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. QTGseq 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''.

Protein-protein interaction (PPI) network maintains proper function of all organisms. Simple high-throughput technologies are desperately needed to delineate the landscape of PPI networks. While recent state-of-the-art yeast two-hybrid (Y2H) systems improved screening efficiency, either individual colony isolation, library preparation arrays, gene barcoding or massive sequencing are still required. Here, we developed a recombination-based ‘library vs library’ Y2H system (RLL-Y2H), by which multi-library screening can be accomplished in a single pool without any individual treatment. This system is based on the phiC31 integrase-mediated integration between bait and prey plasmids. The integrated fragments were digested by MmeI and subjected to deep sequencing to decode the interaction matrix. We applied this system to decipher the trans-kingdom interactome between Mycobacterium tuberculosis and host cells and further identified Rv2427c interfering with the phagosome–lysosome fusion. This concept can also be applied to other systems to screen protein–RNA and protein–DNA interactions and delineate signaling landscape in cells.

The establishment of inflorescence architecture is critical for the reproduction of flowering plant species. The maize plant generates two types of inflorescences, the tassel and the ear, and their architectures have a large effect on grain yield and yield-related traits that are genetically controlled by quantitative trait loci (QTLs). Since ear and tassel architecture are deeply affected by the activity of inflorescence meristems, key QTLs and genes regulating meristematic activity have important impacts on inflorescence development and show great potential for optimizing grain yield. Isolation of yield trait-related QTLs is challenging, but these QTLs have direct application in maize breeding. Additionally, characterization and functional dissection of QTLs can provide genetic and molecular knowledge of quantitative variation in inflorescence architecture. In this review, we summarize currently identified QTLs responsible for the establishment of ear and tassel architecture and discuss the potential genetic control of four ear-related and four tassel-related traits. In recent years, several inflorescence architecture-related QTLs have been characterized at the gene level. We review the mechanisms of these characterized QTLs.

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Interactomes are powerful tools for encoding and decoding complex life systems. Here, we generated a maize interactome map that integrates genomic interactions, transcriptomic co-expression networks, translatomic co-expression networks, and protein–protein interactions throughout the maize lifecycle. This map, containing over 9 million interactions in more than 5,000 functional modules, reveals extensive functional divergence for duplicate genes and a progressive increase in regulatory divergence between the two maize subgenomes during the flow of genetic information. This network enables dissecting and validating gene functions, re-constructing regulatory pathways, and deciphering molecular mechanisms underlying complex traits combining big data mining technique-machine learning. By applying this map to flowering-time, we identified 1,843 high-confidence genes enriched in eight molecular pathways that are related to flowering time. The function of 30 (out of 58 tested) genes, including 27 novel genes, was verified by loss-of-function mutagenesis. Furthermore, a new pathway involving histone modification was identified and confirmed to regulate flowering time. The interactome map illustrates how coherent sets of molecular interactions connect different types of functional elements and pathway modules to map a genome-wide functional wiring landscape, which will be applicable in a wide range of species.

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.

Maize early endosperm development is initiated in coordination with elimination of maternal nucellar tissues. However, the underlying mechanisms are largely unknown. Here, we characterize a major quantitative trait locus for maize kernel size and weight that encodes an EXPANSIN gene, ZmEXPB15. The encoded β-expansin protein is expressed specifically in nucellus, and positively controls kernel size and weight by promoting nucellus elimination. We further show that two nucellus-enriched transcription factors (TFs), ZmNAC11 and ZmNAC29, activate ZmEXPB15 expression. Accordingly, these two TFs also promote kernel size and weight through nucellus elimination regulation, and genetic analyses support their interaction with ZmEXPB15. Importantly, hybrids derived from a ZmEXPB15 overexpression line have increased kernel weight, demonstrates its potential value in breeding. Together, we reveal a pathway modulating the cellular processes of maternal nucellus elimination and early endosperm development, and an approach to improve kernel weight.