Decomposition of Gene Expression State Space Trajectories
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{"title"=>"Decomposition of gene expression state space trajectories", "type"=>"journal", "authors"=>[{"first_name"=>"Jessica C.", "last_name"=>"Mar", "scopus_author_id"=>"8300771200"}, {"first_name"=>"John", "last_name"=>"Quackenbush", "scopus_author_id"=>"7004974520"}], "year"=>2009, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"74549148040", "doi"=>"10.1371/journal.pcbi.1000626", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "issn"=>"1553734X", "scopus"=>"2-s2.0-74549148040", "pmid"=>"20041215", "pui"=>"358137749"}, "id"=>"c87f7ec9-5698-30e6-a49d-a1b620e61954", "abstract"=>"Understanding how cells differentiate from one state to another is a fundamental problem in biology with implications for better understanding evolution, the development of complex organisms from a single fertilized egg, and the etiology of human disease. One way to view these processes is to examine cells as “complex adaptive systems” where the state of all genes in a cell (more than 20,000 genes) determines that cell's “state” at a given point in time. In this view, differentiating cells move along a path in “state space” from one stable “attractor” to another. In a 2005 paper, Sui Huang and colleagues presented an experimental model in which they claimed to have evidence for such attractors and for the transitions between them. The problem with this approach is that although it is intuitively appealing, it lacks predictive power. Reanalyzing Huang's data, we demonstrate that there is an alternative interpretation that still allows for a state space description but which has greater ability to make testable predictions. Specifically, we show that these abstract state space trajectories can be mapped onto more well-known pathways and represented as a “core” differentiation pathway and “transient” processes that capture the effects of the treatments that initiate differentiation.", "link"=>"http://www.mendeley.com/research/decomposition-gene-expression-state-space-trajectories", "reader_count"=>92, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>7, "Researcher"=>32, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Other"=>3, "Student > Master"=>7, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>7, "Researcher"=>32, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Other"=>3, "Student > Master"=>7, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Agricultural and Biological Sciences"=>60, "Computer Science"=>5, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>2, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Mathematics"=>3, "Medicine and Dentistry"=>3, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>7, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Engineering"=>{"Engineering"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>60}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}}, "reader_count_by_country"=>{"Sweden"=>2, "Hong Kong"=>1, "Hungary"=>1, "United States"=>9, "Luxembourg"=>1, "Denmark"=>1, "Mexico"=>1, "France"=>2, "Germany"=>2, "Spain"=>1, "Russia"=>1}, "group_count"=>4}

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  • {"files"=>["https://ndownloader.figshare.com/files/870445"], "description"=>"<p>Enriched GO terms for the transient group that were statistically significant at the 0.1 level.</p>", "links"=>[], "tags"=>["transient"], "article_id"=>540907, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_GO_terms_for_the_transient_group_that_were_statistically_significant_at_the_0_1_level_/540907", "title"=>"Enriched GO terms for the transient group that were statistically significant at the 0.1 level.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-12-24 00:15:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/431608", "https://ndownloader.figshare.com/files/431631", "https://ndownloader.figshare.com/files/431674", "https://ndownloader.figshare.com/files/431714", "https://ndownloader.figshare.com/files/431767", "https://ndownloader.figshare.com/files/431822", "https://ndownloader.figshare.com/files/431881", "https://ndownloader.figshare.com/files/431937", "https://ndownloader.figshare.com/files/432065", "https://ndownloader.figshare.com/files/432199", "https://ndownloader.figshare.com/files/432273"], "description"=>"<div><p>Representing and analyzing complex networks remains a roadblock to creating dynamic network models of biological processes and pathways. The study of cell fate transitions can reveal much about the transcriptional regulatory programs that underlie these phenotypic changes and give rise to the coordinated patterns in expression changes that we observe. The application of gene expression state space trajectories to capture cell fate transitions at the genome-wide level is one approach currently used in the literature. In this paper, we analyze the gene expression dataset of Huang et al. (2005) which follows the differentiation of promyelocytes into neutrophil-like cells in the presence of inducers dimethyl sulfoxide and <em>all-trans</em> retinoic acid. Huang et al. (2005) build on the work of Kauffman (2004) who raised the attractor hypothesis, stating that cells exist in an expression landscape and their expression trajectories converge towards attractive sites in this landscape. We propose an alternative interpretation that explains this convergent behavior by recognizing that there are two types of processes participating in these cell fate transitions—core processes that include the specific differentiation pathways of promyelocytes to neutrophils, and transient processes that capture those pathways and responses specific to the inducer. Using functional enrichment analyses, specific biological examples and an analysis of the trajectories and their core and transient components we provide a validation of our hypothesis using the Huang et al. (2005) dataset.</p></div>", "links"=>[], "tags"=>["decomposition", "trajectories"], "article_id"=>145191, "categories"=>["Biological Sciences", "Cancer", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000626.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s010", "https://dx.doi.org/10.1371/journal.pcbi.1000626.s011"], "stats"=>{"downloads"=>14, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Decomposition_of_Gene_Expression_State_Space_Trajectories/145191", "title"=>"Decomposition of Gene Expression State Space Trajectories", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-12-24 01:26:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/869901"], "description"=>"<p>Schematic diagram outlining our hypothesis.</p>", "links"=>[], "tags"=>["diagram", "outlining"], "article_id"=>540355, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_diagram_outlining_our_hypothesis_/540355", "title"=>"Schematic diagram outlining our hypothesis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:05:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/870426"], "description"=>"<p>Enriched GO terms for the core group that were statistically significant at the 0.1 level.</p>", "links"=>[], "tags"=>["Computational biology", "computational biology/genomics", "computational biology/systems biology"], "article_id"=>540879, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.t001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_GO_terms_for_the_core_group_that_were_statistically_significant_at_the_0_1_level_/540879", "title"=>"Enriched GO terms for the core group that were statistically significant at the 0.1 level.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-12-24 00:14:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/870127"], "description"=>"<p>ATRA is able to diffuse freely across the cell membrane. A pair of cellular retinoic acid binding proteins act as cell surface receptors for retinoids however these have been shown to be dispensable in retinoic-acid signaling <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000626#pcbi.1000626-Gorry1\" target=\"_blank\">[21]</a>. ATRA binds to a family of nuclear hormone receptors called retinoic-acid receptors (RARs). There are three subtypes of the RAR family, these are encoded by different genes and denoted RAR-α, RAR-β, RAR-γ. Collins et al <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000626#pcbi.1000626-Collins2\" target=\"_blank\">[22]</a> demonstrated that in HL-60 cells, ATRA induced granulocytic differentiation by binding RAR-α directly. RAR-α binds to specific <i>cis</i>-acting DNA sites, known as retinoic-acid response elements (RAREs). These RAREs are located in the promoter sequences of specific genes that are targets of RAR-α. In order to bind DNA efficiently, RARs must however form heterodimers with a second family of nuclear hormone receptors, the retinoid X receptors (RXRs), of which there are three subtypes: RXR-α, RXR-β, RXR-γ. Both RXRs and RARs function as ligand-dependent transcription factors. One of the RAR-target genes whose expression is upregulated is the cell cycle protein p21 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000626#pcbi.1000626-Liu1\" target=\"_blank\">[23]</a>. p21 inhibits the cyclin dependent kinase complex Cyclin E and CDK2. In this way, ATRA induces cell cycle arrest at the G1 to S phase transition checkpoint.</p>", "links"=>[], "tags"=>["signaling"], "article_id"=>540587, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ATRA_signaling_pathway_/540587", "title"=>"ATRA signaling pathway.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:09:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/870295"], "description"=>"<p>The expression mosaics for the ATRA and DMSO-stimulated time course data capture spatial patterns in the data as the system iterates through the time series. These images are a graphical representation of dynamic expression changes in the data, clustered using a self-organizing map algorithm (SOM). We show how the overall expression trajectories for the ATRA and DMSO-stimulated data can be divided into components defined by the core and transient set of genes. Red denotes extreme positive log expression ratios, blue denotes extreme negative log expression ratios.</p>", "links"=>[], "tags"=>["mosaics", "atra", "dmso-stimulated"], "article_id"=>540746, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.g004", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_expression_mosaics_of_the_ATRA_and_DMSO_stimulated_time_course_data_/540746", "title"=>"Gene expression mosaics of the ATRA and DMSO-stimulated time course data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:12:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/870040"], "description"=>"<p>DMSO upregulates the tumor suppressor protein PTEN <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000626#pcbi.1000626-Lee1\" target=\"_blank\">[15]</a> in HL-60 cells. This increase in PTEN expression and activity is brought about by the activation of NF-κB. PTEN is a lipid phosphatase that is located in the cytoplasm and one of its primary roles is to dephosphorylate PIP3, a product of PI3K. The upregulation of PTEN results in a perturbation of the PI3k/Akt pathway, specifically the reduction in Akt phosphorylation levels and hence decreasing the amount of activated Akt. Normally activated Akt leads to phosphorylation of FOXO3, a member of the forkhead transcription factor family and this sets off further pathways that promote cell survival. However, inactive FOXO3 is able to translocate to the nucleus where it acts as a transcription factor, binding to <i>cis</i>-DNA elements and causing an increase in the gene expression of p27 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000626#pcbi.1000626-Wang1\" target=\"_blank\">[16]</a>. The p27 protein inhibits the cyclin-dependent kinase complex Cyclin E and CDK2 which controls the G1 to S phase transition.</p>", "links"=>[], "tags"=>["signaling"], "article_id"=>540503, "categories"=>["Biological Sciences", "Infectious Diseases", "Medicine"], "users"=>["Jessica C. Mar", "John Quackenbush"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000626.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_DMSO_signaling_pathway_/540503", "title"=>"DMSO signaling pathway.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-12-24 00:08:23"}

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Relative Metric

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