Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2009-02-18

AUTHORS

Albert C Huang, Limei Hu, Stuart A Kauffman, Wei Zhang, Ilya Shmulevich

ABSTRACT

BACKGROUND: The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation. RESULTS: Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development. CONCLUSION: Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation. More... »

PAGES

20-20

References to SciGraph publications

  • 2006-05-11. Promoter hypermethylation silences expression of the HoxA4 gene and correlates with IgVh mutational status in CLL in LEUKEMIA
  • 2006-05. Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4 in NATURE
  • 2000-05. Gene Ontology: tool for the unification of biology in NATURE GENETICS
  • 2005-12-25. The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation in NATURE GENETICS
  • 2008-05. NLRs at the intersection of cell death and immunity in NATURE REVIEWS IMMUNOLOGY
  • 2008-05. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells in NATURE
  • 1997-01. Isologous diversification: A theory of cell differentiation in BULLETIN OF MATHEMATICAL BIOLOGY
  • 2006-02-28. Multistable and multistep dynamics in neutrophil differentiation in BMC MOLECULAR AND CELL BIOLOGY
  • 2006-12-21. Hypercalcemia in childhood acute lymphoblastic leukemia: frequent implication of parathyroid hormone-related peptide and E2A-HLF from translocation 17;19 in LEUKEMIA
  • 2004-04-22. MEIS 1 expression is downregulated through promoter hypermethylation in AML1-ETO acute myeloid leukemias in LEUKEMIA
  • 2001-04-01. AML1–ETO downregulates the granulocytic differentiation factor C/EBPα in t(8;21) myeloid leukemia in NATURE MEDICINE
  • 2003-04-01. GoMiner: a resource for biological interpretation of genomic and proteomic data in GENOME BIOLOGY
  • 1997-01. Cloning and characterization of AFX, the gene that fuses to MLL in acute leukemias with a t(X;11)(q13;q23) in ONCOGENE
  • 2001-03. Dominant-negative mutations of CEBPA, encoding CCAAT/enhancer binding protein-α (C/EBPα), in acute myeloid leukemia in NATURE GENETICS
  • 2007-11-26. Dynamic simulation of regulatory networks using SQUAD in BMC BIOINFORMATICS
  • 1994-12. Dimethyl sulphoxide: A review of its applications in cell biology in BIOSCIENCE REPORTS
  • 2005-10-20. Aberrant promoter methylation of the retinoic acid receptor alpha gene in acute promyelocytic leukemia in LEUKEMIA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1752-0509-3-20

    DOI

    http://dx.doi.org/10.1186/1752-0509-3-20

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1015008323

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19222862


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/06", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biological Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0601", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Biochemistry and Cell Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0604", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Genetics", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Binding Sites", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "CD11b Antigen", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cell Differentiation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Computational Biology", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Flow Cytometry", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Gene Expression Regulation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "HL-60 Cells", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Humans", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Microarray Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Neutrophils", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Promoter Regions, Genetic", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Tretinoin", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Institute for Systems Biology, Seattle, Washington, DC, USA", 
              "id": "http://www.grid.ac/institutes/grid.64212.33", 
              "name": [
                "Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, DC, USA", 
                "Institute for Systems Biology, Seattle, Washington, DC, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Huang", 
            "givenName": "Albert C", 
            "id": "sg:person.01076374554.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076374554.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA", 
              "id": "http://www.grid.ac/institutes/grid.240145.6", 
              "name": [
                "Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hu", 
            "givenName": "Limei", 
            "id": "sg:person.016205341247.35", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016205341247.35"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada", 
              "id": "http://www.grid.ac/institutes/grid.22072.35", 
              "name": [
                "Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kauffman", 
            "givenName": "Stuart A", 
            "id": "sg:person.0640635137.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0640635137.24"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA", 
              "id": "http://www.grid.ac/institutes/grid.240145.6", 
              "name": [
                "Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "Wei", 
            "id": "sg:person.016617025407.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016617025407.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Institute for Systems Biology, Seattle, Washington, DC, USA", 
              "id": "http://www.grid.ac/institutes/grid.64212.33", 
              "name": [
                "Institute for Systems Biology, Seattle, Washington, DC, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Shmulevich", 
            "givenName": "Ilya", 
            "id": "sg:person.01354314446.15", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1038/85820", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018452369", 
              "https://doi.org/10.1038/85820"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature06965", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031252068", 
              "https://doi.org/10.1038/nature06965"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/86515", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039956151", 
              "https://doi.org/10.1038/86515"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.leu.2404254", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006867848", 
              "https://doi.org/10.1038/sj.leu.2404254"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.leu.2403377", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012398838", 
              "https://doi.org/10.1038/sj.leu.2403377"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/75556", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044135237", 
              "https://doi.org/10.1038/75556"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.leu.2404496", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010189333", 
              "https://doi.org/10.1038/sj.leu.2404496"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nri2296", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007607511", 
              "https://doi.org/10.1038/nri2296"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02459474", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039406310", 
              "https://doi.org/10.1007/bf02459474"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng1725", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005845651", 
              "https://doi.org/10.1038/ng1725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2121-7-11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007005366", 
              "https://doi.org/10.1186/1471-2121-7-11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2105-8-462", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048042523", 
              "https://doi.org/10.1186/1471-2105-8-462"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01199051", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011412277", 
              "https://doi.org/10.1007/bf01199051"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature04768", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016282305", 
              "https://doi.org/10.1038/nature04768"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.onc.1200814", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024647657", 
              "https://doi.org/10.1038/sj.onc.1200814"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/gb-2003-4-4-r28", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017134389", 
              "https://doi.org/10.1186/gb-2003-4-4-r28"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/sj.leu.2403937", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035747882", 
              "https://doi.org/10.1038/sj.leu.2403937"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2009-02-18", 
        "datePublishedReg": "2009-02-18", 
        "description": "BACKGROUND: The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation.\nRESULTS: Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development.\nCONCLUSION: Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.", 
        "genre": "article", 
        "id": "sg:pub.10.1186/1752-0509-3-20", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.2611993", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2440532", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2518631", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1327442", 
            "issn": [
              "1752-0509"
            ], 
            "name": "BMC Systems Biology", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "3"
          }
        ], 
        "keywords": [
          "transcription factors", 
          "systems-level view", 
          "cell fate", 
          "high-dimensional attractor states", 
          "neutrophil differentiation", 
          "particular cell fate", 
          "set of genes", 
          "multitude of genes", 
          "gene expression changes", 
          "cell cycle progression", 
          "early differentiation markers", 
          "divergent genes", 
          "transcriptional regulation", 
          "hematopoietic differentiation", 
          "transcriptional mechanisms", 
          "cellular differentiation", 
          "promyelocytic leukemia cell line", 
          "molecular networks", 
          "expression changes", 
          "cycle progression", 
          "molecular mechanisms", 
          "cell cycle", 
          "intermediate populations", 
          "leukemia cell lines", 
          "computational analysis framework", 
          "biomolecular networks", 
          "genes", 
          "differentiation markers", 
          "differentiation", 
          "cell lines", 
          "trans retinoic acid", 
          "differentiation agents", 
          "tumor progression", 
          "recent experimental evidence", 
          "site analysis", 
          "flow cytometric measurements", 
          "effective therapeutic strategy", 
          "fate", 
          "same stage", 
          "cytometric measurements", 
          "therapeutic strategies", 
          "promoter", 
          "population", 
          "regulation", 
          "experimental evidence", 
          "attractor states", 
          "mechanism", 
          "level view", 
          "expression", 
          "progression", 
          "concert", 
          "cells", 
          "significant role", 
          "enrichment", 
          "markers", 
          "acid", 
          "factors", 
          "role", 
          "lines", 
          "multitude", 
          "cycle", 
          "stage", 
          "process", 
          "cancer", 
          "subset", 
          "development", 
          "evidence", 
          "respective treatments", 
          "CD11b", 
          "potential", 
          "treatment", 
          "leukemia", 
          "changes", 
          "duration combinations", 
          "analysis", 
          "disease", 
          "products", 
          "set", 
          "agents", 
          "experiments", 
          "combination", 
          "strategies", 
          "network", 
          "study", 
          "state", 
          "utility", 
          "view", 
          "dosage", 
          "days", 
          "approach", 
          "period", 
          "theoretical considerations", 
          "means", 
          "behavior", 
          "analysis framework", 
          "attractors", 
          "duration", 
          "consideration", 
          "network state", 
          "framework", 
          "temporal behavior", 
          "measurements", 
          "complex dynamical biomolecular networks", 
          "dynamical biomolecular networks", 
          "dimensional attractor states", 
          "different cell fate attractors", 
          "cell fate attractors", 
          "fate attractors", 
          "HL60 multipotent promyelocytic leukemia cell line", 
          "multipotent promyelocytic leukemia cell line", 
          "neutrophil attractor", 
          "undifferentiated promyelocytic attractor", 
          "promyelocytic attractor", 
          "represses cell cycle progression", 
          "dynamical systems level view", 
          "HL60 neutrophil differentiation"
        ], 
        "name": "Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation", 
        "pagination": "20-20", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1015008323"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/1752-0509-3-20"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "19222862"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/1752-0509-3-20", 
          "https://app.dimensions.ai/details/publication/pub.1015008323"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-01-01T18:20", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_491.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1186/1752-0509-3-20"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-20'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-20'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-20'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1752-0509-3-20'


     

    This table displays all metadata directly associated to this object as RDF triples.

    338 TRIPLES      22 PREDICATES      172 URIs      146 LITERALS      19 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/1752-0509-3-20 schema:about N016ba86f4aee495fbddd2d17b756794d
    2 N041a5dcd48d949c5884387b6d005c59a
    3 N04a8c18aded44d69a31b16dd28b01a3b
    4 N5058f828dc144256a773f734cddd1ca3
    5 N53d06b05e2ee4fafb89b7559189538db
    6 N5e7d788c93fc470d95f06531ba2fe496
    7 N673da0ff98634164824e1e5d5d14d7af
    8 N7926305aa92d4a7d91d2d85955123f66
    9 N7f7e40d09410474ab081a7c562fabe7c
    10 N8dc3281095b2422b8b8ef9f54fa5b88d
    11 Ncbc87954c05a484d982bcde611f142f1
    12 Nfa07447a6505458c8f45213923d7b0eb
    13 anzsrc-for:06
    14 anzsrc-for:0601
    15 anzsrc-for:0604
    16 schema:author N7729462a8ef24f52a60a7f7ed91c6f01
    17 schema:citation sg:pub.10.1007/bf01199051
    18 sg:pub.10.1007/bf02459474
    19 sg:pub.10.1038/75556
    20 sg:pub.10.1038/85820
    21 sg:pub.10.1038/86515
    22 sg:pub.10.1038/nature04768
    23 sg:pub.10.1038/nature06965
    24 sg:pub.10.1038/ng1725
    25 sg:pub.10.1038/nri2296
    26 sg:pub.10.1038/sj.leu.2403377
    27 sg:pub.10.1038/sj.leu.2403937
    28 sg:pub.10.1038/sj.leu.2404254
    29 sg:pub.10.1038/sj.leu.2404496
    30 sg:pub.10.1038/sj.onc.1200814
    31 sg:pub.10.1186/1471-2105-8-462
    32 sg:pub.10.1186/1471-2121-7-11
    33 sg:pub.10.1186/gb-2003-4-4-r28
    34 schema:datePublished 2009-02-18
    35 schema:datePublishedReg 2009-02-18
    36 schema:description BACKGROUND: The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation. RESULTS: Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development. CONCLUSION: Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.
    37 schema:genre article
    38 schema:inLanguage en
    39 schema:isAccessibleForFree true
    40 schema:isPartOf Nadf6f45639d2495d8806b268f5dd5a96
    41 Nc32d643f49dd45a3a7a9b4925d84614c
    42 sg:journal.1327442
    43 schema:keywords CD11b
    44 HL60 multipotent promyelocytic leukemia cell line
    45 HL60 neutrophil differentiation
    46 acid
    47 agents
    48 analysis
    49 analysis framework
    50 approach
    51 attractor states
    52 attractors
    53 behavior
    54 biomolecular networks
    55 cancer
    56 cell cycle
    57 cell cycle progression
    58 cell fate
    59 cell fate attractors
    60 cell lines
    61 cells
    62 cellular differentiation
    63 changes
    64 combination
    65 complex dynamical biomolecular networks
    66 computational analysis framework
    67 concert
    68 consideration
    69 cycle
    70 cycle progression
    71 cytometric measurements
    72 days
    73 development
    74 different cell fate attractors
    75 differentiation
    76 differentiation agents
    77 differentiation markers
    78 dimensional attractor states
    79 disease
    80 divergent genes
    81 dosage
    82 duration
    83 duration combinations
    84 dynamical biomolecular networks
    85 dynamical systems level view
    86 early differentiation markers
    87 effective therapeutic strategy
    88 enrichment
    89 evidence
    90 experimental evidence
    91 experiments
    92 expression
    93 expression changes
    94 factors
    95 fate
    96 fate attractors
    97 flow cytometric measurements
    98 framework
    99 gene expression changes
    100 genes
    101 hematopoietic differentiation
    102 high-dimensional attractor states
    103 intermediate populations
    104 leukemia
    105 leukemia cell lines
    106 level view
    107 lines
    108 markers
    109 means
    110 measurements
    111 mechanism
    112 molecular mechanisms
    113 molecular networks
    114 multipotent promyelocytic leukemia cell line
    115 multitude
    116 multitude of genes
    117 network
    118 network state
    119 neutrophil attractor
    120 neutrophil differentiation
    121 particular cell fate
    122 period
    123 population
    124 potential
    125 process
    126 products
    127 progression
    128 promoter
    129 promyelocytic attractor
    130 promyelocytic leukemia cell line
    131 recent experimental evidence
    132 regulation
    133 represses cell cycle progression
    134 respective treatments
    135 role
    136 same stage
    137 set
    138 set of genes
    139 significant role
    140 site analysis
    141 stage
    142 state
    143 strategies
    144 study
    145 subset
    146 systems-level view
    147 temporal behavior
    148 theoretical considerations
    149 therapeutic strategies
    150 trans retinoic acid
    151 transcription factors
    152 transcriptional mechanisms
    153 transcriptional regulation
    154 treatment
    155 tumor progression
    156 undifferentiated promyelocytic attractor
    157 utility
    158 view
    159 schema:name Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation
    160 schema:pagination 20-20
    161 schema:productId N38ea8b20cf6c47298975122c70b611d3
    162 N4e08de8793284067810e8fac768a6cb3
    163 N9b55432703664b2da26cc9760640b831
    164 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015008323
    165 https://doi.org/10.1186/1752-0509-3-20
    166 schema:sdDatePublished 2022-01-01T18:20
    167 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    168 schema:sdPublisher N6edc0e1b28be4b5bba06e1a2b32f9cf4
    169 schema:url https://doi.org/10.1186/1752-0509-3-20
    170 sgo:license sg:explorer/license/
    171 sgo:sdDataset articles
    172 rdf:type schema:ScholarlyArticle
    173 N016ba86f4aee495fbddd2d17b756794d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    174 schema:name Computational Biology
    175 rdf:type schema:DefinedTerm
    176 N041a5dcd48d949c5884387b6d005c59a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    177 schema:name Binding Sites
    178 rdf:type schema:DefinedTerm
    179 N04a8c18aded44d69a31b16dd28b01a3b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    180 schema:name Neutrophils
    181 rdf:type schema:DefinedTerm
    182 N38ea8b20cf6c47298975122c70b611d3 schema:name doi
    183 schema:value 10.1186/1752-0509-3-20
    184 rdf:type schema:PropertyValue
    185 N469f87b1070f464b8a21899eb5fa5861 rdf:first sg:person.016205341247.35
    186 rdf:rest Nef5006620815455980c611058b1fb4ec
    187 N4e08de8793284067810e8fac768a6cb3 schema:name pubmed_id
    188 schema:value 19222862
    189 rdf:type schema:PropertyValue
    190 N5058f828dc144256a773f734cddd1ca3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    191 schema:name CD11b Antigen
    192 rdf:type schema:DefinedTerm
    193 N53d06b05e2ee4fafb89b7559189538db schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    194 schema:name Humans
    195 rdf:type schema:DefinedTerm
    196 N5e7d788c93fc470d95f06531ba2fe496 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    197 schema:name Tretinoin
    198 rdf:type schema:DefinedTerm
    199 N673da0ff98634164824e1e5d5d14d7af schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    200 schema:name Microarray Analysis
    201 rdf:type schema:DefinedTerm
    202 N6edc0e1b28be4b5bba06e1a2b32f9cf4 schema:name Springer Nature - SN SciGraph project
    203 rdf:type schema:Organization
    204 N7729462a8ef24f52a60a7f7ed91c6f01 rdf:first sg:person.01076374554.16
    205 rdf:rest N469f87b1070f464b8a21899eb5fa5861
    206 N7926305aa92d4a7d91d2d85955123f66 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    207 schema:name Flow Cytometry
    208 rdf:type schema:DefinedTerm
    209 N7f7e40d09410474ab081a7c562fabe7c schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    210 schema:name Gene Expression Regulation
    211 rdf:type schema:DefinedTerm
    212 N8dc3281095b2422b8b8ef9f54fa5b88d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    213 schema:name Promoter Regions, Genetic
    214 rdf:type schema:DefinedTerm
    215 N9b55432703664b2da26cc9760640b831 schema:name dimensions_id
    216 schema:value pub.1015008323
    217 rdf:type schema:PropertyValue
    218 Naa9af0f9dea941779d33e8e7da69880a rdf:first sg:person.01354314446.15
    219 rdf:rest rdf:nil
    220 Nadf6f45639d2495d8806b268f5dd5a96 schema:volumeNumber 3
    221 rdf:type schema:PublicationVolume
    222 Nc32d643f49dd45a3a7a9b4925d84614c schema:issueNumber 1
    223 rdf:type schema:PublicationIssue
    224 Ncbc87954c05a484d982bcde611f142f1 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    225 schema:name Cell Differentiation
    226 rdf:type schema:DefinedTerm
    227 Nef5006620815455980c611058b1fb4ec rdf:first sg:person.0640635137.24
    228 rdf:rest Nfaabb63c0673408c91d0888de5b4b127
    229 Nfa07447a6505458c8f45213923d7b0eb schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    230 schema:name HL-60 Cells
    231 rdf:type schema:DefinedTerm
    232 Nfaabb63c0673408c91d0888de5b4b127 rdf:first sg:person.016617025407.36
    233 rdf:rest Naa9af0f9dea941779d33e8e7da69880a
    234 anzsrc-for:06 schema:inDefinedTermSet anzsrc-for:
    235 schema:name Biological Sciences
    236 rdf:type schema:DefinedTerm
    237 anzsrc-for:0601 schema:inDefinedTermSet anzsrc-for:
    238 schema:name Biochemistry and Cell Biology
    239 rdf:type schema:DefinedTerm
    240 anzsrc-for:0604 schema:inDefinedTermSet anzsrc-for:
    241 schema:name Genetics
    242 rdf:type schema:DefinedTerm
    243 sg:grant.2440532 http://pending.schema.org/fundedItem sg:pub.10.1186/1752-0509-3-20
    244 rdf:type schema:MonetaryGrant
    245 sg:grant.2518631 http://pending.schema.org/fundedItem sg:pub.10.1186/1752-0509-3-20
    246 rdf:type schema:MonetaryGrant
    247 sg:grant.2611993 http://pending.schema.org/fundedItem sg:pub.10.1186/1752-0509-3-20
    248 rdf:type schema:MonetaryGrant
    249 sg:journal.1327442 schema:issn 1752-0509
    250 schema:name BMC Systems Biology
    251 schema:publisher Springer Nature
    252 rdf:type schema:Periodical
    253 sg:person.01076374554.16 schema:affiliation grid-institutes:grid.64212.33
    254 schema:familyName Huang
    255 schema:givenName Albert C
    256 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076374554.16
    257 rdf:type schema:Person
    258 sg:person.01354314446.15 schema:affiliation grid-institutes:grid.64212.33
    259 schema:familyName Shmulevich
    260 schema:givenName Ilya
    261 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01354314446.15
    262 rdf:type schema:Person
    263 sg:person.016205341247.35 schema:affiliation grid-institutes:grid.240145.6
    264 schema:familyName Hu
    265 schema:givenName Limei
    266 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016205341247.35
    267 rdf:type schema:Person
    268 sg:person.016617025407.36 schema:affiliation grid-institutes:grid.240145.6
    269 schema:familyName Zhang
    270 schema:givenName Wei
    271 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016617025407.36
    272 rdf:type schema:Person
    273 sg:person.0640635137.24 schema:affiliation grid-institutes:grid.22072.35
    274 schema:familyName Kauffman
    275 schema:givenName Stuart A
    276 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0640635137.24
    277 rdf:type schema:Person
    278 sg:pub.10.1007/bf01199051 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011412277
    279 https://doi.org/10.1007/bf01199051
    280 rdf:type schema:CreativeWork
    281 sg:pub.10.1007/bf02459474 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039406310
    282 https://doi.org/10.1007/bf02459474
    283 rdf:type schema:CreativeWork
    284 sg:pub.10.1038/75556 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044135237
    285 https://doi.org/10.1038/75556
    286 rdf:type schema:CreativeWork
    287 sg:pub.10.1038/85820 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018452369
    288 https://doi.org/10.1038/85820
    289 rdf:type schema:CreativeWork
    290 sg:pub.10.1038/86515 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039956151
    291 https://doi.org/10.1038/86515
    292 rdf:type schema:CreativeWork
    293 sg:pub.10.1038/nature04768 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016282305
    294 https://doi.org/10.1038/nature04768
    295 rdf:type schema:CreativeWork
    296 sg:pub.10.1038/nature06965 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031252068
    297 https://doi.org/10.1038/nature06965
    298 rdf:type schema:CreativeWork
    299 sg:pub.10.1038/ng1725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005845651
    300 https://doi.org/10.1038/ng1725
    301 rdf:type schema:CreativeWork
    302 sg:pub.10.1038/nri2296 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007607511
    303 https://doi.org/10.1038/nri2296
    304 rdf:type schema:CreativeWork
    305 sg:pub.10.1038/sj.leu.2403377 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012398838
    306 https://doi.org/10.1038/sj.leu.2403377
    307 rdf:type schema:CreativeWork
    308 sg:pub.10.1038/sj.leu.2403937 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035747882
    309 https://doi.org/10.1038/sj.leu.2403937
    310 rdf:type schema:CreativeWork
    311 sg:pub.10.1038/sj.leu.2404254 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006867848
    312 https://doi.org/10.1038/sj.leu.2404254
    313 rdf:type schema:CreativeWork
    314 sg:pub.10.1038/sj.leu.2404496 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010189333
    315 https://doi.org/10.1038/sj.leu.2404496
    316 rdf:type schema:CreativeWork
    317 sg:pub.10.1038/sj.onc.1200814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024647657
    318 https://doi.org/10.1038/sj.onc.1200814
    319 rdf:type schema:CreativeWork
    320 sg:pub.10.1186/1471-2105-8-462 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048042523
    321 https://doi.org/10.1186/1471-2105-8-462
    322 rdf:type schema:CreativeWork
    323 sg:pub.10.1186/1471-2121-7-11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007005366
    324 https://doi.org/10.1186/1471-2121-7-11
    325 rdf:type schema:CreativeWork
    326 sg:pub.10.1186/gb-2003-4-4-r28 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017134389
    327 https://doi.org/10.1186/gb-2003-4-4-r28
    328 rdf:type schema:CreativeWork
    329 grid-institutes:grid.22072.35 schema:alternateName Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada
    330 schema:name Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada
    331 rdf:type schema:Organization
    332 grid-institutes:grid.240145.6 schema:alternateName Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
    333 schema:name Cancer Genomics Laboratory, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
    334 rdf:type schema:Organization
    335 grid-institutes:grid.64212.33 schema:alternateName Institute for Systems Biology, Seattle, Washington, DC, USA
    336 schema:name Institute for Systems Biology, Seattle, Washington, DC, USA
    337 Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, DC, USA
    338 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...