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The development of therapeutic strategies to combat immune-associated diseases requires the molecular mechanisms of human Th17 cell differentiation to be fully identified and understood. To investigate transcriptional control of Th17 cell differentiation, we used primary human CD4+ T cells in small interfering RNA (siRNA)-mediated gene silencing and chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) to identify both the early direct and indirect targets of STAT3. The integrated dataset presented in this study confirms that STAT3 is critical for transcriptional regulation of early human Th17 cell differentiation. Additionally, we found that a number of SNPs from loci associated with immune-mediated disorders were located at sites where STAT3 binds to induce Th17 cell specification. Importantly, introduction of such SNPs alters STAT3 binding in DNA affinity precipitation assays. Overall, our study provides important insights for modulating Th17-mediated pathogenic immune responses in humans.

Uncontrolled Th17 cell activity is associated with cancer and autoimmune and inflammatory diseases. To validate the potential relevance of mouse models of targeting the Th17 pathway in human diseases we used RNA sequencing to compare the expression of coding and non-coding transcripts during the priming of Th17 cell differentiation in both human and mouse. In addition to already known targets, several transcripts not previously linked to Th17 cell polarization were found in both species. Moreover, a considerable number of human-specific long non-coding RNAs were identified that responded to cytokines stimulating Th17 cell differentiation. We integrated our transcriptomics data with known disease-associated polymorphisms and show that conserved regulation pinpoints genes that are relevant to Th17 cell-mediated human diseases and that can be modelled in mouse. Substantial differences observed in non-coding transcriptomes between the two species as well as increased overlap between Th17 cell-specific gene expression and disease-associated polymorphisms underline the need of parallel analysis of human and mouse models. Comprehensive analysis of genes regulated during Th17 cell priming and their classification to conserved and non-conserved between human and mouse facilitates translational research, pointing out which candidate targets identified in human are worth studying by using in vivo mouse models.

Relative abundance of various lncRNA types along with protein-coding genes. The column names are as follows: AS anti sense lncRNA, AS_L lineage-specifc antisense lncRNA, linc long intergenic non-coding RNA, linc_L lineage-specific lincRNA, PT processed transcript, PT_L lineage-specific processed transcript, SI sense intronic, SI_L lineage-specific sense intronic, SO sense overlapping, SO_L lineage-specific sense overlapping, PC protein-coding mRNA. (PDF 132 kb)

Distribution of lineage-specific genes in platforms when analyzed individually in each platform. (PDF 103 kb)

Comparison of transcriptional profiling platforms. a Genes determined to be present in T-helper cell subsets in the three platforms used for transcriptional profiling. b Gene expression density curves of T-helper cell subsets in the three platforms profiled. Only genes determined to be present were included. c Box plot of expression of genes in T-helper cell subsets based on their detection in the platforms used for transcriptional profiling. (PDF 117 kb)

Analysis design schematic. (PDF 133 kb)

Analysis design schematic. (PDF 133 kb)

Comparison of transcriptional profiling platforms. a Genes determined to be present in T-helper cell subsets in the three platforms used for transcriptional profiling. b Gene expression density curves of T-helper cell subsets in the three platforms profiled. Only genes determined to be present were included. c Box plot of expression of genes in T-helper cell subsets based on their detection in the platforms used for transcriptional profiling. (PDF 117 kb)

Randomization test of epigenetic marks around lineage-specific lncRNAs. a Randomization test reveals that the number of lineage-specific enhancers around lineage-specific lncRNAs is more than anywhere else in the genome. b Randomization test reveals that the number of lineage-specific promoters around the lineage-specific lncRNAs is more than anywhere else in the genome. The distribution of enhancers or promoters in the vicinity of genes in the genome was determined by randomly picking the same number of genes as in the lineage-specific set. The red dashed line indicates the number of lineage-specific enhancers or promoters found in the vicinity of lineage-specific lncRNAs. (PDF 114 kb)

Distribution of lineage-specific genes in platforms when analyzed individually in each platform. (PDF 103 kb)