Models of Self-Peptide Sampling by Developing T Cells Identify Candidate Mechanisms of Thymic Selection
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{"title"=>"Models of Self-Peptide Sampling by Developing T Cells Identify Candidate Mechanisms of Thymic Selection", "type"=>"journal", "authors"=>[{"first_name"=>"Iren", "last_name"=>"Bains", "scopus_author_id"=>"26646225300"}, {"first_name"=>"Hisse M.", "last_name"=>"van Santen", "scopus_author_id"=>"7003995682"}, {"first_name"=>"Benedict", "last_name"=>"Seddon", "scopus_author_id"=>"7006500719"}, {"first_name"=>"Andrew J.", "last_name"=>"Yates", "scopus_author_id"=>"35742438800"}], "year"=>2013, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "pui"=>"369438534", "sgr"=>"84880835331", "doi"=>"10.1371/journal.pcbi.1003102", "scopus"=>"2-s2.0-84880835331", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"23935465"}, "id"=>"86cd22f5-be6d-3882-a13d-acc08edd5433", "abstract"=>"Conventional and regulatory T cells develop in the thymus where they are exposed to samples of self-peptide MHC (pMHC) ligands. This probabilistic process selects for cells within a range of responsiveness that allows the detection of foreign antigen without excessive responses to self. Regulatory T cells are thought to lie at the higher end of the spectrum of acceptable self-reactivity and play a crucial role in the control of autoimmunity and tolerance to innocuous antigens. While many studies have elucidated key elements influencing lineage commitment, we still lack a full understanding of how thymocytes integrate signals obtained by sampling self-peptides to make fate decisions. To address this problem, we apply stochastic models of signal integration by T cells to data from a study quantifying the development of the two lineages using controllable levels of agonist peptide in the thymus. We find two models are able to explain the observations; one in which T cells continually re-assess fate decisions on the basis of multiple summed proximal signals from TCR-pMHC interactions; and another in which TCR sensitivity is modulated over time, such that contact with the same pMHC ligand may lead to divergent outcomes at different stages of development. Neither model requires that T(conv) and T(reg) are differentially susceptible to deletion or that the two lineages need qualitatively different signals for development, as have been proposed. We find additional support for the variable-sensitivity model, which is able to explain apparently paradoxical observations regarding the effect of partial and strong agonists on T(conv) and T(reg) development.", "link"=>"http://www.mendeley.com/research/models-selfpeptide-sampling-developing-t-cells-identify-candidate-mechanisms-thymic-selection", "reader_count"=>27, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>2, "Student > Master"=>3, "Student > Bachelor"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Medicine and Dentistry"=>3, "Agricultural and Biological Sciences"=>14, "Computer Science"=>2, "Immunology and Microbiology"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>1, "Japan"=>1, "United Kingdom"=>2, "India"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1130620"], "description"=>"<p><b>Upper panel</b> If TCR sensitivity remains static throughout thymic development, the simplest one-hit model fails to explain the rise and fall of T numbers with agonist expression, because the predicted probability of receiving a T selecting signal either increases or decreases monotonically. <b>Middle panel</b>. Again with static thresholds, if encounters comprise contacts with pMHC, and , the distribution of signal strengths from each encounter is smoother and shifts rightwards with TIM expression, first increasing then decreasing the probability of triggering T development, as required. <b>Lower panel</b>. The two-phase mode also explains the data and allows for encounters comprising single (, illustrated here) or multiple () TCR-pMHC engagements to dictate fate. The trend in T numbers arises from the balance between an increasing probability of receiving a T -selecting signal with agonist expression in the low-sensitivity phase, and a decreasing probability in the higher-sensitivity phase.</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "agonist"], "article_id"=>754805, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Modelling_T_selection_as_a_function_of_agonist_abundance_/754805", "title"=>"Modelling T selection as a function of agonist abundance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130619"], "description"=>"<p>We use <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003102#pcbi.1003102.e170\" target=\"_blank\">equation 6</a> to connect the TIM peptide abundance to the expression of TIM RNA relative to controls. Shown are three representative functions using different values of the breadth-parameter , with location parameter and saturating TIM abundance .</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "tim", "rna", "peptide"], "article_id"=>754804, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g005", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relating_TIM_RNA_to_peptide_abundance_/754804", "title"=>"Relating TIM RNA to peptide abundance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130615"], "description"=>"<p>An <i>encounter</i> is defined as the simultaneous or temporally proximal binding of TCR to pMHC ligands on a thymic epithelial cell. Here, .</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "encounters"], "article_id"=>754801, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g002", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Thymocyte_encounters_with_self_peptides_/754801", "title"=>"Thymocyte encounters with self-peptides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130614"], "description"=>"<p>Absolute number of clonotype positive (T, red circles) and (T, blue circles) thymocytes in tetracycline-treated TAND mice, as a function of the relative expression level of TIM RNA in the thymus of these animals. Control animals lacked either the transactivator or reporter transgene.</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "taken", "santen"], "article_id"=>754800, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g001", "stats"=>{"downloads"=>1, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_taken_from_van_Santen_et_al_16_/754800", "title"=>"Data taken from van Santen <i>et al.</i>[16].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130624", "https://ndownloader.figshare.com/files/1130625", "https://ndownloader.figshare.com/files/1130626", "https://ndownloader.figshare.com/files/1130627", "https://ndownloader.figshare.com/files/1130628", "https://ndownloader.figshare.com/files/1130629", "https://ndownloader.figshare.com/files/1130630", "https://ndownloader.figshare.com/files/1130631"], "description"=>"<div><p>Conventional and regulatory T cells develop in the thymus where they are exposed to samples of self-peptide MHC (pMHC) ligands. This probabilistic process selects for cells within a range of responsiveness that allows the detection of foreign antigen without excessive responses to self. Regulatory T cells are thought to lie at the higher end of the spectrum of acceptable self-reactivity and play a crucial role in the control of autoimmunity and tolerance to innocuous antigens. While many studies have elucidated key elements influencing lineage commitment, we still lack a full understanding of how thymocytes integrate signals obtained by sampling self-peptides to make fate decisions. To address this problem, we apply stochastic models of signal integration by T cells to data from a study quantifying the development of the two lineages using controllable levels of agonist peptide in the thymus. We find two models are able to explain the observations; one in which T cells continually re-assess fate decisions on the basis of multiple summed proximal signals from TCR-pMHC interactions; and another in which TCR sensitivity is modulated over time, such that contact with the same pMHC ligand may lead to divergent outcomes at different stages of development. Neither model requires that T and T are differentially susceptible to deletion or that the two lineages need qualitatively different signals for development, as have been proposed. We find additional support for the variable-sensitivity model, which is able to explain apparently paradoxical observations regarding the effect of partial and strong agonists on T and T development.</p></div>", "links"=>[], "tags"=>["Computational biology", "immunology", "self-peptide", "sampling", "cells", "mechanisms", "thymic"], "article_id"=>754809, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1003102.s001", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s002", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s003", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s004", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s005", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s006", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s007", "https://dx.doi.org/10.1371/journal.pcbi.1003102.s008"], "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Models_of_Self_Peptide_Sampling_by_Developing_T_Cells_Identify_Candidate_Mechanisms_of_Thymic_Selection_/754809", "title"=>"Models of Self-Peptide Sampling by Developing T Cells Identify Candidate Mechanisms of Thymic Selection", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130622"], "description"=>"<p>(A) Total number of encounters (+) and (B) the proportion of all encounters that take place in phase A, for plausible ranges of , the proportion of endogenous peptides replaced by TIM at maximum RNA expression.</p>", "links"=>[], "tags"=>["Computational biology", "immunology"], "article_id"=>754807, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g008", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_phase_model_/754807", "title"=>"Two phase model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130623"], "description"=>"<p>Partial agonist (left panels) may drive T commitment in the early phase but induce deletion when TCR sensitivity is increased. In contrast, strong agonist (right panels) may drive T commitment early, and despite triggering deletion later, the net effect is still a net increase in T numbers. For clarity we illustrate here with the signal-strength distribution derived from , as in the basic two-phase model, but the argument holds also for a generalised two-phase model with encounters of size (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003102#pcbi.1003102.s002\" target=\"_blank\">Figure S2</a>).</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "two-phase", "dependence", "agonist"], "article_id"=>754808, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g009", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Using_the_two_phase_model_to_explain_the_dependence_of_T_development_on_agonist_strength_/754808", "title"=>"Using the two-phase model to explain the dependence of T development on agonist strength.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130621"], "description"=>"<p>Representative descriptions of the data by the models. T in blue, T in red. <b>Panel A</b>; the one-hit model in which fate decisions are re-evaluated after single TCR-pMHC contacts. The dotted curves, ; dashed curves, . <b>Panel B</b>; the model; <b>Panel C</b>; the two-phase model with .</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "descriptions"], "article_id"=>754806, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g007", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_descriptions_of_the_data_/754806", "title"=>"Model descriptions of the data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130618"], "description"=>"<p>In this instance, TCR sensitivity is assumed to increase during development, such that an encounter with the agonist ligand TIM delivers a signal that initiates T development early in selection (phase A) but causes deletion if encountered later (phase B).</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "two-phase"], "article_id"=>754803, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_two_phase_model_/754803", "title"=>"The two-phase model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/1130617"], "description"=>"<p><b>A</b>: The case . The probabilities ) are those of a signal lying within the different selecting regions. Blue curve; log-normally distributed signal strengths from self pMHC in the absence of TIM expression. Area under the curve = 1. Red curve; TIM expressed at frequency superimposed on this wild-type (endogenous pMHC) distribution. The spike at (shown for convenience here with finite width and height) is a point mass in the probability distribution, of area ; the remainder is of area . <b>B</b>: For the different selecting regions lie beween different signal strength thresholds . Increasing TIM expression (the proportion of pMHC within an encounter, on average) shifts the distribution of signal strengths rightwards.</p>", "links"=>[], "tags"=>["Computational biology", "immunology", "tim", "strengths", "thymocyte"], "article_id"=>754802, "categories"=>["Biological Sciences"], "users"=>["Iren Bains", "Hisse M. van Santen", "Benedict Seddon", "Andrew J. Yates"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1003102.g003", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_influence_of_TIM_expression_on_the_distribution_of_signal_strengths_resulting_from_thymocyte_encounters_/754802", "title"=>"The influence of TIM expression on the distribution of signal strengths resulting from thymocyte encounters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-07-25 01:43:39"}

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Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Evolutionary biology", "average_usage"=>[302, 488, 607, 717, 832, 931, 1024, 1117, 1212, 1302, 1389, 1469, 1535]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[284, 491, 620, 738, 843, 945, 1043, 1137, 1225, 1315, 1400, 1479, 1555]}, {"subject_area"=>"/Medicine and health sciences", "average_usage"=>[264, 460, 584, 692, 794, 887, 978, 1067, 1154, 1241, 1328, 1408, 1474]}]}
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