Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis.

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.

Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.

ChIP-Hub provides an integrative platform for exploring plant regulome.

Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here, we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles >10,000 publicly available datasets reanalyzed from >40 plant species, allowing visualization and meta-analysis. We manually curate the datasets through assessing ~540 original publications and comprehensively evaluate their data quality. As a proof of concept, we extensively survey the co-association of different regulators and construct a hierarchical regulatory network under a broad developmental context. Furthermore, we show how our annotation allows to investigate the dynamic activity of tissue-specific regulatory elements (promoters and enhancers) and their underlying sequence grammar. Finally, we analyze the function and conservation of tissue-specific promoters, enhancers and chromatin states using comparative genomics approaches. Taken together, the ChIP-Hub platform and the analysis results provide rich resources for deep exploration of plant ENCODE. ChIP-Hub is available at https://biobigdata.nju.edu.cn/ChIPHub/ .

Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana.

Floral homeotic transcription factors (TFs) act in a combinatorial manner to specify the organ identities in the flower. However, the architecture and the function of the gene regulatory network (GRN) controlling floral organ specification is still poorly understood. In particular, the interconnections of homeotic TFs, microRNAs (miRNAs) and other factors controlling organ initiation and growth have not been studied systematically so far. Here, using a combination of genome-wide TF binding, mRNA and miRNA expression data, we reconstruct the dynamic GRN controlling floral meristem development and organ differentiation. We identify prevalent feed-forward loops (FFLs) mediated by floral homeotic TFs and miRNAs that regulate common targets. Experimental validation of a coherent FFL shows that petal size is controlled by the SEPALLATA3-regulated miR319/TCP4 module. We further show that combinatorial DNA-binding of homeotic factors and selected other TFs is predictive of organ-specific patterns of gene expression. Our results provide a valuable resource for studying molecular regulatory processes underlying floral organ specification in plants.

The HTPmod Shiny application enables modeling and visualization of large-scale biological data.

The wave of high-throughput technologies in genomics and phenomics are enabling data to be generated on an unprecedented scale and at a reasonable cost. Exploring the large-scale data sets generated by these technologies to derive biological insights requires efficient bioinformatic tools. Here we introduce an interactive, open-source web application (HTPmod) for high-throughput biological data modeling and visualization. HTPmod is implemented with the Shiny framework by integrating the computational power and professional visualization of R and including various machine-learning approaches. We demonstrate that HTPmod can be used for modeling and visualizing large-scale, high-dimensional data sets (such as multiple omics data) under a broad context. By reinvestigating example data sets from recent studies, we find not only that HTPmod can reproduce results from the original studies in a straightforward fashion and within a reasonable time, but also that novel insights may be gained from fast reinvestigation of existing data by HTPmod.

Spatial features for Escherichia coli genome organization.

BACKGROUND: In bacterial genomes, the compactly encoded genes and operons are well organized, with genes in the same biological pathway or operons in the same regulon close to each other on the genome sequence. In addition, the linearly close genes have a higher probability of co-expression and their protein products tend to form protein-protein interactions. However, the organization features of bacterial genomes in a three-dimensional space remain elusive. The DNA interaction data of Escherichia coli, measured by the genome conformation capture (GCC) technique, have recently become available, which allowed us to investigate the spatial features of bacterial genome organization. RESULTS: By renormalizing the GCC data, we compared the interaction frequency of operon pairs in the same regulon with that of random operon pairs. The results showed that arrangements of operons in the E. coli genome tend to minimize the spatial distance between operons in the same regulon. A similar global organization feature exists for genes in biological pathways of E. coli. In addition, the genes close to each other spatially (even if they are far from each other on the genome sequence) tend to be co-expressed and form protein-protein interactions. These results provided new insights into the organization principles of bacterial genomes and support the notion of transcription factory. CONCLUSIONS: This study revealed the organization features of Escherichia coli genomic functional units in the 3D space and furthered our understanding of the link between the three-dimensional structure of chromosomes and biological function.

Dissecting the chromatin interactome of microRNA genes.

Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II-associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA-target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR-MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.

Structural and energetic analyses of SNPs in drug targets and implications for drug therapy.

Mutations in drug targets can alter the therapeutic effects of drugs. Therefore, evaluating the effects of single-nucleotide polymorphisms (SNPs) on drug-target binding is of significant interest. This study focuses on the analysis of the structural and energy properties of SNPs in successful drug targets by using the data derived from HapMap and the Therapeutic Target Database. The results show the following: (i) Drug targets undergo strong purifying selection, and the majority (92.4%) of the SNPs are located far from the drug-binding sites (>12 Å). (ii) For SNPs near the drug-binding pocket (≤12 Å), nearly half of the drugs are weakly affected by the SNPs, and only a few drugs are significantly affected by the target mutations. These results have direct implications for population-based drug therapy and for chemical treatment of genetic diseases as well.

The sequencing bias relaxed characteristics of Hi-C derived data and implications for chromatin 3D modeling.

The 3D chromatin structure modeling by chromatin interactions derived from Hi-C experiments is significantly challenged by the intrinsic sequencing biases in these experiments. Conventional modeling methods only focus on the bias among different chromatin regions within the same experiment but neglect the bias arising from different experimental sequencing depth. We now show that the regional interaction bias is tightly coupled with the sequencing depth, and we further identify a chromatin structure parameter as the inherent characteristics of Hi-C derived data for chromatin regions. Then we present an approach for chromatin structure prediction capable of relaxing both kinds of sequencing biases by using this identified parameter. This method is validated by intra and inter cell-line comparisons among various chromatin regions for four human cell-lines (K562, GM12878, IMR90 and H1hESC), which shows that the openness of chromatin region is well correlated with chromatin function. This method has been executed by an automatic pipeline (AutoChrom3D) and thus can be conveniently used.

Oxidative DNA damage is important to the evolution of antibiotic resistance: evidence of mutation bias and its medicinal implications.

Several (1) studies have revealed that the reactive oxygen species (ROS) induced by antibacterial stimulation accelerates the evolution of antibiotic resistance, which uncovered new links between oxygen rise and evolution and inspired new strategies to prevent antibiotic resistance. Considering many other mechanisms cause DNA mutations aside from ROS damage, evaluating the significance of oxidative DNA damage in the development of antibiotic resistance is of great interest. In this study, we examined the ratio of G:C > T:A transversion to G:C > A:T transition in drug-resistant Escherichia coli and Mycobacterium tuberculosis and found that it is significantly higher than the background values. This finding strongly suggests that ROS damage plays a critical role in the development of antibacterial resistance. Considering the long-term co-evolution between host organisms and pathogenic bacteria, we speculate that the hosts may have evolved strategies for combating antibiotic resistance by controlling DNA damage in bacteria. Analysis of the global transcriptional profiles of Staphylococcus aureus treated with berberine (derived from Berberis, a traditional antibacterial medicine) revealed that the transcription of DNA repair enzymes was markedly upregulated, whereas the antioxidant enzymes were significantly downregulated. Thus, we propose that consolidating the DNA repair systems of bacteria may be a viable strategy for preventing antibiotic resistance. (1)These authors contributed equally to this work.

Can medical genetics and evolutionary biology inspire drug target identification?

New strategies for target identification are urgently needed to tackle the current productivity challenges in drug discovery. By examining successful human drug targets, it can be seen that approximately 50% are associated with genetic disorders. Further analysis shows that these successfully targeted genes share some common evolutionary features, which strongly suggests that evolutionary information can help identify drug targets with the greatest potential for therapeutic development.

Exploring the common molecular basis for the universal DNA mutation bias: revival of Löwdin mutation model.

Recently, numerous genome analyses revealed the existence of a universal G:C→A:T mutation bias in bacteria, fungi, plants and animals. To explore the molecular basis for this mutation bias, we examined the three well-known DNA mutation models, i.e., oxidative damage model, UV-radiation damage model and CpG hypermutation model. It was revealed that these models cannot provide a sufficient explanation to the universal mutation bias. Therefore, we resorted to a DNA mutation model proposed by Löwdin 40 years ago, which was based on inter-base double proton transfers (DPT). Since DPT is a fundamental and spontaneous chemical process and occurs much more frequently within GC pairs than AT pairs, Löwdin model offers a common explanation for the observed universal mutation bias and thus has broad biological implications.