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Cited by 709Gene Regulatory Network AnalysisPsoriasis: Treatment and PathogenesisT-cell and B-cell Immunology

Dynamic regulatory network controlling TH17 cell differentiation

Open access
Yosef, Nir, Shalek, Alex K., Gaublomme, Jellert T., Jin, Hulin, Lee, Youjin, Awasthi, Amit, Wu, Chuan, Karwacz, Katarzyna +15 more·2013·Nature
DOI 10.1038/nature11981PMC PMC3637864
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Abstract

Despite their importance, the molecular circuits that control the differentiation of naive T cells remain largely unknown. Recent studies that reconstructed regulatory networks in mammalian cells have focused on short-term responses and relied on perturbation-based approaches that cannot be readily applied to primary T cells. Here we combine transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based perturbation tools to systematically derive and experimentally validate a model of the dynamic regulatory network that controls the differentiation of mouse TH17 cells, a proinflammatory T-cell subset that has been implicated in the pathogenesis of multiple autoimmune diseases. The TH17 transcriptional network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, the coupled action of which may be essential for maintaining the balance between TH17 and other CD4(+) T-cell subsets. Our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles; it also highlights novel drug targets for controlling TH17 cell differentiation.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper reconstructs and experimentally validates a dynamic transcriptional network for Th17 differentiation by combining a 72-hour expression time course, computational network inference, and nanowire-based perturbation of primary T cells. It identifies two opposing, self-reinforcing regulatory modules and characterizes multiple previously unrecognized regulators.

Key findings

  • Across 18 time points spanning 72 hours, 1,291 genes were differentially expressed during Th17 differentiation and grouped into 20 co-expression clusters; the response resolved into early (up to 4 hours), intermediate (4–20 hours), and late (20–72 hours) phases.
  • The inferred network contained 9,159 interactions linking 71 regulators to 1,266 genes, with 33 regulators active in all networks and 18 active in only one; the authors validated 39 of 65 selected genes by perturbation, including 29 regulators and 10 receptors, 21 of which had not previously been linked to Th17 differentiation.
  • At 48 hours, the functional network split into two antagonistic modules: 22 positive regulators (9 novel) whose loss reduced Th17 signature genes, and 5 negative regulators (3 novel) whose loss increased them; 70% of within-module edges were positive, 88% of between-module edges were negative, and this organization was statistically significant (empirical p<10^-3).
  • Twelve novel factors were characterized further; examples include Mina, whose knockdown reduced IL-17a and increased Foxp3, Fas, whose knockout reduced IL-17a and increased IFN-γ, Pou2af1, whose knockout reduced IL-17a and increased IL-2, and Tsc22d3, whose knockdown increased IL-17a, IL-21, and IL-10.

Methods

  • Naïve CD4+ T cells were differentiated toward Th17 with TGF-β1 and IL-6, with Th0 controls, and profiled by microarray at 18 time points from 0.5 to 72 hours; differential expression was called using four methods, and genes were partitioned by k-means clustering.
  • Regulatory interactions were inferred by overlapping published regulator–target datasets with co-expression clusters, then time-stamping edges using target expression and regulator mRNA plus inferred protein levels; candidate regulators and receptors were ranked and tested by nanowire-delivered siRNA, knockout mice, Nanostring (275 genes), Fluidigm (86 genes), RNA-Seq, and ChIP-seq.
  • Statistical support included Fisher’s exact tests for target overlap, permutation tests for network validation, and shuffled-network tests for module organization.

Limitations

  • The paper states that the functional wiring likely includes both direct and indirect interactions and is a starting point for mapping physical DNA-binding or protein–protein interactions.
  • Evident from the text: only 39 of 65 selected genes were successfully perturbed, knockdown required at least a 60% reduction in target transcript, and network inference depended partly on published binding or perturbation data often generated in other cell types.
  • Evident from the text: the clear two-module organization appeared at 48 hours but not at 10 hours (p>0.5), suggesting the network is still malleable early in differentiation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/nature11981

Cites

85 of 85 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 39 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Fas is a Th17-positive regulator.

    “Fas, the TNF receptor superfamily member 6, is another Th17 positive regulator (Fig. 5b).”
    Quote in the paperSubstantial claim
  • Findingown data

    The inferred network contains 9,159 interactions between 71 regulators and 1,266 genes in at least one time point.

    “Overall, 9,159 interactions between 71 regulators and 1,266 genes were inferred in at least one network.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
  • Findingown data

    Most inferred regulatory interactions are active only during some time window, even for regulators present in all networks.

    “The vast majority of interactions are active only at some time window (Fig. 2c), even for regulators (e.g., Batf) that participate in all networks.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
  • Findingown data

    Th17 differentiation proceeds through three transcriptional phases: early up to 4 h, intermediate 4-20 h, and late 20-72 h.

    “There are three transcriptional phases as the cells transition from a naïve-like state (t=0.5hr) to Th17 (t=72hr; Fig. 1c and Supplementary Fig. 1c): early (up to 4hr), intermediate (4-20hr), and late (20-72hr).”
    Quote in the paperSubstantial claim
  • Findingown data

    Based on similarity of active interactions, the networks fall into three classes that correspond to the three differentiation phases.

    “Based on similarity in active interactions, we identified three network classes (Fig. 2c), corresponding to the three differentiation phases (Fig. 2d).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
  • May combine two claims. 48
  • The quote only partly states this claim. 1
  • Classified as probably not a substantial claim. 9

  • May combine two claims.

    Findingown data

    The reconstructed regulatory network for Th17 differentiation is composed of two self-reinforcing but mutually antagonistic modules and includes 12 previously unrecognized regulators.

    “The network consists of two self-reinforcing, but mutually antagonistic, modules, with 12 novel regulators, whose coupled action may be essential for maintaining the balance between Th17 and other CD4+ T cell subsets.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The study identified and validated 39 regulatory factors and embedded them in a comprehensive temporal network.

    “Overall, our study identifies and validates 39 regulatory factors, embeds them within a comprehensive temporal network and reveals its organizational principles, and highlights novel drug targets for controlling Th17 differentiation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Mina is strongly induced during Th17 differentiation (cluster C7), is down-regulated in IL23r-/- Th17 cells, and is a predicted target of Batf, ROR-γt, and Myc in the model.

    “Mina is strongly induced during Th17 differentiation (cluster C7), is down-regulated in IL23r-/- Th17 cells, and is a predicted target of Batf30, ROR-γt30, and Myc in our model (Fig. 5a).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [30] Glasmacher, E. et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1-IRF complexes. Science 338, 975–980 (2012) source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    Knockdown of a Th17-positive regulator down-regulated genes in otherwise induced clusters and up-regulated genes in otherwise repressed or un-induced clusters, with the opposite for Th17-negative regulators.

    “Consistent with the original definitions, knockdown of a ‘Th17 positive’ regulator down-regulated genes in otherwise induced clusters, and up-regulated genes in otherwise repressed or un-induced clusters (and vice versa for ‘Th17 negative’ regulators; Fig. 4d and Supplementary Fig. 11a,b).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    RNA-Seq after perturbing each factor showed highly significant overlap with predicted targets for Egr2, Irf8, and Sp4 (p<10^-5), and borderline overlap for Smarca4.

    “We found highly significant overlaps (p<10-5) for three of the factors (Egr2, Irf8, and Sp4) that exist in both datasets, and a border-line significant overlap for the fourth (Smarca4), validating the quality of the edges in our network.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    During the transition to the late phase at 20 h, mRNAs for Th17 signature cytokines such as IL-17a and IL-9 are induced, while mRNAs for cytokines of other T cell lineages such as IFN-γ and IL-4 are repressed.

    “During the transition to the late phase (t=20hr), mRNAs of Th17 signature cytokines are induced (e.g., IL-17a, IL-9; cluster C19) whereas mRNAs of cytokines that signal other T cell lineages are repressed (e.g., IFN-γ and IL-4).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Around 48 h, the cells induce IL23r, which plays an important role in the late phase.

    “Around 48hr, the cells induce IL23r (data not shown), which plays an important role in the late phase”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In the authors' cells, Mina knockdown did not induce Th2 genes, suggesting an alternative mechanism involving positive feedback loops between Mina, Batf, and ROR-γt.

    “However, in our cells, Mina knockdown did not induce Th2 genes, suggesting an alternative mode of action via positive feedback loops between Mina, Batf and ROR-γt (Fig. 5a, left).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Cytokine analysis of supernatants from Mina-/- cells confirmed decreased IL-17a production and increased IFN-γ and TNFα.

    “Cytokine analysis of the corresponding supernatants confirmed a decrease in IL-17a production and an increase in IFN-γ (Fig. 5a) and TNFα (Supplementary Fig. 12a).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Within each module, 70% of intra-module edges are positive self-reinforcing interactions, and 88% of inter-module interactions are negative.

    “Each of the modules is tightly intra-connected through positive, self-reinforcing interactions between its members (70% of the intra-module edges), whereas most (88%) inter-module interactions are negative.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The network includes many novel factors as predicted regulators, induced targets, or both, and includes receptor genes as induced targets, some known and some novel.

    “In addition to known Th17 regulators, our network includes dozens of novel factors as predicted regulators (Fig. 2d), induced target genes, or both”
    • Also stated in: § Inference of dynamic regulatory interactions · plain
      “It also contains receptor genes as induced targets, both previously known in Th17 cells (e.g., IL-1R1, IL-17RA) and novel (e.g., Fas, Itga3).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.

  • May combine two claims.

    Findingown data

    Perturbing Th17-positive factors also induces signature genes of other T cell subsets, whereas perturbing Th17-negative factors suppresses them.

    “Indeed, perturbing Th17 positive factors also induces signature genes of other T cell subsets (e.g., Gata3, Fig. 4b, grey nodes, top), whereas perturbing Th17 negative factors suppresses them (e.g., Foxp3, Gata3, Stat4, and Tbx21).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Altogether, 39 of the 65 selected genes were successfully perturbed: 29 regulators and 10 receptors, including 21 genes not previously linked to Th17 differentiation.

    “Altogether, we successfully perturbed 39 of the 65 selected genes – 29 regulators and 10 receptors – including 21 genes not previously associated with Th17 differentiation.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Pou2af1 knockdown strongly decreases Th17 signature genes and intermediate/late-induced genes (clusters C19, C20; p<10^-7), while increasing regulators of other CD4+ subsets and genes in non-induced clusters (C2, C16; p<10^-9).

    “Knockdown of Pou2af1 (OBF1) strongly decreases the expression of Th17 signature genes (Fig. 5c) and of intermediate- and late-induced genes (clusters C19 and C20, p<10-7; Supplementary Tables 5 and 7), while increasing the expression of regulators of other CD4+ subsets (e.g., Foxp3, Stat4, Gata3) and of genes in non-induced clusters (clusters C2 and C16 p<10-9; Supplementary Table 5 and 7).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Some early induced genes have sustained expression and are enriched for transcription factors including Stat3, Irf4, and Batf, and for cytokines and receptors IL-21, Lif, and Il2ra.

    “Some early induced genes display sustained expression (e.g., Cluster C10, Fig. 1b); these are enriched for transcription factors (TFs), including the key Th17 factors Stat3, Irf4 and Batf, and the cytokine and cytokine receptors IL-21, Lif, and Il2ra”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Among the regulators, 33 are active in all networks, while 18 are active in only one network.

    “Among the regulators, 33 are active in all of the networks (e.g. many known master regulators such as Batf1, Irf4, and Stat3), whereas 18 are active in only one (e.g. Stat1 and Irf1 in the early network; ROR-γt in the late network).”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.

  • May combine two claims.

    Findingown data

    At 10 h and 48 h, 30% of functional interactions have the same activation/repression logic at both times, while the rest appear at only one time point.

    “We found that 30% of the functional interactions are present with the same activation/repression logic at both 10hr and 48hr, whereas the rest are present only in one time point (Supplementary Fig. 9).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Genes induced by Mina knockdown significantly overlap with genes bound by Foxp3 in Treg cells and with a cluster previously linked to Foxp3 activity in Treg cells.

    “Finally, the genes induced by Mina knockdown significantly overlap with those bound by Foxp3 in Treg cells26,27 (P<10−25; Supplementary Table 7) and with a cluster previously linked to Foxp3 activity in Treg cells32”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [26] Marson, A. et al. Foxp3 occupancy and regulation of key target genes during T-cell stimulation. Nature 445, 931–935 (2007) source

      Not checked in the free run.

    • cites [27] Zheng, Y. et al. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature 445, 936–940 (2007) source

      Not checked in the free run.

    • cites [32] Hill, J. A. et al. Foxp3 transcription-factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. Immunity 27, 786–800 (2007) source

      Not checked in the free run.

  • May combine two claims.

    Findingown data

    ROR-γt mRNA is induced around 4 h, but ROR-γt protein increases around 20 h and continues to rise, matching the model.

    “Indeed, while ROR-γt mRNA levels are induced at ∼4h, ROR-γt protein levels increase at approximately 20h and further rise over time, consistent with our model”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Differentiating T cells from Fas-/- mice showed strongly repressed IL-17a and strongly increased IFN-γ under both Th17 and Th0 polarizing conditions.

    “Consistent with our knockdown analysis, expression of IL-17a was strongly repressed and IFN-γ production was strongly increased under both Th17 and Th0 polarizing conditions (Fig. 5b).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Mina knockdown represses signature Th17 cytokines and transcription factors (ROR-γt, Batf, Irf4) and late-induced genes (clusters C9, C19; p<10^-5), while increasing Foxp3.

    “Knockdown of Mina, a chromatin regulator from the Jumonji C (JmjC) family, represses the expression of signature Th17 cytokines and TFs (e.g. ROR-γt, Batf, Irf4) and of late-induced genes (clusters C9, C19; p<10-5; Supplementary Tables 5 and 7), while increasing the expression of Foxp3, the master TF of Treg cells.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At 48 h, the network is organized into two antagonistic modules: 22 Th17-positive factors (9 novel) whose perturbation decreased Th17 signature gene expression, and 5 Th17-negative factors (3 novel) whose perturbation increased it.

    “we find that at 48hr the network is organized into two antagonistic modules: a module of 22 ‘Th17 positive factors’ (Fig. 4b, blue nodes: 9 novel) whose perturbation decreased the expression of Th17 signature genes (Fig. 4b, grey nodes, bottom), and a module of 5 ‘Th17 negative factors’ (Fig. 4b, red nodes: 3 novel) whose perturbation did the opposite.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Fas is induced early and is a target of Stat3 and Batf in the model.

    “Fas is induced early, and is a target of Stat3 and Batf in our model.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Mina expression is reduced in Th17 cells from ROR-γt-knockout mice, and the Mina promoter is bound by ROR-γt by ChIP-Seq.

    “Consistent with this model, Mina expression is reduced in Th17 cells from ROR-γt-knockout mice, and the Mina promoter was found to be bound by ROR-γt by ChIP-Seq (data not shown).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    At 10 h, the same regulators do not show the clear two-module pattern (p>0.5), suggesting the network is still malleable at that time.

    “At 10hrs, the same regulators do not yield this clear pattern (p>0.5), suggesting that at that point, the network is still malleable.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    Fas knockdown represses key Th17 genes (IL-17a, IL-17f, Hif1a, Irf4, Rbpj) and induced cluster C14, and promotes Th1-related genes including IFN-γ receptor 1 and Klrd1.

    “Fas knockdown represses the expression of key Th17 genes (e.g., IL-17a, IL-17f, Hif1a, Irf4, and Rbpj) and of the induced cluster C14, and promotes the expression of Th1-related genes, including IFN-γ receptor 1 and Klrd1 (Cd94; by RNA-Seq, Fig. 4, Fig. 5b, Supplementary Table 7 and Supplementary Fig. 11).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The top-ranked factors are enriched for manually curated Th17 regulators (p<10^-3) and correlate with a supervised ranking (Spearman r>0.86).

    “Supporting their quality, our top-ranked factors are enriched (p<10-3) for manually curated Th17 regulators (Supplementary Fig. 6), and correlate well (Spearman r>0.86) with a ranking learned by a supervised method (Methods).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Mina-/- cells have decreased IL-17a and increased Foxp3 compared to wild-type cells by intracellular staining.

    “Mina-/- cells had decreased IL-17a and increased Foxp3 compared to wild-type (WT) cells, as detected by intracellular staining (Fig. 5a).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The authors systematically ranked 127 putative regulators (80 novel) and tested top-ranking ones experimentally.

    “Building on this model, we systematically ranked 127 putative regulators (80 novel; Supplementary Table 4), and tested top ranking ones experimentally.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The model consists of three consecutive, densely intra-connected networks, implicates 71 regulators (46 novel), and suggests substantial rewiring in three phases.

    “The model consists of three consecutive, densely intra-connected networks, implicates 71 regulators (46 novel), and suggests substantial rewiring in 3 phases.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.

  • May combine two claims.

    Findingown data

    Tsc22d3 binds near IL-21 and Irf4, which also become up-regulated in Tsc22d3 knockdown.

    “For example, Tsc22d3 binds in proximity to IL-21 and Irf4, which also become up regulated in the Tsc22d3 knockdown.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Within the two modules are 12 novel factors, four of which were further characterized in the main text.

    “Within the two modules are 12 novel factors (Fig. 4 and 5), which we further characterized, highlighting four of the factors”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Findingnone given

    The network in Fig. 4b is divided into a positive module and a negative module.

    “The functional network in Fig. 4b consists of two modules: positive and negative.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    IL-17a production was also strongly repressed in Oct1-deficient cells, suggesting Pou2af1 may exert some effects through this co-factor.

    “IL-17a production was also strongly repressed in Oct1-deficient cells (Supplementary Fig. 12d), suggesting that Pou2af1 may exert some of its effects through this co-factor.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Tsc22d3 knockdown increases Th17 cytokines (IL-17a, IL-21) and transcription factors (ROR-γt, Rbpj, Batf), and reduces Foxp3 expression.

    “Knockdown of the TSC22 domain family protein 3 (Tsc22d3) increases the expression of Th17 cytokines (IL-17a, IL-21) and TFs (ROR-γt, Rbpj, Batf), and reduces Foxp3 expression.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    TGF-β1 alone induces Foxp3+ regulatory T cells, whereas IL-6 inhibits iTreg and promotes Th17 differentiation.

    “While TGF-β1 alone induces Foxp3+ regulatory T cells (iTreg)2, the presence of IL-6 inhibits iTreg and induces Th17 differentiation1.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] Zhou, L. et al. TGF-β-induced Foxp3 inhibits TH17 cell differentiation by antagonizing RORγt function. Nature 453, 236–240 (2008) source

      Not checked in the free run.

    • cites [1] Bettelli, E., Oukka, M. & Kuchroo, V. K. TH-17 cells in the circle of immunity and autoimmunity. Nature Immunol. 8, 345–350 (2007) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisenone given

    Th17 differentiation occurs over several days, and the components and connections of its regulatory network likely change during that period.

    “Th17 differentiation, however, spans several days, during which the components and wiring of the regulatory network likely change.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Pou2af1's role in T cell differentiation had not been explored.

    “Pou2af1's role in T cell differentiation has not been explored35.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Teitell, M. A. OCA-B regulation of B-cell development and function. Trends Immunol. 24, 546–553 (2003) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Mina was previously shown to suppress Th2 bias by interacting with NFAT and repressing the IL-4 promoter.

    “Mina was shown to suppress Th2 bias by interacting with the TF NFAT and repressing the IL-4 promoter31.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Okamoto, M. et al. Mina, an Il4 repressor, controls T helper type 2 bias. Nature Immunol. 10, 872–879 (2009) source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The results suggest a model in which Mina, induced by ROR-γt and Batf, promotes ROR-γt transcription while suppressing Foxp3 induction, thereby favoring rapid Th17 differentiation and affecting the Treg/Th17 balance.

    “This suggests a model where Mina, induced by ROR-γt and Batf, promotes transcription of ROR-γt, while suppressing induction of Foxp3, thus affecting the reciprocal Tregs/Th17 balance33 by favoring rapid Th17 differentiation.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [33] Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory TH17 cells. Nature 448, 484–487 (2007) source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    Silicon nanowire-based delivery was optimized to deliver siRNA into naïve T cells with >95% efficiency without activating them.

    “We therefore used a new delivery technology based on silicon nanowires (NWs)10,23, which we optimized to effectively (>95%) deliver siRNA into naïve T cells without activating them (Figs. 3b and c)23.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [10] Shalek, A. K. et al. Vertical silicon nanowires as a universal platform for delivering biomolecules into living cells. Proc. Natl Acad. Sci. USA 107, 1870–1875 (2010) source

      Not checked in the free run.

    • cites [23] Shalek, A. K. et al. Nanowire-mediated delivery enables functional interrogation of primary immune cells: application to the analysis of chronic lymphocytic leukemia. Nano Lett. 12, 6498–6504 (2012) source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Fas and Fas-ligand deficient mice are resistant to EAE induction but have no defect in IFN-γ or Th1 responses.

    “Fas and Fas-ligand deficient mice are resistant to the induction of autoimmune encephalomyelitis (EAE)34, but have no defect in IFN-γ or Th1 responses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Waldner, H., Sobel, R. A., Howard, E. & Kuchroo, V. K. Fas- and FasL-deficient mice are resistant to induction of autoimmune encephalomyelitis. J. Immunol. 159, 3100–3103 (1997) source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The results suggest that Fas, besides being a death receptor, may control the Th1/Th17 balance, and that Fas-/- mice may resist EAE because they lack Th17 cells.

    “These results suggest that besides being a death receptor, Fas may play an important role in controlling the Th1/Th17 balance, and Fas-/- mice may be resistant to EAE due to lack of Th17 cells.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    IL-10 production has been shown to render Th17 cells less pathogenic in autoimmunity, while co-production of IL-10 and IL-17a may be the indicated response for clearing certain infections like Staphylococcus aureus at mucosal sites.

    “Although IL-10 production has been shown33,38,39 to render Th17 cells less pathogenic in autoimmunity, co-production of IL-10 and IL-17a may be the indicated response for clearing certain infections like Staphylococcus aureus at mucosal sites40.”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [33] Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory TH17 cells. Nature 448, 484–487 (2007) source

      Not checked in the free run.

    • cites [38] Peters, A., Lee, Y. & Kuchroo, V. K. The many faces of Th17 cells. Curr. Opin. Immunol. 23, 702–706 (2011) source

      Not checked in the free run.

    • cites [39] Chaudhry, A. et al. Interleukin-10 signaling in regulatory T cells is required for suppression of Th17 cell-mediated inflammation. Immunity 34, 566–578 (2011) source

      Not checked in the free run.

    • cites [40] Zielinski, C. E. et al. Pathogen-induced human TH17 cells produce IFN-γ or IL-10 and are regulated by IL-1β. Nature 484, 514–518 (2012) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Previous studies in macrophages showed Tsc22d3 expression is stimulated by glucocorticoids and IL-10 and plays a key role in their anti-inflammatory and immunosuppressive effects.

    “Previous studies in macrophages have shown that Tsc22d3 expression is stimulated by glucocorticoids and IL-10, and it plays a key role in their anti-inflammatory and immunosuppressive effects37.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Choi, S.-J. et al. Tsc-22 enhances TGF-β signaling by associating with Smad4 and induces erythroid cell differentiation. Mol. Cell. Biochem. 271, 23–28 (2005) source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Replicate measurements were taken at eight time points and had high reproducibility (r2 > 0.98).

    “Biological replicates were measured in eight of the eighteen time points (1hr, 2hr, 10hr, 20hr, 30hr, 42hr, 52hr, 60hr) with high reproducibility (r2>0.98).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Every siRNA underwent at least three independent experiments, and each pool was run in quadruplicate.

    “In each knockdown experiment, each individual siRNA pool was run in quadruplicate; each siRNA was tested in at least three separate experiments (Supplementary Fig. 9).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The coordinated action of the two modules may explain how the balance between Th17, Treg, and other effector T cell subsets is maintained and how progressive directional Th17 differentiation is achieved.

    “whose coordinated action may explain how the balance between Th17, Treg, and other effector T cell subsets is maintained, and how progressive directional differentiation of Th17 cells is achieved”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The results suggest a model in which Tsc22d3 is part of a negative feedback loop that induces a Th17 subtype co-producing IL-17 and IL-10 and limits its pro-inflammatory capacity.

    “This suggests a model where Tsc22d3 is part of a negative feedback loop for the induction of a Th17 cell subtype that coproduce IL-17 and IL-10 and limits their pro-inflammatory capacity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The time course of differentiation was checked against previously published microarray profiles of Th17 and naïve T cells.

    “For further validation we compared the differentiation time course to published microarray data of Th17 cells and naïve T cells51 (Supplementary Fig. 1c).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [51] Wei, G. et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155–167 (2009) source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Tsc22d3 is induced in other cells in response to dexamethasone, which represses Th17 differentiation and ROR-γt expression.

    “Tsc22d3 is induced in other cells in response to the steroid Dexamethasone41, which represses Th17 differentiation and ROR-γt expression42.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [41] Jing, Y. et al. A mechanistic study on the effect of dexamethasone in moderating cell death in Chinese Hamster Ovary cell cultures. Biotechnol. Prog. 28, 490–496 (2012) source

      Not checked in the free run.

    • cites [42] Hu, S. M., Luo, Y. L., Lai, W. Y. & Chen, P. F. Effects of dexamethasone on intracellular expression of Th17 cytokine interleukin 17 in asthmatic mice [in Chinese]. Nan Fang Yi Ke Da Xue Xue Bao 29, 1185–1188 (2009)

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The wiring of the positive and negative modules uncovers some functional logic of the Th17 program, but likely involves both direct and indirect interactions.

    “The wiring of the positive and negative modules (Fig. 4 and 5) uncovers some of the functional logic of the Th17 program, but likely involve both direct and indirect interactions.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    Candidate regulators of Th17 differentiation, including transcription factors and chromatin modifiers, were ranked using an unbiased method.

    “We used an unbiased approach to rank candidate regulators – transcription factor or chromatin modifier genes – of Th17 differentiation.”
    Quote in the paperSubstantial claim
53 of 53

  • The authors state this as an assumption.

    Genes in the same co-expression cluster share regulators that are active at the relevant time points.

    “We hypothesized that each of the clusters (Fig. 1b, Supplementary Table 2) encompasses genes that share regulators active in the relevant time points.”

  • The paper relies on this without support.

    The set of 74 manually annotated regulators had genuine prior links to Th17 differentiation.

    “we manually annotated 74 regulators that were previously associated with Th17 differentiation.”
2 of 2

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Th17 cells in different tissue sites can show distinct transcriptional profiles associated with homeostatic or pathogenic functions.

    “analysis of Th17 cells in different tissue sites has revealed that Th17 cells may exhibit distinct transcriptional profiles associated with either homeostatic or pathogenic functions”

    From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [37]

  • SFB induction of Th17 cells in the small intestine does not cause a proinflammatory reaction but induces a homeostatic population that supports barrier function.

    “the induction of Th17 cells by SFB in the small intestine does not induce a proinflammatory reaction in the intestine, but instead induces a homeostatic Th17 population that supports intestinal barrier function”

    From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [37]

  • The role of Th17 cells in mouse models of IBD is controversial, likely due to their dichotomous nature as mediators of tissue homeostasis that can become drivers of tissue inflammation.

    “The role of Th17 cells in mouse models of inflammatory bowel disease (IBD) is controversial, which is likely due to the dichotomous nature of intestinal Th17 cells, as they are mediators of tissue homeostasis yet can become drivers of tissue inflammation [37].”

    From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [37]

  • Fate-mapping studies show IL-23R-dependent differentiation of IL-17-producing cells into encephalitogenic IFNγ-producing Th1/Th17 cells.

    “Fate-mapping studies using IL-17-fate reporter mice showed IL-23R-dependent differentiation of IL-17-producing cells into encephalitogenic IFNγ-producing Th1/Th17 cells”

    From TH17 cell heterogeneity and its role in tissue inflammation (2023) · cites this paper as [37]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Conflicting knockdown thresholds

    Major

    The results count knockdowns with less than 60% of the transcript remaining as efficient. The methods instead require a 60% reduction, equivalent to less than 40% remaining. These thresholds would classify some experiments differently.

1 of 1

Cites

Flagged first, then by citations

3 of the 88 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Glasmacher, E. et al. A genomic regulatory element that directs assembly and function of immune-specific AP-1-IRF complexes. Science 338, 975–980 (2012) · DOI · Paper page
  2. Durant, L. et al. Diverse targets of the transcription factor STAT3 contribute to T cell pathogenicity and homeostasis. Immunity 32, 605–615 (2010) · DOI

Cited by

Sorted by citations

Related

Authors
Yosef, N., Shalek, A. K., Gaublomme, J. T., Jin, H., Lee, Y., Awasthi, A., Wu, C., Karwacz, K., Xiao, S., Jorgolli, M., Gennert, D., Satija, R., Shakya, A., Lu, D. Y., Trombetta, J. J., Pillai, M. R., Ratcliffe, P. J., Coleman, M. L., Bix, M., Tantin, D., Park, H., Kuchroo, V. K., & Regev, A.
Affiliations
  • Broad Institute (US)
  • Harvard University (US)
  • Brigham and Women's Hospital (US)
  • University of Utah (US)
  • St. Jude Children's Research Hospital (US)
  • University of Oxford (GB)
Corresponding author
  • Park, Hongkun
  • Kuchroo, Vijay K.
  • Regev, Aviv
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2013-03-06
Volume
496
Issue
7446
Pages
461-468
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/nature11981
PMC
PMC3637864
OpenAlex
W2102158172
Cited by
682
References
88
Citations by year
2026: 17 · 2025: 23 · 2024: 39 · 2023: 38 · 2022: 44 · 2021: 55 · 2020: 46 · 2019: 62 · 2018: 52 · 2017: 55 · 2016: 73 · 2015: 77 · 2014: 71 · 2013: 29 · 2012: 1
FWCI
37.30
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders
  • Howard Hughes Medical Institute
  • British Heart Foundation (RG/11/1/28684)
  • National Institutes of Health (DP1OD003893, DP1OD003958)
  • National Human Genome Research Institute (P50HG006193)
  • National Institute of Allergy and Infectious Diseases (AI 45757, R01AI100873, P01AI045757, P01AI073748, AI073748)
  • National Institute of Diabetes and Digestive and Kidney Diseases (K01DK090105)
  • National Institute of Neurological Disorders and Stroke (R01NS030843, R29NS030843, NS045937, NS30843, R37NS030843, R01NS045937)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
Keywords
Th17 cell differentiation, regulatory networks, naive T cells, CD4+ T cells, transcriptional regulatory networks, network inference, IL-17, therapeutic target, autoimmune disease, proinflammatory T cells
MeSH terms
Animals, Cell Differentiation, Cells, Cultured, DNA, Interferon-gamma, Interleukin-2, Mice, Inbred C57BL, Neoplasm Proteins, Nuclear Proteins, RNA, Messenger, Silicon, Time Factors, Transcription Factors, Transcription, Genetic, Reproducibility of Results, Trans-Activators, Genome, fas Receptor, Mice, Forkhead Transcription Factors, Gene Regulatory Networks, Nanowires, Gene Knockdown Techniques, Th17 Cells

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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