Quantitative Proteomics Reveals the Dynamic Protein Landscape during Initiation of Human Th17 Cell Polarization.

Th17 cells contribute to the pathogenesis of inflammatory and autoimmune diseases and cancer. To reveal the Th17 cell-specific proteomic signature regulating Th17 cell differentiation and function in humans, we used a label-free mass spectrometry-based approach. Furthermore, a comprehensive analysis of the proteome and transcriptome of cells during human Th17 differentiation revealed a high degree of overlap between the datasets. However, when compared with corresponding published mouse data, we found very limited overlap between the proteins differentially regulated in response to Th17 differentiation. Validations were made for a panel of selected proteins with known and unknown functions. Finally, using RNA interference, we showed that SATB1 negatively regulates human Th17 cell differentiation. Overall, the current study illustrates a comprehensive picture of the global protein landscape during early human Th17 cell differentiation. Poor overlap with mouse data underlines the importance of human studies for translational research.

Genome-wide Analysis of STAT3-Mediated Transcription during Early Human Th17 Cell Differentiation.

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.

Comparative analysis of human and mouse transcriptomes of Th17 cell priming.

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.

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation.

MOTIVATION: Gene expression profiling using RNA-seq is a powerful technique for screening RNA species' landscapes and their dynamics in an unbiased way. While several advanced methods exist for differential expression analysis of RNA-seq data, proper tools to anal.yze RNA-seq time-course have not been proposed. RESULTS: In this study, we use RNA-seq to measure gene expression during the early human T helper 17 (Th17) cell differentiation and T-: cell activation (Th0). To quantify Th17-: specific gene expression dynamics, we present a novel statistical methodology, DyNB, for analyzing time-course RNA-seq data. We use non-parametric Gaussian processes to model temporal correlation in gene expression and combine that with negative binomial likelihood for the count data. To account for experiment-: specific biases in gene expression dynamics, such as differences in cell differentiation efficiencies, we propose a method to rescale the dynamics between replicated measurements. We develop an MCMC sampling method to make inference of differential expression dynamics between conditions. DyNB identifies several known and novel genes involved in Th17 differentiation. Analysis of differentiation efficiencies revealed consistent patterns in gene expression dynamics between different cultures. We use qRT-PCR to validate differential expression and differentiation efficiencies for selected genes. Comparison of the results with those obtained via traditional timepoint-: wise analysis shows that time-course analysis together with time rescaling between cultures identifies differentially expressed genes which would not otherwise be detected. AVAILABILITY: An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/

Identification of early gene expression changes during human Th17 cell differentiation.

Th17 cells play an essential role in the pathogenesis of autoimmune and inflammatory diseases. Most of our current understanding on Th17 cell differentiation relies on studies carried out in mice, whereas the molecular mechanisms controlling human Th17 cell differentiation are less well defined. In this study, we identified gene expression changes characterizing early stages of human Th17 cell differentiation through genome-wide gene expression profiling. CD4(+) cells isolated from umbilical cord blood were used to determine detailed kinetics of gene expression after initiation of Th17 differentiation with IL1ß, IL6, and TGFß. The differential expression of selected candidate genes was further validated at protein level and analyzed for specificity in initiation of Th17 compared with initiation of other Th subsets, namely Th1, Th2, and iTreg. This first genome-wide profiling of transcriptomics during the induction of human Th17 differentiation provides a starting point for defining gene regulatory networks and identifying new candidates regulating Th17 differentiation in humans.