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.

Tomato is considered as the genetic model for climacteric fruits, in which three major players control the fruit ripening process: ethylene, ripening transcription factors, and DNA methylation. The fruitENCODE project has now shown that there are multiple transcriptional circuits regulating fruit ripening in different species, and H3K27me3, instead of DNA methylation, plays a conserved role in restricting these ripening pathways. In addition, the function of the core tomato ripening transcription factors is now being questioned. We have employed CRISPR/Cas9 genome editing to mutate the SBP-CNR and NAC-NOR transcription factors, both of which are considered as master regulators in the current tomato ripening model. These plants only displayed delayed or partial non-ripening phenotypes, distinct from the original mutant plants, which categorically failed to ripen, suggesting that they might be gain-of-function mutants. Besides increased DNA methylation genome-wide, the original mutants also have hyper-H3K27me3 in ripening gene loci such as ACS2, RIN, and TDR4. It is most likely that multiple genetic and epigenetic factors have contributed to their strong non-ripening phenotypes. Hence, we propose that the field should move beyond these linear and two-dimensional models and embrace the fact that important biological processes such as ripening are often regulated by highly redundant network with inputs from multiple levels.

Because of their unique and excellent photophysical properties, lead halide perovskites are widely used in photoelectronic devices such as photodetectors, light-emitting diodes, solar cells, and lasers. Recently it was found that lead halide perovskites also exhibit excellent nonlinear optical properties in nonlinear optics (NLO), including saturated absorption, two-or multiphoton absorption, and nonlinear refraction. It is believed that perovskites will be serious nonlinear optical materials in the future. The nonlinear optical devices prepared from perovskites on the basis of their optical nonlinearity may open up a new viewpoint for the application of information and communication technology. Here the research on lead halide perovskites in NLO is reviewed. The mechanism of nonlinear optical phenomena of lead halide perovskites is analyzed, and some possible methods to improve the optical nonlinearity are summarized. Their applications in passive Q-switched lasers, upconversion lasers, optical limiting, and infrared detection are introduced. Several future research directions and challenges of lead halide perovskites in nonlinear optics are proposed.

Non-coding cis-regulatory variants in animal genomes are an important driving force in the evolution of transcription regulation and phenotype diversity. However, cistrome dynamics in plants remain largely underexplored. Here, we compare the binding of GOLDEN2-LIKE (GLK) transcription factors in tomato, tobacco, Arabidopsis, maize and rice. Although the function of GLKs is conserved, most of their binding sites are species-specific. Conserved binding sites are often found near photosynthetic genes dependent on GLK for expression, but sites near non-differentially expressed genes in the glk mutant are nevertheless under purifying selection. The binding sites’ regulatory potential can be predicted by machine learning model using quantitative genome features and TF co-binding information. Our study show that genome cis-variation caused wide-spread TF binding divergence, and most of the TF binding sites are genetically redundant. This poses a major challenge for interpreting the effect of individual sites and highlights the importance of quantitatively measuring TF occupancy.

No abstract