N. A. Omelyanchuk

Plants of the genus Miscanthus are a promising perennial energy crop, combining high biomass productivity, resistance to abiotic stress, and low agricultural technology requirements. This review summarizes recent advances in genomic and transcriptomic studies of the molecular genetic mechanisms underlying the economically valuable traits of Miscanthus . Data on whole-genome assemblies of key species – M. sinensis ( Msi ), M. sacchariflorus ( Msa ), M. floridulus ( Mfl ), and M. lutarioriparius ( Mlu ) – are presented using various technologies (Illumina, PacBio, Oxford Nanopore, Hi-C). Genomic studies have revealed the complex evolution of the genus, including paleoallopolyploidy, chromosomal fusions, and duplications, which accounts for the high genetic diversity of these species. Their genome assemblies at the complete chromosome level have become the basis for comparative genomics, establishing taxonomic relationships (including the recognition of Mlu as a subspecies of Msa and Mfl as a subtype of Msi ), and studying synteny with related crops such as sorghum. The information on the Miscanthus genome allows for the complete and accurate identification of a set of genes targeting breeding for the most important biotechnological traits. At the same time, the commercial hybrid M. × giganteus ( M × g ) is characterized by extremely low levels of genetic polymorphism, making it vulnerable to pathogens and climate fluctuations. The integration of genetic polymorphism data, phylogeography, and functional annotation of genomes opens up opportunities for the development of new, productive, and environmentally friendly Miscanthus varieties through interspecific crossings, ploidy modification, and genetic engineering. These advances contribute to the optimization of the biotechnological potential of Miscanthus for the production of biofuels and other biomaterials and the restoration of degraded lands.

The development of high-throughput sequencing has expanded the possibilities for studying the regulation of gene expression, including the reconstruction of gene regulatory networks and transcription factor regulatory networks (TFRNs). Identifying the molecular aspects for regulation of biological processes via these networks remains a challenge. Solving this problem for plants will significantly advance the understanding of the mechanisms shaping agronomically important traits. Previously, we developed the PlantReg program to reconstruct the transcriptional regulation of biological processes in the model species Arabidopsis thaliana L. The links established by this program between TFRNs and the genes regulating biological processes specify the type of regulation (activation/suppression). However, the program does not determine whether activation/suppression of the target gene is due to the cooperative or competitive interaction of transcription factors (TFs). We assumed that using information on the mutual arrangement of TF binding sites (BSs) in the target gene promoter as well as data on the activity type of TF effector domains would help to identify the cooperative/competitive action of TFs. We improved the program and created PlantReg 1.1, which enables precise localization of TF BSs in extended TF binding regions identified from genome-wide DAP-seq profiles (https://plamorph.sysbio.ru/fannotf/). To demonstrate the capabilities of the program, we used it to investigate the regulation of target genes in previously reconstructed TFRNs for auxin response and early reaction to salt stress in A. thaliana. The study focused on genes encoding proteins involved in chlorophyll and lignin biosynthesis, ribosome biogenesis, and abscisic acid (ABA) signaling. We revealed that the frequency of competitive regulation under the influence of auxin or salt stress could be quite high (approximately 30 %). We demonstrated that competition between bZIP family TFs for common BS is a significant mechanism of transcriptional repression in response to auxin, and that auxin and salt stress can engage common competitive regulatory mechanisms to modulate the expression of some genes in the ABA signaling pathway.

The description of the path from a gene to a trait, as the main task of many areas in biology, is currently being equipped with new methods affecting not only experimental techniques, but also analysis of the results. The pleiotropic effect of a gene is due to its participation in numerous biological processes involved in different traits. A widespread use of genome-wide sequencing of transcripts and transcription factor (TF) binding regions has made the following tasks relevant: unveiling pleiotropic effects of TFs based on the functions of their target genes; compiling the lists of TFs that regulate biological processes of interest; and describing the ways of TF functioning (their primary and secondary targets, higher order targets, TF interactions in the process under study). We have previously developed a method for the reconstruction of TF regulatory networks and proposed an approach that allows identifying which biological processes are controlled by these networks and how this control is exerted. In this paper, we have implemented the approach as PlantReg, a program available as a web service. The paper describes how the program works. The input consists of a list of genes and a list of TFs – known or putative transcriptional regulators of these genes. As an output, the program provides a list of biological processes enriched for these genes, as well as information about by which TFs and through which genes these processes are controlled. We illustrated the use of PlantReg deciphering transcriptional regulation of processes initiated at the early salt stress response in Arabidopsis thaliana L. With PlantReg, we identified biological processes stimulated by the stress, and specific sets of TFs that activate each process. With one of these processes (response to abscisic acid) as an example, we showed that salt stress mainly affects abscisic acid signaling and identified key TFs in this regulation. Thus, PlantReg is a convenient tool for generating hypotheses about the molecular mechanisms that control plant traits.

Gene regulatory networks (GRNs) – interpretable graph models of gene expression regulation – are a pivotal tool for understanding and investigating the mechanisms utilized by cells during development and in response to various internal and external stimuli. Historically, the first approach for the GRN reconstruction was based on the analysis of published data (including those summarized in databases). Currently, the primary GRN inference approach is the analysis of omics (mainly transcriptomic) data; a number of mathematical methods have been adapted for that. Obtaining omics data for individual cells has made it possible to conduct large-scale molecular genetic studies with an extremely high resolution. In particular, it has become possible to reconstruct GRNs for individual cell types and for various cell states. However, technical and biological features of single-cell omics data require specific approaches for GRN inference. This review describes the approaches and programs that are used to reconstruct GRNs from single-cell RNA sequencing (scRNA-seq) data. We consider the advantages of using scRNA-seq data compared to bulk RNA-seq, as well as challenges in GRN inference. We pay specific attention to state-of-the-art methods for GRN reconstruction from single-cell transcriptomes recruiting other omics data, primarily transcription factor binding sites and open chromatin profiles (scATAC-seq), in order to increase inference accuracy. The review also considers the applicability of GRNs reconstructed from single-cell omics data to recover and characterize various biological processes. Future perspectives in this area are discussed.