Genome-wide analysis of NAC transcription factors and exploration of candidate genes regulating selenium metabolism in Broussonetia papyrifera.

MAIN CONCLUSION: Genome-wide identification revealed 79 BpNAC genes belonging to 16 subfamilies, and their gene structures and evolutionary relationships were characterized. Expression analysis highlighted their importance in plant selenium stress responses. Paper mulberry (Broussonetia papyrifera), a deciduous arboreal plant of the Moraceae family, is distinguished by its leaves, which are abundant in proteins, polysaccharides, and flavonoids, positioning it as a novel feedstock. NAC transcription factors, exclusive to plant species, are crucial in regulating growth, development, and response to biotic and abiotic stress. However, extensive characterization of the NAC family within paper mulberry is lacking. In this study, 79 BpNAC genes were identified from the paper mulberry genome, with an uneven distribution across 13 chromosomes. A comprehensive, genome-wide analysis of BpNACs was performed, including investigating gene structures, promoter regions, and chromosomal locations. Phylogenetic tree analysis, alongside comparisons with Arabidopsis thaliana NACs, allowed for categorizing these genes into 16 subfamilies in alignment with gene structure and motif conservation. Collinearity analysis suggested a significant homologous relationship between the NAC genes of paper mulberry and those in Morus notabilis, Ficus hispida, Antiaris toxicaria, and Cannabis sativa. Integrating transcriptome data and Se content revealed that 12 BpNAC genes were associated with selenium biosynthesis. Subsequent RT-qPCR analysis corroborated the correlation between BpNAC59, BpNAC62 with sodium selenate, and BpNAC55 with sodium selenite. Subcellular localization experiments revealed the nuclear functions of BpNAC59 and BpNAC62. This study highlights the potential BpNAC transcription factors involved in selenium metabolism, providing a foundation for strategically breeding selenium-fortified paper mulberry.

Genome-wide identification of HMT gene family explores BpHMT2 enhancing selenium accumulation and tolerance in Broussonetia papyrifera.

Broussonetia papyrifera, a valuable feed resource, is known for its fast growth, wide adaptability, high protein content and strong selenium enrichment capacity. Selenomethionine (SeMet), the main selenium form in selenium fortification B. papyrifera, is safe for animals and this enhances its nutritional value as a feed resource. However, the molecular mechanisms underlying SeMet synthesis remain unclear. This study identified three homocysteine S-methyltransferase genes from the B. papyrifera genome. The phylogenetic tree demonstrated that BpHMTs were divided into two classes, and BpHMT2 in the Class 2-D subfamily evolved earlier and possesses more fundamental functions. On the basis of the correlation between gene expression levels and selenium content, BpHMT2 was identified as a key candidate gene associated with selenium tolerance. Subcellular localization experiments confirmed the targeting of BpHMT2 in nucleus, cell membrane and chloroplasts. Moreover, three BpHMT2 overexpression Arabidopsis thaliana lines were confirmed to enhance plant selenium tolerance and SeMet accumulation. Overall, our finding provides insights into the molecular mechanisms of selenium metabolism in B. papyrifera, highlighting the potential role of BpHMT2 in SeMet synthesis. This research contributes to our understanding of selenium-enriched feed resources, with increased SeMet content contributing to the improved nutritional value of B. papyrifera as a feed resource.

Transcriptome-wide identification of WRKY family genes and their expression profiling toward salicylic acid in Camellia japonica.

The ornamental plant Camellia japonica is widely distributed worldwide and is susceptible to various environmental stresses. The WRKY transcription factor (TF) is an important node of plant tolerance. However, WRKY TFs from C. japonica have not been reported yet. In this study, 48 CjWRKYs, namely, CjWRKY1 to CjWRKY48, were identified. Protein structure analysis revealed that CjWRKY proteins contain a highly conserved motif (WRKYGQK) and two variant motifs (WRKYGKK and WRKYGRK). Phylogenetic analysis indicated that the 48 CjWRKYs can be divided into three groups, which are further classified into six subgroups, namely, I-C, II-a, II-b, II-c, II-e, and III, which contain 10, 6, 8, 13, 7, and 4 members, respectively. The expression patterns of 15 CjWRKYs under salicylic acid (SA) treatment were investigated by real-time quantitative PCR (qRT-PCR). Results showed that the 15 CjWRKYs could be induced by SA treatment. This study is the first to screen CjWRKYs and identify the expression profile of CjWRKYs under SA treatment and provides a theoretical basis for analyzing the function of CjWRKY genes to SA stress tolerance in C. japonica.

Emotion recognition using Kinect motion capture data of human gaits.

Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker's emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants' gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.