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RORγt and RORα are transcription factors of the RAR-related orphan nuclear receptor (ROR) family. They are expressed in Th17 cells and have been suggested to play a role in Th17 differentiation. Although RORγt signature genes have been characterized in mouse Th17 cells, detailed information on its transcriptional control in human Th17 cells is limited and even less is known about RORα signature genes which have not been reported in either human or mouse T cells. In this study, global gene expression of human CD4 T cells activated under Th17 skewing conditions was profiled by RNA sequencing. RORγt and RORα signature genes were identified in these Th17 cells treated with specific siRNAs to knock down RORγt or RORα expression. We have generated selective small molecule RORγt modulators and they were also utilized as pharmacological tools in RORγt signature gene identification. Our results showed that RORγt controlled the expression of a very selective number of genes in Th17 cells and most of them were regulated by RORα as well albeit a weaker influence. Key Th17 genes including IL-17A, IL-17F, IL-23R, CCL20 and CCR6 were shown to be regulated by both RORγt and RORα. Our results demonstrated an overlapping role of RORγt and RORα in human Th17 cell differentiation through regulation of a defined common set of Th17 genes. RORγt as a drug target for treatment of Th17 mediated autoimmune diseases such as psoriasis has been demonstrated recently in clinical trials. Our results suggest that RORα could be involved in same disease mechanisms and gene signatures identified in this report could be valuable biomarkers for tracking the pharmacodynamic effects of compounds that modulate RORγt or RORα activities in patients. [ABSTRACT FROM AUTHOR]

Kv1.3 is a voltage-gated potassium channel expressed on T cells that plays an important role in T cell activation. Previous studies have shown that blocking Kv1.3 channels in human T cells during activation results in reduced calcium entry, cytokine production, and proliferation. The aim of the present study was to further explore the effects of Kv1.3 blockers on the response of different human T cell subsets under various stimulation conditions. Our studies show that, unlike the immune suppressor cyclosporine A, the inhibitory effect of Kv1.3 blockers was partial and stimulation strength dependent, with reduced inhibitory efficacy on T cells under strengthened anti-CD3/CD28 stimulations. T cell responses to allergens including house dust mites and ragweed were partially reduced by Kv1.3 blockers. The effect of Kv1.3 inhibition was dependent on T cell subsets, with stronger effects on CCR7- effector memory compared to CCR7+ central memory CD4 T cells. Calcium entry studies also revealed a population of CD4 T cells resistant to Kv1.3 blockade. Activation of CD4 T cells was accompanied with an increase in Kv1.3 currents but Kv1.3 transcripts were found to be reduced, suggesting a posttranscriptional mechanism in the regulation of Kv1.3 activities. In summary, Kv1.3 blockers inhibit T cell activation in a manner that is highly dependent on the T cell identity and stimulation strength, These findings suggest that Kv1.3 blockers inhibit T cells in a unique, conditional manner, further refining our understanding of the therapeutic potential of Kv1.3 blockers. [ABSTRACT FROM AUTHOR]

To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis. [ABSTRACT FROM AUTHOR]