Highly interwoven communities of a gene regulatory network unveil topologically important genes for maize seed development

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Striking a growth-defense balance: Stress regulators that function in maize development

Maize (Zea mays) cultivation is strongly affected by both abiotic and biotic stress, leading to reduced growth and productivity. It has recently become clear that regulators of plant stress responses, including the phytohormones abscisic acid (ABA), ethylene (ET), and jasmonic acid (JA), together with reactive oxygen species (ROS), shape plant growth and development. Beyond their well established functions in stress responses, these molecules play crucial roles in balancing growth and defense, which must be finely tuned to achieve high yields in crops while maintaining some level of defense. In this review, we provide an in-depth analysis of recent research on the developmental functions of stress regulators, focusing specifically on maize. By unraveling the contributions of these regulators to maize development, we present new avenues for enhancing maize cultivation and growth while highlighting the potential risks associated with manipulating stress regulators to enhance grain yields in the face of environmental challenges.

The tuber-specific StbHLH93 gene regulates proplastid-to-amyloplast development during stolon swelling in potato

In potato, stolon swelling is a complex and highly regulated process, and much more work is needed to fully understand the underlying mechanisms. We identified a novel tuber-specific basic helix-loop-helix (bHLH) transcription factor, StbHLH93, based on the high-resolution transcriptome of potato tuber development. StbHLH93 is predominantly expressed in the subapical and perimedullary region of the stolon and developing tubers. Knockdown of StbHLH93 significantly decreased tuber number and size, resulting from suppression of stolon swelling. Furthermore, we found that StbHLH93 directly binds to the plastid protein import system gene TIC56 promoter, activates its expression, and is involved in proplastid-to-amyloplast development during the stolon-to-tuber transition. Knockdown of the target TIC56 gene resulted in similarly problematic amyloplast biogenesis and tuberization. Taken together, StbHLH93 functions in the differentiation of proplastids to regulate stolon swelling. This study highlights the critical role of proplastid-to-amyloplast interconversion during potato tuberization.

ABA-induced phosphorylation of basic leucine zipper 29, ABSCISIC ACID INSENSITIVE 19, and Opaque2 by SnRK2.2 enhances gene transactivation for endosperm filling in maize

Opaque2 (O2) functions as a central regulator of the synthesis of starch and storage proteins and the O2 gene is transcriptionally regulated by a hub coordinator of seed development and grain filling, ABSCISIC ACID INSENSITIVE 19 (ZmABI19), in maize (Zea mays). Here, we identified a second hub coordinator, basic Leucine Zipper 29 (ZmbZIP29) that interacts with ZmABI19 to regulate O2 expression. Like zmabi19, zmbzip29 mutations resulted in a dramatic decrease of transcript and protein levels of O2 and thus a significant reduction of starch and storage proteins. zmbzip29 seeds developed slower and had a smaller size at maturity than those of the wild type. The zmbzip29;zmabi19 double mutant displayed more severe seed phenotypes and a greater reduction of storage reserves compared to the single mutants, whereas overexpression of the two transcription factors enhanced O2 expression, storage-reserve accumulation, and kernel weight. ZmbZIP29, ZmABI19, and O2 expression was induced by abscisic acid (ABA). With ABA treatment, ZmbZIP29 and ZmABI19 synergistically transactivated the O2 promoter. Through liquid chromatography tandem-mass spectrometry analysis, we established that the residues threonine(T) 57 in ZmABI19, T75 in ZmbZIP29, and T387 in O2 were phosphorylated, and that SnRK2.2 was responsible for the phosphorylation. The ABA-induced phosphorylation at these sites was essential for maximum transactivation of downstream target genes for endosperm filling in maize.

Rejuvenation remodels transcriptional network to improve rhizogenesis in mature Juglans tree

Adventitious rooting of walnut species (Juglans L.) is known to be rather difficult, especially for mature trees. The adventitious root formation (ARF) capacities of mature trees can be significantly improved by rejuvenation. However, the underlying gene regulatory networks (GRNs) of rejuvenation remain largely unknown. To characterize such regulatory networks, we carried out the transcriptomic study using RNA samples of the cambia and peripheral tissues on the bottom of rejuvenated and mature walnut (Juglans hindsii × J. regia) cuttings during the ARF. The RNA sequencing data suggested that zeatin biosynthesis, energy metabolism and substance metabolism were activated by rejuvenation, whereas photosynthesis, fatty acid biosynthesis and the synthesis pathways for secondary metabolites were inhibited. The inter- and intra-module GRNs were constructed using differentially expressed genes. We identified 35 hub genes involved in five modules associated with ARF. Among these hub genes, particularly, beta-glucosidase-like (BGLs) family members involved in auxin metabolism were overexpressed at the early stage of the ARF. Furthermore, BGL12 from the cuttings of Juglans was overexpressed in Populus alba × P. glandulosa. Accelerated ARF and increased number of ARs were observed in the transgenic poplars. These results provide a high-resolution atlas of gene activity during ARF and help to uncover the regulatory modules associated with the ARF promoted by rejuvenation.

A Transcriptome Community-and-Module Approach of the Human Mesoconnectome

Graph analysis allows exploring transcriptome compartments such as communities and modules for brain mesostructures. In this work, we proposed a bottom-up model of a gene regulatory network to brain-wise connectome workflow. We estimated the gene communities across all brain regions from the Allen Brain Atlas transcriptome database. We selected the communities method to yield the highest number of functional mesostructures in the network hierarchy organization, which allowed us to identify specific brain cell functions (e.g., neuroplasticity, axonogenesis and dendritogenesis communities). With these communities, we built brain-wise region modules that represent the connectome. Our findings match with previously described anatomical and functional brain circuits, such the default mode network and the default visual network, supporting the notion that the brain dynamics that carry out low- and higher-order functions originate from the modular composition of a GRN complex network.

Maize unstable factor for orange1 is essential for endosperm development and carbohydrate accumulation

Maize (Zea mays L.) Ufo1-1 is a spontaneous dominant mutation of the unstable factor for orange1 (ufo1). We recently cloned ufo1, which is a Poaceae-specific gene highly expressed during seed development in maize. Here, we have characterized Ufo1-1 and a loss-of-function Ds insertion allele (ufo1-Dsg) to decipher the role of ufo1 in maize. We found that both ufo1 mutant alleles impact sugars and hormones, and have defects in the basal endosperm transfer layer (BETL) and adjacent cell types. The Ufo1-1 BETL had reduced cell elongation and cell wall ingrowth, resulting in cuboidal shaped transfer cells. In contrast, the ufo1-Dsg BETL cells showed a reduced overall size with abnormal wall ingrowth. Expression analysis identified the impact of ufo1 on several genes essential for BETL development. The overexpression of Ufo1-1 in various tissues leads to ectopic phenotypes, including abnormal cell organization and stomata subsidiary cell defects. Interestingly, pericarp and leaf transcriptomes also showed that as compared with wild type, Ufo1-1 had ectopic expression of endosperm development-specific genes. This study shows that Ufo1-1 impacts the expression patterns of a wide range of genes involved in various developmental processes.

Synonymous mutation in Growth Regulating Factor 15 of miR396a target sites enhances photosynthetic efficiency and heat tolerance in poplar

Heat stress damages plant tissues and induces multiple adaptive responses. Complex and spatiotemporally specific interactions among transcription factors (TFs), microRNAs (miRNAs), and their targets play crucial roles in regulating stress responses. To explore these interactions and to identify regulatory networks in perennial woody plants subjected to heat stress, we integrated time-course RNA-seq, small RNA-seq, degradome sequencing, weighted gene correlation network analysis, and multi-gene association approaches in poplar. Results from Populus trichocarpa enabled us to construct a three-layer, highly interwoven regulatory network involving 15 TFs, 45 miRNAs, and 77 photosynthetic genes. Candidate gene association studies in a population of P. tomentosa identified 114 significant associations and 696 epistatic SNP–SNP pairs that were linked to 29 photosynthetic and growth traits (P<0.0001, q<0.05). We also identified miR396a and its target, Growth-Regulating Factor 15 (GRF15) as an important regulatory module in the heat-stress response. Transgenic plants of hybrid poplar (P. alba × P. glandulosa) overexpressing a GRF15 mRNA lacking the miR396a target sites exhibited enhanced heat tolerance and photosynthetic efficiency compared to wild-type plants. Together, our observations demonstrate that GRF15 plays a crucial role in responding to heat stress, and they highlight the power of this new, multifaceted approach for identifying regulatory nodes in plants.

Maize endosperm development

Recent breakthroughs in transcriptome analysis and gene characterization have provided valuable resources and information about the maize endosperm developmental program. The high temporal-resolution transcriptome analysis has yielded unprecedented access to information about the genetic control of seed development. Detailed spatial transcriptome analysis using laser-capture microdissection has revealed the expression patterns of specific populations of genes in the four major endosperm compartments: the basal endosperm transfer layer (BETL), aleurone layer (AL), starchy endosperm (SE), and embryo-surrounding region (ESR). Although the overall picture of the transcriptional regulatory network of endosperm development remains fragmentary, there have been some exciting advances, such as the identification of OPAQUE11 (O11) as a central hub of the maize endosperm regulatory network connecting endosperm development, nutrient metabolism, and stress responses, and the discovery that the endosperm adjacent to scutellum (EAS) serves as a dynamic interface for endosperm-embryo crosstalk. In addition, several genes that function in BETL development, AL differentiation, and the endosperm cell cycle have been identified, such as ZmSWEET4c, Thk1, and Dek15, respectively. Here, we focus on current advances in understanding the molecular factors involved in BETL, AL, SE, ESR, and EAS development, including the specific transcriptional regulatory networks that function in each compartment during endosperm development.

Conserved co-functional network between maize and Arabidopsis aid in the identification of seed defective genes in maize

BACKGROUND

The biological pathways related to Arabidopsis seed development have been well studied and functional genes involved in it have been discovered. However, functional studies about maize seed development were more limited compared to Arabidopsis.

OBJECTIVE

Therefore, transferring knowledge from Arabidopsis into maize would facilitate functional studies about maize seed development.

METHOD

In this study, public transcriptome data of the two species related to seed development were obtained. Co-expression network in each species was compared by integrating orthology information.

RESULTS

This conserved co-functional network contained 4510 maize and 4808 Arabidopsis genes, respectively. Most of these genes were expressed in throughout embryo, early or later endosperm/seed. These conserved co-functional genes were significantly enriched for members of PPR protein family, which was consistent with that PPR proteins play an important role in maize seed development. Spatial-temporally co-functional genes were discovered in the seed coat and embryo. Furthermore, 66 well-studied genes involved in Arabidopsis seed development were co-functional with 319 maize genes and one maize gene (GRMZM2G036050) was further confirmed using an EMS-induced seed defective mutant by bulked segregating RNA sequencing (BSR) analysis.

CONCLUSIONS

Altogether, these results showed the potential of this approach to support functional studies in maize seed development by transferring knowledge from Arabidopsis.

Deciphering transcriptional regulators of banana fruit ripening by regulatory network analysis

Fruit ripening is a critical phase in the production and marketing of fruits. Previous studies have indicated that fruit ripening is a highly coordinated process, mainly regulated at the transcriptional level, in which transcription factors play essential roles. Thus, identifying key transcription factors regulating fruit ripening as well as their associated regulatory networks promises to contribute to a better understanding of fruit ripening. In this study, temporal gene expression analyses were performed to investigate banana fruit ripening with the aim to discern the global architecture of gene regulatory networks underlying fruit ripening. Eight time points were profiled covering dynamic changes of phenotypes, the associated physiology and levels of known ripening marker genes. Combining results from a weighted gene co-expression network analysis (WGCNA) as well as cis-motif analysis and supported by EMSA, Y1H, tobacco-, banana-transactivation experimental results, the regulatory network of banana fruit ripening was constructed, from which 25 transcription factors were identified as prime candidates to regulate the ripening process by modulating different ripening-related pathways. Our study presents the first global view of the gene regulatory network involved in banana fruit ripening, which may provide the basis for a targeted manipulation of fruit ripening to attain higher banana and loss-reduced banana commercialization.

Maize kernel development

Maize (Zea mays) is a leading cereal crop in the world. The maize kernel is the storage organ and the harvest portion of this crop and is closely related to its yield and quality. The development of maize kernel is initiated by the double fertilization event, leading to the formation of a diploid embryo and a triploid endosperm. The embryo and endosperm are then undergone independent developmental programs, resulting in a mature maize kernel which is comprised of a persistent endosperm, a large embryo, and a maternal pericarp. Due to the well-characterized morphogenesis and powerful genetics, maize kernel has long been an excellent model for the study of cereal kernel development. In recent years, with the release of the maize reference genome and the development of new genomic technologies, there has been an explosive expansion of new knowledge for maize kernel development. In this review, we overviewed recent progress in the study of maize kernel development, with an emphasis on genetic mapping of kernel traits, transcriptome analysis during kernel development, functional gene cloning of kernel mutants, and genetic engineering of kernel traits.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

The encyclopedia of maize kernel gene expression

This commentary highlights the recent two studies which uncovered dynamic maize kernel RNA-seq transcriptomes from early seed development, storage filling to maturation, day by day, hour by hour. These two studies provide a 'maize kernel gene expression dictionary' that will be powerful for the future studies in seed biology.

Plant Networks as Traits and Hypotheses: Moving Beyond Description

Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.

Recent advances in gene function prediction using context-specific coexpression networks in plants

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks-created by integrating multiple expression datasets-connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional "global" to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

Computational prediction of gene regulatory networks in plant growth and development

Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.