Non-syntenic genes drive RTCS-dependent regulation of the embryo transcriptome during formation of seminal root primordia in maize (Zea mays L.)

Temporal and spatial auxin responsive networks in maize primary roots

Auxin is a key regulator of root morphogenesis across angiosperms. To better understand auxin-regulated networks underlying maize root development, we have characterized auxin-responsive transcription across two time points (30 and 120 min) and four regions of the primary root: the meristematic zone, elongation zone, cortex and stele. Hundreds of auxin-regulated genes involved in diverse biological processes were quantified in these different root regions. In general, most auxin-regulated genes are region unique and are predominantly observed in differentiated tissues compared with the root meristem. Auxin gene regulatory networks were reconstructed with these data to identify key transcription factors that may underlie auxin responses in maize roots. Additionally, Auxin-Response Factor subnetworks were generated to identify target genes that exhibit tissue or temporal specificity in response to auxin. These networks describe novel molecular connections underlying maize root development and provide a foundation for functional genomic studies in a key crop.

Whole-genome resequencing of three Coilia nasus population reveals genetic variations in genes related to immune, vision, migration, and osmoregulation

Background

Coilia nasus is an important anadromous fish, widely distributed in China, Japan, and Korea. Based on morphological and ecological researches of C. nasus, two ecotypes were identified. One is the anadromous population (AP). The sexually mature fish run thousands of kilometers from marine to river for spawning. Another one is the resident population which cannot migrate. Based on their different habitats, they were classified into landlocked population (LP) and sea population (SP) which were resident in the freshwater lake and marine during the entire lifetime, respectively. However, they have never been systematically studied. Moreover, C. nasus is declining sharply due to overfishing and pollution recently. Therefore, further understandings of C. nasus populations are needed for germplasm protection.

Results

Whole-genome resequencing of AP, LP, and SP were performed to enrich the understanding of different populations of C. nasus. At the genome level, 3,176,204, 3,307,069, and 3,207,906 single nucleotide polymorphisms (SNPs) and 1,892,068, 2,002,912, and 1,922,168 insertion/deletion polymorphisms (InDels) were generated in AP, LP, and SP, respectively. Selective sweeping analysis showed that 1022 genes were selected in AP vs LP; 983 genes were selected in LP vs SP; 116 genes were selected in AP vs SP. Among them, selected genes related to immune, vision, migration, and osmoregulation were identified. Furthermore, their expression profiles were detected by quantitative real-time PCR. Expression levels of selected genes related to immune, and vision in LP were significantly lower than AP and SP. Selected genes related to migration in AP were expressed significantly more highly than LP. Expression levels of selected genes related to osmoregulation were also detected. The expression of NKAα and NKCC1 in LP were significantly lower than SP, while expression of NCC, SLC4A4, NHE3, and V-ATPase in LP was significantly higher than SP.

Conclusions

Combined to life history of C. nasus populations, our results revealed that the molecular mechanisms of their differences of immune, vision, migration, and osmoregulation. Our findings will provide a further understanding of different populations of C. nasus and will be beneficial for wild C. nasus protection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08182-0.

Increased seminal root number associated with domestication improves nitrogen and phosphorus acquisition in maize seedlings

BACKGROUND AND AIMS

Domesticated maize (Zea mays ssp. mays) generally forms between two and six seminal roots, while its wild ancestor, Mexican annual teosinte (Zea mays ssp. parviglumis), typically lacks seminal roots. Maize also produces larger seeds than teosinte, and it generally has higher growth rates as a seedling. Maize was originally domesticated in the tropical soils of southern Mexico, but it was later brought to the Mexican highlands before spreading to other parts of the continent, where it experienced different soil resource constraints. The aims of this study were to understand the impacts of increased seminal root number on seedling nitrogen and phosphorus acquisition and to model how differences in maize and teosinte phenotypes might have contributed to increased seminal root number in domesticated maize.

METHODS

Seedling root system architectural models of a teosinte accession and a maize landrace were constructed by parameterizing the functional-structural plant model OpenSimRoot using plants grown in mesocosms. Seedling growth was simulated in a low-phosphorus environment, multiple low-nitrogen environments, and at variable planting densities. Models were also constructed to combine individual components of the maize and teosinte phenotypes.

KEY RESULTS

Seminal roots contributed ~35 % of the nitrogen and phosphorus acquired by maize landrace seedlings in the first 25 d after planting. Increased seminal root number improved plant nitrogen acquisition under low-nitrogen environments with varying precipitation patterns, fertilization rates, soil textures and planting densities. Models suggested that the optimal number of seminal roots for nutrient acquisition in teosinte is constrained by its limited seed carbohydrate reserves.

CONCLUSIONS

Seminal roots can improve the acquisition of both nitrogen and phosphorus in maize seedlings, and the increase in seed size associated with maize domestication may have facilitated increased seminal root number.

Homoeolog Inference Methods Requiring Bidirectional Best Hits or Synteny Miss Many Pairs

Homoeologs are pairs of genes or chromosomes in the same species that originated by speciation and were brought back together in the same genome by allopolyploidization. Bioinformatic methods for accurate homoeology inference are crucial for studying the evolutionary consequences of polyploidization, and homoeology is typically inferred on the basis of bidirectional best hit (BBH) and/or positional conservation (synteny). However, these methods neglect the fact that genes can duplicate and move, both prior to and after the allopolyploidization event. These duplications and movements can result in many-to-many and/or nonsyntenic homoeologs-which thus remain undetected and unstudied. Here, using the allotetraploid upland cotton (Gossypium hirsutum) as a case study, we show that conventional approaches indeed miss a substantial proportion of homoeologs. Additionally, we found that many of the missed pairs of homoeologs are broadly and highly expressed. A gene ontology analysis revealed a high proportion of the nonsyntenic and non-BBH homoeologs to be involved in protein translation and are likely to contribute to the functional repertoire of cotton. Thus, from an evolutionary and functional genomics standpoint, choosing a homoeolog inference method which does not solely rely on 1:1 relationship cardinality or synteny is crucial for not missing these potentially important homoeolog pairs.

Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

BACKGROUND

Transcription factors (TFs) are proteins that can bind to DNA sequences and regulate gene expression. Many TFs are master regulators in cells that contribute to tissue-specific and cell-type-specific gene expression patterns in eukaryotes. Maize has been a model organism for over one hundred years, but little is known about its tissue-specific gene regulation through TFs. In this study, we used a network approach to elucidate gene regulatory networks (GRNs) in four tissues (leaf, root, SAM and seed) in maize. We utilized GENIE3, a machine-learning algorithm combined with large quantity of RNA-Seq expression data to construct four tissue-specific GRNs. Unlike some other techniques, this approach is not limited by high-quality Position Weighed Matrix (PWM), and can therefore predict GRNs for over 2000 TFs in maize.

RESULTS

Although many TFs were expressed across multiple tissues, a multi-tiered analysis predicted tissue-specific regulatory functions for many transcription factors. Some well-studied TFs emerged within the four tissue-specific GRNs, and the GRN predictions matched expectations based upon published results for many of these examples. Our GRNs were also validated by ChIP-Seq datasets (KN1, FEA4 and O2). Key TFs were identified for each tissue and matched expectations for key regulators in each tissue, including GO enrichment and identity with known regulatory factors for that tissue. We also found functional modules in each network by clustering analysis with the MCL algorithm.

CONCLUSIONS

By combining publicly available genome-wide expression data and network analysis, we can uncover GRNs at tissue-level resolution in maize. Since ChIP-Seq and PWMs are still limited in several model organisms, our study provides a uniform platform that can be adapted to any species with genome-wide expression data to construct GRNs. We also present a publicly available database, maize tissue-specific GRN (mGRN, https://www.bio.fsu.edu/mcginnislab/mgrn/ ), for easy querying. All source code and data are available at Github ( https://github.com/timedreamer/maize_tissue-specific_GRN ).