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
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
Cites
Statistics
Verification
Claims the paper makes
54 Own result · 22 Not checked yet · 7 Own interpretation · 3 Own method · 2 No basis given
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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 48
- The quote only partly states this claim. 1
- Probably not a substantial claim. 9
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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 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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
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
“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
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Dynamic regulatory network controlling Th17 cell differentiation > Results > Nanowire-based perturbation of primary T cells
“achieved efficient knockdown (<60% transcript remaining at 48hr post activation) for 34 genes”
§ Dynamic regulatory network controlling Th17 cell differentiation > Methods > Gene knockdown using silicon nanowires
“A 60% reduction in target mRNA was used as the knockdown threshold.”
Citations
Cites
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Bettelli, E., Carrier, Y., Gao, W., Korn, T., Strom, T. B., Oukka, M., Weiner, H. L., & Kuchroo, V. K. (2006) · Nature · doi:10.1038/nature04753 · Cited by 6,992
Self-citationGlobal quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Protein Identification and Analysis Tools in the ExPASy Server
Wilkins, M. R., Gasteiger, E., Bairoch, A., Sanchez, J., Williams, K. L., Appel, R. D., & Hochstrasser, D. F. (2003) · Humana Press eBooks · doi:10.1385/1-59259-584-7:531 · Cited by 6,244
Korn, T., Bettelli, E., Oukka, M., & Kuchroo, V. K. (2009) · Annual Review of Immunology · doi:10.1146/annurev.immunol.021908.132710 · Cited by 4,873
Self-citationHost–microbe interactions have shaped the genetic architecture of inflammatory bowel disease
The International IBD Genetics Consortium (IIBDGC), Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., Lee, J. C., Philip Schumm, L., Sharma, Y., et al. (2012) · Nature · doi:10.1038/nature11582 · Cited by 4,722
Pruitt, K. D., Tatusova, T. A., & Maglott, D. R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl842 · Cited by 4,441
Direct multiplexed measurement of gene expression with color-coded probe pairs
Geiss, G. K., Bumgarner, R. E., Birditt, B., Dahl, T., Dowidar, N., Dunaway, D. L., Fell, H. P., Ferree, S., George, R. D., Grogan, T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1385 · Cited by 2,066
Research works printed but not linked: 2
- 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
- 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
Transcriptome-Based Network Analysis Reveals a Spectrum Model of Human Macrophage Activation
Xue, J., Schmidt, S. V., Sander, J., Draffehn, A., Krebs, W., Quester, I., De Nardo, D., Gohel, T. D., Emde, M., Schmidleithner, L., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.01.006 · Cited by 2,210
“Several elegant studies have demonstrated the value of analyzing networks based on expression profiling in macrophages (Martinez et al., 2006; Nau et al., 2002; Ramsey et al., 2008) or T helper 17 (Th17) cells (Ciofani et al., 2012; Yosef et al., 2013).”
Immune regulation by glucocorticoids
Cain, D. W., & Cidlowski, J. A. (2017) · Nature Reviews Immunology · doi:10.1038/nri.2017.1 · Cited by 1,846
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670
25th Anniversary Article: Semiconductor Nanowires – Synthesis, Characterization, and Applications
Dasgupta, N. P., Sun, J., Liu, C., Brittman, S., Andrews, S. C., Lim, J., Gao, H., Yan, R., & Yang, P. (2014) · Advanced Materials · doi:10.1002/adma.201305929 · Cited by 901
Getting Across the Cell Membrane: An Overview for Small Molecules, Peptides, and Proteins
Yang, N. J., & Hinner, M. J. (2015) · Methods in Molecular Biology · doi:10.1007/978-1-4939-2272-7_3 · Cited by 826
Th17 cells transdifferentiate into regulatory T cells during resolution of inflammation
Gagliani, N., Vesely, M. C. A., Iseppon, A., Brockmann, L., Xu, H. Y., Palm, N. W., de Zoete, M. R., Licona-Limón, P., Paiva, R., Ching, T. H., et al. (2015) · Nature · doi:10.1038/nature14452 · Cited by 827
Innate lymphoid cells regulate CD4+ T-cell responses to intestinal commensal bacteria
Hepworth, M. R., Monticelli, L. A., Fung, T. C., Ziegler, C. G. K., Grunberg, S., Sinha, R., Mantegazza, A. R., Ma, H., Crawford, A., Angelosanto, J., et al. (2013) · Nature · doi:10.1038/nature12240 · Cited by 816
Intracellular Delivery by Membrane Disruption: Mechanisms, Strategies, and Concepts
Stewart, M. P., Langer, R., & Jensen, K. F. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.7b00678 · Cited by 767
Applications of nanotechnology for immunology
Smith, D. M., Simon, J. K., & Baker, J. R. (2013) · Nature reviews. Immunology · doi:10.1038/nri3488 · Cited by 787
“This therefore has facilitated the identification of crucial points in the molecular network that regulates the immune response 121 .”
Single-cell genomics unveils critical regulators of Th17 cell pathogenicity
Gaublomme, J. T., Yosef, N., Lee, Y., Gertner, R. S., Yang, L. V., Wu, C., Pandolfi, P. P., Mak, T., Satija, R., Shalek, A. K., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.009 · Cited by 575
“…supporting the interpretation of gradual progression, most in vivo cells are maximally correlated with bulk profiles at 48–72h during Th17 cell differentiation in vitro (Yosef et al., 2013) (Figure S4B), but some cells, especially from the LN, correlate most strongly with earlier time points.”
InfluentialSelf-citationCytokine Regulation and Function in T Cells
Dong, C. (2021) · Annual Review of Immunology · doi:10.1146/annurev-immunol-061020-053702 · Cited by 625
Metabolic modeling of single Th17 cells reveals regulators of autoimmunity
Wagner, A., Wang, C., Fessler, J., DeTomaso, D., Avila-Pacheco, J., Kaminski, J., Zaghouani, S., Christian, E., Thakore, P., Schellhaass, B., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.045 · Cited by 524
Self-citationTh17 Cell Pathway in Human Immunity: Lessons from Genetics and Therapeutic Interventions
Patel, D. D., & Kuchroo, V. K. (2015) · Immunity · doi:10.1016/j.immuni.2015.12.003 · Cited by 495
Self-citationSaravia, J., Chapman, N. M., & Chi, H. (2019) · Cellular & Molecular Immunology · doi:10.1038/s41423-019-0220-6 · Cited by 489
Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
Schafflick, D., Xu, C. A., Hartlehnert, M., Cole, M. B., Schulte‐Mecklenbeck, A., Lautwein, T., Wolbert, J., Heming, M., Meuth, S. G., Kuhlmann, T., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14118-w · Cited by 452
Self-citationMetabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α
Mascanfroni, I. D., Takenaka, M. C., Yeste, A., Patel, B., Wu, Y., Kenison, J. E., Siddiqui, S., Basso, A. S., Otterbein, L. E., Pardoll, D. M., et al. (2015) · Nature Medicine · doi:10.1038/nm.3868 · Cited by 457
Data integration in the era of omics: current and future challenges
Gómez-Cabrero, D., Abugessaisa, I., Maier, D., Teschendorff, A. E., Merkenschlager, M., Gisel, A., Ballestar, E., Bongcam‐Rudloff, E., Conesa, A., & Tegner, J. N. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-s2-i1 · Cited by 426
“[60]).”
The dichotomous nature of T helper 17 cells
Stockinger, B., & Omenetti, S. (2017) · Nature reviews. Immunology · doi:10.1038/nri.2017.50 · Cited by 419
CD5L/AIM Regulates Lipid Biosynthesis and Restrains Th17 Cell Pathogenicity
Wang, C., Yosef, N., Gaublomme, J. T., Wu, C., Lee, Y., Clish, C. B., Kaminski, J., Xiao, S., zu Hörste, G. M., Pawlak, M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.10.068 · Cited by 405
“We analyzed the expression of a 312 gene signature of Th17 cell differentiation and function (Yosef et al., 2013) in SFA- or control-treated WT Th17 cells and in PUFA- or control-treated CD5L−/− Th17 cells differentiated with TGF-β1+IL-6.”
InfluentialSelf-citationKurachi, M., Barnitz, R. A., Yosef, N., Odorizzi, P. M., DiIorio, M. A., Lemieux, M. E., Yates, K. B., Godec, J., Klatt, M. G., Regev, A., et al. (2014) · Nature Immunology · doi:10.1038/ni.2834 · Cited by 392
Self-citationShi, W., Liao, Y., Willis, S. N., Taubenheim, N., Inouye, M., Tarlinton, D. M., Smyth, G. K., Hodgkin, P. D., Nutt, S. L., & Corcoran, L. M. (2015) · Nature Immunology · doi:10.1038/ni.3154 · Cited by 397
ISMARA: automated modeling of genomic signals as a democracy of regulatory motifsPreprint
Balwierz, P. J., Pachkov, M. V., Arnold, P., Gruber, A. J., Zavolan, M., & van Nimwegen, E. (2014) · Genome Research · doi:10.1101/gr.169508.113 · Cited by 306
“…modeling of observed expression and chromatin dynamics is a powerful approach to reconstructing regulatory circuitry (Novershtern et al. 2011; Yosef et al. 2013) (to give just two examples) and show that MARA-like approaches can be extended to include miRNA regulation (Setty et al. 2012) and…”
A comprehensive evaluation of module detection methods for gene expression data
Saelens, W., Cannoodt, R., & Saeys, Y. (2018) · Nature Communications · doi:10.1038/s41467-018-03424-4 · Cited by 305
T Helper Cell Differentiation, Heterogeneity, and PlasticityPreprint
Zhu, J. (2017) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a030338 · Cited by 374
“Although networks of transcription factors regulate Th-cell differentiation (Zhu and Paul 2010b; Ciofani et al. 2012; Hu et al. 2013; Yosef et al. 2013), lineage-specific master transcription factors play the most critical roles during the differentiation process (Fig.”
Developmental gene networks: a triathlon on the course to T cell identity
Yui, M. A., & Rothenberg, E. V. (2014) · Nature Reviews Immunology · doi:10.1038/nri3702 · Cited by 342
Sodium chloride inhibits the suppressive function of FOXP3+ regulatory T cells
Hernandez, A., Kitz, A., Wu, C., Lowther, D. E., Rodriguez, D. M., Vudattu, N. K., Deng, S., Herold, K. C., Kuchroo, V. K., Kleinewietfeld, M., et al. (2015) · Journal of Clinical Investigation · doi:10.1172/jci81151 · Cited by 345
“Because SGK1 is a central node in high-salt–mediated Th17 induction, it was of interest to study whether it is responsible for high-salt–mediated loss of suppression in Tregs (25, 33, 34).”
Self-citationScanning the Immunopathogenesis of Psoriasis
Chiricozzi, A., Romanelli, P., Volpe, E., Borsellino, G., & Romanelli, M. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19010179 · Cited by 331
Models of human core transcriptional regulatory circuitriesPreprint
Saint‐André, V., Alexander Joel Federation, Lin, C. Y., Abraham, B. J., Reddy, J., Lee, T. I., Bradner, J. E., & Young, R. A. (2016) · Genome Research · doi:10.1101/gr.197590.115 · Cited by 303
“The model of extended hESC regulatory circuitry contains many genes that are known to play prominent roles in ESC biology (Young 2011).”
InfluentialEmmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282
“Also, information about temporal changes in the network structure are important to understand immune response, infection and differentiation processes (Rozenblatt-Rosen et al., 2012; Yosef et al., 2013).”
Gideon, H. P., Hughes, T. K., Tzouanas, C. N., Wadsworth, M. H., Tu, A. A., Gierahn, T. M., Peters, J. M., Hopkins, F. F., Wei, J.-R., Kummerlowe, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.04.004 · Cited by 282
“…classical Th1-associated genes, including IFNG and TNF (Raphael et al., 2015), as well as transcription factors associated with Th17 differentiation (Yosef et al., 2013), including RORA (Yang et al., 2008), RORC (Ivanov et al., 2006), RBPJ (Meyer Zu Horste et al., 2016), and BHLHE40 (Huynh UMAP…”
Self-citationThe role of diet in the aetiopathogenesis of inflammatory bowel disease
Khalili, H., Chan, S. S. M., Lochhead, P. J., Ananthakrishnan, A. N., Hart, A. R., & Chan, A. T. (2018) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-018-0022-9 · Cited by 307
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 303
“150 Algorithm have been designed to model this phenomenon, for instance, DICER, in which the expression profiles of genes within each module of the pair are correlated across all samples and the correlation between the two modules differ dramatically between the disease and normal samples.”
TH17 cell heterogeneity and its role in tissue inflammation
Schnell, A., Littman, D. R., & Kuchroo, V. K. (2023) · Nature Immunology · doi:10.1038/s41590-022-01387-9 · Cited by 286
Self-citationXiao, S., Yosef, N., Yang, J., Wang, Y., Zhou, L., Zhu, C. Y., Wu, C., Baloglu, E., Schmidt, D. R., Ramesh, R., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.04.004 · Cited by 283
Self-citationStem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity
Schnell, A., Huang, L., Singer, M., Singaraju, A., Barilla, R. M., Regan, B. M. L., Bollhagen, A., Thakore, P. I., Dionne, D., Delorey, T. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.018 · Cited by 268
Self-citationScriba, T. J., Penn‐Nicholson, A., Shankar, S., Hraha, T., Thompson, E. G., Sterling, D., Nemes, E., Darboe, F., Suliman, S., Amon, L. M., et al. (2017) · PLoS Pathogens · doi:10.1371/journal.ppat.1006687 · Cited by 255
Synthetic recombinase-based state machines in living cells
Roquet, N., Soleimany, A. P., Ferris, A. C., Aaronson, S., & Lu, T. K. (2016) · Science · doi:10.1126/science.aad8559 · Cited by 245
“For example, in human development, progenitor cells differentiate into specific cell types with disparate functions determined by the timing and order of transcription factor (TF) activation (2, 3).”
Using EAE to better understand principles of immune function and autoimmune pathology
Rangachari, M., & Kuchroo, V. K. (2013) · Journal of Autoimmunity · doi:10.1016/j.jaut.2013.06.008 · Cited by 256
“expression of IL-23 receptor on Th17 cells [89,90].”
Self-citationA microbiota-modulated checkpoint directs immunosuppressive intestinal T cells into cancers
Fidelle, M., Rauber, C., Alves Costa Silva, C., Tian, A.-L., Lahmar, I., de La Varende, A.-L. M., Zhao, L., Thelemaque, C., Lebhar, I., Messaoudene, M., et al. (2023) · Science · doi:10.1126/science.abo2296 · Cited by 243
“Recent intestinal Treg17 emigrants found in tdLNs exhibit gutspecific Treg cell features (69, 70), TH17 cell– related gene expression patterns (71), as well as immunosuppressive traits such as Dusp2/ PCA1 (47) and Pik3ip1 (72).”
Sterol metabolism controls TH17 differentiation by generating endogenous RORγ agonists
Hu, X., Wang, Y., Hao, L.-Y., Liu, X., Lesch, C. A., Sanchez, B., Wendling, J. M., Morgan, R. W., Aicher, T. D., Carter, L., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1714 · Cited by 225
Pouring fuel on the fire: Th17 cells, the environment, and autoimmunity
Burkett, P. R., zu Hörste, G. M., & Kuchroo, V. K. (2015) · Journal of Clinical Investigation · doi:10.1172/jci78085 · Cited by 225
“org Volume 125 Number 6 June 2015 SGK1, which was identified as an important downstream target of IL-23R signaling (50, 54).”
Self-citationHeterogeneity in immune responses: from populations to single cells
Satija, R., & Shalek, A. K. (2014) · Trends in Immunology · doi:10.1016/j.it.2014.03.004 · Cited by 215
Self-citationDemocratizing systems immunology with modular transcriptional repertoire analyses
Chaussabel, D., & Baldwin, N. E. (2014) · Nature reviews. Immunology · doi:10.1038/nri3642 · Cited by 212
Transcriptional Control of Early T and B Cell Developmental Choices
Rothenberg, E. V. (2014) · Annual Review of Immunology · doi:10.1146/annurev-immunol-032712-100024 · Cited by 245
Systems vaccinology: Probing humanity’s diverse immune systems with vaccines
Pulendran, B. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400476111 · Cited by 218
Deletion of CTLA-4 on regulatory T cells during adulthood leads to resistance to autoimmunity
Paterson, A. M., Lovitch, S. B., Sage, P. T., Juneja, V. R., Lee, Y., Trombley, J. D., Arancibia-Cárcamo, C. V., Sobel, R. A., Rudensky, A. Y., Kuchroo, V. K., et al. (2015) · The Journal of Experimental Medicine · doi:10.1084/jem.20141030 · Cited by 220
“To test this hypothesis, we further characterized the phenotype of T conv and T reg cells from UBC Cre/ERT2+ Ctla4 fl/fl mice by performing a focused transcriptional analysis of CTLA-4–deleted T conv and T reg cells using a pathogenic Th17 NanoString codeset (Lee et al., 2012; Yosef et al., 2013).”
Self-citationSkin-resident innate lymphoid cells converge on a pathogenic effector state
Bielecki, P., Riesenfeld, S. J., Hütter, J.-C., Triglia, E. T., Kowalczyk, M. S., Ricardo-González, R. R., Lian, M., Vesely, M. C. A., Kroehling, L., Xu, H. Y., et al. (2021) · Nature · doi:10.1038/s41586-021-03188-w · Cited by 223
Self-citationAn IL-27/NFIL3 signalling axis drives Tim-3 and IL-10 expression and T-cell dysfunction
Zhu, C. Y., Sakuishi, K., Xiao, S., Sun, Z., Zaghouani, S., Gu, G., Wang, C., Tan, D. J., Wu, C., Rangachari, M., et al. (2015) · Nature Communications · doi:10.1038/ncomms7072 · Cited by 208
“With this information, we built a transcriptional network that could identify transcription factors based on their kinetics and by labeling each edge with a time stamp to identify a connection between a regulator and its target 20 .”
Self-citationMechanical Model of Vertical Nanowire Cell Penetration
Xie, X., Xu, A. M., Angle, M. R., et al. (2013) · Nano Letters · doi:10.1021/nl403201a · Cited by 191
Autoimmune and autoinflammatory mechanisms in uveitis
Lee, R., Nicholson, L. B., Sen, H. N., Chan, C., Wei, L., Nussenblatt, R. B., & Dick, A. D. (2014) · Seminars in Immunopathology · doi:10.1007/s00281-014-0433-9 · Cited by 189
“When naive CD4 T helper cells are activated, they reorganise transcriptional networks [52].”
van Hamburg, J. P., & Tas, S. W. (2017) · Journal of Autoimmunity · doi:10.1016/j.jaut.2017.12.006 · Cited by 192
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173
Self-citationTranscriptional Regulators of T Helper 17 Cell Differentiation in Health and Autoimmune Diseases
Capone, A., & Volpe, E. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00348 · Cited by 185
“In fact, BATF and IRF4 are responsible of initial chromatin accessibility in Il17a locus and, with STAT3, of initiation of the transcriptional program that is then globally tuned by the lineage-specific transcription factor RORγt, which plays a pivotal deterministic role at key loci (24, 63).”
Escobar, T. M., Kanellopoulou, C., Kugler, D. G., Kilaru, G., Nguyen, C. K., Nagarajan, V., Bhairavabhotla, R., Northrup, D. L., Zahr, R., Burr, P., et al. (2014) · Immunity · doi:10.1016/j.immuni.2014.03.014 · Cited by 182
“At this stage of Th17 cell differentiation, RORgt expression was already induced by the combined action of STAT3, BATF, and IRF4 (Ciofani et al., 2012; Yosef et al., 2013).”
InfluentialShin, B., Benavides, G. A., Geng, J., Koralov, S. B., Hu, H., Darley-Usmar, V. M., & Harrington, L. E. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.01.022 · Cited by 187
“TCR activation is a known factor that induces BATF expression, and the strength of TCR signaling affects the binding ability of BATF-IRF4 complexes to the target loci in Th2 cells (Ciofani et al., 2012; Iwata et al., 2017; Murphy et al., 2013; Schraml et al., 2009; Yosef et al., 2013).”
Chong, W. P., Mattapallil, M. J., Raychaudhuri, K., Bing, S. J., Wu, S., Zhong, Y., Wang, W., Chen, Z., Silver, P. B., Jittayasothorn, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.022 · Cited by 191
“The groups of Regev and Kuchroo identified expression of the IL-24 gene during the late phase of Th17 polarization (Yosef et al., 2013) but did not pursue this observation further.”
IL ‐17: overview and role in oral immunity and microbiome
Abusleme, L., & Moutsopoulos, N. M. (2016) · Oral Diseases · doi:10.1111/odi.12598 · Cited by 189
“High throughput transcriptional network analyses have revealed that Th17 cell development is tightly controlled by 22 genes that act as positive regulators and a module of 5 genes that are negative regulators of differentiation, illustrating the complexity of the Th17 cell developmental program (Yosef et al., 2013).”
Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes
Kiner, E., Willie, E., Vijaykumar, B., Chowdhary, K., Schmutz, H., Chandler, J. S., Schnell, A., Thakore, P. I., Gros, G. S. L., Mostafavi, S., et al. (2021) · Nature Immunology · doi:10.1038/s41590-020-00836-7 · Cited by 167
Self-citationChambers, E. S., Nanzer, A. M., Pfeffer, P., Richards, D. F., Timms, P. M., Martineau, A. R., Griffiths, C., Corrigan, C. J., & Hawrylowicz, C. M. (2015) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2015.01.026 · Cited by 181
“A recent article described a detailed transcriptional time course during TH17 differentiation, suggesting a number of potential candidates for further study.(49) Previous data from our laboratory suggest that glucocorticoids do not inhibit proliferation of IL-17A–expressing cells; the mechanisms behind these different…”
Occupancy maps of 208 chromatin-associated proteins in one human cell type
Partridge, E. C., Chhetri, S. B., Prokop, J. W., Ramaker, R. C., Jansen, C. S., Goh, S.-T., Mackiewicz, M., Newberry, K. M., Brandsmeier, L. A., Meadows, S. K., et al. (2020) · Nature · doi:10.1038/s41586-020-2023-4 · Cited by 154
Massoud, A. H., Charbonnier, L., Lopez, D., Pellegrini, M. A., Phipatanakul, W., & Chatila, T. A. (2016) · Nature Medicine · doi:10.1038/nm.4147 · Cited by 179
Fang, D., & Zhu, J. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20170494 · Cited by 175
“To understand T cell differentiation, ILC development, and their functional regulation at a systems level, several genome-wide studies have been performed to compare the transcriptomes of Th cell subsets, ILC subsets, and ILC progenitors (Wei et al., 2011; Hu et al., 2013; Yosef et al., 2013; Yagi et al., 2014; Robine…”
Emerging Principles of Gene Expression Programs and Their Regulation
Pope, S. D., & Medzhitov, R. M. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.07.017 · Cited by 161
ZFP36 RNA-binding proteins restrain T cell activation and anti-viral immunity
Moore, M. J., Blachère, N. E., Fak, J. J., Park, C. Y., Sawicka, K., Parveen, S., Zucker-Scharff, I., Moltedo, B., Rudensky, A. Y., & Darnell, R. B. (2018) · eLife · doi:10.7554/elife.33057 · Cited by 160
Yi, B., Titze, J. M., Рыкова, М. П., Feuerecker, M., Vassilieva, G. Y., Ничипорук, И. А., Schelling, G., Morukov, B. V., & Choukèr, A. (2014) · Translational research · doi:10.1016/j.trsl.2014.11.007 · Cited by 171
The Roles of Jumonji-Type Oxygenases in Human Disease
Johansson, C., Tumber, A., Che, K., Cain, P., Nowak, R. P., Gileadi, C., & Oppermann, U. C. T. (2014) · Epigenomics · doi:10.2217/epi.13.79 · Cited by 167
Quantification of nanowire penetration into living cells
Xu, A. M., Aalipour, A., Leal‐Ortiz, S., Mekhdjian, A. H., Xie, X., Dunn, A. R., Garner, C. C., & Melosh, N. A. (2014) · Nature Communications · doi:10.1038/ncomms4613 · Cited by 152
Identification of environmental factors that promote intestinal inflammation
Sanmarco, L. M., Chao, C., Wang, Y., Kenison, J. E., Li, Z., Rone, J. M., Rejano-Gordillo, C. M., Polonio, C. M., Gutiérrez‐Vázquez, C., Piester, G., et al. (2022) · Nature · doi:10.1038/s41586-022-05308-6 · Cited by 158
JunB promotes Th17 cell identity and restrains alternative CD4+ T-cell programs during inflammation
Carr, T. M., Wheaton, J. D., Houtz, G. M., & Ciofani, M. (2017) · Nature Communications · doi:10.1038/s41467-017-00380-3 · Cited by 146
Harbour, S. N., DiToro, D. F., Witte, S. J., Zindl, C. L., Gao, M., Schoeb, T. R., Jones, G. W., Jones, S. A., Hatton, R. D., & Weaver, C. T. (2020) · Science Immunology · doi:10.1126/sciimmunol.aaw2262 · Cited by 151
“STAT3 induces the expression of Th17-related genes including Il17a, Il17f, Il22, Il23r and the master transcription factor Rorc (6-8), which itself is a positive regulator of key Th17 genes and a negative regulator of alternative lineage fates (9-12).”
T helper 17 (Th17) cells and interleukin-17 (IL-17) in cancer
Chang, S. H. (2019) · Archives of Pharmacal Research · doi:10.1007/s12272-019-01146-9 · Cited by 149
Unifying immunology with informatics and multiscale biology
Kidd, B. A., Peters, L. A., Schadt, E. E., & Dudley, J. T. (2014) · Nature Immunology · doi:10.1038/ni.2787 · Cited by 145
Liu, S.-Q., Jiang, S., Li, C., Zhang, B., & Li, Q.-J. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m114.550723 · Cited by 145
“Transcriptional network mapping has uncovered the key modulators of TH17 lineage initiation, commitment and stabilization (22, 23).”
Cheung, K. L., Zhang, F., Jaganathan, A., Sharma, R., Zhang, Q., Konuma, T., Shen, T., Lee, J., Ren, C., Chen, C., et al. (2017) · Molecular Cell · doi:10.1016/j.molcel.2016.12.022 · Cited by 143
“…Okamoto et al., 2010; Schraml et al., 2009; Yang et al., 2007), that work in concert with chromatin-modifying enzymes and effector proteins to ensure proper timing, duration, and amplitude for ordered gene transcription during Th17 cell differentiation (Ciofani et al., 2012; Yosef et al., 2013).”
Lessons Learned From Trials Targeting Cytokine Pathways in Patients With Inflammatory Bowel Diseases
Abraham, C., Dulai, P. S., Vermeire, S., & Sandborn, W. J. (2016) · Gastroenterology · doi:10.1053/j.gastro.2016.10.018 · Cited by 142
Engineering vertically aligned semiconductor nanowire arrays for applications in the life sciences
Elnathan, R., Kwiat, M., Patolsky, F., & Voelcker, N. H. (2014) · Nano Today · doi:10.1016/j.nantod.2014.04.001 · Cited by 142
Digital cell quantification identifies global immune cell dynamics during influenza infection
Altboum, Z., Steuerman, Y., David, E., Barnett‐Itzhaki, Z., Valadarsky, L., Keren‐Shaul, H., Meningher, T., Mendelson, E., Mandelboim, M., Gat‐Viks, I., et al. (2014) · Molecular Systems Biology · doi:10.1002/msb.134947 · Cited by 125
“To map the regulatory entities acting during influenza infection (Supplementary Fig S6B-D), we used a previously assembled network of 1550 regulators and 409,000 regulator-target gene connections (Yosef et al, 2013).”
Pseudotime estimation: deconfounding single cell time series
(2016) · doi:10.1093/bioinformatics/btw372 · Cited by 128
“This results in cross-sectional data wherein each sample is taken from a different cell.”
Mechanisms of Pediatric Inflammatory Bowel Disease
Peloquin, J. M., Goel, G., Villablanca, E. J., & Xavier, R. J. (2016) · Annual Review of Immunology · doi:10.1146/annurev-immunol-032414-112151 · Cited by 141
Salehipour, Z., Haghmorad, D., Sankian, M., Rastin, M., Nosratabadi, R., Dallal, M. M. S., Tabasi, N. S., Khazaee, M., Nasiraie, L. R., & Mahmoudi, M. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.08.117 · Cited by 143
Bereshchenko, O., Coppo, M., Bruscoli, S., Biagioli, M., Cimino, M., Frammartino, T., Sorcini, D., Venanzi, A., Di Sante, M., & Riccardi, C. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.03.004 · Cited by 131
Deubiquitinase DUBA is a post-translational brake on interleukin-17 production in T cells
Rutz, S., Kayagaki, N., Phung, Q. T., Eidenschenk, C., Noubade, R., Wang, X., Lesch, J., Lu, R. O., Newton, K., Huang, O. W., et al. (2014) · Nature · doi:10.1038/nature13979 · Cited by 131
Cheung, K., Lu, G., Sharma, R., Vincek, A. S., Zhang, R., Plotnikov, A. N., Zhang, F., Zhang, Q., Ju, Y., Hu, Y., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615601114 · Cited by 132
“These transcription factors work with chromatin modifying enzymes and effector proteins to ensure proper timing, duration, and amplitude for ordered gene transcription in Th17 cell differentiation (15, 21).”
Regulatory T cells in atherosclerosis: critical immune regulatory function and therapeutic potential
Spitz, C., Winkels, H., Bürger, C., Weber, C., Lutgens, E., Hansson, G. K., & Gerdes, N. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-2080-2 · Cited by 134
“further characterized by expression of retinoic acid receptor-related orphan receptor (ROR)-ct [67].”
Dynamic Gene Regulatory Networks of Human Myeloid Differentiation
Ramírez, R. N., El-Ali, N. C., Mager, M. A., Wyman, D., Conesa, A., & Mortazavi, A. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.005 · Cited by 114
Impulse model-based differential expression analysis of time course sequencing data
Fischer, D. S., Theis, F. J., & Yosef, N. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky675 · Cited by 126
Self-citationZhang, X., Mozeleski, B., Lemoine, S., Dériaud, E., Lim, A., Zhivaki, D., Azria, É., Ray, C. L., Roguet, G., Launay, O., et al. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3008748 · Cited by 125
“The biased analysis of TH17-associated genes in CCR6+ TEM cells in comparison with TN cells shows an increased expression of transcription factors such as RORA, MAF, PRDM1, and BATF in addition to more recently described genes such as FAS, ETV6, POU2AF1, and TSC22D3 (32).”
Iwata, A., Durai, V., Tussiwand, R., Briseño, C. G., Wu, X., Grajales‐Reyes, G. E., Egawa, T., Murphy, T. L., & MURPHY, K. M. M. (2017) · Nature Immunology · doi:10.1038/ni.3714 · Cited by 120
Tutorial: using nanoneedles for intracellular delivery
Chiappini, C., Chen, Y., Aslanoglou, S., Mariano, A., Mollo, V., Mu, H., Rosa, E. D., He, G., Tasciotti, E., Xie, X., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00600-7 · Cited by 123
Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
Cannoodt, R., Saelens, W., Deconinck, L., & Saeys, Y. (2021) · Nature Communications · doi:10.1038/s41467-021-24152-2 · Cited by 124
Inoue, F., Kreimer, A., Ashuach, T., Ahituv, N., & Yosef, N. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.09.010 · Cited by 109
“, 2017), suggesting that these TFs may provide a parsimonious explanation for the MPRA signal (Kashtan and Alon, 2005), and (2) an added requirement that the mRNA that codes for the TF is induced compared to previous time points, which may also suggest functional importance (Rosenfeld et al., 2005; Setty et al., 2003;…”
Self-citationNatividad, J. M., Pinto-Sánchez, M. I., Galipeau, H. J., Jury, J., Jordana, M., Reinisch, W., Collins, S. M., Bercik, P., Surette, M. G., Allen‐Vercoe, E., et al. (2015) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000000422 · Cited by 119
The Th17 Lineage: From Barrier Surfaces Homeostasis to Autoimmunity, Cancer, and HIV-1 Pathogenesis
Wacleche, V. S., LANDAY, A. L., Routy, J., & Ancuța, P. (2017) · Viruses · doi:10.3390/v9100303 · Cited by 109
“STAT3 is one of the first transcription factors up-regulated within the first hours of Th17 polarization [75].”
Paramsothy, S., Rosenstein, A. K., Mehandru, S., & Colombel, J. (2018) · Mucosal Immunology · doi:10.1038/s41385-018-0050-3 · Cited by 115
Campmans-Kuijpers, M. J. E., & Dijkstra, G. D. H. (2021) · Nutrients · doi:10.3390/nu13041067 · Cited by 124
Miraldi, E. R., Pokrovskii, M. V., Watters, A., Castro, D. M., Veaux, N. D., Hall, J. A., Lee, J., Ciofani, M., Madar, A., Carriero, N., et al. (2019) · Genome Research · doi:10.1101/gr.238253.118 · Cited by 109
RBPJ Controls Development of Pathogenic Th17 Cells by Regulating IL-23 Receptor Expression
zu Hörste, G. M., Wu, C., Wang, C., Cong, L., Pawlak, M., Lee, Y., Elyaman, W., Xiao, S., Regev, A., & Kuchroo, V. K. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.05.088 · Cited by 118
“…controlled by the lineage-defining transcription factor RORgt (Ivanov et al., 2006; Xiao et al., 2014), and the transcriptional networks controlling Th17 cell differentiation have recently been identified in large-scale transcriptomic analyses (Ciofani et al., 2012; Yosef et al., 2013).”
InfluentialSelf-citationPham, D., Moseley, C. E., Gao, M., Savic, D., Winstead, C. J., Sun, M., Kee, B., Myers, R., Weaver, C., & Hatton, R. D. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.064 · Cited by 98
“In CD4 T cells, Batf and Irf4 are induced within 4 h after TCR engagement (Yosef et al., 2013) and are cooperatively recruited to AICEs.”
Gene regulatory networks in the immune system
Singh, H., Khan, A. A., & Dinner, A. R. (2014) · Trends in Immunology · doi:10.1016/j.it.2014.03.006 · Cited by 101
Arid5a, an RNA-Binding Protein in Immune Regulation: RNA Stability, Inflammation, and Autoimmunity
Nyati, K. K., Zaman, M. M.-U., Sharma, P., & Kishimoto, T. (2020) · Trends in Immunology · doi:10.1016/j.it.2020.01.004 · Cited by 85
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
