Identification of nuclear pore complexes (NPCs) and revealed outer-ring component BnHOS1 related to cold tolerance in B. napus.

Nuclear pore complexes (NPCs) consist of ~30 different nucleoporins (Nups), are the unique channels that govern development, hormonal response, and roles in both biotic and abiotic responses, as well as the transport and information exchange of biomacromolecules between nucleoplasms. Here, we report the comprehensive identification of 77 BnNups throughout the zhongshuang11 (ZS11) genome, which were classified into 29 distinct categories based on their evolutionary connections. We compared and contrasted different BnNups by analyzing at their gene structures, protein domains, putative three-dimensional (3D) models and expression patterns. Additional examples of genome-wide duplication events and cross-species synteny are provided to demonstrate the proliferation and evolutionary conservation of BnNups. When BnHOS1 was modified using CRISPR/Cas9 technology, the resulting L10 and L28 lines exhibited substantial freezing resistance. This not only demonstrated the negative regulatory impact of BnHOS1 on cold stress, but also offered a promising candidate gene for cold tolerance breeding and augmented the available B. napus material. These findings not only help us learn more about the composition and function of BnNPCs in B. napus, but they also provide light on how NPCs in other eukaryotic organism functions.

Genome-Wide Survey of Leucine-Rich Repeat Receptor-Like Protein Kinase Genes and CRISPR/Cas9-Targeted Mutagenesis BnBRI1 in Brassica napus.

The leucine-rich repeat receptor-like protein kinase (LRR-RLK) family represents the largest group of RLKs in plants and plays vital roles in plant growth, development and the responses to environmental stress. Although LRR-RLK families have been identified in many species, they have not yet been reported in B. napus. In this study, a total of 444 BnLRR-RLK genes were identified in the genome of Brassica napus cultivar "Zhongshuang 11" (ZS11), and classified into 22 subfamilies based on phylogenetic relationships and genome-wide analyses. Conserved motifs and gene structures were shared within but not between subfamilies. The 444 BnLRR-RLK genes were asymmetrically distributed on 19 chromosomes and exhibited specific expression profiles in different tissues and in response to stress. We identified six BnBRI1 homologs and obtained partial knockouts via CRISPR/Cas9 technology, generating semi-dwarf lines without decreased yield compared with controls. This study provides comprehensive insight of the LRR-RLK family in B. napus. Additionally, the semi-dwarf lines expand the "ideotype" germplasm resources and accelerate the breeding process for B. napus.

Genome-wide identification and expression analysis of U-box gene family in wild emmer wheat (Triticum turgidum L. ssp. dicoccoides).

In this study, 82 U-box genes were identified in wild emmer wheat (TdPUBs) through a genome-search method. Phylogenetic analysis classified them into seven groups and the genes belonging to the same group shared the similar exon-intron structure, motif organization and cis-element compositions. Synteny analysis of the U-box genes between different species revealed that segmental duplication and polyploidization mainly contributed to the expansion of TdPUBs. Furthermore, the genetic variations of U-box were investigated in wild emmer, domesticated emmer and durum wheat. Results showed that significant genetic bottleneck has occurred during domestication process of tetraploid emmer wheat. Meanwhile, 12 TdPUBs were co-located with known domestication related QTLs. Finally, the tissue-specific and stress-responsive TdPUB genes were identified through RNA-seq analysis. Combined with qPCR validation of 19 salt-responsive TdPUBs, the candidates involving in salt response were obtained. It lays the foundation to better understand the regulatory roles of U-box family in emmer wheat and beyond.

Combined feature selection and cancer prognosis using support vector machine regression.

Prognostic prediction is important in medical domain, because it can be used to select an appropriate treatment for a patient by predicting the patient's clinical outcomes. For high-dimensional data, a normal prognostic method undergoes two steps: feature selection and prognosis analysis. Recently, the L1-L2-norm Support Vector Machine (L1-L2 SVM) has been developed as an effective classification technique and shown good classification performance with automatic feature selection. In this paper, we extend L1-L2 SVM for regression analysis with automatic feature selection. We further improve the L1-L2 SVM for prognostic prediction by utilizing the information of censored data as constraints. We design an efficient solution to the new optimization problem. The proposed method is compared with other seven prognostic prediction methods on three realworld data sets. The experimental results show that the proposed method performs consistently better than the medium performance. It is more efficient than other algorithms with the similar performance.