Use the sword of biological network big data to solve the mystery of corn genetics

Inheritance and innovation kicked off the era of maize multi-dimensional network map

On December 30, 2022, the journal Nature and Genetics published the collaborative research paper "A multi-omics integrative network map of maize" by Li Lin's research group, Yang Fang's research group and Yan Jianbing's
research group online 。 In this study, multidimensional omics big data assays were performed on samples from different tissues/periods of the reference inbred line B73 in the whole growth stage, and mRNA-Seq data of 31 different tissues or developmental periods, circRNA-Seq, sRNA-Seq data and Ribo-Seq data
of 21 tissues or developmental periods were obtained.
At the same time, a maize protein interaction network was constructed using the high-throughput yeast system RLL-seq, and more than 360,000 protein-protein interaction pairs were obtained, and 56243 interactions were made with high confidence
.
The existing genome-level ChIA-PET network was integrated with the transcriptome-level co-expression network, the translationome-level co-translation network and the protein interaction network generated in this study, and the first-generation multiomics integration network map of maize was constructed, involving 2 million interactions
.

Fig.
1 Atlas of the first generation multidimensional omics network of maize

Biological breeding is the foundation and core of agriculture, and genome breeding technology is the common key core technology
of biological breeding in China.
Biological breeding has gone through several different stages of development: empirical breeding, molecular marker breeding and whole genome selection breeding, and is making great strides towards the era of genomic intelligent design breeding
.
No matter what stage of biological breeding is inseparable, it is inseparable from the cloning of functional genes that control biological genetic variation and the analysis
of molecular mechanisms.
Classical genetics and molecular biology methods carry out localization, cloning and molecular interaction experiments on a single important site of an important trait, so as to clarify the upstream regulatory gene, molecular chaperone, and downstream target site of the important target gene, and then construct the regulatory network of functional genes, and finally analyze a molecular mechanism
of gene control of important trait variation.
The classical method works, cloning and resolving a number of important functional genes
.
However, even more than two decades after the development of functional genomes, the cloned functional genes in rice and maize still account for less than 10% of all their genes, and new functions of the cloned genes continue to be discovered
.
How to quickly clone functional genes and analyze the molecular mechanism of important trait variation, and solve the mystery of genetic variation of important crops from a global perspective, is still a huge challenge and an important constraint to moving towards the era of intelligent design breeding
.

Biological research has entered the era of
biological big data.
Based on biological big data, it has become possible to build upstream and downstream and chaperone networks of all genes from the global level, providing us with an unprecedented opportunity
to analyze as many gene functions as possible globally and comprehensively solve the mystery of biological genetic variation.
Project leader Li Lin has been planning and designing since the end of 2013, aiming to construct genes and gene regulation networks at various scales and mesoscale levels from the perspective of multidimensional omics, so as to comprehensively analyze the mysteries
of biological inheritance.
After Li Lin returned to China in 2016 to establish a laboratory, he joined hands with Yang Fang's research group and Yan Jianbing's research group of Huazhong Agricultural University to officially kick off the construction of maize multidimensional network atlases at the levels of genome, transcriptome, translation group, and proteomics
.

The Sword of Network Big Data Cracking the Genetic Complex Function Network

The genes inside a living organism are very similar
to those in human society.
To determine the social value of a person in human society, in sociology, it is not only necessary to look at what the family relationship is in which the person lives, who he marries and what the kinship relationship is; It also depends on the person's circle of friends and who he/she is friends with; It also depends on who the colleagues it works for and what company it works
for.
Through family relationships, friend relationships, and work relationships, you can understand the social value and contribution
of this person.
The first generation of multiomics integrated network maps of maize are maps that describe the hierarchical relationships of global genes in maize
.
Understanding the global relationship map will provide a systematic understanding of the function of the global gene, and then the molecular mechanism
of trait genetic variation can be systematically analyzed.
After six years of hard work, the researchers forged the sword of biological network big data, laying the foundation
for a comprehensive and systematic analysis of the genetic variation mechanism of maize.

Based on the successfully constructed maize multidimensional network big data map, this study explores the functional differentiation of duplicate genes in the network at the genome-wide level, and finds that there are significant differences in the functional differentiation of different types of duplicate genes and repeat genes in different eras, revealing that the two ancient subgenomes of maize show progressive functional differentiation from transcriptome to protein interaction group.
At the same time, the molecular networks of cloned functional genes of plant type, grain quality and grain size were reconstructed, and new genes and molecular networks
affecting target traits were successfully predicted.
Interestingly, although the functional genes cloned by different groups of maize grain size were from different sources, these functional genes of grain size were significantly enriched in the maize multidimensional omics integration network map, and six sub-networks were systematically identified to coerciously regulate maize grain development
.

Fig.
2 The molecular mechanism of maize grain development was analyzed based on the first-generation multidimensional omics network map of maize

Crack the mystery of genetic variation in corn flowering period

In order to further use millions of molecular regulatory network relationships to solve the mystery of the inheritance of complex quantitative genetic traits in maize, the authors have carried out a lot of explorations and attempts
based on artificial intelligence algorithms 。 Taking the flowering period of important agronomic traits of maize as an example, an artificial intelligence algorithm for predicting multiple functional genes at the flowering stage of maize was developed, and the big data of the first generation of integrated multi-omics network of maize was mined, and 2,651 candidate flowering stage genes were predicted, divided into 8 sub-network pathways, covering the key networks reported in previous studies, and at the same time identifying a new molecular pathway in maize to regulate flowering period, involving histone acetyltransferase regulation
。 From the eight flowering stage subnetworks identified, nearly 100 key candidate flowering stage genes were excavated, and their mutants
were created through CRISPR gene editing technology and EMS mutation technology.
The mutants of candidate genes at the flowering stage were planted in multi-point fields for many years, and their wild-type materials were identified, and 20 predicted genes were identified to correlate with the traits of the flowering stage, and their molecular mechanisms were preliminarily explained, which deepened the understanding of the flowering period of maize and provided a theoretical basis and genetic resources
for intelligent design breeding at the flowering stage.

Fig.
3 The genetic mechanism of maize flowering stage was analyzed by artificial intelligence algorithm

Li Lin, Yang Fang and Yan Jianbing of the National Key Laboratory of Crop Genetic Improvement and Hubei Hongshan Laboratory are co-corresponding authors
of the paper.
Our doctoral students Han Linqian, Zhong Wanshun, Qian Jia, postdoctoral fellows Jin Minliang, Zhu Wanchao and Sun Yonghao, graduated master's degree Tian Peng and associate researcher Zhang Hongwei of the Chinese Academy of Agricultural Sciences are the co-first authors of the paper, Professor Chen Hong and Associate Professor Li Weifu of the School of Science of our university, Liu Xiangguo, researcher of Jilin Academy of Agricultural Sciences, Professor Chen Lingling of Guangxi University and other teachers and students participated in the research
.
The research was supported
by the National Natural Science Foundation of China's Major Research Program Integration Project, the National Outstanding Youth Fund, Hubei Hongshan Laboratory, and Hainan Yazhou Bay Seed Laboratory.