Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics

As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.

Transcription Factor CsWRKY65 Participates in the Establishment of Disease Resistance of Citrus Fruits to Penicillium digitatum

Penicillium digitatum is the primary pathogen that causes serious yield losses worldwide. In our previous study, CsWRKY transcription factors (TFs) and some genes associated with immunity were identified in citrus fruits after P. digitatum infection, but little information is available in the literature on the mechanisms of TFs in citrus disease resistance. In this study, the possible mechanisms of CsWRKY65 participating in the establishment of disease resistance were investigated. Results show that CsWRKY65 was a transcriptional activator in the nucleus. The dual-luciferase transient assays and electrophoretic mobility shift assays showed that CsWRKY65 bound with CsRbohB, CsRbohD, CsCDPK33, and CsPR10 promoters to activate gene transcription. Besides, the transient overexpression of CsWRKY65 induced reactive oxygen species accumulation and increased PR gene expression in Nicotiana benthamiana leaves. The transient overexpression of CsWRKY65 in the citrus peel enhanced the disease resistance against P. digitatum. In conclusion, CsWRKY65 is likely to be involved in regulating the disease resistance to P. digitatum of citrus fruits by directly activating the expressions of CsRbohB, CsRbohD, CsCDPK33, and CsPR10. This study provides new information for the mechanism of citrus WRKY TFs participating in the establishment of disease resistance.

Functional characterization of an inducible bidirectional promoter from Fusarium oxysporum f. sp. cubense

Bidirectional promoters (BDPs) are regulatory DNA sequences (~1000 bp long) intervening two genes arranged on opposite strands with their 5' ends in close proximity. These genes are mostly co-expressed; but, instances of anti-correlation and independent transcription have been observed. In fungal systems, BDPs have shown to provide an improved genetic circuit by assembling and regulating transcription of different genes of a common metabolic pathway. We have identified an intergenic region (1063 bp) from the genome of Fusarium oxysporum f. sp. cubense (Foc), a banana root pathogen. This intergenic region regulates the expression of a gene pair required for the breakdown of hemicellulose. For characterization, it was cloned into pCSN44 vector backbone between two reporter genes, namely β-glucuronidase (GUS) and enhanced green fluorescent protein (EGFP). The newly formed vector was transformed into Foc and tested for its bidirectional expression activity. Using histochemical staining and fluorescence microscopy, the kinetics for both, GUS and EGFP expression were tested under different growth conditions respectively. The activity was differentially regulated by inducers such as xylan, arabinogalactan and pectin. This is the first report on the isolation of the intergenic region with inducible bidirectional promoter activity from Fusarium. Characterization of such BDPs will find applications in genetic engineering, metabolic engineering and synthetic biology using fungal systems.

Transcription closed and open complex formation coordinate expression of genes with a shared promoter region

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli, the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.