 2024, 7: 130. PMID: 38760474, PMCID: PMC11101464, DOI: 10.1038/s41746-024-01120-w.Peer-Reviewed Original ResearchElectronic health recordsWeighted F1MIMIC-IIIClinical decision support systemsMulti-class classificationNatural language processingMIMIC-III datasetHealth recordsMachine learning classifiersDecision support systemArtificial intelligence toolsVascular neurologistsLearning classifiersBinary classificationCross-validation accuracyLanguage processingMeta-modelIntelligence toolsStroke prevention effortsAcute ischemic strokeStroke etiologySupport systemStroke etiology classificationClassification toolClassifierEarly cellular and molecular signatures correlate with severity of West Nile virus infection
Lee H, Zhao Y, Fleming I, Mehta S, Wang X, Vander Wyk B, Ronca S, Kang H, Chou C, Fatou B, Smolen K, Levy O, Clish C, Xavier R, Steen H, Hafler D, Love J, Shalek A, Guan L, Murray K, Kleinstein S, Montgomery R. Early cellular and molecular signatures correlate with severity of West Nile virus infection. IScience 2023, 26: 108387. PMID: 38047068, PMCID: PMC10692672, DOI: 10.1016/j.isci.2023.108387.Peer-Reviewed Original ResearchWest Nile virusEffective anti-viral responseInnate immune cell typesWest Nile virus infectionPro-inflammatory markersAcute time pointsImmune cell typesAnti-viral responseMolecular signaturesHost cellular activitiesAcute infectionAsymptomatic donorsPeripheral bloodSevere infectionsVirus infectionImmune responseSevere casesCell activityIll individualsSerum proteomicsInfectionInfection severityHigh expressionTime pointsNile virusMultiomic characterization of pancreatic cancer-associated macrophage polarization reveals deregulated metabolic programs driven by the GM-CSF–PI3K pathway
Boyer S, Lee H, Steele N, Zhang L, Sajjakulnukit P, Andren A, Ward M, Singh R, Basrur V, Zhang Y, Nesvizhskii A, di Magliano M, Halbrook C, Lyssiotis C. Multiomic characterization of pancreatic cancer-associated macrophage polarization reveals deregulated metabolic programs driven by the GM-CSF–PI3K pathway. ELife 2022, 11: e73796. PMID: 35156921, PMCID: PMC8843093, DOI: 10.7554/elife.73796.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCell Line, TumorCell Transformation, NeoplasticGene Expression ProfilingGranulocyte-Macrophage Colony-Stimulating FactorHumansMetabolic Networks and PathwaysMetabolomicsMiceMice, Inbred C57BLPancreatic NeoplasmsProteomicsSignal TransductionTranscription FactorsTumor-Associated MacrophagesConceptsTumor-educated macrophagesSingle-cell RNA sequencing datasetsCancer cellsMultiomics characterizationRNA sequencing datasetsTumor-associated macrophagesPI3K-Akt pathwayPI3K pathwayMetabolic programsSequencing datasetsGene expressionMetabolic crosstalkFunction of TAMsCell typesK pathwayGM-CSFGranulocyte-macrophage colony-stimulating factorTumor promotingModel systemEpithelial cellsPathwayColony-stimulating factorMetabolic signaturesMutant KrasMalignant epithelial cellsA large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics
Lee HJ, Kremer DM, Sajjakulnukit P, Zhang L, Lyssiotis CA. A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics. Metabolomics 2019, 15: 103. PMID: 31289941, PMCID: PMC6616221, DOI: 10.1007/s11306-019-1564-8.Peer-Reviewed Original ResearchMetabolic signatures of regulation by phosphorylation and acetylation
Smith K, Shen F, Lee H, Chandrasekaran S. Metabolic signatures of regulation by phosphorylation and acetylation. IScience 2022, 25: 103730. PMID: 35072016, PMCID: PMC8762462, DOI: 10.1016/j.isci.2021.103730.Peer-Reviewed Original ResearchPosttranslational modificationsTarget of phosphorylationGenome-scale metabolic networksRational rewiringRegulatory circuitsMammalian cellsCellular metabolismMetabolic networksPhosphorylated enzymeMetabolic regulationPhosphoproteome datasetEnzyme propertiesPhosphorylationAcetylationMetabolic signaturesRegulationAcetylomeTranscriptomeDiverse conditionsProteomePhosphoproteomicsTargetCondition-specific factorsEssentialityOrganismsProteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation
Lee HJ, Jedrychowski MP, Vinayagam A, Wu N, Shyh-Chang N, Hu Y, Min-Wen C, Moore JK, Asara JM, Lyssiotis CA, Perrimon N, Gygi SP, Cantley LC, Kirschner MW. Proteomic and Metabolomic Characterization of a Mammalian Cellular Transition from Quiescence to Proliferation. Cell Reports 2017, 20: 721-736. PMID: 28723573, PMCID: PMC5626450, DOI: 10.1016/j.celrep.2017.06.074.Peer-Reviewed Original ResearchConceptsCell cycleCancer-related metabolic pathwaysAmino acid synthesisUpregulation of glycolysisNormal proliferative cellsCellular transitionsMetabolic machineryOxidative phosphorylationLymphocyte cell linesEssential amino acidsMetabolic pathwaysAmino acidsNucleotide synthesisCancer cellsCell linesProteomicsMetabolomic characterizationIL-3Proliferative cellsLipid metabolismUrea cycleCellsMetabolic changesMetabolomic profilingPotential linkControllability analysis of the directed human protein interaction network identifies disease genes and drug targets
Vinayagam A, Gibson TE, Lee HJ, Yilmazel B, Roesel C, Hu Y, Kwon Y, Sharma A, Liu YY, Perrimon N, Barabási AL. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proceedings Of The National Academy Of Sciences Of The United States Of America 2016, 113: 4976-4981. PMID: 27091990, PMCID: PMC4983807, DOI: 10.1073/pnas.1603992113.Peer-Reviewed Original ResearchConceptsPPI networkDisease genesProtein-protein interaction networkDrug targetsCellular information processingHuman PPI networkNovel disease genesCopy number alteration dataPotential drug targetsNumber alteration dataDisease-causing mutationsIndispensable proteinsInteraction networksCell deathGenesProteinCell proliferationDifferent cancersHuman virusesPrimary targetAlteration dataDisease statesTargetMutationsNetwork control propertiesCysteine depletion induces pancreatic tumor ferroptosis in mice
Badgley MA, Kremer DM, Maurer HC, DelGiorno KE, Lee HJ, Purohit V, Sagalovskiy IR, Ma A, Kapilian J, Firl CEM, Decker AR, Sastra SA, Palermo CF, Andrade LR, Sajjakulnukit P, Zhang L, Tolstyka ZP, Hirschhorn T, Lamb C, Liu T, Gu W, Seeley ES, Stone E, Georgiou G, Manor U, Iuga A, Wahl GM, Stockwell BR, Lyssiotis CA, Olive KP. Cysteine depletion induces pancreatic tumor ferroptosis in mice. Science 2020, 368: 85-89. PMID: 32241947, PMCID: PMC7681911, DOI: 10.1126/science.aaw9872.Peer-Reviewed Original ResearchConceptsReactive oxygen speciesLipid reactive oxygen speciesPancreatic ductal adenocarcinomaLipid ROS productionAmino acid cysteineCell deathPDAC growthCysteine depletionCoenzyme APDAC cellsTumor ferroptosisROS productionFerroptosisCysteineOxygen speciesCatastrophic accumulationTranslatable meansCancer mortalityDuctal adenocarcinomaLeading causeSystem xTumor typesSubunitsSpeciesDeletionAbnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma
Xu J, Reznik E, Lee HJ, Gundem G, Jonsson P, Sarungbam J, Bialik A, Sanchez-Vega F, Creighton CJ, Hoekstra J, Zhang L, Sajjakulnukit P, Kremer D, Tolstyka Z, Casuscelli J, Stirdivant S, Tang J, Schultz N, Jeng P, Dong Y, Su W, Cheng EH, Russo P, Coleman JA, Papaemmanuil E, Chen YB, Reuter VE, Sander C, Kennedy SR, Hsieh JJ, Lyssiotis CA, Tickoo SK, Hakimi AA. Abnormal oxidative metabolism in a quiet genomic background underlies clear cell papillary renal cell carcinoma. ELife 2019, 8: e38986. PMID: 30924768, PMCID: PMC6459676, DOI: 10.7554/elife.38986.Peer-Reviewed Original ResearchConceptsClear cell papillary renal cell carcinomaMtDNA-encoded proteinsPapillary renal cell carcinomaMetabolic phenotypeRenal cell carcinomaNuclear genomeDistinct metabolic phenotypesMitochondrial DNACell carcinomaRespiratory metabolismGenomic sequencingMolecular phenotypesAbnormal oxidative metabolismSugar alcohol sorbitolPresence of glycogenStudy of cancerMajority of cancersOncogenic alterationsPhenotypeOxidative stressOxidative metabolismCytoplasmic clarityDriver lesionsImmunohistochemical stainingKidney tumors
Links
Media
- This is an overview of our general strategy to study SARS-CoV-2 infection by integrative computational approaches in the context of systems biology and network medicine.
- (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.
- Dynamic metabolic network modeling of a cytokine-induced mammalian cell cycle using time-course metabolomics and proteomics. Presented at the 68th ASMS Conference on Mass Spectrometry and Allied Topics 2020.
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300 Cedar Street, Rm N-212
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