QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize.

Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.

Next-generation bulked segregant analysis for Breeding 4.0.

Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.

A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize.

BACKGROUND: Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome. RESULTS: We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31. CONCLUSIONS: Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.

A multi-omics integrative network map of maize.

Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.

Single-molecule long-read sequencing reveals extensive genomic and transcriptomic variation between maize and its wild relative teosinte (Zea mays ssp. parviglumis).

Teosinte (Zea mays ssp. parviglumis), the wild progenitor of maize (Zea mays L.), is an important germplasm resource for improvement of modern maize lines. However, we have limited genetic and genomic information about teosinte and lack state-of-the-art tools to annotate transcriptomes assembled by single-molecule long-read sequencing without a reference genome. Here, we employed single-molecule long-read sequencing of cDNA libraries from five tissues of the teosinte inbred line TIL11 and identified 70,044 nonredundant transcript isoforms. We devised a state-of-the-art, machine learning-based bioinformatics pipeline DenovoAS_Finder to annotate the TIL11 transcriptome without a complete reference genome with an accuracy of up to 91%, providing a robust gene classifier of complex genomes. Additionally, we constructed a draft TIL11 genome with 16,633 high-quality contigs and a N50 of 112 kb by Nanopore sequencing. Genes from families that expanded from teosinte to maize were significantly enriched in the gene ontology (GO) term "RNA modification pathway" and had more transcript isoforms in TIL11 than in the maize inbred line B73. Genes showed collinearity between TIL11 and B73, and intergenic regions were extensively altered by transposable elements. Our study furthers the understanding of maize domestication and provides a resource for the utilization of wild germplasm in maize breeding.

Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding.

The functional genes underlying phenotypic variation and their interactions represent "genetic mysteries". Understanding and utilizing these genetic mysteries are key solutions for mitigating the current threats to agriculture posed by population growth and individual food preferences. Due to advances in high-throughput multi-omics technologies, we are stepping into an Interactome Big Data era that is certain to revolutionize genetic research. In this article, we provide a brief overview of current strategies to explore genetic mysteries. We then introduce the methods for constructing and analyzing the Interactome Big Data and summarize currently available interactome resources. Next, we discuss how Interactome Big Data can be used as a versatile tool to dissect genetic mysteries. We propose an integrated strategy that could revolutionize genetic research by combining Interactome Big Data with machine learning, which involves mining information hidden in Big Data to identify the genetic models or networks that control various traits, and also provide a detailed procedure for systematic dissection of genetic mysteries,. Finally, we discuss three promising future breeding strategies utilizing the Interactome Big Data to improve crop yields and quality.

3D genome architecture coordinates trans and cis regulation of differentially expressed ear and tassel genes in maize.

BACKGROUND: Maize ears and tassels are two separate types of inflorescence which are initiated by similar developmental processes but gradually develop distinct architectures. However, coordinated trans and cis regulation of differentially expressed genes determining ear and tassel architecture within the 3D genome context is largely unknown. RESULTS: We identify 56,055 and 52,633 open chromatin regions (OCRs) in developing maize ear and tassel primordia using ATAC-seq and characterize combinatorial epigenome features around these OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets. Our integrative analysis of coordinated epigenetic modification and transcription factor binding to OCRs highlights the cis and trans regulation of differentially expressed genes in ear and tassel controlling inflorescence architecture. We further systematically map chromatin interactions at high-resolution in corresponding tissues using in situ digestion-ligation-only Hi-C (DLO Hi-C). The extensive chromatin loops connecting OCRs and genes provide a 3D view on cis- and trans-regulatory modules responsible for ear- and tassel-specific gene expression. We find that intergenic SNPs tend to locate in distal OCRs, and our chromatin interaction maps provide a potential mechanism for trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through chromatin loops. CONCLUSIONS: Our comprehensive epigenome annotations and 3D genome maps serve as valuable resource and provide a deep understanding of the complex regulatory mechanisms of genes underlying developmental and morphological diversities between maize ear and tassel.

RNA Isolation and Analysis of LncRNAs from Gametophytes of Maize.

The explosion of RNA-Seq data has enabled the identification of expressed genes without relying on gene models with biases toward open reading frames, allowing the identification of many more long noncoding RNAs (lncRNAs) in eukaryotes. Various tissue enrichment strategies and deep sequencing have also enabled the identification of an extensive list of genes expressed in maize gametophytes, tissues that are intractable to both traditional genetic and gene expression analyses. However, the function of very few genes from the lncRNA and gametophyte sets (or from their intersection) has been tested. Methods for isolating and identifying lncRNAs from gametophyte samples of maize are described here. This method is transferable to any maize gametophyte mutant enabling the development of gene networks involving both protein-coding genes and lncRNAs. Additionally, these methods can be adapted to apply to other grass model systems to test for evolutionary conservation of lncRNA expression patterns.

Circular RNAs exhibit extensive intraspecific variation in maize.

MAIN CONCLUSION: Comprehensive transcriptome profiling uncovers extensive intraspecific variation of circular RNAs in maize, shedding light on genomic and phenotypic variation among maize inbred lines. Circular RNAs (circRNAs) are single-strand, covalently closed transcripts. A substantial number of circRNAs have been identified and shown to be associated with phenotypic variation in various species. However, little is known about the intraspecific variation of circRNAs in maize (Zea mays L.). Here, we collected a large transcriptomic dataset (by circRNA-seq and mRNA-seq) from seedling leaves of the reference maize inbred lines B73 and Mo17. We identified over 1500 circRNAs in these lines using two circRNA detection methods, CIRCexplorer2 and CIRI. Notably, a substantial proportion of circRNAs varied in terms of sequence or expression level between lines, pointing to extensive intraspecific variation of circRNAs in maize. GO and KEGG analyses showed that genes producing circRNAs with intraspecific variation were more likely to be enriched in multiple functional groups, compared with those that did not produce circRNAs. These findings suggest that circRNAs could be utilized as an indicator of genomic and phenotypic variation among maize inbred lines. Ribosomal profiling revealed that several circRNAs might have translational capacity in maize. These results uncover the extensive intraspecific variation of circRNAs and pave the way for further understanding the molecular mechanisms underlying phenotypic variation at the circRNA level in maize.

QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples.

Deciphering the genetic mechanisms underlying agronomic traits is of great importance for crop improvement. Most of these traits are controlled by multiple quantitative trait loci (QTLs), and identifying the underlying genes by conventional QTL fine-mapping is time-consuming and labor-intensive. Here, we devised a new method, named quantitative trait gene sequencing (QTG-seq), to accelerate QTL fine-mapping. QTG-seq combines QTL partitioning to convert a quantitative trait into a near-qualitative trait, sequencing of bulked segregant pools from a large segregating population, and the use of a robust new algorithm for identifying candidate genes. Using QTG-seq, we fine-mapped a plant-height QTL in maize (Zea mays L.), qPH7, to a 300-kb genomic interval and verified that a gene encoding an NF-YC transcription factor was the functional gene. Functional analysis suggested that qPH7-encoding protein might influence plant height by interacting with a CO-like protein and an AP2 domain-containing protein. Selection footprint analysis indicated that qPH7 was subject to strong selection during maize improvement. In summary, QTG-seq provides an efficient method for QTL fine-mapping in the era of "big data".

New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize.

Long non-coding RNAs (lncRNAs), whose sequences are approximately 200 bp or longer and unlikely to encode proteins, may play an important role in eukaryotic gene regulation. Although the latest maize (Zea mays L.) reference genome provides an essential genomic resource, genome-wide annotations of maize lncRNAs have not been updated. Here, we report on a large transcriptomic dataset collected from 749 RNA sequencing experiments across different tissues and stages of the maize reference inbred B73 line and 60 from its wild relative teosinte. We identified 18,165 high-confidence lncRNAs in maize, of which 6,873 are conserved between maize and teosinte. We uncovered distinct genomic characteristics of conserved lncRNAs, non-conserved lncRNAs, and protein-coding transcripts. Intriguingly, Shannon entropy analysis showed that conserved lncRNAs are likely to be expressed similarly to protein-coding transcripts. Co-expression network analysis revealed significant variation in the degree of co-expression. Furthermore, selection analysis indicated that conserved lncRNAs are more likely than non-conserved lncRNAs to be located in regions subject to recent selection, indicating evolutionary differentiation. Our results provide the latest genome-wide annotation and analysis of maize lncRNAs and uncover potential functional divergence between protein-coding, conserved lncRNA, and non-conserved lncRNA genes, demonstrating the high complexity of the maize transcriptome.