Synergistic interplay of redox homeostasis and polysaccharide synthesis promotes cotton fiber elongation.

Cell polarity is the foundation of cell development and tissue morphogenesis. The investigation of polarized growth provides opportunities to gain profound insights into morphogenesis and tissue functionality in organisms. Currently, there are still many mysteries surrounding the mechanisms that regulate polarized cell growth. Cotton fiber cells serve as an excellent model for studying polarized growth, and provide important clues for unraveling the molecular mechanisms, signaling pathways, and regulatory networks of polarized growth. In this study, we characterized two functional genes, GhMDHAR1AT/DT and GhDHAR2AT/DT with predominant expression during fiber elongation. Loss of function of both genes contributed to a significant increase in fiber length. Transcriptomic data revealed up-regulated expression of antioxidant genes in CRISPR mutant lines, along with delayed expression of secondary wall-related genes and temporally prolonged expression of primary wall-related genes. Experimental evidence demonstrated that the increase in GSH content and glutathione peroxidase (GPX) enzyme activity led to enhanced total antioxidant capacity (T-AOC), resulting in reduced H2O2 levels, which contributed to the extension of fiber elongation stage in CRISPR mutant lines. Moreover, the increased polysaccharide synthesis in CRISPR mutant lines was found to provide an abundant supply of raw materials for fiber cell wall elongation, suggesting that synergistic interplay between redox homeostasis and polysaccharide synthesis in fiber cells may facilitate cell wall remodeling and fiber elongation. This study provides valuable insights for deciphering the mechanisms of cell polarized growth and improving cotton fiber quality.

Genomic innovation and regulatory rewiring during evolution of the cotton genus Gossypium.

Phenotypic diversity and evolutionary innovation ultimately trace to variation in genomic sequence and rewiring of regulatory networks. Here, we constructed a pan-genome of the Gossypium genus using ten representative diploid genomes. We document the genomic evolutionary history and the impact of lineage-specific transposon amplification on differential genome composition. The pan-3D genome reveals evolutionary connections between transposon-driven genome size variation and both higher-order chromatin structure reorganization and the rewiring of chromatin interactome. We linked changes in chromatin structures to phenotypic differences in cotton fiber and identified regulatory variations that decode the genetic basis of fiber length, the latter enabled by sequencing 1,005 transcriptomes during fiber development. We showcase how pan-genomic, pan-3D genomic and genetic regulatory data serve as a resource for delineating the evolutionary basis of spinnable cotton fiber. Our work provides insights into the evolution of genome organization and regulation and will inform cotton improvement by enabling regulome-based approaches.

Dynamic 3D genome architecture of cotton fiber reveals subgenome-coordinated chromatin topology for 4-staged single-cell differentiation.

BACKGROUND: Despite remarkable advances in our knowledge of epigenetically mediated transcriptional programming of cell differentiation in plants, little is known about chromatin topology and its functional implications in this process. RESULTS: To interrogate its significance, we establish the dynamic three-dimensional (3D) genome architecture of the allotetraploid cotton fiber, representing a typical single cell undergoing staged development in plants. We show that the subgenome-relayed switching of the chromatin compartment from active to inactive is coupled with the silencing of developmentally repressed genes, pinpointing subgenome-coordinated contribution to fiber development. We identify 10,571 topologically associating domain-like (TAD-like) structures, of which 25.6% are specifically organized in different stages and 75.23% are subject to partition or fusion between two subgenomes. Notably, dissolution of intricate TAD-like structure cliques showing long-range interactions represents a prominent characteristic at the later developmental stage. Dynamic chromatin loops are found to mediate the rewiring of gene regulatory networks that exhibit a significant difference between the two subgenomes, implicating expression bias of homologous genes. CONCLUSIONS: This study sheds light on the spatial-temporal asymmetric chromatin structures of two subgenomes in the cotton fiber and offers a new insight into the regulatory orchestration of cell differentiation in plants.

Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.

Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.