Continuous presence of IL-4 is required for optimal Th2 development
IL-4 drives both the initiation and the reinforcement stages of the Th2 polarization response1,20
. In view of the kinetics of the process though, we speculated that endogenous IL-4 would be specifically involved during the reinforcement/commitment of Th2 lineage specification. To test this we generated parallel cultures of either Th activation, or Th2 differentiation and added to these a combination of neutralizing antibodies against IL-4 and IL-4R. Antibodies were first added at day three after initiation of the cultures, and then supplemented every 24 h up to day 5. This timing of addition was based on the fact that IL-4 transcription peaks at around day3 post stimulation7
. Neutralization of exogenous IL-4 significantly reduced the extent of Th2 cell generation (P
≤0.05, ). Further, this inhibition was also observed in TCR activated T cells (“Data not shown”), supporting that this was likely due to depletion of the endogenously produced cytokine (). The partial attenuation seen may well derive from the possibility that the added antibodies were only partly effective at neutralizing the autocrine/paracrine action of the endogenously produced IL-4.
Continuous IL-4 is required for optimal Th2 development.
We also probed the puzzling question of why endogenous IL-4 was necessary even under Th2 polarizing conditions, where a saturating amount of exogenous IL-4 was added at the beginning of the culture. For this, we collected supernatants at regular intervals from cultures of naive cells subjected to Th2 polarization, and estimated amount of IL-4 present. In parallel, we also took aliquots of the cells at select time points to determine the levels of surface expression of the IL-4 receptor (IL-4R). We found that the concentration of the externally added IL-4 was continuously declining such that IL-4 levels were barely detectable by 120 hours. Interestingly, this decline correlated well with a reciprocal increase in cell surface expression of IL-4R that was evident from the 48 hours time point onwards (). Thus, the observed decrease in IL-4 concentration in the culture supernatants likely derives from its consumption by IL-4R, whose surface levels were rapidly being upregulated over the same time span. This would then rationalize the requirement for endogenous IL-4, during the later stages of polarization.
Endogenous IL-4 helps in differentiation versus proliferation decision making process
We recently showed that ERK regulates human Th2 differentiation and it does so by activating IL-4 transcription and that siRNA mediated silencing of ERK leads to significant reduction in number of IL-4 producing cells7
. Therefore we wanted to test whether endogenous IL-4 helps in decision of proliferation versus differentiation processes of the cell. For this, GFP- or ERK-silenced cells were labeled with CFSE and their proliferation rates were compared under conditions of Th2 polarization. Relative to that of GFP-silenced cells, depletion of ERK resulted in a significant increase in the number of cell doublings by day 6 of the culture. The addition of IL-4 at and after day 3 to day 5, however, substantially reversed this effect () suggesting that continuous presence of endogenous IL-4 during the process of Th2 differentiation may act by limiting the proliferative capacity of cells and, presumably, thereby biasing towards differentiation.
At this juncture, it was of interest to investigate the global effects of IL-4 during reinforcement stage of Th2 differentiation. We deployed similar neutralization strategy as in . Whole genome transcription profiling was done to identify the target of IL-4 and eventually novel players of Th2 transcription network.
Identification of differentially regulated genes
To recapitulate the methodology, Stimulation of cells was done at day 0 through TCR in the presence of IL-4 and at day 3 half the cells were treated with anti IL-4 and anti-IL-4R neutralizing antibodies. RNA was isolated and hybridized to a human genome array. Feature extraction and normalization was done as described in the methods section. Differentially expressed genes thus identified are shown in . More than 3000 genes were upregulated in both the samples as compared to unstimulated cells, and similar statistics were obtained for down regulated genes. The complete list of differentially regulated genes can be accessed through Geo database at NCBI (Accession Number GSE 16022).
Differentially regulated genes in different samples
Identification of IL-4 dependent genes their GO classes
IL-4 dependent genes were identified using the methodology detailed in the method section. The exercise resulted in identification of 128 IL-4 dependent upregulated genes, and 151 IL-4 dependent down regulated genes. The list of these up and down regulated genes along with their functions, as described in Biobase Knowledge Library, (BKL) (www.biobase-international.com
) are given as Supplementary Table 2
and 3 respectively. To identify if any Gene Ontology (GO) classes are enriched in any of these two differentially expressed gene sets, GO analysis was performed on “Explain Analysis System” of Biobase (TRANSFAC) database. Many GO classes were enriched in the upregulated gene set including cell activation, cell differentiation and regulation of T cell anergy at the p value threshold of 0.01 as shown in . However at this cut off only two GO classes were enriched in down regulated gene set (Developmental process and Multi-cellular organismal process) and the overall low enrichment was retained even at p value threshold of 0.1. The observed difference in the enrichment in the two classes may partly be attributed to the ability of IL-4 to specifically upregulate target genes.
Gene Ontology of IL-4 dependent upregulated genes.
MATCH analysis identifies key TFBS in the promoter of IL-4 dependent genes
Further to identify which transcription factors might be important in regulating the expression of these genes, MATCH analysis was done again on “Explain Analysis System” of Biobase database. MATCH analysis was done on three test-background set combinations. In the first two cases up and down regulated gene sets were each used as test set against the background of human house keeping gene promoter sets. In the third analysis, TFBS search was performed in the upregulated set down regulated sets using downregulated set as background. Results are displayed in . Enriched matrices are shown along with Yes/No ratio and p value. “Yes/No” is the ratio of the average number of putative binding sites per 1000 base pair for the query and the background sets. Value greater than 1 indicates an over representation of the motif in the query set.
Overrepresented matrices in IL-4 dependent upregulated genes. Promoters of human house keeping genes were taken as background set. Yes/No is the ratio of occurrence of these matrices in test and background set
Only two matrices were enriched in Up versus Down MATCH () analysis pointing towards the fact that most of transcription factors (TFs) required for up and down regulation are probably the same in case of IL-4. However, two matrices unique to upregulated group are V$HTF_01 (“V$” is matrix nomenclature adapted by TRANSFAC database) and V$NFKB_C. Although NFKB is a well studied molecule and known to function in IL-4 dependent Th2 differentiation process21
, role of Xbp-1 (TF represented by V$HTF) in T cell activation or differentiation is not investigated. Xbp-1 is known to be necessary for plasma B cell differentiation and this analysis suggest future experiment for investigating the role of this molecule in T cell differentiation.
Overrepresented matrices in IL-4 dependent upregulated gene set with downregulated genes as background set
A closer look at and also suggests that there are two matrices that are present in Up versus housekeeping comparison but absent in Down versus housekeeping comparison and these are NFKB and MYOD suggesting that MYOD, which is critical for muscle cell differentiation, could also play a prominent role in T cell differentiation.
Overrepresented matrices in IL-4 dependent downregulated genes. Promoters of human house keeping genes were taken as background set. Yes/No is the ratio of occurrence of these matrices in test and background set
Gene Set Enrichment Analysis of whole dataset reveals significant enriched pathways
Analysis of gene expression data by identifying differentially expressed genes and interpreting biological information independently for each gene has several limitation as outlined elsewhere17
. These include low signal to noise ratio owing to high noise in microarray data, and the possibility of missing crucial effects on pathways. GSEA, on the contrary, analyzes whole data at biological pathway level by performing unbiased global searches for genes that are coordinately up or down regulated in predefined biological gene sets. GSEA first ranks the original microarray data on the basis of expression profile in two groups test and control. And then in the ranked list it asks whether a significant number of genes from predefined biological sets occur towards the top or bottom of ranked list.
To identify pathways enriched in TIL4 treated samples over the samples treated with neutralizing antibodies, GSEA was performed on day 6transcription data. Results are shown in . Overview of GSEA analysis is illustrated in . Panel a shows distribution of enrichment score (ES) of all the gene sets enriched. On the X axis, gene sets that are present before “0” mark were overrepresented in the background set and those that are present after it were the sets enriched in the test set (TIL4 as opposed to TIL4ABS group). A dot plot of significance and Normalized Enrichment Score NES for is given in . This Figure () shows that gene sets bearing NES, either less than -1 or more than +1 are significant at the cut off FDR q value of less than 25 %. Although in TIL4ABS samples, there were many gene sets with nominal p value less than 0.25, or even 0.05, but none of them was enriched at FDR q value of less than 0.25. The nominal p value is less relevant for comparing across the gene sets than FDR q value. Parameters used for the analysis are detailed in Methods section. Details on statistics used are described elsewhere17
. As shown in many pathways known to be associated with cytokine signaling were enriched in TCR + IL-4 treated sample. Enriched pathways can be classified under the heads of signaling pathways and metabolic pathways.
GSEA enriched pathways at day 6. Only pathways that are enriched at p value threshold of 0.05 and FDR q value of 0.25 are shown
Gene Set Enrichment Analysis (GSEA).
Signaling pathways associated with IL-4
Many of the signaling pathways known to be essential for T cell activation and differentiation are enriched in TCR + IL-4 treated samples. Although the genes in these pathways were marginally upregulated and would have been missed these in a single gene analysis, GSEA successfully identifies pathways known to be central in transducing cytokine signal owing to its ability to analyze the whole transcriptome data at pathway level. The most significant hit was JAK-STAT pathway followed by “Cytokine and cytokine receptor interaction”. None of these pathways came as a surprise because these were expected to be enriched.
Gene set “Differentiation pathway in PC12 cells” from STKE (Signal Transduction Knowledge Environment) is significantly enriched at FDR q value of less than 0.04 indicating that the pathway is enriched in TIL4 samples at the highest level of significance (Less significant but acceptable levels for FDR q value being 0.1 and 0.25). It may, therefore, be of critical importance to study some of the enriched genes from this pathway for their involvement in T helper cell differentiation. Some of the core-enriched genes from this pathway such as Egr1/2/3 are already known to be important for Th1/2 differentiation22,23
. Other signaling pathways enriched were Calcineurine NF-AT signaling and ATR BRCA pathway. Because Calcineurine NFAT signaling plays a crucial role in T cell activation, enrichment of this pathway along with other pathway of known implications in Th2 differentiation strengthen the reliability of GSEA results.
Although IL-4 is known to have both positive and negative effects in various types of cancers24,25,26,27
its association to leukemia's of Fanconi anemia (FA) type has not been investigated in detail. Enrichment of ATR BRCA pathway that has many genes common with FA suggests a possible role of IL-4 in these lymphocyte malignancies. Cell cycle Pathway is also enriched, albeit at low significance (FDR q value 0.21). Core enriched genes of this pathway are pro-proliferative and probably signal for “go ahead” to the cell cycle at various stages. These genes include CCND2 (required for G1/S transition), CDC25C (triggers mitosis and suppresses growth arrest by p53) and CCNA (required both at the start and G2/M transition). The whole list of coordinately upregulated genes in all the enriched pathways is given as Supple Table II
. A closer inspection of the ranked list reveals many noteworthy points. For example, DUSP2 and DUSP9, which are known to dephosphorylate ERK, are at the bottom of the ranked list while other dual specificity phosphatase, DUSP13, which is not specific for ERK1/2, is towards the top indicating the possibility that ERK1/2 plays a dominant role in transmitting signals from IL-4R.
Metabolic Pathways associated with IL-4
Metabolic pathways enriched were Folate biosynthesis, amino acid and prostaglandin metabolism as outlined in . Folate plays a critical role in protein and nucleic acid synthesis28
, and low Folate level has been associated with decreased expression of immune related proteins especially those involved in inflammation and complement activation29,30
. Given its functions it was cogent for it to be crucial in the pro-proliferative function of IL-4, which explains its enrichment in TIL4 group over TIL4ABS group. Genes from enriched metabolism pathways listed in suggest that differentiation supports or requires synthesis of more amino acids of acidic nature (ASP GLU) while at the same time degrading hydrophobic residue (ALA) indicating the possibility of existence of preference for amino acids of a kind for a particular cellular process. Although this may be exciting, it requires further experimental validation.
Network of core enriched genes follow a power law degree distribution
Although analysis of individual enriched pathways gave fascinating insights into the pleiotropic functions of IL-4, it was pertinent to study whether these individual pathways integrate to form a functional network. For this, a functional association network was made out of all core enriched genes from all enriched pathways using STRING database. No additional white nodes (Additional interactors in the literature but not in the input data) were allowed to ensure that only associations among this set of proteins be picked up. The functional association networks thus generated () followed scale free topology (), which is said to be existing in a network if the degree distribution follows the power law. Power law in this context would mean that there are extremely few nodes with exceedingly high degree while a majority of the nodes only have few edges. This hub topological feature of biological networks make them robust against random node failure.
STAT3 and JUN are key nodes of Th2 network at day 6
The network thus generated was analyzed using “network analyzer” plug-in of Cytoscape. This plug-in calculated all the relevant parameters such as degree, clustering coefficient and other centrality measures. Betweenness centrality (BC) is one such parameter, which, for a vertex, is a measure of how frequently the vertex lies on a randomly chosen shortest path. It shows how much is the load share of this vertex in a network31
meaning the extent of information flow through this node. Both node size and intensity of color represent Betweenness centrality. As is evident PCNA, STAT3 and JUN as the nodes of highest “importance”(). The observation that STAT3 is coming as a key regulatory node as opposed STAT6 was initially surprising because STAT6 and not STAT3, along with GATA3, was known to be the lineage specific transcription factor for Th2 development4,6,9
. However, a recent report showed that STAT3 synergizes with STAT6 to give optimal Th2 development5
LIF-IL-6-STAT3 axis may regulate outcome of Th2 differentiation
Next we sought to investigate which upstream molecules control STAT3 activity during Th2 development. All the known upstream molecules that target STAT3 were searched in the microarray data. We found that LIF was the only molecule that was going up in TCR + IL-4 treated sample as compared to the unstimulated and this upregulation was reversed upon addition of neutralization antibody against IL-4 and IL-4R (). Given the role of LIF as a negative regulator of Th17 differentiation19
and the fact that STAT3 is a known target of LIF and that STAT3 is a positive regulator of Th2 differentiation5
, we hypothesized that LIF may positively regulate Th2 differentiation.
LIF is secreted by th2 cells and it negatively regulates IL-4 production.
To investigate the role of LIF in Th2 development we decided to test: A) whether LIF is secreted by Th2 cells, B) can LIF substitute for IL-4 in Th2 development and C) what is the effect of LIF on phosphorylation of STAT3.
LIF is secreted by Th2 cells
LIF is known to be secreted by activated T cells32,33
, so here we sought to investigate if LIF is secreted in the supernatant of T cell cultures which are differentiated in Th2 environment for 7 days. Results are shown in . Although the overall expression is very low (in pg only), a definite trend can be seen. TCR + IL-4 + anti-IFNγ sample had highest LIF secretion followed by TCR + IL-4 and TCR alone. Interestingly in the sample where IL-4 signaling was blocked at the later stage, the level of LIF was comparable to that what is produced in the unstimulated sample suggesting that possibly Th2 cells produce more LIF than other subsets. At the mRNA level also, the TCR + IL-4 samples had significantly higher level of LIF over TCR + IL-4 + anti-IL-4 samples. Given the known antagonism of LIF and IL-6 we also checked the expression of IL-6 in the whole genome transcriptome data and found that indeed there exists antipathy between the two ().
LIF negatively regulates Th2 differentiation
To address whether LIF has positive or negative effect on Th2 development, cells were stimulated through TCR in the presence of cytokine and or neutralizing antibody as mentioned in the . Stimulation media were supplemented again at day 3, and after 6 days cells were washed and restimulated with PMA for 24 hours and ELISPOT assay was done to determine the fraction of IL-4 secreting cells. As expected more IL-4 producing cells were produced where IL-4 was present (TCR + IL-4) during trigger, as opposed to those where no IL-4 was added initially. Most notably though, addition of anti-LIF neutralizing antibody yielded exactly the opposite result and IL-4 producing cell population grew significantly over TCR + IL-4 sample. Even in the presence of anti-IFNγ, LIF appears to downgrade development of Th2 differentiation (). Thus, overall interpretation of supports the notion that LIF is a negative regulator of Th2 differentiation process. This role of LIF is in line with its role in promoting stemness in stem cells34
and preventing it from differentiating to any lineages.
LIF down modulates STAT3 phosphorylation
A recent study reported the role of STAT3 in Th2 differentiation and there it was observed that STAT3 phosphorylation peaks at day 3 followed by a steady decline. We, therefore, studied STAT3 phosphorylation by LIF and IL-4 at day 3 post stimulation. shows pSTAT3 profile either in unstimulated cells (Red) or after TCR triggering in presence (Green) or absence (Blue) of IL-4. We observed that both TCR and TCR + IL-4 show similar profile with TCR + IL-4 having slightly more phosphorylation than TCR alone. TCR + LIF treated cells, on the other hand, had much less phosphorylation, as compared to TCR or TCR + IL-4 treated cells suggesting that LIF may inhibit sustained STAT3 phosphorylation (). We also observed pSTAT3 level in the presence of anti-IFNγ and found that the profile for TCR + IL-4 in the presence (Blue) or absence (Red) of anti-IFNγ were similar (). Thus, taken together results in suggest that although LIF is secreted by Th2 cells it inhibits the differentiation of naïve cells into Th2 by reducing sustained phosphorylation of STAT3.
STAT3 Phosphorylation in response to different stimuli.