With the accumulation of massive data sets from high-throughput experiments and the rapid emergence of new types of omics data, gene sets have become more diverse and essential for the refinement of gene annotation at multidimensional levels. Accordingly, we collected and defined 236 007 gene sets across different categories for 44 plant species in the Plant Gene Set Annotation Database (PlantGSAD). These gene sets were divided into nine main categories covering many functional subcategories, such as trait ontology, co-expression modules, chromatin states, and liquid-liquid phase separation. The annotations from the collected gene sets covered all of the genes in the Brassicaceae species Arabidopsis and Poaceae species Oryza sativa. Several GSEA tools are implemented in PlantGSAD to improve the efficiency of the analysis, including custom SEA for a flexible strategy based on customized annotations, SEACOMPARE for the cross-comparison of SEA results, and integrated visualization features for ontological analysis that intuitively reflects their parent-child relationships. In summary, PlantGSAD provides numerous gene sets for multiple plant species and highly efficient analysis tools. We believe that PlantGSAD will become a multifunctional analysis platform that can be used to predict and elucidate the functions and mechanisms of genes of interest. PlantGSAD is publicly available at http://systemsbiology.cau.edu.cn/PlantGSEAv2/.

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Plant Knotted1-like homeobox (KNOX) genes encode homeodomain-containing transcription factors. In rice (Oryza sativa L.), little is known about the downstream target genes of KNOX Class II subfamily proteins.Here we generated chromatin immunoprecipitation (ChIP)-sequencing datasets for HOS59, a member of the rice KNOX Class II subfamily, and characterized the genome-wide binding sites of HOS59. We conducted trait ontology (TO) analysis of 9705 identified downstream target genes, and found that multiple TO terms are related to plant structure morphology and stress traits. ChIP-quantitative PCR (qPCR) was conducted to validate some key target genes. Meanwhile, our IP-MS datasets showed that HOS59 was closely associated with BELL family proteins, some grain size regulators (OsSPL13, OsSPL16, OsSPL18, SLG, etc.), and some epigenetic modification factors such as OsAGO4a and OsAGO4b, proteins involved in small interfering RNAmediated gene silencing. Furthermore, we employed CRISPR/Cas9 editing and transgenic approaches to generate hos59 mutants and overexpression lines, respectively. Compared with wild-type plants, the hos59 mutants have longer grains and increased glume cell length, a loose plant architecture, and drooping leaves, while the overexpression lines showed smaller grain size, erect leaves, and lower plant height. The qRT-PCR results showed that mutation of the HOS59 gene led to upregulation of some grain size-related genes such as OsSPL13, OsSPL18, and PGL2. In summary, our results indicate that HOS59 may be a repressor of the downstream target genes, negatively regulating glume cell length, rice grain size, plant architecture, etc. The identified downstream target genes and possible interaction proteins of HOS59 improve our understanding of the KNOX regulatory networks.

Histone deacetylases (HDACs) influence chromatin state and gene expression. Eighteen HDAC genes with important biological functions have been identified in rice. In this study, we surveyed the gene presence frequency of all 18 rice HDAC genes in 3,010 rice accessions. HDA710/OsHDAC2 showed insertion/deletion (InDel) polymorphisms in almost 98.8% japonica accessions but only 1% indica accessions. InDel polymorphism association analysis showed that accessions with partial deletions in HDA710 tended to display early leaf senescence. Further transgenic results confirmed that HDA710 delayed leaf senescence in rice. The over-expression of HDA710 delayed leaf senescence, and the knock-down of HDA710 accelerated leaf senescence. Transcriptome analysis showed that photosynthesis and chlorophyll biosynthesis related genes were up-regulated in HDA710 over-expression lines, while some programmed cell death and disease resistance related genes were down-regulated. Co-expression network analysis with gene expression view revealed that HDA710 was co-expressed with multiple genes, particularly OsGSTU12, which was significantly up-regulated in 35S::HDA710-sense lines. InDels in the promoter region of OsGSTU12 and in the gene region of HDA710 occurred coincidentally among more than 90% accessions, and we identified multiple W-box motifs at the InDel position of OsGSTU12. Over-expression of OsGSTU12 also delayed leaf senescence in rice. Taken together, our results suggest that both HDA710 and OsGSTU12 are involved in regulating the process of leaf senescence in rice.

The herbaceous peony (Paeonia lactiflora Pall.) is a well-known ornamental flowering and pharmaceutical plant found in China. Its high medicinal value has long been recognized by traditional Chinese medicine (as Radix paeoniae Alba and Radix paeoniae Rubra), and it has become economically valued for its oilseed in recent years; like other Paeonia species, it has been identified as a novel resource for the α-linolenic acid used in seed oil production. However, its genome has not yet been sequenced, and little transcriptome data on Paeonia lactiflora are available. To obtain a comprehensive transcriptome for Paeonia lactiflora, RNAs from 10 tissues of the Paeonia lactiflora Pall. cv Shaoyou17C were used for de novo assembly, and 416,062 unigenes were obtained. Using a homology search, it was found that 236,222 (approximately 57%) unigenes had at least one BLAST hit in one or more public data resources. The construction of co-expression networks is a feasible means for improving unigene annotation. Using in-house transcriptome data, we obtained a co-expression network covering 95.13% of the unigenes. Then we integrated co-expression network analyses and lipid-related pathway genes to study lipid metabolism in Paeonia lactiflora cultivars. Finally, we constructed the online database HpeNet (http://bioinformatics.cau.edu.cn/HpeNet) to integrate transcriptome data, gene information, the co-expression network, and so forth. The database can also be searched for gene details, gene functions, orthologous matches, and other data. Our online database may help the research community identify functional genes and perform research on Paeonia lactiflora more conveniently. We hope that de novo transcriptome assembly, combined with co-expression networks, can provide a feasible means to predict the gene function of species that do not have a reference genome.