Dysregulation of Th17 cell differentiation and pathogenicity contributes to multiple autoimmune and inflammatory diseases. Previously growth hormone releasing hormone receptor (GHRH-R) deficient mice have been reported to be less susceptible to the induction of experimental autoimmune encephalomyelitis. Here, we show GHRH-R is an important regulator of Th17 cell differentiation in Th17 cell-mediated ocular and neural inflammation. We find that GHRH-R is not expressed in naïve CD4+ T cells, while its expression is induced throughout Th17 cell differentiation in vitro. Mechanistically, GHRH-R activates the JAK-STAT3 pathway, increases the phosphorylation of STAT3, enhances both non-pathogenic and pathogenic Th17 cell differentiation and promotes the gene expression signatures of pathogenic Th17 cells. Enhancing this signaling by GHRH agonist promotes, while inhibiting this signaling by GHRH antagonist or GHRH-R deficiency reduces, Th17 cell differentiation in vitro and Th17 cell-mediated ocular and neural inflammation in vivo. Thus, GHRH-R signaling functions as a critical factor that regulates Th17 cell differentiation and Th17 cell-mediated autoimmune ocular and neural inflammation.

The transcription regulatory network inside a eukaryotic cell is defined by the combinatorial actions of transcription factors (TFs). However, TF binding studies in plants are too few in number to produce a general picture of this complex network. In this study, we use large-scale ChIP-seq to reconstruct it in the maize leaf, and train machine-learning models to predict TF binding and co-localization. The resulting network covers 77% of the expressed genes, and shows a scale-free topology and functional modularity like a real-world network. TF binding sequence preferences are conserved within family, while co-binding could be key for their binding specificity. Cross-species comparison shows that core network nodes at the top of the transmission of information being more conserved than those at the bottom. This study reveals the complex and redundant nature of the plant transcription regulatory network, and sheds light on its architecture, organizing principle and evolutionary trajectory.

Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs-the reference panel and the chain-type designs-true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.

Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.