The DOI matches its Crossref record; 85 of the 88 references it cites were found in a registry or an index; none of the 39 sources its claims cite is checked for support yet.
Dynamic regulatory network controlling TH17 cell differentiation
Open accessAbstract
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.
Overview
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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.
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In the paper
“Fas, the TNF receptor superfamily member 6, is another Th17 positive regulator (Fig. 5b).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“Overall, 9,159 interactions between 71 regulators and 1,266 genes were inferred in at least one network.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Three main waves of transcription and differentiation
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In the paper
“Based on similarity in active interactions, we identified three network classes (Fig. 2c), corresponding to the three differentiation phases (Fig. 2d).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“partitioned them into 20 co-expression clusters (k-means clustering, Methods, Fig. 1b and Supplementary Fig. 2) with distinct temporal profiles”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > A transcriptional time course of Th17 differentiation
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In the paper
“We identified 1,291 genes that were differentially expressed specifically during Th17 differentiation (Methods, Supplementary Table 1) and partitioned them into 20 co-expression clusters”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > A transcriptional time course of Th17 differentiation
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In the paper
“The early phase is characterized by transient induction (e.g., Cluster C5, Fig. 1b) of immune response pathways (e.g., IL-6 and TGF-β signaling; Fig. 1d and Supplementary Table 2).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Three main waves of transcription and differentiation
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In the paper
“Supporting the original network model (Fig. 2), there is a significant overlap between the genes affected by a regulator's knockdown and its predicted targets (p ≤ 0.01, permutation test; Supplementary Methods).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“The genes affected by either positive or negative regulators also significantly overlap with those bound by key CD4+ transcription factors (e.g., Foxp326,27, Batf, Irf4, and ROR-γt28,29, Xiao et al., unpublished data).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [28] 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 yetcites [29] Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012) source
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Abstract
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Abstract
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Three main waves of transcription and differentiation
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In the paper
“Around 48hr, the cells induce IL23r (data not shown), which plays an important role in the late phase”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Three main waves of transcription and differentiation
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
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- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
- Also stated in: § Inference of dynamic regulatory interactions · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“Altogether, we successfully perturbed 39 of the 65 selected genes – 29 regulators and 10 receptors – including 21 genes not previously associated with Th17 differentiation.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Three main waves of transcription and differentiation
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
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- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“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”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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 yetcites [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 yetcites [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
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In the paper
“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”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“Fas is induced early, and is a target of Stat3 and Batf in our model.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“At 10hrs, the same regulators do not yield this clear pattern (p>0.5), suggesting that at that point, the network is still malleable.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Inference of dynamic regulatory interactions
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In the paper
“Mina-/- cells had decreased IL-17a and increased Foxp3 compared to wild-type (WT) cells, as detected by intracellular staining (Fig. 5a).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“Building on this model, we systematically ranked 127 putative regulators (80 novel; Supplementary Table 4), and tested top ranking ones experimentally.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Discussion
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In the paper
“The model consists of three consecutive, densely intra-connected networks, implicates 71 regulators (46 novel), and suggests substantial rewiring in 3 phases.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Discussion
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- The authors state this as an assumption: Genes in the same co-expression cluster share regulators that are active at the relevant time points.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, Tsc22d3 binds in proximity to IL-21 and Irf4, which also become up regulated in the Tsc22d3 knockdown.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
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In the paper
“Within the two modules are 12 novel factors (Fig. 4 and 5), which we further characterized, highlighting four of the factors”
§ Dynamic regulatory network controlling Th17 cell differentiation > Discussion
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“The functional network in Fig. 4b consists of two modules: positive and negative.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Estimating statistical significance of monochromatic interactions between modules
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While TGF-β1 alone induces Foxp3+ regulatory T cells (iTreg)2, the presence of IL-6 inhibits iTreg and induces Th17 differentiation1.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Introduction
What the sources say
- Not checked yetcites [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 yetcites [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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Th17 differentiation, however, spans several days, during which the components and wiring of the regulatory network likely change.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Introduction
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Pou2af1's role in T cell differentiation has not been explored35.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [35] Teitell, M. A. OCA-B regulation of B-cell development and function. Trends Immunol. 24, 546–553 (2003) source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Mina was shown to suppress Th2 bias by interacting with the TF NFAT and repressing the IL-4 promoter31.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [31] Okamoto, M. et al. Mina, an Il4 repressor, controls T helper type 2 bias. Nature Immunol. 10, 872–879 (2009) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [33] Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory TH17 cells. Nature 448, 484–487 (2007) source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
What the sources say
- Not checked yetcites [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 yetcites [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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [33] Korn, T. et al. IL-21 initiates an alternative pathway to induce proinflammatory TH17 cells. Nature 448, 484–487 (2007) source
- Not checked yetcites [38] Peters, A., Lee, Y. & Kuchroo, V. K. The many faces of Th17 cells. Curr. Opin. Immunol. 23, 702–706 (2011) source
- Not checked yetcites [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 yetcites [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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Microarray data
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Gene knockdown using silicon nanowires
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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”
§ Dynamic regulatory network controlling Th17 cell differentiation > Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“For further validation we compared the differentiation time course to published microarray data of Th17 cells and naïve T cells51 (Supplementary Fig. 1c).”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Microarray data
What the sources say
- Not checked yetcites [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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Tsc22d3 is induced in other cells in response to the steroid Dexamethasone41, which represses Th17 differentiation and ROR-γt expression42.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Validation and characterization of novel factors
What the sources say
- Not checked yetcites [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 yetcites [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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Discussion
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used an unbiased approach to rank candidate regulators – transcription factor or chromatin modifier genes – of Th17 differentiation.”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Selection of perturbation targets
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial 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.
“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.”
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?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
MajorThe 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.
“achieved efficient knockdown (<60% transcript remaining at 48hr post activation) for 34 genes”
“A 60% reduction in target mRNA was used as the knockdown threshold.”
Citations
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3 of the 88 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
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Metadata
- 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
- Published
- 2013-03-06
- Volume
- 496
- Issue
- 7446
- Pages
- 461-468
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- 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
- FWCI
- 37.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- 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.
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