Overriding Mendelian inheritance in Arabidopsis with a CRISPR toxin-antidote gene drive that impairs pollen germination.

Synthetic gene drives, inspired by natural selfish genetic elements and transmitted to progeny at super-Mendelian (>50%) frequencies, present transformative potential for disseminating traits that benefit humans throughout wild populations, even facing potential fitness costs. Here we constructed a gene drive system in plants called CRISPR-Assisted Inheritance utilizing NPG1 (CAIN), which uses a toxin-antidote mechanism in the male germline to override Mendelian inheritance. Specifically, a guide RNA-Cas9 cassette targets the essential No Pollen Germination 1 (NPG1) gene, serving as the toxin to block pollen germination. A recoded, CRISPR-resistant copy of NPG1 serves as the antidote, providing rescue only in pollen cells that carry the drive. To limit potential consequences of inadvertent release, we used self-pollinating Arabidopsis thaliana as a model. The drive demonstrated a robust 88-99% transmission rate over two successive generations, producing minimal resistance alleles that are unlikely to inhibit drive spread. Our study provides a strong basis for rapid genetic modification or suppression of outcrossing plant populations.

A designer synthetic chromosome fragment functions in moss.

Rapid advances in DNA synthesis techniques have enabled the assembly and engineering of viral and microbial genomes, presenting new opportunities for synthetic genomics in multicellular eukaryotic organisms. These organisms, characterized by larger genomes, abundant transposons and extensive epigenetic regulation, pose unique challenges. Here we report the in vivo assembly of chromosomal fragments in the moss Physcomitrium patens, producing phenotypically virtually wild-type lines in which one-third of the coding region of a chromosomal arm is replaced by redesigned, chemically synthesized fragments. By eliminating 55.8% of a 155 kb endogenous chromosomal region, we substantially simplified the genome without discernible phenotypic effects, implying that many transposable elements may minimally impact growth. We also introduced other sequence modifications, such as PCRTag incorporation, gene locus swapping and stop codon substitution. Despite these substantial changes, the complex epigenetic landscape was normally established, albeit with some three-dimensional conformation alterations. The synthesis of a partial multicellular eukaryotic chromosome arm lays the foundation for the synthetic moss genome project (SynMoss) and paves the way for genome synthesis in multicellular organisms.

Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions.

Genotyping and phenotyping large natural populations provide opportunities for population genomic analysis and genome-wide association studies (GWAS). Several rice populations have been re-sequenced in the past decade; however, many major Chinese rice cultivars were not included in these studies. Here, we report large-scale genomic and phenotypic datasets for a collection mainly comprised of 1,275 rice accessions of widely planted cultivars and parental hybrid rice lines from China. The population was divided into three indica/Xian and three japonica/Geng phylogenetic subgroups that correlate strongly with their geographic or breeding origins. We acquired a total of 146 phenotypic datasets for 29 agronomic traits under multi-environments for different subpopulations. With GWAS, we identified a total of 143 significant association loci, including three newly identified candidate genes or alleles that control heading date or amylose content. Our genotypic analysis of agronomically important genes in the population revealed that many favorable alleles are underused in elite accessions, suggesting they may be used to provide improvements in future breeding efforts. Our study provides useful resources for rice genetics research and breeding.

Systematic discovery of novel and valuable plant gene modules by large-scale RNA-seq samples.

Motivation: The complex cellular networks underlying phenotypes are formed by the interacting gene modules. Building and analyzing genome-wide and high-quality Gene Co-expression Networks (GCNs) is useful for uncovering these modules and understanding the phenotypes of an organism. Results: Using large-scale RNA-seq samples, we constructed high coverage and confident GCNs in two monocot species rice and maize, and two eudicot species Arabidopsis and soybean, and subdivided them into co-expressed gene modules. Taking rice as an example, we discovered many interesting and valuable modules, for instance, pollen-specific modules and starch biosynthesis module. We explored the regulatory mechanism of modules and revealed synergistic effects of gene expression regulation. In addition, we discovered that the modules conserved among plants participated in basic biological processes, whereas the species-specific modules were involved in spatiotemporal-specific processes linking genotypes to phenotypes. Our study suggests gene regulatory relationships and modules relating to cellular activities and agronomic traits in several model and crop plants, and thus providing a valuable data source for plant genetics research and breeding. Availability and implementation: The analyzed gene expression data, reconstructed GCNs, modules and detailed annotations can be freely downloaded from ftp://47.94.193.106/pub. Supplementary information: Supplementary data are available at Bioinformatics online.

NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples.

Accurately reconstructing gene co-expression network is of great importance for uncovering the genetic architecture underlying complex and various phenotypes. The recent availability of high-throughput RNA-seq sequencing has made genome-wide detecting and quantifying of the novel, rare and low-abundance transcripts practical. However, its potential merits in reconstructing gene co-expression network have still not been well explored. Using massive-scale RNA-seq samples, we have designed an ensemble pipeline, called NetMiner, for building genome-scale and high-quality Gene Co-expression Network (GCN) by integrating three frequently used inference algorithms. We constructed a RNA-seq-based GCN in one species of monocot rice. The quality of network obtained by our method was verified and evaluated by the curated gene functional association data sets, which obviously outperformed each single method. In addition, the powerful capability of network for associating genes with functions and agronomic traits was shown by enrichment analysis and case studies. In particular, we demonstrated the potential value of our proposed method to predict the biological roles of unknown protein-coding genes, long non-coding RNA (lncRNA) genes and circular RNA (circRNA) genes. Our results provided a valuable and highly reliable data source to select key candidate genes for subsequent experimental validation. To facilitate identification of novel genes regulating important biological processes and phenotypes in other plants or animals, we have published the source code of NetMiner, making it freely available at https://github.com/czllab/NetMiner.